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Wind turbines: current status, obstacles, trends and technologies

E I Konstantinidis 1 and P N Botsaris 1

Published under licence by IOP Publishing Ltd IOP Conference Series: Materials Science and Engineering , Volume 161 , 20th Innovative Manufacturing Engineering and Energy Conference (IManEE 2016) 23–25 September 2016, Kozani, Greece Citation E I Konstantinidis and P N Botsaris 2016 IOP Conf. Ser.: Mater. Sci. Eng. 161 012079 DOI 10.1088/1757-899X/161/1/012079

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1 Democritus University of Thrace, School of Engineering, Department of Production Engineering and Management, Section of Materials, Processes and Engineering Vas. Sofias str. Building I, Office 103, Central University Campus, 67100 Xanthi, Greece

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The last decade the installation of wind farms around the world is spreading rapidly and wind energy has become a significant factor for promoting sustainable development. The scope of the present study is to indicate the present status of global wind power expansion as well as the current state of the art in the field of wind turbine technology. The RAM (reliability/availability/maintenance) section is also examined and the Levelized Cost of Energy for onshore/ offshore electricity production is presented. Negative consequences that go with the rapid expansion of wind power like accidents, environmental effects, etc. are highlighted. Especially visual impact to the landscape and noise pollution are some factors that provoke social reactions. Moreover, the complicated and long permitted process of a wind power plant, the high capital cost of the investment and the grid instability due to the intermittent nature of wind, are also significant obstacles in the development of the wind energy production. The current trends in the field of research and development of onshore and offshore wind power production are analyzed. Finally the present study is trying to achieve an estimation of where the wind industry targets for the years to come.

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wind turbine technology research paper

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  • WES, 7, 2003–2037, 2022
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wind turbine technology research paper

Current status and grand challenges for small wind turbine technology

Alessandro bianchini, galih bangga, ian baring-gould, alessandro croce, josé ignacio cruz, rick damiani, gareth erfort, carlos simao ferreira, david infield, christian navid nayeri, george pechlivanoglou, mark runacres, gerard schepers, brent summerville, alice orrell.

While modern wind turbines have become by far the largest rotating machines on Earth with further upscaling planned for the future, a renewed interest in small wind turbines (SWTs) is fostering energy transition and smart grid development. Small machines have traditionally not received the same level of aerodynamic refinement as their larger counterparts, resulting in lower efficiency, lower capacity factors, and therefore a higher cost of energy. In an effort to reduce this gap, research programs are developing worldwide. With this background, the scope of the present study is 2-fold. In the first part of this paper, an overview of the current status of the technology is presented in terms of technical maturity, diffusion, and cost. The second part of the study proposes five grand challenges that are thought to be key to fostering the development of small wind turbine technology in the near future, i.e. (1) improving energy conversion of modern SWTs through better design and control, especially in the case of turbulent wind; (2) better predicting long-term turbine performance with limited resource measurements and proving reliability; (3) improving the economic viability of small wind energy; (4) facilitating the contribution of SWTs to the energy demand and electrical system integration; (5) fostering engagement, social acceptance, and deployment for global distributed wind markets. To tackle these challenges, a series of unknowns and gaps are first identified and discussed. Based on them, improvement areas are suggested, for which 10 key enabling actions are finally proposed.

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Bianchini, A., Bangga, G., Baring-Gould, I., Croce, A., Cruz, J. I., Damiani, R., Erfort, G., Simao Ferreira, C., Infield, D., Nayeri, C. N., Pechlivanoglou, G., Runacres, M., Schepers, G., Summerville, B., Wood, D., and Orrell, A.: Current status and grand challenges for small wind turbine technology, Wind Energ. Sci., 7, 2003–2037, https://doi.org/10.5194/wes-7-2003-2022, 2022.

A major portion of today's installed wind power is in the form of large wind power plants, which mainly consist of multi-megawatt machines (GWEC, 2020), while a clear trend in further upscaling of both rated power and dimension is ongoing (Veers et al., 2019). Small wind turbines (SWTs) are, however, still visible around the world for a variety of applications, including electric power generation for households, industrial centers, farms, and isolated communities; combining with other energy sources and storage in hybrid energy systems for electricity to support remote monitoring and telecommunications; and providing direct energy services for applications such as water pumping, desalination, and purification (Chagas et al., 2020). The use of wind turbines in rural areas is of particular relevance for some countries; for example, around the Horn of Africa, small wind systems are the most viable solution in the scarcely electrified parts of those countries (Gabra et al., 2019). Karekezi (2002) reported that South Africa has more than 100 000 wind pumps in operation used at over 45 818 farms. SWTs are a subset of a larger distributed wind market segment that can include large turbines installed in distributed applications. Figure 1 associates typical distributed turbine sizes with their main types of application.

https://wes.copernicus.org/articles/7/2003/2022/wes-7-2003-2022-f01

Figure 1 Small and distributed wind turbine dimensions and rated power outputs as a function of various applications.

When SWTs are used for a variety of ancillary purposes other than electricity production such as ventilation or water pumping, different turbine concepts can come into play. These applications may use the Savonius vertical-axis turbine (Akwa et al., 2012) or the multi-blade American windmill (Baker, 1985), which constitute a small space on the market. Although these machines are in all respects SWTs, they are not discussed in the present study, which instead focuses on SWTs for electricity production.

Before moving forward, a key element of this study is defining what is meant by “small wind turbine”. A universal consensus on this has not been reached, with the International Electrotechnical Commission (IEC) Standards (IEC – International Standard, 2019b) defining SWTs as turbines with a maximum rotor swept area of 200 m 2 ; the same threshold is applied to eligible turbines for certification by the AWEA Small Wind Turbine Performance and Safety Standard 9.1-2009; however, a new American National Standards Institute consensus standard, ACP 101-1, is being developed by the American Clean Power Association (ACP), the successor to the AWEA. ACP 101-1 is intended to eventually supersede the AWEA 9.1-2009 standard (Summerville et al., 2021). Several countries use rated power as the key differentiator, and ACP 101-1 thus defines SWTs as having a peak power of 150 kW or less and microturbines as having a peak power up to 1 kW. In Brazil, small wind systems are categorized as power stations (which could be composed of one or many wind turbines) with a total rated capacity below 100 kW, according to Resolution 438/2012 of the Brazilian Electricity Regulatory Agency (ANEEL) (Chagas et al., 2020).

The importance of having a more comprehensive definition of “small wind” has been recently put in the spotlight. For example, it has been suggested by the Small Wind Turbine Technical Committee of the European Academy of Wind Energy (EAWE) that many problems and technical challenges of SWTs are common to the majority of the rotors up to 500 kW (EAWE, 2020), i.e., also extending to distributed wind turbines (DWTs). As is further discussed in the present study, it is important to more clearly define those characteristics that make SWTs unique from utility-scale turbines. However, this is not an easy task because significant variability in wind turbine design is also apparent, with no specific size-based design threshold. Additionally, there are a variety of “alternative” configurations available on the open market (Bianchini, 2019), such as vertical-axis turbines (Aslam Bhutta et al., 2012), diffuser-augmented wind turbines (Evans et al., 2020), or first prototypes of airborne wind energy (AWE) converters (Meghana et al., 2022). Even though SWTs may still represent a niche application within the wind energy market, they have recently been exhibiting a notable rate of growth concomitant with the diffusion of smart energy systems (Tzen, 2020). This diffusion, however, is still hindered by the typically higher costs of small wind systems. These increased costs are driven by several factors, including a lack of development and system optimization and issues related to those cost items (i.e., electrical connection, resource assessment expenses, installation cost, etc.) that are not proportionally lower for smaller projects (Simic et al., 2013). The growth of the SWT sector is further notable in light of the several published reports showing that SWT installations have failed to reach their expected energy yield, resulting in underperforming turbines. This is particularly true in the case of installations in the urban or built environment (WINEUR project, 2005; Fields et al., 2016). Development in highly complex areas, such as urban locations, is complicated due to the wind conditions in the city's canopy layer, which typically have low intensity, high variability, high levels of turbulence, and inclined or even reversed airflows. While several studies have shown a theoretically good potential for urban wind (Balduzzi et al., 2012; Toja-Silva et al., 2013), a number of challenges still need to be tackled to effectively fit wind energy converters to this environment, as recently discussed by Micallef and Bussel (2018) and Stathopoulos et al. (2018). In the present study, the authors decided not to include a specific technical analysis of the needs for urban wind, although future work on the topic has to be encouraged (Battisti, 2018).

Even so, projections of SWT deployment in future scenarios of distributed energy production within smart grids (thus in proximity to populated areas) are considered promising. In this sense, SWTs are expected to provide a significant contribution, especially in combination with other renewable energy sources. However, the higher levelized cost of energy (LCOE) of SWTs, especially compared to residential solar photovoltaics (PVs), still hampers the massive diffusion of this technology.

1.1  A guide to this article

The present study has two main focuses. First, it provides an overview of the status of SWT technology. We present the market diffusion and economics of SWTs (Sects. 2–3) with the goal of placing the technology in the current energy market and defining some important threshold values. We then provide a description of the main technical features of SWTs (Sect. 4) and compare them to those of their utility-scale counterparts. Section 5 pursues the second focus of the work, defining five grand challenges that – per the authors' assessment – are key to fostering the development of SWTs in the near future. More specifically, a series of unknowns and gaps for SWTs is first defined, and then the main improvement areas and prospects are proposed to address those gaps. Finally, Sect. 6 synthesizes the main outcomes of the study into concluding remarks and defines 10 key enabling actions for achieving the grand challenges in the near future.

https://wes.copernicus.org/articles/7/2003/2022/wes-7-2003-2022-f02

Figure 2 Evolution of the country's share in the newly installed SWT capacity for that year for a number of key European countries and China. Data from Orrell et al. (2021).

There is at least ∼  1.8 GW of installed small wind capacity globally from over 1 million turbines (Orrell et al., 2021). The global spread of this electrical capacity, including all types of turbines and based on available reports from some key surveyed countries, is shown in Table 1 (asterisks denote a lack of validated data for that specific year). Figure 2 provides a more focused insight into several of those countries, which showed notably different trends in the first years of the last decade, where SWT technology saw one of its more interesting phases. While Denmark, the United Kingdom, and the United States have a long-recorded history of small wind installations, China has added larger amounts of small wind capacity more consistently in recent years. On the other hand, Italy and the United Kingdom, which saw many installations in the first decade of the century, both experienced recent decreases due to feed-in tariff (FIT) policy changes. FITs provide payments to owners of small-scale renewable generators at a fixed rate per unit of electricity produced, verifying that the cost of the installation is recovered over the lifetime of the generator. In the case of Italy, in particular, the significant increase in installations seen around 2016–2017 was due to a special program of incentives for turbines under 60 kW. The FIT rate in Italy declined over time before expiring in 2017. It was replaced by the FER1 Decree in 2019 (Dentons, 2020). In line with these changes, an estimated 77.46 MW of wind projects using turbines sized up through 250 kW was installed in Italy in 2017, no installation reports were available for 2018 and 2019, and 0.65 MW of projects was reported for 2020. The United Kingdom closed its FIT program to new applicants in 2019 and introduced the Smart Export Guarantee program. Under that program, applicants now receive a tariff determined by the buyer rather than a fixed price determined by the government (Ofgem, 2021). Consequently, small wind deployment went from 28.53 MW in 2014 to only 0.43 MW in 2019 (Orrell et al., 2021). In a scenario of decaying government incentives, an outlier case in Europe is Greece (Greek Government Gazette, 2021), which still offers an FIT for SWTs. At the time of writing this paper, the program was for 20 MW installed capacity, starting with a tariff of EUR 157 per megawatt-hour (USD 181 per megawatt-hour) that will be automatically reduced based on the cumulative contracted power of the projects. A bonus with respect to the tax break is also in place, which brings the FIT to EUR 163 per megawatt-hour (USD 187 per megawatt-hour).

Table 1 Small wind turbine installations through 2020. Data from Orrell et al. (2021) and Chagas et al. (2020). Values in bold refer to years/countries with FIT schemes in place. Asterisks refer to missing data for that specific year.

wind turbine technology research paper

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Other examples of these tariffs include those in Japan and the Republic of Korea. Japan's FIT program was established in the wake of the Fukushima Daiichi nuclear disaster. Its rates have steadily declined, from a peak of JPY 55 per kilowatt-hour in 2015 to JPY 19 (approximately 1 EUR 0.125 or USD 0.175) per kilowatt-hour as of 2019 for turbines less than 20 kW (Orrell et al., 2021). The Republic of Korea also had an FIT program, but it was ended in 2012 and replaced with a renewable portfolio standard (Lo, 2018). While the switch from the FIT program increased capacities in some renewables in the Republic of Korea, such as biomass co-firing and fuel cell deployment, small wind installations dropped (Orrell et al., 2021).

The discontinuous nature of incentives and national programs makes it difficult for manufacturers to stay in the market, even in those countries where SWT technology is more present, as in the UK, Italy, and the United States. Six small wind manufacturers in the United States reported international exports in 2015, with just three doing so in 2020 (Orrell et al., 2021). Similarly, sales in China and exports from China have fluctuated with the number of Chinese small wind manufacturers in that market. In 2017, only 15 Chinese small wind turbine manufacturers reported sales, a decrease from 28 in 2014 (Duo, 2017), corresponding to a 60 % drop in sales from 2014 to 2017 (Orrell et al., 2021).

https://wes.copernicus.org/articles/7/2003/2022/wes-7-2003-2022-f03

Figure 3 Global SWT market status in terms of revenues (Global Info Research, 2021).

From a global perspective, at the time of writing this paper, the largest market for small wind still came from Europe, the United States, and China. SWTs are most commonly used for off-grid applications, such as telecommunication towers and farming. They are also used to power individual homes and small businesses, which can be tied to the grid. In 2019, 94 % of SWT sales went to off-grid applications (Global Info Research, 2021). Unfortunately, 2020 saw only about 30 MW worth of units being sold around the world (Orrell et al., 2021), with a global market in terms of revenues (Fig. 3) still on a flat trend. Regarding future perspectives (Global Info Research, 2021), no clear agreement on future perspectives was found at the time of writing, mainly as a consequence of the financial crisis connected to the global COVID-19 pandemic in 2020. Global Info Research (Global Info Research, 2021) predicted the SWT global market would reach USD 190 million (EUR 165 million) in 2025, with a compound annual growth rate of 11.45 % from 2020 to 2025. The market could thus become promising again, especially in connection with the increasing attention on the transition toward cleaner energy systems. Regarding the future share by region, Europe, Asia-Pacific, and the United States are expected to remain the key players in this sector. In particular, the Asia-Pacific market will lead the total worldwide SWT sales, while the European market will show a reduction in the global relative share (Fig. 4). In Asia, Japan is expected to deploy renewable energy generation at large scales following the Fukushima Daiichi nuclear disaster, whereas other countries such as Malaysia – which represents an untapped market with suitable conditions for SWTs (Wen et al., 2019) – might also see significant deployment.

https://wes.copernicus.org/articles/7/2003/2022/wes-7-2003-2022-f04

Figure 4 Global SWT sales forecast by region (2020–2025). Data from Global Info Research (2021).

As described in Sect. 2, the diffusion of SWTs has often gone hand in hand with dedicated financial incentive programs from individual countries. This is unfortunately because the high LCOE of SWTs has represented the main obstacle hampering wider deployment of SWT technology (Predescu, 2016).

The economic evaluation of small wind systems is particularly critical for three main reasons: (1) the capital investment is strongly dependent on the specific turbine and country; (2) the correct selection of the installation site has a much higher impact on actual annual energy production (AEP) than in the case of turbines with large rotors; and (3) as discussed, the real viability of a project may depend completely on the incentives ensured by the specific country.

To give the reader an overview of the aforementioned issues, the main cost factors are analyzed in the following subsections to facilitate the comparison of costs by country or region for the same technologies and to enable the identification of the key drivers in any cost differences. The four key indicators are total installation cost, operation and maintenance cost, capacity factors, and LCOE.

https://wes.copernicus.org/articles/7/2003/2022/wes-7-2003-2022-f05

Figure 5 Turbine purchase cost survey for rated power lower than 20 kW (Kaldellis and Zafirakis, 2012) and around 50 kW (authors' experience).

3.1  Total installation cost

The total investment for installation can be expressed as the sum of the purchase cost and installation cost. The purchase cost for a SWT is notably variable not only as a function of the turbine size but also over time, depending on the attention given to the technology. Kaldellis and Zafirakis (2012) present a survey on 142 SWT models up to 20 kW, showing – as expected – a turbine cost reduction as a function of the rated power (black square markers in Fig. 5). Recent data from the authors' direct experience are also added as red diamonds in Fig. 5 for the SWTs with rated power outputs around 50 kW. As seen in the figure, the decreasing cost trend for lower rated power values is somehow stopped for rated power outputs around 50 kW. This can be explained considering that, from this size up, turbines become more complex, requiring specific features (e.g., the yawing system) and a manufacturing quality higher than that of smaller turbines. Finally, Bortolini et al. (2014) provide a more up-to-date market survey considering several producers located worldwide and confirm that purchasing costs are not so highly correlated to the plant sizes because of aspects related to the specific producer, e.g., producer country, producer cost structure, and market policies. Having direct information on how the global, or total installed, cost comes together is very rare. In this study, thanks to support from the Eunice Energy Group, a cost breakdown is presented in Table 2 for the 60 kW machine EW16 Thesis (Eunice Energy Group, 2021).

Table 2 Capital cost breakdown of a 60 kW turbine (courtesy of the Eunice Energy Group). Weights reported in metric tons.

wind turbine technology research paper

Wood (2011) reported a similar breakdown for a smaller machine (10 kW), showing how – in that case – the relative cost for blades becomes more relevant (7 %), while that of the generator becomes less significant (6 %) due to the lower power output.

The installation cost is probably the most critical parameter to evaluate and includes seven primary factors.

Raw material cost, i.e., expenditures to purchase the materials required for the turbine installation as well as to lay the foundation. All these elements are correlated to the wind turbine's weight and height and to the rotor diameter.

Earthworks' cost, i.e., foundations, grounding, etc., to enable SWTs' operation. This is more crucial for countries with higher seismic activity that require more expensive foundations and is dependent on the type of soil.

Installation labor cost, i.e., workers' salary, crane rental, standby times on windy days.

Engineering cost, i.e., expenditures for the preliminary and executive drawings, feasibility study and engineering, and site assessment and wind resource assessment activities to estimate expected AEP as well as documentation of all deliverables.

Land purchase cost, i.e., cost for the required ground surface. Considering the tower height, a surface area of the same swept radius is assumed to be necessary. Additional cost for access roads, where not present, may be necessary.

Grid connection cost, i.e., cables, power unit, and control system, including license fees.

Transportation costs, i.e., the expenditures necessary to get the turbine to the installation site. Transportation costs can include two different types of trips. In the case of imported turbines, transportation by both sea (e.g., to reach the EU mainland) and land (i.e., to reach the final site) is needed.

The relative impact of these factors has been quantified by Bortolini et al. (2014) and reported in Table 3.

Table 3 Impact of different cost factors on a SWT project.

wind turbine technology research paper

The engineering cost in Table 3 includes the wind resource and site assessment activities conducted to estimate a SWT's expected AEP. The low percentage of total cost for this cost factor is in line with similar research that found that many small wind installers do only minimal wind resource and site assessments (Orrell and Poehlman, 2017). This is partly because of the challenges involved in achieving a low-cost and accurate wind resource assessment. First, there are not many tools available and appropriate for small wind assessments. Next, for those installers who do attempt assessments, the tools regularly do not provide accurate AEP estimates because they mischaracterize the wind resource and perform poorly in areas of complex terrain well (Sheridan et al., 2022). Based on the experience of some of the authors, the cost for a resource assessment for a SWT project may be in the order of EUR 15 000 (USD 172 000), although this price is strongly variable from case to case, especially as a function of the site topography. In addition, one should also remember that the complexity of the terrain also affects accessibility to the grid, roads, price of the land, foundations, and the excavation works needed, thus also impacting the other items of the table.

Referring again to the 60 kW EW16 Thetis machine by the Eunice Energy Group, even though real costs are strictly project-dependent, the foundation cost can be broken down into approximately EUR 3000 (USD 3450) for the excavation (23 %), EUR 8000 (USD 9200) for the concrete (61 %), and EUR 2000 (USD 2300) for civil works (16 %). The transportation cost is approximately EUR 5000 per day (USD 5750 per day) (up to two trucks and up to 600 km), while the crane costs for a 50 t, 40 m crane are about EUR 6000 (USD 7200).

https://wes.copernicus.org/articles/7/2003/2022/wes-7-2003-2022-f06

Figure 6 Installed cost per kilowatt for newly installed or retrofit projects in the United States (Orrell et al., 2021).

An overview of the overall average annual and project-specific small wind installed cost (in 2020 USD) in the United States for 2010 through 2020 is presented in Fig. 6 (data from Orrell et al., 2021). Only new and retrofit projects with reported installed costs that use turbines with known rated capacities are included. Annual average capacity-weighted installed costs for new US small wind projects range from around EUR 3480 per kilowatt (USD 4000 per kilowatt) to nearly EUR 9565 per kilowatt (USD 11 000 per kilowatt). The small sample sizes and high variance in project-specific costs both contribute to this wide cost range. With the exception of 2018, the overall annual average capacity-weighted installed cost for this US dataset has remained relatively flat at approximately EUR 9260 per kilowatt (USD 9500 per kilowatt) (Orrell et al., 2021). This cost trend is in contrast with residential solar PV costs, which have been steadily dropping over several years (Barbose and Darghouth, 2015).

3.2  Operations and maintenance cost

Operations and maintenance (O&M) are conventionally clustered into a single cost term, but operation costs differ from maintenance costs, and not all distributed wind projects experience them equally. Operation costs for wind projects may include land lease payments, remote monitoring, various operations contracts, insurance, and property taxes. Operations are a significant expense for wind farms and large distributed wind projects; however, they typically are not substantial, or even present, for small, distributed wind projects. On the other hand, all wind projects, distributed or otherwise, require a significant maintenance cost (Orrell et al., 2021). For small wind systems, and especially in the case of complex areas, experience shows that usually an investor does not opt for installation sites with more than two SWTs in the same field or from the same owner. This consequently decreases the available room for the economy scaling on the O&M costs.

In most cases, the project installer or developer performs the maintenance for the system owner. Maintenance costs include labor, travel to the site, consumables, and any other related costs. Therefore, small wind maintenance costs can depend on the maintenance provider's proximity to the project site (i.e., travel costs), the availability of spare parts, and the complexity of maintenance and repairs. Maintenance costs can be categorized as scheduled or unscheduled. Scheduled maintenance activities can include inspecting the turbine, controller, and/or tower; adjusting blades; checking production meter and communications components; and providing an overall annual scheduled maintenance visit per the manufacturer's manual. Unscheduled maintenance activities can include a wide variety of activities, ranging from responding to a customer's complaint of noise from the turbine to replacing the generator, electrical components, inverter, blades, or anemometer. Scheduled maintenance site visit costs for a sample of small wind projects were collected for the Benchmarking US Small Wind Costs report (Orrell and Poehlman, 2017). Scheduled maintenance is typically performed annually. That data showed that the average scheduled maintenance cost per visit is about EUR 32 per kilowatt (USD 37 per kilowatt); the same value was confirmed by some European companies (Eunice Energy Group, personal communication, 2022). In general, upon combining different reference sources, it is reasonable to consider O&M cost for small wind projects in the range of 1 %–3 % of the initial investment (Tzen, 2020).

3.3  Capacity factors

The economic viability of SWTs depends in a complex way on several factors, including the life-cycle energy production and the possible presence of incentives. To address the first issue, i.e., to correctly evaluate actual production, a key metric is the capacity factor.

https://wes.copernicus.org/articles/7/2003/2022/wes-7-2003-2022-f07

Figure 7 The 3-year-average capacity factor for several US wind projects. Data from Orrell et al. (2020).

Boccard observed mean values below 21 % in 2009 (Boccard, 2009), while more recent works observed values between 37 % and 40 % (US DOE, 2015). Figure 7 presents calculated capacity factors for SWTs installed in the United States, based on the average of the first 3 years of reported generation for each project from the New York State Energy Research and Development Authority and US Department of Agriculture Rural Energy for America Program datasets and the turbine rated capacity (Orrell et al., 2020).

The 3-year-average capacity factor for small wind is 17 %, but the dataset includes a range from as small as 2 % to as high as 36 %. This large variability reflects, more than other variables, the challenges to SWT siting and site suitability. For example, the capacity factors for the 8.9 kW rated capacity turbines range from 5 % to 29 %. This means that the same turbine model sited in different locations can achieve very different capacity factors. Overall, the wind resource quality has the largest impact on capacity factors, even though technology improvements have raised turbine power outputs significantly. Therefore, the wide variation in capacity factors across markets is predominantly due to differing wind resource qualities and, to a lesser extent, the different site configurations and technologies used.

3.4  Levelized cost of energy

Scattered data regarding the LCOE of SWTs can be found in the literature and relevant reports. One of the most complete databases is provided by Orrell et al. (2020), who collected the data reported in Fig. 8 (prices are in cents of USD/EUR) for the US market.

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Figure 8 Measured LCOE for SWT projects in the US data from Orrell et al. (2020).

The small wind average LCOE after incentives was EUR 0.2 per kilowatt-hour (USD 0.23 per kilowatt-hour) (from 86 US projects totaling 2 MW in rated capacity). To put these numbers in perspective, the LCOE of SWTs may be compared to the average residential retail electric rates ranging from approximately EUR 0.07 to EUR 0.17 per kilowatt-hour (USD 0.08 to 0.20 per kilowatt-hour) in the continental United States (Orrell et al., 2019) and to the LCOE of residential PVs, which is below EUR 0.087 per kilowatt-hour (USD 0.10 per kilowatt-hour). Recent experiences in Europe for turbines in the range of 50 to 60 kW showed potential for a significantly lower LCOE on the order of EUR 0.12 per kilowatt-hour (USD 0.0014 per kilowatt-hour) (Eunice Energy Group, personal communication, 2022). The relationship between calculated LCOEs after incentives and capacity factors is shown in Fig. 9. As expected, the higher the capacity factor, the lower the LCOE in general. Higher capacity factors, which in turn can reduce LCOEs, can be achieved by better siting, which can help increase energy production and better turbine operations (i.e., higher turbine availability).

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Figure 9 Relationship between LCOE and capacity factor for SWT projects. Data from Orrell et al. (2020).

Regarding the European Union, to the best of the authors' knowledge, there is no systematic study of the LCOE of SWTs, but there are a number of studies that point to higher LCOEs than those reported for the United States. For a site with a mean annual wind speed of 4.77 m s −1 , Bukala et al. (2016) estimate a yearly energy production of 7551 kWh for a SWT with a rated power of 5 kW, neglecting downtime. They estimate the investment cost of such a wind turbine at EUR 36 500 (USD 42 000), which is lower than that in the data reported for the United States. For a discount factor of 4 % and assuming a yearly operation and maintenance cost of 2 % of the investment cost, an LCOE of EUR 0.45 per kilowatt-hour (USD 0.52 per kilowatt-hour) is produced without incentives.

For a SWT with a rated power of 3.5 kW installed at an agricultural site in Belgium with a mean wind speed of 4.13 m s −1 , Tordeur (2018) reports an LCOE of EUR 0.36 per kilowatt-hour (USD 0.415 per kilowatt-hour) without incentives. This, coupled with all the incentives from which a small to medium agricultural enterprise may benefit in Belgium at the time of the measurement campaign (2016) and accounting for a discount rate of 4 %, gives a discounted payback time of 19 years. It is worth noting that the true cost of this project was a very low EUR 4300 per kilowatt (USD 4950 per kilowatt). The low cost is partly explained by the fact that the farmer acquired the tower separately at reduced cost and performed most of the installation himself. Even with such major cost-cutting, the SWT is not economically viable, indicating that a mean wind speed of 4.13 m s −1 is too low for a viable SWT project.

Bryne (2017) reports the metered energy output for a number of sites in Ireland. For a site with a mean wind speed of 6.1 m s −1 , the AEP of a 5.2 kW rated wind turbine is 14 947 kWh, and for a site with a mean wind speed of 4.7 m s −1 , the AEP of a 2.1 kW rated wind turbine is 3816 kWh. Assuming again a discount rate of 4 %, a yearly operation and maintenance cost of 2 % of the investment cost results in LCOEs of EUR 0.33 per kilowatt-hour (USD 0.38 per kilowatt-hour) and EUR 0.51 per kilowatt-hour (USD 0.59 per kilowatt-hour) for the 5.2 kW and 2.1 kW turbines, respectively, if the average installed cost per kilowatt from Orrell et al. (2019) is used. LCOEs of EUR 0.14 per kilowatt-hour (USD 0.16 per kilowatt-hour) and EUR 0.22 per kilowatt-hour (USD 0.25 per kilowatt-hour) are produced, respectively, if the average installed cost per kilowatt from Tordeur (2018) is used.

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Figure 10 LCOE trends versus annual average wind speed at different specific investment values in EU. Data from Predescu (2016).

Figure 10 presents the results of a study of the LCOE trend versus annual average wind speed at different specific investment values, with the household energy purchasing prices in EU also shown as references (Predescu, 2016).

Financial viability for small wind investment occurs in the region where the LCOE curve, computed for a specific investment value, is lower than the household energy price at the implementation location. The break-even point for a specific investment value is at the intersection of the respective LCOE curve with the line representing the household energy price. Beyond this point toward higher wind speeds, the savings obtained when using small wind technology brings long-term tax-free profit and savings to the investor. In countries where the household energy price is lower, financial viability can be reached at smaller specific investment costs and higher annual average wind speeds, which limits the geographical area where grid-connected small wind systems can be efficient. This analysis shows that in most situations, SWTs cannot compete with residential PVs in terms of economic viability (European Court of Auditors, 2018). Even at sites with high wind speeds, the cost reduction required to achieve viability is still substantial. Taking the best case from Bryne (2017) as a close-to-optimal performance example with a capacity factor of 33 %, the investment cost would need to be less than EUR 6000 per kilowatt (USD 6900 per kilowatt) for the LCOE to fall below EUR 0.20 per kilowatt-hour (USD 0.23 per kilowatt-hour), which is typical for residential retail electric rates in many European countries. This illustrates the main conclusion from the above analysis: SWTs may be viable, but only at very windy sites and with a serious additional effort to reduce the investment cost.

While Sects. 1–3 reported the status of the technology in terms of diffusion and costs, this section shifts the focus to the specific features of SWTs, which are the core of small wind systems. The philosophy with which this study has been prepared highlights those features that make SWTs different from utility-scale machines. This is important for introducing the resulting challenges that must be tackled to further progress SWT technology.

4.1  Typical features of small wind turbines compared to utility-scale turbines

Utility-scale wind turbines are usually located in clusters and in areas with high wind resources, from a few turbines to large wind plants located far (e.g., offshore) from the consumer. Although some utility-scale wind turbines may provide energy to the owner, they are typically owned by or provide power to a utility company. In contrast, SWTs are typically owned by the individual or organization that will use the power, such as a home or business, and are installed close to those loads. Because the siting driver for SWTs is proximity to loads and not the optimal wind resource, the winds at these locations often have low average speeds; are highly turbulent; and are more likely to have obstacles nearby, which can create flow structures of a scale commensurable to that of the turbine. On the one hand, this usually leads to lower peak power coefficients, ranging approximately from 0.25 to 0.40 (Wood, 2011), compared to values higher than 0.5 for utility-scale machines (Veers et al., 2019). However, full transparency regarding the real efficiency of SWTs is often missing. For example, in a relatively recent study, it was shown that 15 out of 43 manufacturers claim a power coefficient above the theoretical maximum or Betz–Joukowsky limit (Simic et al., 2013). Notwithstanding this, it is undisputable that the peculiar environment these rotors work in implies that SWTs must be specifically designed to work effectively in both low- and turbulent-wind-resource conditions. The implications of these peculiar working conditions are many and involve all aspects of turbine design and operation, as summarized below.

4.1.1  Aerodynamics

The combination of dimensions much smaller than those of utility-scale machines with turbulent winds may present significant problems for the aerodynamics of SWTs. First, the resulting low Reynolds numbers ( R e ) may cause a laminar separation bubble, which is associated with a local maximum of the drag coefficient in the polar and a reduced lift-to-drag ratio ( L / D ) (Selig, 2003). The presence of transition and the relative impact of inflow turbulence on it is key for airfoil performance (Abbott and Von Doenhoff, 2010). This has many implications for design, including the fact that airfoils for SWTs must be selected from those that provide good performance at low R e numbers, which favors airfoils with lower thicknesses that are, however, more sensitive to stall. A compromise in this regard must be pursued. The presence of transition makes the L / D dependent on R e and thus is particularly challenging for blade designers. Because the angle needed for maximum L / D is also R e -dependent, a constant-pitch turbine would not operate at maximum efficiency at a constant tip-speed ratio, making the control strategy in below-rated conditions more complicated (see the following subsection).

The aforementioned issues are particularly challenging in terms of proper simulation. Panel methods usually employed by companies to define polars likely fail to correctly model these phenomena in many instances, especially in the near- and post-stall regions. However, accurately modeling these phenomena is crucial for SWTs, particularly stall-controlled ones (Papi et al., 2021). High-fidelity models used in academia are often not affordable for SWT companies, and airfoil selection is therefore often based on published performance data. Examples of airfoils with good performance characteristics at low (around 5×10 5 ) Reynolds numbers can be found in Gigue`re and Selig (1998) and Timmer and van Rooij (2003). Even high-fidelity turbulence models, however, often do not predict lift and drag accurately in the presence of transition, let alone laminar separation, and the designer should rely on lift and drag data measured in reliable wind tunnel tests (Van Treuren, 2015).

The problem of low Reynolds numbers is further exacerbated by the possible installation of SWTs at high altitude (Pourrajabian et al., 2014), where the air density reduction can substantially reduce R e (up to more than 10 %), bringing it to those values where the effect of transition is more relevant. In this sense, it has been shown that the correction methods proposed in the standards (wind or power correction) often fail in correctly representing reality.

The influence of blade roughness, due to insect accumulation in dry areas or leading-edge erosion for example, also differs between SWTs and large turbines. Holst et al. (2016), for example, discuss the effects of roughness by comparing lift polars of low- R e airfoils to high- R e utility-scale wind turbine airfoils. Experiments in that study revealed lift deficits of up to 50 % and confirmed the importance of a proper profile selection. In addition, simulations showed that roughness can reduce AEP by up to 50 %. Furthermore, roughness sensitivity could lead to premature separation, especially near the blade root that is characterized by highly three-dimensional flow (Bangga et al., 2017). Thus, employing airfoils with good aerodynamic characteristics for the specific blade span and expected operational regime is compelling.

4.1.2  Control

Large wind turbines have yaw-drive mechanisms to align the rotor to the mean wind direction. Such devices are much more expensive for SWTs, especially for small rated power values (10 kW or less): in these applications, some form of free or passive yaw has been typically used. The most popular options are then a tail fin or the use of a downwind rotor, e.g., SD Wind (SD Wind Energy, 2022), Skystream (XZERES Wind Turbines, 2022), Carter Wind (Carter Wind Energy, 2022), and others. The downwind configuration solution is experiencing a revival for some specific applications in utility-scale machines, especially for floating offshore applications (Bortolotti et al., 2021). For larger turbines, the same yaw-drive technology in use for utility-scale machines is instead being increasingly applied.

Another control actuation commonly found in large wind turbines is the blade pitch system that can both regulate power and slow down the rotor for overspeed protection by aerodynamically changing the blades' angle of attack. However, pitch control is often not available at the scale of SWTs for economic reasons. Designing and manufacturing a fail-safe pitch system within the physical constraint of a small hub and the capital cost constraints needed to keep an overall low LCOE are one of the biggest challenges for the SWT industry. The need for a redundant brake mechanism, in fact, translates into either having independent pitch actuation (as for the utility-scale machines) or an oversized mechanical brake that could bring the rotor to a stop in the case of grid connection failure and associated runaway rotor. Both options have proven to be prohibitively expensive in the DWT space thus far, and more economical solutions for avoiding overspeed that have been widely adopted include stall regulation and/or rotating the rotor out of the wind direction via a furling mechanism. An attractive option for smaller SWTs is “electromagnetic braking” by shorting the generator output (McMahon et al., 2015). This obviates the need for a mechanical brake. Several current commercial SWTs such as the Bergey XL 15 (Bergey Wind Power, 2022) use this cost-reducing strategy. Regarding active pitch, however, a recent study (Papi et al., 2021) highlights how the use of advanced pitch-to-feather control strategies can significantly improve the performance of SWTs through more effective power regulation. It is speculated that the aerodynamic power coefficient could be improved significantly to reach C P ≈0.5 , which, together with simpler and therefore more accurate aerodynamic modeling performance, could then justify the higher cost of pitch actuation in a SWT. Also, another study (Papi et al., 2022) showed that a pitch control strategy can reduce peak loads in extreme conditions, thus potentially leading to lighter and more cost-effective blade designs. Blade pitch can also help with start-up torque at low wind speeds, whereas a fixed-pitch rotor must rely on its low wind speed and high angle-of-attack performance to overcome the resistive torque of the drivetrain and generator. A quick starting characteristic is crucial for SWTs because they tend to have more start and stop events compared to their larger counterparts due to higher turbulence levels and lower average wind speeds.

Due to the aforementioned technical and economic issues, stall control is still largely used in SWTs. This latter strategy, however, generates peak loads on the blades that are relatively much higher than those seen in utility-scale machines because the pitch cannot be varied in parking conditions. In addition to the lower efficiency in terms of regulation across the functioning range, the stall control strategy inherently introduces difficulties in predicting the aerodynamics of SWTs because three-dimensional flow aspects and unsteady characteristics make the near- and post-stall regions of the polar curves difficult to capture in aerodynamic models, especially in engineering methods (which can be economically used during the design phase). These difficulties are further compounded in the case of passive-yaw configurations. Skewed inflow and dynamic wake physics are still a topic of research in the wind energy community (Ning et al., 2015; Schepers et al., 2021) and in the case of SWTs, given their more dynamic nature (e.g., higher yaw rates, rotational velocities, and passive yaw), introduce further nonlinearities and unsteadiness in the rotor and tail induction fields, rotor aeroelasticity, and overall turbine response.

4.1.3  Structural design and (scarce) aeroelasticity modeling

In the field of large wind turbines, the use of aeroelastic simulation tools has been a consolidated practice for years (Bottasso et al., 2006) and is required for the certification of the machine itself. In the case of SWTs, the common approach up to a few years ago was to build stiff blades characterized by high safety factors in the structural design in order to avoid significant aeroelastic effects. As discussed, however, somewhat larger SWTs (from about 60 kW and up) are now practically equal in complexity to large wind turbines (e.g., they usually have a variable-speed pitch-torque control system and an active yaw control system and, because they often have a single actuation system for the blades, for safety they require mechanical brakes for the emergency stop). In addition, they are often designed for low to medium wind speeds, so the blade is very large (for the 60 kW blades, it is possible to reach 14–15 m). The experience of many authors of this paper, who had the opportunity in the last decade to collaborate with the small or medium enterprises (SMEs) producing these rotors (IEA, 2014), shows that the use of aeroelastic simulation tools is important to ensure a quality, safe, and economically sustainable project but is still very uncommon. One of the few aeroelastic analyses of a 5 kW turbine is described by Evans et al. (2018). The less frequent use of aeroelastic models in industry is due mainly to a lack of experience of these companies, which very often come from other industrial fields (e.g., producers of boats or heavy mechanical systems) where other design tools such as finite-element codes are primarily used. These companies are often not aware of the availability of good aeroelastic tools in the public domain (e.g., OpenFAST from the National Renewable Energy Laboratory, NREL; NREL, 2022). Finally, another limitation to the use of aeroelastic simulation tools for SWTs is connected to the lack of easy-to-handle post-processing tools. In fact, standards require the designer to simulate the wind turbine in power production for different wind values and gusts, but also for a variety of other operating conditions (starting phase, normal and emergency shutdown, transportation, faults, etc.). This results in a few thousand simulations that must be analyzed to extract maximum loading values for the various sub-components of the wind turbine, including blades, tower, and drivetrain, but also pitch and yaw, air gap in the generator, supports, bearings, brake discs, foundation, etc. In turn, these loads, together with fatigue loads and stress range cycles, need to be delivered to the different partner manufacturers. This process therefore requires automated tools and specific skills that are not always available outside academia or large manufacturers.

4.2  Innovative concepts and vertical-axis wind turbines

Whereas conventional horizontal-axis wind turbines (HAWTs) have become the reference technology for all scales up to 15 MW or higher, alternative concepts are still being proposed for SWTs (Damota et al., 2015).

A popular modification to small HAWTs is to enclose the rotor with a diffuser to induce more airflow through the blades and thereby increase the power output. This produces a diffuser-augmented wind turbine (DAWT), some examples of which are shown in the first row of Fig. 10. Adding a diffuser is indeed more attractive for small turbines than large ones because the additional structural and wind loads on the latter are likely to be excessive. A diffuser is a relatively simple modification to basic turbine design, but it is still not clear how to optimize the diffuser and rotor to extract maximum power and whether the extra power is worth the cost of the diffuser. An interesting review demonstrating the enduring fascination of the concept has been recently reported by Bontempo and Manna (2020). There are other advantages of DAWTs: the diffuser may contain a blade if it detaches from the rotor and probably make the turbine quieter and less harmful to birds. These may well be significant advantages for DAWTs in urban settings (Micallef and van Bussel, 2018). At least two companies have recently commercialized small DAWTs, as showcased by Evans et al. (2020) and Visser (2020). They have found a wide range of applications from remote communication systems where the turbine partners a photovoltaic system to more common stand-alone systems.

Beyond other pioneering studies on novel energy-conversion systems such as DAWTs, most of the research on novel SWT architectures has been directed to vertical-axis wind turbines (VAWTs) (Aslam Bhutta et al., 2012).

Among these, drag-type rotors like the Savonius turbine (Akwa et al., 2012) are relegated to very small applications due to their low power coefficients and high mass-to-power ratio. Nevertheless, thanks to their simplicity, Savonius VAWTs are still considered suitable in remote rural areas (e.g., the first electrification of developing countries) (Senthilvel et al., 2020).

On the other hand, despite a long absence from research agendas after the first generation of research culminated in the mid-1990s, lift-driven VAWTs (or Darrieus concepts) are being increasingly studied (Bianchini et al., 2019). Despite popular claims, the new understanding of the complex aerodynamics of Darrieus VAWTs achieved in the last decade has proven that these machines can achieve power coefficients comparable to those of small HAWTs (Bianchini et al., 2015a). More importantly, VAWTs present several advantages for small-scale applications, namely an intrinsic insensitivity to wind direction, misaligned flows (Bianchini et al., 2012), or turbulence (Balduzzi et al., 2020) as well as lower acoustic noise generation associated with generally lower tip speeds (Möllerström et al., 2016). The advantage of low blade speed, however, is offset by the need to have a physically bigger, and therefore more expensive, generator and mechanical brake. In addition, VAWTs allow for a variety of design solutions, which are considered aesthetically pleasant by the public and thus also suitable for integration in buildings (Dayan, 2006) or with other infrastructure such as streets (Khan et al., 2017). Therefore, a variety of small manufacturers entered the market either with downscaled VAWTs or with alternative concepts specifically intended for use on rooftops (Mertens, 2003). Among others, one concept that is receiving increasing attention is the exploitation of the so-called Magnus effect, which is a phenomenon associated with a solid object spinning in a fluid. This concept has been studied for both HAWT (e.g., Sedaghat, 2014) and VAWT (Shimizu, 2013) designs. The potential advantage of these solutions lies in the fact that they can operate in relatively low winds (Bychkov et al., 2007), thus covering a range of winds not typically exploited by conventional wind turbines.

For very small VAWTs ( < 3  kW), recent designs chose high-solidity rotors, i.e., rotors with larger chord-to-radius ratios, mainly because of the need for sufficiently long chords to increase the aerodynamic forces and the Reynolds number. Based on recent analyses, this aerodynamic solution seems to provide unprecedented specific power values for small rotors (Bianchini et al., 2015a). On the other hand, these models showed the significant shortcomings of existing simulation models (Bianchini et al., 2019), which were resolved largely by the new understanding of the role of flow curvature effects (Bianchini et al., 2015b, 2016). Renewed research efforts are being undertaken to determine whether VAWTs can fit the scope of distributed energy production in complex installation areas, as testified to by the recent EU project (Aeolus4Future, 2022). Parallel to these research trends, VAWTs are being investigated for deep-water offshore applications with floating substructures (Paulsen et al., 2013). The more favorable structural loads of the VAWT architecture and the possibility of placing the generator on the floating platform – and thus lowering the system's center of mass – may lead to smaller floating supporting structures, better control, reduced logistics and capital cost, and ultimately a lower LCOE (Arredondo-Galeana and Brennan, 2021). In the realm of offshore SWTs, floating VAWTs could be deployed in some niche applications like integration with beacons at the entrance of a port. A recent book, for example, explores the relationships between small wind and hydrokinetic turbines (Clausen et al., 2021). Overall, despite the benefits that could be provided by VAWTs in some applications, they still lack both theoretical understanding and technical maturity compared to HAWTs. Whereas the theoretical gap could be overcome by modern investigation techniques, gaining the same level of industrial maturity as HAWTs seems out of reach at this time. The potential impact of funded research projects at a national or a broader level could be relevant in proving the real prospects of the technology and driving their development.

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Figure 11 Currently proposed DAWT (upper row) and AWE kite archetypes (lower row). First row (from left to right): the Diffuse Energy Hyland 920 diffuser-augmented turbine as part of a remote power system for a communication tower (the 200 W turbine has a maximum diameter of 0.92 m; photo supplied by Dr Joss Kesby), HAWT with flanged diffuser (Ohya et al., 2008), DonQi urban windmill (photo credit: DonQui Global). Second row (from left to right): cross-wind or fly-gen (a.k.a. drag-power) devices (image credit: Windlift), ground-gen (a.k.a. lift power) flexible kite (photo credit: KPS), ground-gen rigid kite (photo credit: Ampyx Power), aerostat ducted wind turbine (photo credit: Altaeros).

Other touted devices that, at least on paper, have demonstrated the potential for low LCOEs are airborne wind energy (AWE) kites (Fig. 11). They propose to extract wind power either through cross-wind by using lift and therefore flying faster than the wind speed and carrying turbine generators on board (fly-gen) or by pulling and unwinding a tether connected to a generator on the ground (ground-gen). Other concepts expect to take advantage of very-high-altitude winds via buoyant aerostat ducts. None of these concepts has thus far demonstrated an economically viable power curve or has shown successful size scalability in real-world settings. Yet, there is significant momentum in AWE research, with some pioneering industrial products already on the market, and the applicability of these devices will likely be in the distributed wind space. While it is difficult to assess the real costs and LCOE of AWE kites due to their nascent stage, the key advantage they provide is the absence of hefty and expensive support structures while maintaining a generous rotor swept area. This would have favorable effects on the balance of station costs that have plagued the DWT industry to date; this is the main reason why they are mentioned here as potential actors of the small- and, more likely, distributed wind market of the future. The challenges these devices face are numerous, however, from flight safety and reliability to the efficiency of power generation and from the issuing of design and certification standards to their acceptance by public and aviation authorities, and only future deployments will indicate whether they can compete in the DWT market.

4.3  Turbine archetypes and design standards

Unlike the typical utility-scale, three-bladed, upwind machines, SWTs have not coalesced into a dominant archetype, with many different layouts still being offered on the market. The variety of archetypes (upwind vs. downwind; HAWTs vs. VAWTs; two vs. three or more blades; even one like PowerHouse Wind, 2022; active pitch vs. stall-controlled; etc.; see Figs. 12 and 13) creates a challenge for the design standardization and certification of SWTs (Damiani et al., 2022). This challenge is made stronger by the intention of standards to facilitate the development of SWTs at relatively low cost; the “simplified loads methodology” (SLM) in IEC 61400-2 for small horizontal-axis turbines is the main example.

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Figure 12 Common HAWT archetypes found on the current DWT market. From left to right: upwind, active pitch and yaw (photo credit: Tozzi Nord); upwind, stall-controlled and active yaw (photo credit: Eunice); upwind, stall-controlled and tailed passive yaw (photo credit: NREL pix 49511); downwind, stall-controlled and passive yaw (photo credit: Eocycle – formerly XANT); upwind, tailed passive yaw, furling (photo credit: Bornay); downwind, pitch- or pitch-coning-controlled, passive yaw (photo credit: SD Wind, formerly Proven); downwind, stall-controlled, passive yaw and teeter (photo credit: Ryse Energy, formerly Gaia).

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Figure 13 Common VAWT archetypes found on the current DWT market. From left to right: Darrieus Troposkien (photo credit: Chava Wind), H-Darrieus (photo credit: Xflow Energy), H-Darrieus with helix shape (photo credit: PRAMAC), Savonius (photo credit: BE Wind), combined Savonius–Darrieus (photo credit: HiVAWT).

The lack of dominant archetypes complicates the development of standards and design tools for SWTs, resulting in a reduced refinement and robustness for all the archetypes as their counterparts for utility-scale machines.

Type certification for large wind turbines, which primarily follows IEC 61400-1 (IEC: International Standard, 2019a), are typically performed by large companies with extensive design teams who can afford multidisciplinary development departments, highly refined turbine-specific aeroelastic models, high-performance computing, and testing facilities. The much smaller companies that manufacture SWTs do not have access to such resources. For example, even though estimating the loads according to the design standards would require only a few hours of computational time with state-of-the-art engineering codes, these codes require resources and staff with very specific skills to be utilized correctly, and correlation to archetype-specific load measurements is needed to demonstrate confidence in the results.

The IEC standard for wind turbine design also includes the IEC 61400-2, dedicated to SWTs (IEC: International Standard, 2019b). It covers all mechanical and electrical subsystems and includes support structure and foundations as well as the grid connection (including power electronics where applicable). The section applies to wind turbines with a rotor swept area smaller than or equal to 200 m 2 generating at a voltage below 1000 V AC or 1500 V DC and covers both grid-connected turbines and off-grid applications. IEC 61400-2 allows for a number of simplifications to the design and analysis of turbines, including the use of the SLM and a reduced number of design load cases (DLCs). However, the SLM currently captured in the standards is more than double the safety factor for ultimate loads, which may make the SLM process easier to use for the design phase and helps keep costs low but will create a heavier and more expensive product, which results in turbines that may not be competitive on the distributed generation market. By their nature, use of the SLM normally leads to a safe but over-designed product. For example, for very small SWTs, the critical DLC includes the gyroscopic loads on the blade roots and main shaft under yaw; however, in general terms our knowledge of the yaw behavior of SWTs is poor across the range of turbine configurations. The magnitude of the gyroscopic moment is given by a simplified load equation involving the blade moment of inertia, the blade angular velocity, and the yaw rate. Although the equation captures in principle the actual physics responsible for the gyroscopic moment (Wilson et al., 2008), the safety factor for this load is 3. The SLM stipulates the maximum yaw rate as a function of rotor area and then requires this to be multiplied by the maximum blade angular velocity. The limited information available on SWT yaw behavior (e.g., Wright and Wood, 2007, and Bradney et al., 2019) suggests however that high blade speed correlates with low yaw rate, but this is not used in the SLM.

As an alternative or if the turbine configuration is not covered by the SLM equations, then alternative simulation modeling or load measurements can be used, which may result in a more optimized final design. Additionally, many aspects of the turbine aeroelastic response that are missed by the SLM approach could, in principle, be captured by higher-fidelity aero-servo-elastic modeling. However, using aeroelastic modes for the design and certification of SWTs is challenged by the fact that while models are well tuned for active-yaw and active-pitch HAWTs, they are less validated for stall-controlled, passive-yaw HAWTs and progressively less so for non-traditional archetypes (e.g., teetering hubs, VAWTs, AWE kites) (Damiani et al., 2022).

Regardless of the initial design approach, the reliability of SWTs is guaranteed through duration testing, where at least 6 months of operation is required during which minimum operation at high winds is stipulated. The standard requires comprehensive documentation of the testing. In addition to the whole turbine testing, specific component tests are prescribed.

Some SWTs come with design variations. To limit the demands on the original equipment manufacturers, a full design evaluation is only required on a selected representative configuration. Other variations need only be evaluated or tested in the ways in which they are different from the representative configuration. Guidance on the conformity assessment, however, is rather limited in the design standards, and this has been lamented by the industry as an obstacle to the commercialization of new fleet products or in the case where changes to the product line, such as the use of a new manufacturing process for an individual component, may open the product to extensive work to maintain certification.

For power performance testing, IEC 61400-12-1 includes a normative Annex H specifically for the power performance testing of small turbines. This reflects the fact that testing according to the general standard using 10 min averages, where the complete wind speed range must be covered by sufficient data to minimize statistical uncertainty, can be a time-consuming and expensive process. To get around this difficulty, testing SWTs involves using 1 min averaged data, thus considerably reducing the time needed for testing, but also because 1 min averaging extends the frequency distribution of wind speed, making high-wind-speed data points more common.

The SWT test standard also covers battery charging. Procedures are prescribed that minimize the influence of the specific battery configuration and condition (state of charge). SWTs that use inverters for grid connection are tested together with the inverters, and the power measured is the power available to the consumer. Most SWTs lack a clear definition of rated power and wind speed; instead, a reference power is defined as the averaged power in the 11 m s −1 bin.

Comparisons of 10 min averaged power curves with those based on 1 min averaged data have been presented in Elliott and Infield (2014). Fortunately, the systematic distortion of power curves due to so-called errors in bins was found to be small. However, if the 1 min power curve is used together with a 10 min averaged wind speed distribution, then an error of 1.15 % in the estimated annual energy yield is shown in the study. To avoid this, the energy yield calculation should ideally be based on 1 min averaged wind speed data. Because the calculation of turbulence intensity depends strongly on the averaging period, it would be better for this aspect of site characterization to be based on 10 min data, even if the power curve itself is based on 1 min data as prescribed in the SWT test standard.

From this overview, it is clear that the SWT design standards can be substantially improved on multiple fronts, from the design requirements to the testing, validation, and conformity assessment. The preparation of a new edition of IEC 61400-2 has just started. It is anticipated that the SLM will be improved and there are likely to be further divisions of SWTs depending on size, power rating, and archetype. Rotor swept area combined with rotor orientation, type of power regulation, and type of yaw control, for example, can lead to a matrix organization to determine requirements for design load calculations, structural verification, and numerical model validation that would also depend on the experience of the numerical codes with the different turbine archetypes (Damiani et al., 2022). A rigorous differentiation of certification requirements that depend on the turbine configuration appears as the most urgent need in the design standards to arrive at a substantiated assessment of the load categories for SWT. All these desired changes should make the standard significantly more useful to the manufacturers and end-users of SWTs.

The transition to a more distributed production of energy, combined with the evolution of grids toward “smart” architectures and control logics, which are more resilient, is leading to an evolution in the way electric services are being provided. Distributed solar has already demonstrated wide-scale acceptance (IEA, 2019) in this more distributed energy system. While SWTs have yet to reach general acceptance, they can play a similar and supporting role. To become more commercially accepted, marked cost and performance improvements are needed. Although significant reductions can be achieved through understood technology improvements, additional innovations are needed that lie beyond our current knowledge of critical physics, with particular reference to turbulence, applicability of design assumptions, and the existing modeling and simulation capabilities. Cost reductions that have been demonstrated within the distributed wind industry show that with adequate investment, significant hardware cost reductions are possible (NREL, 2022). However, the generally low investment in small wind technology research and a lack of consistent and substantial incentive programs have relegated SWTs to niche applications with minimal economies of scale. The success of solar PVs, which has benefited from significantly more incentive programs than SWTs on the distributed generation market, demonstrates the importance of stable incentive programs of this type in achieving market share.

Among other considerations, a recurring research gap noted in many studies is that SWTs often fail to achieve predicted or published AEP. This is likely due to a host of considerations such as overly optimistic resource assessments, rotor underperformance at low wind speeds and during high turbulence, or poor final turbine siting. The two flow features, rotor underperformance in low winds and/or turbulent winds, are typical of installations on top of short towers and in proximity to natural or artificial obstacles.

Based on the status of the technology described in the previous sections, the present study identifies five specific grand challenges (GCs) that must be overcome to spur SWT development and meet the globally expected demand for a wider variety of distributed energy resources. The grand challenges are visually presented in Fig. 14, which represents the graphical abstract of this study. To address these challenges, a number of unknowns and gaps to be filled are identified (Sect. 5.1). Future enablers (Sect. 5.2) are also suggested as the keys to elevate SWTs to a more mature technology.

https://wes.copernicus.org/articles/7/2003/2022/wes-7-2003-2022-f14

Figure 14 Visual synopsis on how the key enablers identified in this study may help tackle the five grand challenges for SWT technology.

Grand challenge 1 – improve energy conversion of modern SWTs through better design and control, especially in the case of turbulent wind

Because SWTs are typically installed in areas with lower (less energetic) and more turbulent wind resources, maximizing the amount of energy that can be harvested from the wind (i.e., maximizing the SWT's capacity factor) while ensuring turbine longevity and survival through infrequent high-wind events is critical. Many wind turbines have been shown to underperform in comparison to performance based on simulations. This is due to a combination of simulation tools that overpredict turbine performance, driven largely by the simplification of flow features that these turbines are subject to and the actual complexity of the oncoming flow. In particular, better insight into the impact of turbulence and gustiness on turbine performance is needed. This can be achieved with a combination of more detailed testing data and more advanced design tools capable of modeling the complex blade–flow interactions. Additionally, advancements focused to exploit oncoming winds more effectively, including the use of taller towers or the design of lower specific power rotors to better exploit lower winds, must be continued.

To this end, it is now possible to undertake multidimensional blade design to minimize starting time, blade mass, and noise while maintaining good power extraction and adequate blade strength (e.g., Sessarego and Wood, 2015). Among other aspects, blade mass is paramount because it correlates with manufacturing costs and blade inertia. In turn, the ability of a turbine to start quickly to maximize power extraction at low wind speeds depends on the inertia, as do the gyroscopic loads discussed above, giving this feature an importance that it does not have for large turbines. SWT blades are naturally stiff and benefit from additional centrifugal stiffening at high angular speeds, so further optimization should be possible. Because the gyroscopic loads are major fatigue (as well as ultimate) loads, an improved understanding of turbine yaw behavior should allow more optimized turbine design. This should be seen as the key challenge in the modeling of complex unsteady aerodynamics in the presence of passively yawing rotors, either downwind of the tower or yawed by tail fins.

Grand challenge 2 – improve prediction and reliability of long-term turbine performance despite limited resource measurements

Going beyond accurately optimizing and then predicting the power production of a SWT based on specific wind characteristics, for SWT projects to receive financing, the industry must be able to accurately predict turbine power production over the full life of the project. This accuracy of long-term performance prediction is needed to lower the risk associated with SWTs as seen from the perspective of consumers, insurers, city planning professionals, project financiers, and regulators.

Long-term performance prediction is built on a number of factors, primarily the turbine performance characteristics combined with accurate wind resource estimation and any changes due to local obstacles over the life of the project. Additionally, turbine availability due to mechanical, electrical, and weather conditions at the specific site must be considered in addition to long-term turbine reliability and performance degradation. Although not directly related to turbine design, the availability of spare and replacement parts, approved turbine repair technicians, company warranty commitments, and specific turbine location relative to all these factors will also drive long-term power generation.

Beyond corporate credibility of the installer and turbine manufacturer, long-term production reliability can be categorized in two main areas, i.e., wind-driven resource performance and turbine reliability. Discussions with the SWT development community have identified several key challenges to conducting low-cost but accurate resource assessments (Fields et al., 2016). These include the availability of low-cost anemometer and remote sensing; the lack of high-quality mesoscale-modeled wind speed data at heights typical for SWT installation; and the availability of validated and easy-to-run obstacle modeling to understand the potential impacts of local obstacles on the wind resource, especially in complex terrain (Duplyakin et al., 2021). Once an accurate assessment of the resource at the site in question is available, typically for a model year, additional parameters such as the conditional changes over time, growth of obstructions such as tree cover, and potential weather-driven availability reduction will need to be added. Tools making resource recommendations must also be verified, providing confidence to installers, consumers, and the financial community (Tinnesand and Sethuraman, 2019).

Many turbine manufacturers can point to turbines that have operated reliably for many years, but to be successful in today's market, a long turbine life must be balanced with economic viability (see GC 3). The second element of this challenge is developing methods that prove that SWT technology will operate reliably over the turbine's design life. For example, the SLM of IEC 61400-2 mandates a simple determination of the total number of fatigue cycles experienced by the blades of a SWT. Because of the higher angular velocities of SWTs, the fatigue cycles for SWT blades are on the order of 100 times the number for large turbine blades. Despite this, the standard does not mandate fatigue tests for small blades, and there is not strong operational evidence that fatigue is a major issue for most SWT blades. On the other hand, the fatigue load case in the SLM appears to be very conservative (Evans et al., 2021), which increases turbine costs and may not identify the likely locations for fatigue-driven failures in operating turbines. Addressing this challenge will center on developing a better understanding of the likely failure modes of SWTs, improved knowledge of the role of yaw behavior in generating gyroscopic fatigue loads, the development and use of validated design tools that address the likely failure modes, and standards and certification processes to help ensure that turbines operate reliably over their design life. This improved understanding and these improved tools will also need to be validated for the wide array of SWT configurations, including free and damped yaw. For the future SWT market to be successful, this effort will need to be accepted by large-scale financial organizations, which are driving investment in distributed-scale power generations.

Grand challenge 3 – improve the economic viability of small wind energy

For a SWT to be economically successful, it must provide reliable power at a cost comparable to other similar technologies, such as distributed solar PVs, and be acceptable by the market. A reduction in the LCOE can be achieved by balancing better capacity factors (see GC 1) and reducing unit installed cost. Reductions can come from design optimization; using new materials and manufacturing techniques; developing standardized solutions for components that can be applied across multiple turbine models, such as power inverters; and promoting or incentivizing production economies of scale. Moreover, improvements in installation techniques, reducing the cost of foundations, and other related balance-of-station costs will be needed.

Many strategies have been considered to lower the cost of turbine hardware, with some solid success in specific turbines. A balance must however be made to optimize lower turbine costs, which is largely driven by reducing turbine materials and ensuring successful operation over the turbine's designed life (see GC 2). This optimization must also be balanced with international standards, which may drive up turbine system costs through the SLM. For example, tools used to predict the impact of turbulence on component fatigue while load-reducing turbine control, such as adopting pitch regulation typical in larger rotors, can also help ensure long-term turbine operation while optimizing turbine material needs. The expanded use of validated aeroelastic design tools will also become more critical to help optimize this balance of reliability and low cost.

Recent increases in commodity prices as well as supply chain interruptions are causing increased costs for most SWT manufacturers. Although some of these challenges could be overcome with expanded manufacturing, leading to larger economies of scale and increased industry purchasing power, expanded research into material substitution for high-cost or hard-to-access materials would help lower and stabilize turbine manufacturing costs. Expanded work in aligning component supply across multiple SWT vendors may also help address some high costs and lower component availabilities, especially if supply chain disruption becomes more common.

Overall, a lower LCOE will also help communities access SWT technology (see GC 5), allowing wind technology to play a more active role in addressing issues of energy poverty and energy access while reducing the needs for financial incentives, which typically favor wealthier consumers.

Grand challenge 4 – facilitate the contribution of SWTs to energy demand and electrical system integration

Having more distributed wind in the energy mix could contribute significantly to energy justice and power system decarbonization. The ability of distributed wind to provide low-cost energy close to consumers with a higher energy density and smaller footprint of other distributed technologies provides an important tool to achieve low-carbon-energy-system goals. Additionally, SWT lends itself to local development and deployment. Many developing countries, for example, are more likely to have the capacity to build an indigenous SWT than the solar cells necessary for a PV system. If the fulfillment of GC 2 is pivotal to make investment in SWTs attractive to many more customers, the introduction of many SWTs to the grid is non-trivial, although the expanded use of distributed energy resources will generally require improved energy control and likely distribution system enhancements. The highly discontinuous power production of SWTs, which can be hampered by some energy grids with restrictive ramp rate requirements or that are particularly susceptible to faults, requires additional thinking. SWT technology must not only advance to meet the rapidly evolving grid code requirements for distributed generation (Preus et al., 2021), but the value they may add to grid reliability and resilience should be highlighted and monetized. Standardization through improved future revisions of IEC 61400-2 will bring the industry to a similar technical level for remote control and safety in the smart grids of tomorrow. Due to their distributed nature, the ability of SWTs to assist load reduction or load shifting in behind-the-meter applications, especially in markets that are expanding electrification in an effort to reduce carbon production, must be fully assessed and articulated. The ability of SWTs to complement distributed solar PV technologies will allow improved cost and operability to high-renewable-contribution systems for both behind- and in-front-of-the-meter applications (Reiman et al., 2020), especially with expanded consumer electrification for heating and transportation. The role of energy storage, and particularly of batteries, will be important not only for wind, but in general for enabling the transition to a smart-user-based grid paradigm.

The increasing interconnection requirements of all distributed generation, including in many cases two-way communication with grid control systems, require new SWTs to be more responsive, such as providing low-voltage ride-through, more advanced grid services, and potentially direct grid support. Additionally, with these expanded communication needs, additional cybersecurity considerations will be required of future SWT technology.

The role of SWTs, however, should not be limited to grid-connected installations. Large global markets for isolated energy systems and the provision of energy access as well as off-grid energy services such as ice making, water pumping, irrigation, or direct heat could further increase the market potential of the technology and again aid in global decarbonization by offsetting typically fossil-based means of providing these services.

Grand challenge 5 – foster engagement, social acceptance, and deployment for global distributed wind markets

Engaging communities, societies, and regulatory authorities is key for SWT development. Actions need to be taken to enhance the social understanding of SWTs and to provide evidence that modern turbines are expected to be significantly more efficient than their predecessors. Turbines must also be designed and deployed while taking into account their installation in proximity to people and within communities, with a clear understanding of their social and environmental impacts. Expanded research on community-based impact, such as ice throw and safety setbacks, needs to be carried out, leading to improved standards and guidelines for turbine installation. While some virtuous examples have been presented recently (e.g., the RELY COST Action; Roth et al., 2018; US DOE WindExchange, 2022) additional programs are seen as key enablers to increase awareness and acceptance about the technology.

Political and regulatory actions, especially if coordinated among countries on a larger scale, must be enhanced to allow deployment of the technology in a more effective way. Common regulatory and permitting requirements, based on science and modern understandings of potential impacts, are needed to streamline development timelines and reduce costs. Incentives, standards, and promotional policies should also be aligned. This is needed not only in the context of governments, but also within multi-lateral nongovernmental organizations, development banks, and foundations. For example, the creation of equal incentives across nations, including a clearly defined timeline for them to stay in place, is needed to encourage investment and the creation of economies of scale that will be important to sustain each of the other grand challenges.

5.1  Unknowns and knowledge gaps

Associated with the grand challenges identified above, the following Sects. 5.1.1–5.1.6 identify specific areas that will need ongoing global focus if SWT technology is going to be successfully developed to support long-term global needs for power generation to meet local loads. In particular, these sections identify the main unknowns and knowledge gaps that need to be addressed to allow the five grand challenges to be resolved.

5.1.1  Higher LCOE due to a lack of an economy of scales, resulting in high balance-of-station cost

As discussed, the total global installed cumulative small wind 2 capacity was estimated to be about 1.8 GW as of 2020 (Orrell et al., 2021). In contrast, an estimated 19 GW of residential solar PVs was installed worldwide in 2020 alone (IEA, 2020). The difference in installed capacities is driven by a number of factors, including intrinsic siting requirements, availability of incentives, market acceptance, and differences in costs. High deployment costs are driven by a number of factors. In particular, a lack of economies of scale and high balance-of-station costs.

Currently, most manufacturing of SWTs is conducted in small plants using batch processes because of the relatively small manufacturing volume and limited corporate cash flow. Small commercial volumes increase component costs, reduce purchasing power, and in times of restricted supply chains necessitate the ability to substitute components if traditional ones are unavailable. Each of these items increase cost and complexity and reduce the reliability of SWT products. As has been clearly demonstrated within the solar industry, large efficiencies and cost reductions can be gained across the SWT industry by significantly increasing production (Pillai, 2015). A transition to serial production, large-volume component purchasing, and advanced manufacturing techniques will significantly reduce the equipment costs for small turbines while also improving product quality control. An effort to greatly expand manufacturing capacity should be placed against an industry desire to continue using small plants that are located in the communities they are serving to meet energy justice, diversity, local development, and product reliability while also reducing climate impacts associated with global shipping.

Balance-of-station costs include all costs of a turbine system outside of the wind turbine and tower equipment and can represent up to 60 % of a small wind project's total installed cost (Orrell and Poehlman, 2017). These costs typically include customer acquisition; zoning, permitting, inspection, and incentive application; engineering and design; transportation and logistics; foundation design and installation; electrical infrastructure; turbine and tower installation and erection; taxes; and overhead and profit. Zoning and permitting costs in particular can be burdensome for small wind. For example, at one point it was reported that potential customers in the Republic of Korea needed written approval from neighbors within a given radius to install a SWT (Kim, 2018).

Although not typically a direct one-to-one substitution, the generally lower cost, in great part due to governmental incentives, and easier siting of solar PVs gives it a competitive advantage over small wind. From 2008 to 2012, the drop in the overall installed cost of PV systems was mainly due to the drop in cost of crystalline silicon. Since 2012, installed costs have continued to drop due to decreases in other costs, focusing on greatly reducing balance-of-station costs (Barbose and Darghouth, 2015). In addition, as demand for solar PVs increases, production of PV modules can enjoy the benefit of economies of scale, helping to further decrease installed costs.

5.1.2  Uncertainty in power curves and local wind conditions, resulting in poor estimations of AEP

The estimation of the AEP of a wind turbine has two main components: the power curve of the wind turbine and the knowledge of the wind conditions on the site. Nordic Folkecenter's Catalogue of Small Wind Turbines (8th edition) lists 302 types of wind turbines with a rated power below 50 kW, only a fraction of which have independently measured power curves (Nordic Folkecenter for Renew. Energ., 2016). This is in stark contrast to large wind turbines, where the vast majority of turbines have independently measured power curves.

Over the past decades, there have been multiple facilities developed for SWT testing, some of which are still in operation, providing the performance testing needed to increase the number of SWTs with independently measured power curves. However, the IEC 61400-2 is still the most credited reference to standardized performance measurements, but some discrepancies still exist with other references, and some aspects are still not completely covered. Further improving this standard could contribute significantly to closing the gap between small-scale and large-scale wind turbines. Only when a standard is applied to all these aspects will wind turbines be reliable. Generally, PV modules, inverters, and ancillary systems are more standardized than SWTs, and this is one of their keys to lower costs and market success.

Because tower heights are commensurate with rotor diameter, SWTs are placed on relatively short towers. Furthermore, tower heights are often restricted below their optimal values by local planning regulations. Due to wind shear, low towers result in lower mean wind speeds and therefore lower production. As discussed, SWTs are also strongly affected by installation at high altitude, where the reduction in air density leads to low Reynolds numbers and in turn to a lower aerodynamic efficiency. Furthermore, the wind flow for SWTs is more likely to be perturbed by nearby obstacles. This has two important effects: (1) the wind pattern can change over very short distances, making the micrositing of SWTs complex, and (2) the wind is likely more turbulent. As a result, even when power curves have been independently measured at a certified test site, those power curves may not be representative of real-life performance at the installation site.

The uncertainty in power curves and local wind conditions leads to considerable uncertainty in the estimate of the AEP.

In absence of new remote-sensing- or model-based assessment technologies, the way to reduce uncertainty in the characterization of local wind conditions is to take on-site wind measurements. However, site assessment through on-site measurement is often expensive in relation to the installed cost of SWTs and their generation potential. Deploying instruments for measurement is also far more expensive and more time-consuming than using model-based approaches to estimate a wind resource, which has led to limited uptake in the use of on-site measurements for small wind (Tinnesand and Sethuraman, 2019). Although expanded consideration of remote sensing and high-fidelity, model-based resource assessment techniques are being developed, which may prove reliable for energy production estimation, these are likely to be insufficient in areas with complex terrain, especially because the SWTs are close to the ground. In these cases, a site assessment is necessary for the project to be successful.

5.1.3  Intermittent incentives and regulations between countries

Incentives applicable to small wind can include net-metering, FITs, other types of production-based payments, grants, rebates, and tax credits. Regulations that affect small wind can include government renewable energy goals and mandates, interconnection standards and rules, and utility programs and interconnection rules. Both incentive programs and regulations vary widely across countries and utilities. Incentive programs can vary with respect to the amount and type of funding they provide, what types of projects are eligible to apply, the cap on the number of projects they support, and the length of time they are available. Regulations are highly country- and utility-specific. For example, in countries with complex terrain good spots are mostly remote (on hills and mountains rather than in large land fields); to exploit these remote areas, network expansion from low-voltage to medium-voltage connection is therefore needed. This increases costs for the investment but simultaneously – and indirectly – helps the distribution companies expand their network with new equipment.

As discussed in Sect. 2, Japan, Italy, the United Kingdom, and the Republic of Korea are examples of countries where intermittent incentive availability and funding levels have changed greatly due to the changes to their FIT programs over the past approximately 10 years. Changing the availability of incentives is one reason why many SWT manufacturers have not been able to remain in the market or do not participate in certain markets. The fluctuating sales presence of small wind manufacturers both in and exporting from the United States and China provides examples of how small wind manufacturers must adapt to different market conditions across countries. In the past, Japan, Italy, and the United Kingdom had been key export markets for SWT manufacturers. With the programs discontinued or drastically reduced, the markets are much less attractive, and this contributes to manufacturers leaving the market. Long-term consistency across incentive programs would greatly improve the development of the SWT sector. The lack of consistency also holds for national certification requirements and is another possible reason for manufacturers leaving the SWT market. If there was a unification (IEC certification, for example), then all the manufacturers could sell globally. For example, six US small wind manufacturers reported international exports in 2015, with just three in 2020 (Orrell et al., 2021). Similarly, sales in China and exports from China have fluctuated with the number of Chinese small wind manufacturers in that market. In 2017, only 15 Chinese SWT manufacturers reported sales, a decrease from 28 in 2014 (Duo, 2017), corresponding to a 60 % drop in sales from 2014 to 2017 (Orrell et al., 2021).

5.1.4  Lack of openly available data for detailed validation and development of design tools

Aeroelastic modeling should be the primary methodology for structural and performance assessment of any wind turbine. Such modeling allows the turbine designer to understand and predict the load and power behavior of the turbine before witnessing it in the field, to demonstrate and optimize the control parameters that have the highest impact on the design, and to optimize the configuration most efficiently.

For the results of an aeroelastic model to be used for design and certification, the aeroelastic code (the software), the turbine-specific inputs, the aeroelastic model setup and usage with those inputs, and the post-processing of the results must achieve a certain level of verification and validation. Most distributed wind modelers utilize the open-source aeroelastic code OpenFAST or the proprietary code HAWC2. While these tools have received adequate validation in past research work, there remains a need for experimental field data to validate turbine-specific models, especially in the case of SWTs. Publicly available aeroelastic models are well tuned for traditional three-bladed HAWTs, although less so for downwind HAWTs, and are progressively less and less validated for passive yaw, pitch-to-stall, furling, and VAWT machines (Forsyth et al., 2019). Scarcity of these data is seen in many aspects related to SWTs.

In the validation process, the model results are compared to experimental datasets to ascertain the degree to which the model represents the actual physics. Therefore, the validation datasets must be properly collected and quality assured. Validation, however, is not a binary statement about whether a model is valid or invalid, but rather a critical part in the overall assessment of the suitability of the computational model for the intended application (Hills et al., 2015).

A successful validation exercise requires close collaboration between the experimentalists, the modelers, certification bodies, and the relevant stakeholders throughout the conceptualization, design, execution, and post-processing phases of the experiments. Additionally, the computational model should be used to help design the details of the experimental campaign, which is effectively another (physical) simulation of the true behavior of the systems.

5.1.5  Social acceptance and environmental issues (noise, visual impact, vibrations)

In 2016, some studies suggested that around 70 % to 80 % of people in Europe support wind farms (Allen, 2016), although there were still concerns around noise and aesthetics. However, little was known about public attitudes toward locally developed SWTs. According to Ellis and Ferraro (2016), the social acceptance of wind energy is influenced by a much wider and complex set of mutual effects between individuals, communities, place, wind energy operators, regulatory regimes, and technology operating at a variety of geographical scales. Social acceptance should therefore be viewed within this wider set of relationships and as part of the transition to a low-carbon economy. In particular, small wind is commonly located closer to the customers that benefit but may also have more expanded impacts on the other local members of the community. For this reason, SWTs may stimulate social acceptance of wind energy if the installation and the technology used is really adequate and if local benefits are shown.

In 2016, a research survey was completed looking at the drivers of public attitudes toward SWTs in the UK (Tatchley et al., 2016). The results showed that half of respondents felt that SWTs were acceptable across a range of settings, with those on road signs being most accepted and those in hedgerows and gardens being least accepted.

Similar to the results obtained in a survey developed in Europe for the SWIP Project (SWIP Project, 2014) about the awareness level and public opinion of SWTs, more than 75 % of people interviewed showed a positive reaction to the installation of SWTs in their environment and only 5 % showed a negative reaction. Even for all demographic groups involved, the response was more positive to SWTs than large, utility-scale wind turbines. “Energy Communities” schemes increased this acceptance rate because more people are able to invest and benefit from a wind turbine investment. Generally, people feel detached from large, utility-scale wind facilities because they do not see the same direct benefits as in the case of SWT investments. Another conclusion was that industrial sites were regarded as the most acceptable places for installing SWTs, far ahead of the second-place response of roofs in residential areas. Even so, a bad attitude toward SWTs is still noticeable in politics and local administration in many regions, especially in those countries where historical or aesthetic restrictions are present (e.g., Italy).

In relation to noise emissions, SWT manufacturers have identified noise as a concern (also because some countries do require noise emission evaluations), and new SWT designs are typically less noisy. However, the general opinion is still that SWTs are noisy, especially if they are compared with solar PVs.

For visual impact (including visual flicker), noise, or safety issues, considerably less concern was shown than toward performance issues or high investment costs. This is supported by the fact that when an adequate support program for small wind is established, social concerns decline. Nevertheless, their visual impact in an urban area can still be a source of concern. According to Emblin (2017), developers must find smart ideas and designs to integrate turbines into communities and to educate local populations about the long-term benefits and impacts that SWT can bring. The visual impact can also be minimized if the turbines are placed carefully and sensitively, although turbine design also plays a significant role. These are all issues that may be addressed through expanded social science research, science-based community engagement, and innovations in design and software.

Vibration is another relevant issue, especially in roof-mounted wind turbines with no adequate damping solution and/or SWTs operating under high-wind conditions regulated by passive power regulation techniques. In those cases, vibration is transmitted through the pole to the roof or to the ground. When the turbine is sited near dwellings, residents have been known to express annoyance.

5.1.6  Real and perceived concerns with SWT reliability and the high cost of certification

As discussed, financial incentives in the form of FITs, direct-pay grants, and tax credits help strengthen the global distributed wind market. Incentive agencies and other industry stakeholders have worked to formulate and implement program eligibility requirements to ensure the public funds used in these programs are directed to successful projects, and embarrassing failures are avoided. One common strategy is to require third-party certification of the wind turbine system according to national and international standards. The goal of the standards is to provide meaningful criteria upon which to assess the quality of the engineering that has gone into a SWT and to provide consumers with performance data that will help them make informed purchasing decisions (e.g., IEC: International Standard, 2019b). While certification attests that a wind turbine has been tested and designed according to requirements in the relevant standards, a third party cannot guarantee that a turbine model will exhibit perfect reliability in the field. Therefore, a level of surveillance must be put in place by the certification body to monitor and respond to field failures, in collaboration with the turbine manufacturer.

While certification helps improve the reliability of deployed wind turbines, it comes at a significant cost, although efforts have been made to reduce the complexity and cost of meeting standards for SWTs. To achieve certification, the turbine must be field-tested for power performance, acoustic noise, safety and function, and durability. The turbine designer must also generate a significant engineering report documenting the calculation of turbine loads, both extreme and fatigue, and the structural analysis of the major components in the load path. These test and design reports are then evaluated by a third party, usually an internationally accredited certification body. If the work is found to conform to the applicable standards, certification is granted, making the turbine model eligible for financial incentives. The validity of the certificate must then be maintained because of design changes or other factors.

Other certifications or dedicated studies are typically required as part of the installation process, including structural engineering of the tower, the foundation (mostly within the permitting phase), and electrical safety (part of the IEC certification) related to protection from electrical shock and fire.

While it is very difficult to find publicly available data for field-testing and reporting, industrial contacts of the authors in Europe determined that it costs about EUR 200 000 (USD 230 000) for the complete design assessment of a SWT, while field-testing and reporting alone can cost upwards of EUR 85 000 (USD 100 000) and third-party certification can cost up to about EUR 43 000 (USD 50 000). Small and medium wind turbine manufacturers in the United States have reported that certification costs, including fees, direct expenses, and labor time, range from USD 150 000 (EUR 134 000) to USD 500 000 (EUR 435 000) (Orrell et al., 2020).

5.2  Improvement areas

By addressing the five identified grand challenges, SWT technology is expected to decrease significantly in cost, become more accepted within the distributed energy investment community, and demonstrate acceptable community impact to allow direct community-based acceptance. To this scope, the following section reviews some main improvement areas where major research and development is suggested to allow the global SWT market to flourish.

5.2.1  Changes in turbine design and control

The task of designing, manufacturing, and installing SWTs has always been challenging. Suppliers of small wind technology must produce a product that will be deployed in a wide variety of sites around the globe, maintain reliable operation with minimal maintenance, and be an economically viable choice. For small wind to maintain a competitive stance on the international distributed clean energy market, future designs must be further optimized, lowering the LCOE. Unlike the process used largely for current SWT products on the market, future optimized SWT designs will need to utilize validated aero-servo-elastic modeling as a design tool starting at the concept phase; utilize low-cost, reliable overspeed protection methods; and incorporate strategies including design for manufacturing, design for certification, design for installation, and design for recycling, all before initial prototype testing and ideally in the framework of improved and more detailed, internationally accepted design standards.

While addressing all these things is beyond the scope of this study, some key enabling actions are proposed in the following, clustered together based on the main technical areas.

Aerodynamics

Basic wind turbine aerodynamics lead to the statement that a good blade is composed of good airfoils: “good” in the sense of having a high lift-to-drag ratio. At the low Reynolds numbers of SWTs, this is a major design challenge that has languished for over 2 decades. Given the developments in Reynolds-averaged Navier–Stokes (RANS) turbulence and transition models, a design methodology is becoming available to overcome the limitations of conventional panel methods in use up to now. In particular, better modeling of the near- and post-stall region of airfoil polars is key not only to improve stall-controlled machines, but also to get more reliable estimations of loads in a variety of DLCs prescribed by the standards, thus leading to better prediction of turbine lifetime and possibly enabling lower safety factors. Innovations at the airfoil level should focus not only on pure aerodynamic performance (in terms of high glide ratio, resistance to stall, and low sensitivity to R e variations), but also on further lowering noise levels to make turbines more suitable for installations in proximity to populated areas (improved certification labeling could also be useful in this regard).

The introduction of smart blade technologies for flow control in SWTs may provide a significant boost toward better designs in the near future. For example, the potential of retrofitting SWTs with passive flow control elements such as vortex generators and Gurney flaps to improve their starting behavior and to reduce the risk of stall caused by roughness has recently shown very promising prospects (Holst et al., 2017).

Aeroelastic modeling

Up to now, SWT blades have been much stiffer and protected by large safety factors in their structural design than blades for large turbines. To enable wider use of this simulation tool for design and optimization, gaps and barriers to its use must be identified and solutions implemented (Damiani et al., 2022). Growth in the theoretical knowledge possessed by SWT-producing companies and a wider availability of easy-to-set, open-source tools will also be required. To evaluate the impact of the above, Evans et al. (2018, 2021) investigated blade fatigue by undertaking aeroelastic simulations of six SWTs up to 50 kW in rated power using OpenFAST (NREL, OpenFAST, 2019). Their research shows that the fatigue DLC in IEC 61400-2 is unduly pessimistic and that more detailed aeroelastic modeling to allow the design of fatigue-resistant blades at lower cost will be needed. To support more efficient designs while reducing blade cost and weight, aeroelastic modeling should be increasingly used in SWT design, as it has been used for utility-scale turbines. To enable wider use of this simulation tool for design and optimization, several gaps and barriers to its use across the SWT industry must be identified and addressed (Damiani et al., 2022). Growth in the theoretical knowledge possessed by typically small SWT-producing companies and a wider availability of easy-to-set-up, open-source tools will also be required. Additionally, the challenge of expanding the use of aeroelastic models must be supported through dedicated verification and validation campaigns on a number of different turbine archetypes, sizes, and computational codes. One particular area of importance for very small turbines is the need for better understanding of yaw behavior of turbines with a tail fin. Yaw response gives rise to gyroscopic ultimate and fatigue loads, which can be the largest loads on a turbine of around 1 kW (Wood, 2011). None of the currently available aeroelastic codes contain a tail fin model.

Control strategies for SWTs must also evolve to become more robust and cost-effective. We see an example of this evolution in the contemporary trend of turbine designers moving from tail furling to stall regulation and in some cases pitch regulation. An example of this transition is the evolution of the Bergey Excel 10 turbine toward the Excel 15 (Bergey Wind Power, 2022). The change was in both the increase in power capture via a larger, more efficient rotor and the moving away from the furling strategy toward a more controlled-stall strategy. Other manufacturers (e.g., Tozzi Nord, 2022) are proposing models with both active yaw and pitch. The difficulty here is to package these controls in relatively tight spaces while still guaranteeing reliability and redundancy. A recent research article (Damiani and Davis, 2022) explores the technical and economic viability of retrofitting a stall-controlled turbine with pitch control together with an extended rotor for increased power capture. Both pitch-to-stall and pitch-to-feather approaches are investigated, and the advantages of each solution are discussed. The authors devise a compact, redundant independent pitch control system but conclude that, for power regulation, the economics do not warrant the extra complexity of the pitch control, which is then relegated to overspeed protection alone. More research and technical support in this direction are needed because the experience of utility-scale machines is not directly applicable in SWTs due to cost and physical constraints. However, as discussed in Sect. 4, recent studies suggest that the use of pitch control could significantly improve the efficiency of SWTs (Papi et al., 2021), and new grid integration requirements being driven by the expanded use of distributed generation may require more active power control than what can be achieved through traditional controlled stall designs.

Generator and drivetrain

The unsteady behavior of SWTs, especially during start-up, depends on drivetrain and generator resistance (Vaz et al., 2018). Typically, the wind speed at which a SWT begins power production as the wind increases in strength is significantly higher than the speed at which it ceases production as the wind dies away (Wood, 2011). The cut-in wind speed is usually an average of these two speeds and therefore can give a misleading indication of what wind speed is needed for a SWT to start producing power. In particular, the cogging torque of permanent magnet generators (PMGs) can be a major impediment to very-low-wind-speed start-up of small turbines. This problem is exacerbated because, due to their relatively small size, SWT manufacturers are typically forced to purchase third-party generators that may not match their blade design, resulting in the need for higher wind speeds to overcome the cogging torque of the generator. Additionally, because there appears to be few uses for PMGs in the sub-10 kW capacity, there is little market pressure on generator manufacturers to optimize their designs for SWT applications. Eventually, SWT manufacturers may design and build their own generators, but turbine sales must expand greatly to warrant this large investment. The design of turbine-specific generators, optimized with specific blade and rotor design, would require improved understanding of generators, control systems, permanent magnet design, and the use of modern additive manufacturing.

Design strategies

Knowing that a SWT must be manufactured, tested, certified, installed, maintained, and then recycled at the end of its life puts pressure on the designer to incorporate this thinking into the design from the initial concept. Key market drivers, such as subsidies that may incentivize capital costs compared to operational costs, must be considered carefully to balance up-front and operating costs, in turn making the LCOE of SWTs more competitive. Several, sometimes competing, additional design strategies that may be implemented that will impact turbine performance and cost include design for manufacturing (incorporating the manufacturing in the design process to avoid future issues in fabrication and assembly); design for certification (incorporating conformity with the relevant design standards early in the design process to avoid future issues in the design evaluation and turbine certification); and lastly, since the SWT must be shipped, installed, and commissioned, design for installation strategies, which must be considered, especially if the turbine is to be deployed in remote or isolated locations. With this in mind, the complete small wind system, including the foundation, tower, inverter, wiring, disconnects, monitoring, nacelle, access platforms, and rotor, will need to be designed in a way that makes the installation process efficient, well thought-out, innovative, and safe.

Novel concepts

While continuously improving existing concepts and archetypes, the recent novel designs discussed in Sect. 4 like DAWT, Darrieus VAWTs, and mostly recently AWE still deserve attention and research efforts, since they could represent an important future contribution to distributed power production. Novel turbine concepts, however, are not limited only to the individual turbine performance but should also include holistic considerations of different elements, from economics to social perspectives, which are further discussed in subsequent sections.

5.2.2  Open data from field experiments

Many, but not all, SWT manufacturers remotely monitor the operation of their turbine fleets. For many smaller turbines, monitoring focuses on electrical parameters that are measured as part of the inverter system, but ongoing measurement of many turbine-specific parameters simply increases the cost and maintenance requirements of turbine systems. Sharing any available remote monitoring data is an opportunity for researchers and manufacturers to collaborate on a variety of potential research areas that could expand small wind markets while also helping reduce costs. These areas include isolating and identifying the factors that affect why actual performance differs from predicted performance in real-world conditions and then improving performance prediction tools accordingly, improving wind resource assessment data and models for small wind, calculating actual LCOEs, using the performance data to understand wind's complementarity to solar PVs, and enabling wind to complement and communicate with other distributed energy resources in the grid of the future. The inability to predict performance consistently and accurately can negatively affect customer confidence in small wind and access to financing. Increasing investor confidence, reducing perceived risk, and decreasing assessment costs with improved tools and datasets will help small wind achieve large-scale deployment. In this regard, however, it must be clarified that the real “performance” of a wind turbine system is the amount of achievable AEP. As discussed in Sect. 5.1.2, this actually is driven by variables beyond just turbine technology, including, but not limited to, the project's available wind resource, siting (i.e., tower height, local obstructions, and other micrositing issues), and turbine availability (i.e., downtime for expected or unexpected maintenance or grid outages). These variables contribute to why accurately estimating small wind project performance can be challenging. A better prediction of performance can then be synthesized into the proper combination of good resource estimation coupled with accurate power performance and then with the guarantee that the turbine will provide that same level of power over its design life. While the current performance prediction tools generally focus on the first of these questions, which is driven by good resource assessment and accurate representation of the turbine power curve as discussed above, they largely do not address the second part, which is failure analysis. Open data on turbine failure mechanisms for the verification and tuning of performance prediction tools will then need to cover not only turbine performance vs. actual wind resource but also real production vs. time, fatigue, and failure analyses.

Regarding prediction tools, in particular, special attention is also needed to make available open data to calibrate and further develop and design aero-servo-elastic tools (see Sect. 5.2.1) in operating conditions outside of turbine-specific validation that may be needed as part of turbine certification processes. Having detailed field data that may only be available from heavily instrumented research-grade turbines in the wind tunnel (e.g., those shared in internationally coordinated programs like those from the International Energy Agency (IEA) Wind Technical Collaboration Programme) will foster the development of more robust design tools for SWTs, enabling the modelers to improve the accuracy of the turbine design tools. Data must also be collected over a wide range of operating conditions, from the standard steady-state operation to predicting the turbine loads, performance, and lifetime in actual operating conditions. In this sense, the tools can be validated for scenarios that can be significantly different from one particular site to another site, e.g., different turbulence levels, anisotropy, wind speed, wind direction, and ground stability. An overview of measurement data collected within IEA projects is given in Schepers and Schreck (2019). These projects also provide examples of how international consensus on sharing data will help the users validate models while maintaining any needed confidentiality.

5.2.3  Improvements in installation, maintenance, and life-cycle analysis

Over the 10 years from 2010 to 2020, the cost for installing residential-scale solar PV systems in the United States has seen an approximately 64 % reduction in benchmark costs. A total of 42 % of these costs has been attributed to installation labor and additional soft costs, such as siting, permitting, sales tax, and overhead (IEA, 2020). Although a smaller percentage of overall total costs, significant reductions are seen in structural and electrical hardware costs outside of the inverter and solar module. These installation costs (the total cost outside of the module and inverter) now make up almost 70 % of the total installed cost of a modern residential-scale solar PV system (Feldman et al., 2021). Limited published data exist for similar installation-specific balance-of-station costs for small wind (Orrell et al., 2021, as an example), but a 2017 study of the US distributed wind market shows that similar costs represent 63 % of the cost of residential wind systems (Orrell and Poehlman, 2017), which indicates that if a cost reduction of a similar magnitude as that demonstrated in the solar industry can be achieved for small wind, this would represent a 25 % reduction in the installed costs of small wind systems.

To date, limited systematic analysis has been undertaken to identify methods to reduce the installation costs of small wind technology. Having more of these studies for different countries and environments is considered a key research area for the evolution of small wind systems.

The SMART Wind Roadmap (DWEA, 2016) identifies a set of potential cost-reduction opportunities based on a consensus-based collaboration of small wind industry members. Most of the focus of this work was in the area of turbine hardware cost reductions, but the report does identify tower, foundation, and turbine erection costs as significant cost drivers for small wind, on par with the costs of the turbine hardware itself. Recent work by industry has focused primarily on reducing the costs of towers, primarily developing self-erecting mono-pole towers that provide lower installation and turbine maintenance costs. Recent efforts to reduce installation costs through the DOE-funded Competitiveness Improvement Project (NREL, 2021) have focused on tower and foundation design, including the use of low or no concrete foundations for SWTs, which can greatly reduce turbine installation timelines and costs. Expanded cost reductions could also be expected in site assessment with the expanded use of modeling tools, simplified installation procedures, and reductions in project acquisition and project permitting, each of which needs to be explored in more detail.

Similarly, a full understanding of O&M costs of DWTs is limited. As introduced in Sect. 3.2, the most recent US Distributed Wind Market Report (Orrell et al., 2021) provides an estimate of cost of USD 37 per kilowatt (EUR 32 per kilowatt) per scheduled maintenance site visit, which is typically required annually. This cost has not seemed to decrease over time. In comparison, O&M expenses on the basis of USD (EUR) per kilowatt-hour per year for residential-scale solar PV systems has dropped by almost 50 % over the last 10 years, again demonstrating strong potential for cost savings (Feldman et al., 2021). Maintenance needs of small turbines cover a range of requirements. Most residential and small commercial turbines are designed to require minimal ongoing maintenance, such as bi-annual inspections and potentially blade reconditioning, depending on the environment. Turbines greater than 50 kW in capacity are assumed to undergo more ongoing maintenance, similar to large wind turbines. Ideas that have been identified to support lower long-term maintenance costs include the expanded use of remote monitoring to understand service needs before maintenance is required and expanded turbine structural modeling to eliminate unplanned maintenance. Systematic approaches to reduce maintenance for the distributed wind fleet should also be pursued. Although individual manufacturers have a good sense of long-term turbine-specific component failure rates, no system-wide assessment has been undertaken to focus research efforts on components that have higher service requirements, such as power electronics. This would also represent a key enabler. Focusing on local and national standards will isolate the SWT manufacturers in the borders of their countries. Unification under a common standard (such as IEC) should be proposed as for PVs. History also shows how the SWT market has failed to follow the large wind turbine and PV pace for growth.

Although stories abound of particular SWTs operating for decades, factual data on the full life-cycle cost and performance of many SWTs is limited, reducing the ability to assess the long-term cost of energy for small wind systems. Additionally, the wide variety of turbines, their almost constant change in design, and limited number of operational small turbines that have undergone a full certification to national and international standards also make it challenging to develop meaningful, information-based estimates of life-cycle cost as has been done with other technologies. To support the better full assessment of life-cycle costs, NREL developed a cost taxonomy for distributed wind (Forsyth et al., 2017) that has been applied in a small number of cases such as Orrell and Poehlman (2017). Most work today focuses on articulating costs based on the installed cost of wind technology, making assumptions on maintenance costs and long-term turbine performance. Estimates of life-cycle costs for SWTs at and below USD 0.10 per kilowatt-hour (EUR 0.087 per kilowatt-hour) are being reported but have not been independently demonstrated or verified. A better estimation of life-cycle costs of SWTs is considered a key enabler. In doing so, of critical concern is an accurate accounting of long-term turbine production. Work has been undertaken in relation to an improved estimation of the site-specific wind resource, a topic that is more complicated due to the higher likelihood of local obstructions (Drew et al., 2015). Long-term performance production, which could include consideration of long-term wind turbine availability, turbine performance degradation, and increased impact of obstacles such as vegetation growth, has not been systematically considered to date and would definitely improve these estimations (see also Sect. 5.2.2).

5.2.4  Regional appreciation of distributed generation and integration with storage systems

Although historically used in remote and edge-of-grid applications (Hemeida et al., 2022; Duchaud et al., 2019), the continued decrease in the costs of renewable energy generation and storage technologies, combined with incentive programs and policies to support local generation, has resulted in a wider acceptance of grid-connected distributed generation. With the advent of lower-cost controls, advanced power electronics, and improved communication systems, the use of more distributed power generation is becoming common. Additionally, new efforts to expand clean energy development, paired with the high costs and typically long project development timelines for transmission development, make the use of distributed generation even more cost-effective as a way to support local power development. Lastly, although it typically requires additional expenses and planning, distributed generation can also be used to support grid resilience when combined with storage and other grid-forming technologies. The bold plans of the European Union as well as many other countries around the world in the direction of e-mobility require significant infrastructure investments to facilitate the millions of electric vehicle chargers that will be installed. This expansion will, however, put an additional large load on existing low-voltage grid infrastructure that, in most countries, is old and extremely expensive to upgrade. The strain on the low-voltage grid cascades toward the medium-voltage infrastructure, which is also coming much closer to its capacity limits.

Enhancing this development while maintaining a reasonable cost involves simultaneously unloading the low- and medium-voltage grid from some capacity through local energy generation and storage. This is possible when buildings and households in local communities are able to become “net prosumers”, meaning that they are simultaneously energy producers and consumers. In the future, these prosumers can serve as active members of the energy system network with the ability to exchange energy and offer stabilizing services to the grid. This is achieved through the integration of renewables with storage in combination with decentralized control. Solar has been the first technology to be successfully combined with storage on a residential or local community level, contributing effectively to the “net prosumer” concept. SWTs have been traditionally very simplistic with respect to their design and control, making their combination with storage more difficult. However, numerous current designs include variable-speed full-converter AC–DC–AC turbine concepts and have been successfully integrated with modern storage technologies. The combination of SWTs with fast-response storage systems allows for the generation of significant quantities of energy at the low-voltage grid level with a simultaneous grid stabilization capability that is able to unload capacity in an effective manner from the grid. Similarly, combining wind, solar, and storage in many parts of the world where wind and solar are not typically coincident, either daily or seasonally, could provide expanded benefits to the low- and medium-voltage energy distribution network. Actions can also be carried out directly on wind turbine design and control, e.g., integrating fault ride through technologies.

The biggest challenges for this integration involve the volatile nature of wind turbine operation, which requires a very fast response from the power electronics and storage technology to maintain constant production levels and allow for fast-response voltage and frequency regulation. However, building on the distributed generation concept into regional development, the wider use of distributed wind combined with solar and storage at small scales across a region will reduce the variability experienced with just single units, providing more reliable and less transient power, likely at a reduced cost and certainly faster than large-scale transmission system development.

To address the expanded need for energy to remote areas not served by current energy infrastructure across the globe, SWTs in combination with solar, storage, and advanced load control technology are likely to play an expanding role. Although most investments within the energy access space currently focus on solar and storage, growing energy needs will make it difficult and expensive to rely on oversized solar and storage facilities to provide full-time power. The use of SWTs and other renewable energy devices such as pico hydro and biomass can provide energy at different times than solar, reducing the cost and space requirements of large storage systems. The limited civil infrastructure and difficulties in providing the on-site service expertise that is required for larger wind turbines will make SWT technologies more applicable for these more remote applications.

5.2.5  Shared programs of incentives and social actions to improve acceptance

The majority of renewable energy incentives are targeted at large-scale wind projects and wind farms, where scale is a critical component in a country's wind energy development success rate (Wolsink, 2013). Social acceptability can also be construed as commercial acceptance in the case of small wind. Wind energy is naturally more complex to diffuse than other energy alternatives such as solar panels because it frequently involves infrastructure (foundation, tower, and grid interconnection).

If the economic competitiveness of SWTs can progress significantly as a result of improvements in efficiency, manufacturing, and siting, then the technology could be sustained in the transitory phase by more coordinated political and regulatory actions at a large scale. For example, a federation like Europe could promote the harmonization of incentives between the countries, although energy policies are still managed individually by the members. This could in turn create a common, broader market for SWTs, promoting the development of an economy of scales. Moreover, different from previous practices, the time framework for these incentives to stay in place should be clearly assessed to reassure investors and companies and prompt them to bid on the technology. In this context, networks of research institutions like EAWE in Europe and NAWEA in the United States or of wind energy industries like WindEurope can play an important role advising regulatory bodies and politicians.

Social acceptance of SWTs could potentially be improved if the drawback on local ecology such as the habitats of birds, insects, and other small animals, as well as noise and vibrations, can be minimized. While these concerns are largely debated in utility-scale machines, and a vast body of literature does exist, the environmental impacts of SWTs are not so well defined as a result of less scientific research on the topic. Additional studies and projects on the topic would also represent an important enabler to improve acceptance of small wind.

Finally, it is worth mentioning that the diffusion of small wind technology could also be supported by actions that are somehow a combination of technical and social aspects. A good example of this is a virtual net-metering approach (Hellenic Electricity Distribution Network Operator S.A., 2021). Under this scheme, consumers could install SWTs away from the consumption meter and liquidate the energy as a classic net-metering. There is a trend where companies try to get “green electricity” from their providers or through their own investments to compensate for their footprint (Wang, 2013). This will and should get amplified in the next few years as companies of all sizes try to become greener. These efforts will boost the sector but also in a more secure and professional way because this “green point system” will push the wind turbine makers toward real power curves and better products (Simic et al., 2013). Additionally, a link between this type of investment with ESG (environmental, social, and governance) policies will boost the market even more due to the comparative advantages of SWTs. For example, many industrial consumers who have already installed PVs may be eager to increase their green electricity, but they may not have space available for additional PVs.

5.3  Key enablers

As a final product of the work, the aforementioned areas of focus are synthesized below in 10 key enablers that, in the authors' opinion, more than others would represent the catalysts for a significant development of SWTs worldwide.

Aeroelasticity for SWTs. If aeroelasticity has represented the main driver of the size and capacity factor of utility-scale machines, its diffusion to SWTs could also be extremely beneficial. For example, an improved aeroelastic design could contribute to reducing the structural safety factors, in turn enabling a blade weight and cost reduction and more efficient designs. To enable wider use of aero-servo-elastic simulation tools for design and optimization, gaps and barriers still need to be identified and solutions implemented, including growth in the theoretical knowledge possessed by SWT-producing companies and wider availability of easy-to-set, open-source tools.

Improvement in control strategies. To achieve more effective and robust control, thus maximizing the energy conversion, a transition away from furling toward more controlled-stall strategies is also seen in very small machines. Moreover, some manufacturers are proposing models with both active yaw and pitch. While the implementation of these controls in SWTs is not straightforward due to the difficulty of packaging them in relatively tight spaces while still guaranteeing reliability and redundancy, recent studies suggest that the use of active-pitch and active-yaw controls could significantly improve the efficiency of future SWTs.

Improvement in design, with a focus on the characterization of airfoil aerodynamics at low Re . Improvements in the design of SWTs will be needed at any level, from the rotor–nacelle assembly (e.g., minimization of drivetrain and generator resistance, with particular reference to the cogging torque) to blades' material and cost or use of cheaper materials for some of the most expensive components such as the towers. Among others, a key area for improvement is defining (possibly validated with experiments) accurate and reliable airfoil polars with the low-Reynolds-number range that SWT blades usually work with, remembering their strong sensitivity to air density variations due to installations in altitude for example. Having those data available will produce benefits at different levels, including more effective aerodynamic designs, better prediction of loads, and a more reliable definition of turbine control (especially in stall-controlled machines). Special attention should also be given to aerodynamic noise in view of turbine installation in proximity to populated areas.

Open data from both wind tunnel and field experiments. Open data for verification, validation, and optimization of SWTs are seen as a key enabler for the future evolution of the technology. In particular, thanks to the smaller size of SWTs compared to utility-scale machines, they can be placed at full scale or at low scale in a wind tunnel, meaning that reliable testing can take place in the controlled and known wind tunnel environment. Data collected in these conditions would be of particular use for the evolution and calibration of simulation tools. On the other hand, there is also an urgent need for different open datasets, i.e., related to field measurements of real turbine performance. These will need to cover not only turbine performance vs. actual wind resource, but also real production vs. time, fatigue, and failure analyses.

More accurate performance and resource assessments. More accurate assessments of both the real performance of SWTs and the wind resource are key to improving design, siting, and operation. Regarding performance assessment, a better quantification of several factors could be beneficial, including the impact of turbulence or the effect of obstacles. For example, a DOE-funded project plans to include obstacle modeling research results as an add-on feature to wind resource data for the United States available via an application programming interface. Regarding resource assessment, high-fidelity computational fluid dynamics (CFD) simulations could provide a significant contribution, even though the economic convenience of their computational cost must still be proven.

Variable validation and verification of SWTs, especially for non-traditional archetypes. Balancing certification requirements from a regulatory point of view, which prioritizes design thoroughness, model validation, and public safety, against requests from the original equipment manufacturers for more streamlined and economical approaches to certification is difficult. Therefore, there is an immediate need for breaking SWTs into categories for load assessment and validation requirements that account for both size and archetype. Smaller turbines and more established archetypes would benefit from less onerous requirements in terms of load assessment and validation, whereas more complicated machines would require a more in-depth review of the prediction capabilities of the code used for design and load analysis. Verification and validation guidance in the current design standards is limited, and this is one area that requires more research and data to increase the diffusion of DWT and SWTs.

Standardization. Standardization at different levels is key for further development of SWT technology. First, standardization is needed for components to promote an economy of scale. In particular, it is suggested that generic products are designed and produced to achieve economies of scale, in turn enabling reduction in the purchase cost of SWTs. Examples of this could be the design and production of a generic rotor blade family or lighter and easier-to-install towers. Similarly, research must be focused on the utilization of lower-cost generators, possibly available on the market with a standardized design. Standardization would come with non-negligible technical challenges but could represent the key catalyst for reducing the LCOE in the near future. Moreover, more effective standardization is needed for regulations and standards. Regulations for SWT installation among different countries are also largely variable, and making those regulations more uniform through international coordination would represent another pillar toward the creation of a stable market for the technology. Standards should instead evolve along with the changes in the design and operation of new machines, with a special focus on aeroelastic design and certification. In particular, we suggest that a major enabler could be the differentiation of standards as a function of turbine archetype.

Detailed studies on cost and life-cycle analysis. To date, limited systematic analysis has been undertaken to identify methods to reduce the installation costs of small wind technology. Having more of these studies for different countries and environments is proposed as a key enabler for the evolution of small wind systems, in connection with the impulse toward standardization. The same applies to life-cycle costs, in which a critical concern is accurate accounting for long-term turbine production. This should include consideration of long-term wind turbine availability; turbine performance degradation; and increased impact of obstacles, such as vegetation growth, which have not been systematically considered to date and would definitely improve the estimations.

Grid compliance and integration, including storage systems. To comply with most of the current grid codes as well as the upcoming grid code modifications, SWTs of larger rated power should probably mostly become variable speed and make full use of AC–DC–AC converters. Also, new SWT developments will likely make larger use of fault ride through technologies because they are becoming compulsory for small-scale generating systems. Beyond this, the combination of SWTs with fast-response storage systems is thought to be key for allowing generation of significant quantities of energy at the low-voltage grid level with a simultaneous grid stabilization capability that is able to unload capacity in an effective manner from the grid. Similarly, combining wind, solar, and storage in many parts of the world where wind and solar are not typically coincident, either daily or seasonally, could provide expanded benefits to the low- and medium-voltage energy distribution network and support the establishment of a significant market for small wind technology.

Shared programs of incentives and new paradigms to support SWT diffusion, with a special focus on social acceptance. Both incentive programs and regulations have been widely variable across different countries, making it difficult for producers to stay in the market. More coordinated political and regulatory actions at a large scale should be fostered in view of the creation of a broader market for SWTs, thus promoting the development of an economy of scale. Different from previous practices, the time framework for these incentives to stay in place should be clearly assessed to assure investors and companies and prompt them to bid on the technology. In this context, networks of research institutions or wind energy industrials could play an important role in advising regulatory bodies and politicians. All these actions must be coordinated with a better understanding of the environmental impacts of SWTs so that greater social acceptance can be achieved.

For SWTs to be widely successful, tomorrow's technology will require a new generation of turbines optimized for complex, low-wind-speed locations with high turbulence that can also successfully and reliably operate throughout their design life, producing the power expected when they were installed. Such turbine designs will require higher-fidelity modeling and simulation to support lower-order tools for design and optimization of turbine systems in complex installation contexts. These models will need additional open data for validation and calibration, which are currently very scarce. Also, advancements in control and materials will be needed to improve the energy capture in gusty flows and to reduce the overall cost. Additionally, these higher-efficiency and reliable turbines must be paired with accurate performance assessment tools to ensure life-cycle power production, providing confidence to consumers and financiers alike. Finally, these turbines will be more effectively integrated with storage systems to achieve higher appreciation of small wind for distributed generation.

To make this scenario possible in the near future, the present study suggests five grand challenges for the small wind community, to which common and synergic efforts should be devoted. These grand challenges translate into

improving energy conversion of modern SWTs through better design and control, especially in the case of turbulent wind;

better predicting long-term turbine performance with limited resource measurements and proving reliability;

improving the economic viability of small wind energy;

facilitating the contribution of SWTs to the energy demand and electrical system integration;

fostering engagement, social acceptance, and deployment for global distributed wind markets.

To overcome these challenges, the main unknowns and gaps that must be filled have been presented, as well as the main improvement areas to which major research and development actions should be devoted. As a final product of the work, 10 key enablers are proposed by the authors as the proper catalysts for a significant development of SWTs worldwide:

more effective use of aeroelasticity for SWTs;

improvement in control strategies;

improvement in design, with a focus on the characterization of airfoil aerodynamics at low R e ;

open data from both wind tunnel and field experiments;

more accurate performance and resource assessments;

variable validation and verification of SWTs, especially for non-traditional archetypes;

standardization;

detailed studies on cost and life-cycle analysis;

grid compliance and integration, including storage systems;

shared programs of incentives and new paradigms to support SWT diffusion, with a special focus on social acceptance.

All relevant data are included in the paper.

All authors were involved in the original draft preparation, review, and editing. AB directed the work and was responsible for much of the introduction, recommendations, and summary material. AO was the main author responsible for Sects. 2 and 3, with IBG, GE, and RD. GB, AC, JIC, RD, CSF, DI, CNN, GP, MR, GS, BS, and DW contributed with all their expertise to Sect. 4. All the authors contributed to Sects. 5 and 6. Much material was shared or moved between sections, and editing responsibilities were comprehensive, so section authorship is never exclusive.

At least one of the (co-)authors is a member of the editorial board of Wind Energy Science . The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This study was originally promoted by the Small Wind Turbine Technical Committee of the European Academy of Wind Energy (EAWE), which is sincerely acknowledged for their support. Experts from around the world have joined the authors' group in a joint effort to provide a comprehensive view of the topic and a critical analysis on the status of the SWT and DWT technology. Special thanks are also due to the National Renewable Energy Laboratory (NREL) and Pacific Northwest National Laboratory for providing access to their most recent data. Moreover, appreciation is shown to the Eunice Energy Group for supporting the study with its industrial experience. The authors would like to acknowledge the members of “IEA Task 41: Enabling Wind to Contribute to a Distributed Energy Future” for great collaborations and to the numerous experts who did not author the paper but directly contributed via private discussions and friendly reviews; in particular, the assistance provided by Vasilis Papatsiros from the Eunice Energy Group was greatly appreciated. Finally, Francesco Papi from Università degli Studi di Firenze is acknowledged for his contribution in the consolidation of the document and the final editing.

This work was supported in part by the National Renewable Energy Laboratory (preparation of some graphics), the Pacific Northwest National Laboratory (text editing), and the Publications Committee of the European Academy of Wind Energy (coverage of part of the APC).

This paper was edited by Gerard J. W. van Bussel and reviewed by Phil Clausen and one anonymous referee.

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Conversion rates used in the paper at the time of writing: JPY 1  =  EUR 0.008, EUR 1  =  USD 1.15.

This small wind capacity value mostly represents wind turbines up through 100 kW in size, with some capacity from wind turbines up through 250 kW in size.

  • Introduction
  • Diffusion of small wind turbines
  • Economic aspects
  • Status of the technology
  • Grand challenges for small wind turbine technology
  • Conclusions
  • Code and data availability
  • Author contributions
  • Competing interests
  • Acknowledgements
  • Financial support
  • Review statement

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Introduction

Why do we need wind energy, current status of wind power technology, comparison of vertical axis wind turbine and horizontal axis wind turbine, wind turbine blade design, special treatment on wind turbines, conclusions, acknowledgment, review of wind turbine research in 21st century.

Contributed by the Advanced Energy Systems Division of ASME for publication in the J OURNAL OF E NERGY R ESOURCES T ECHNOLOGY . Manuscript received August 21, 2017; final manuscript received August 22, 2017; published online September 11, 2017. Editor: Hameed Metghalchi.

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Amano, R. S. (September 11, 2017). "Review of Wind Turbine Research in 21st Century." ASME. J. Energy Resour. Technol . September 2017; 139(5): 050801. https://doi.org/10.1115/1.4037757

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Wind energy is a well proven and cost-effective technology and expected to be a promising technology in which industry responds to the environmental targets—so becoming an important source of power generation in years to come. This paper focuses on the current status of wind energy and more advanced subjects needed to understand the current technology in the wind power engineering.

As green energy industry emerges from initial stage caused by the global economic downturn, it is a new stage of rapid change of power science and technology. The worldwide demand for electricity is expected to triple by 2050, when fossil fuels account for no more than 60% of all energy consumed, compared with 80% of the energy consumed today. Traditional fossil resources such as oil, natural gas, and coal are nonrenewable and generate air pollution by releasing a huge amount of pollutants into the atmosphere, thereby damaging the environment, from acid rain to climate change. In order to help combat these problems, 158 countries have ratified of 197 parties to the Convention the Paris Agreement put into force on Nov. 4, 2016. The first session of the parties serving as the Meeting of the Parties to the Paris Agreement (CMA 1) was held in Morocco during Nov. 15–18, 2016 for controlling the environmental problems [ 1 ].

Wind power has recently become the world's fastest-growing source of renewables. According to the U.S. Department of Energy, it is expected that wind energy contributes to a significant portion of the U.S. electricity supply in two decades. For this reason, there has been a keen interest in wind energy because they are emission-free and the wind is cost-free renewable. Nevertheless, the amount of electricity generated and obtained by wind energy conversion systems is still unsteady, relatively expensive, and difficult for integrating into conventional electrical systems because of the variation in wind source and unresolved energy storage issues. On a large-scale, spatial variability articulates the situation that there are various climatic regions throughout the regions, some much windier than others. These areas are mostly illustrated by the latitude, which affects the amount of insulation. Within any one climatic region, there is lot of variation on a smaller scale, largely dictated by physical geographies such as the land, lake, sea, the size of land masses, and the presence of mountains and hills or plains. The wind energy resource map in the U.S. [ 2 ] indicates that the wind resource data are based on geographical wind data, coastal ocean area data, and higher-air data, wherever applicable. In wind-sparse regions, three fundamental indicators of wind velocity or power levels are employed: topographic/meteorological indicators (e.g., valleys, mountains, and chasm), wind-deformed ravine, and eolian landforms (e.g., plains and dunes). The data were analyzed at a regional level to produce 12 regional wind resource categories; the local groups are thus incorporated into the national wind resource assessment.

In the U.S., while the wind makes up only 2% of total power supply, it is one of the highest sources of wind electricity in the country, second only to natural gas generation regarding new capacity built each year since 2005. During the last few years, wind power in the U.S. has been increasing sharply. Thousands of wind turbine units larger than 1.8 megawatts (MW) are now in operation in the U.S. Larger turbines are demanded due to economic benefits: a taller turbine with a larger blade radius produces higher power at a modest cost per kilowatt-hour. The rapid quest for more electrical power, moving from 2 MW turbines in 2009 to 6 MW turbines sited in early 2012 has shown the tower head mass went from 140 to 360 tonne, with more variations in structural loading and fatigue. Stabilization at the current power level will improve opportunities for consistent design and manufacture of the structures within a farm.

As shown in Fig. 1 , the wind capacity was 24 GW in 2001, but it monotonically grew up to 490 GW in 2016. Total wind capacity in the U.S. reached 82 GW by the end of 2017. Wind power accounted for 35% of the country's new power-production capacity from 2007 to 2016. According to AWEA, Texas is the number one state with the most installed wind power with 21 GW, with Iowa being a leader in wind generation with 3675 MW installed, while California and Minnesota harvest significant amounts of the wind with 4322 MW and 2733 MW, respectively [ 4 ] (see Fig. 2 ). 1

Global cumulative installed wind capacity 2001–2016 [3]. Permission granted by Lasma Livzeniece of GWEC.

Global cumulative installed wind capacity 2001–2016 [ 3 ]. Permission granted by Lasma Livzeniece of GWEC.

U.S. wind map [7]. Permission granted by Nate Blair of National Renewable Energy Laboratory.

U.S. wind map [ 7 ]. Permission granted by Nate Blair of National Renewable Energy Laboratory.

Although the installation mentioned earlier is a significant growth for wind power, this is still a minor portion of the entire energy resources in the U.S. electricity supply. The Department of Energy released the target of 20% by wind by 2013. Therefore it could provide a significant increase in the current economy. Factors pushing for growth in the U.S. wind power include the high cost of fossil fuels and concern over national energy security. As a result, policymakers consider a broad range of legislation that would support and enhance wind power growth.

The progressive public policy has been the main ingredient both for encouraging wind energy technology development and assisting in determining what forms that growth will take. Future growth will likely come from commercial-scale wind farms, which are typically vast arrays of turbines owned and operated by large corporations.

There are now over a quarter million wind turbines operating with a total capacity of 282,482 MW as of end 2015 [ 4 ] worldwide. The U.S. developed wind farms and led the world in installed capacity in the 1980s and into the 1990s. In 1997, German installed capacity exceeded the U.S. and led until once again overtaken by the U.S. in 2008.

The size of the turbines becomes larger every year. The longest wind turbine blade is 88.4 m of an offshore turbine manufactured by the Adwen AD-180, 10% longer than even those of the MHI Vestas V164. Wind power is expected to reach 8% by 2018 [ 5 ]. As of 2017, 90 countries in the world will be using wind power on a commercial basis [ 6 ].

As shown in Fig. 1 , the size of the wind turbines is getting larger and larger for off-shore wind, and a large size of modern turbines, such as MHI Vestas or Adwen AD-180, can go as large as most of the commercial airplanes, like Hughes H-4 (Fig. 3 ). The largest wind turbines are built-in Denmark and then shipped across the world.

Comparison of turbine blades with major aircrafts

Comparison of turbine blades with major aircrafts

The diameter of the turbine blade alone is considerably greater than the London Eye, which is a giant Ferris wheel situated on the South Bank of the Thames River in London. The wind turbine blade size has grown over the last two decades. It went from 17 m to over 90 m in 2015 with the capacity went up from 50 kW to 8 MW.

The earth is becoming hotter by 1 °C in land area, which is attributed to the human activities, particularly the emission of greenhouse CO 2 [ 7 ]. The energy sector is by far the largest source of greenhouse carbon dioxide emissions. Thus, it is clear that current power generation technologies need to move away from the limited amount of fossil fuel resources to more sustainable and renewable sources of energy. As well as being good for the earth, this step from fossil to renewable sources is also good for the earth's resources as it decreases the dependency on fossil-fuel imports while also increasing energy supply security. The U.S. has set a target for 20% of the U.S.'s electricity to come from renewable sources by 2030 [ 8 ].

Windpower, one of the major renewables, can play a significant role in meeting U.S.'s high demand for electricity, 20% wind energy by 2030. Increasing Wind Energy's Contribution to U.S. electricity supply was prepared by the U.S. Department of Energy, the American Wind Energy Association, Black & Veatch, and others from the energy sector. In increasing wind power capability, more wind power installations can increase to more than 16,000 MW per year by 2020 and continue at that rate through 2030. Wind energy siting costs and performance are expected to drop slowly over the next two decades.

During the next decades, the U.S. wind industry could support more than 500,000 jobs with an annual average of more than 170,000 workers employed by the wind manufacturers. It also supports more than 150,000 jobs in associated industries, supports more than 250,000 jobs through economic expansion, and increases annual property tax revenues to as high as $1.5 billion in 20 years.

Owing to the rise in energy prices and the demand, there are now hundredths of wind farms developed around the world. Currently, major electric power companies are going green and actively proclaiming it too from rooftops.

Over the two decades, average wind turbine ratings have increased linearly ten times as large as the 2000s. Wind turbine designers have estimated that their machines and capacity are greater than ever be. However, with each new generation of wind turbines, the size has increased along the linear curve and has achieved reductions in the life-cycle cost of energy. The scope of developing larger turbines stems from a desire to take advantage of wind shear by placing rotors in the higher, stronger powered winds at a greater elevation above ground (wind speed increases with height above the ground). For this reason, the capacity factor of wind turbines grew. However, the continued growth in larger sizes has some constraints since it results in higher costs to build a larger turbine. The primary rule for a size limit for wind turbines is that the power is proportional to the area of the turbine blade area. That is, as a diameter of wind turbine rotor blade increases in size, its energy output goes up as the rotor-swept area increases. At the same time, the volume of material also increases, and because of that, its volume and price increase as the cube of the diameter. In other words, at some size the cost for a larger turbine will grow faster than the resulting energy output gains, making scaling a losing economic game. Manufacturers have successfully applied this law by increasing size and by using material more efficiently to reduce weight and cost. According to the wind blade scaling law, the size of wind turbine blade mass is approximately an exponent of 2.3 as opposed to the expected 3.

Vertical axis wind turbines (VAWTs) have emerged as a potential unit to the need, after being shelved by wind turbine companies in the late 1980s as a result of the greater success of horizontal axis wind turbines (HAWTs). VAWTs present several advantages over HAWTs, however, which is especially pertinent in the built environment. There are, however, also significant challenges that have prevented their widespread adoption. These challenges must overcome if VAWTs significantly contribute to meeting the 2030 wind power goal of the Department of Energy.

Vertical axis wind turbines require the equipment that needs maintenance near the ground. VAWTs are used for smaller capacity of the electric generator and are used only in small applications such as a residential use and office buildings. VAWT is used for powerless than 50 kW. There are no commercial-sized VAWTs because they do not make a profit, but if they exist, they would spin at speeds from 1.6 km/h to 200's of km/h. VAWTs would, of course, fly apart as soon as the wind went over about 30 km/h, which is why they are not in commercial operation, as centripetal forces may cause the blade damage. There are many types of VAWTs, because they are all under development stage. VAWT's cost less in total dollars, but significantly more in dollars per kilowatt generated because they do not make much power for their cost.

Drawback of VAWT.

While VAWT has several advantages over HAWT such as the low levels of noise and the independence from the wind direction, they are not used for the electric supplier as a replacement of fossil energy power plants. One of the disadvantages that VAWT possesses is that they must be installed close to the ground. Since the wind blows at a higher speed and evenly at greater heights, an installation that is not on a mast loses lots of efficiencies. If with this type of setup, the generator is installed in a machine room on the ground, and thus, maintenance is more straightforward and less expensive. Despite this arrangement, it is doubtful that the lower yield because of the weaker winds close to the ground would be balanced out by the money saved in maintenance costs. It remains to be seen whether plans to use existing tall structures to mount planned megawatt-level installations with vertical spindles can be realized. It will probably not be easy to find buildings or structures that would be able to handle the static and dynamic loads from a large wind power installation with a vertical axis. And it goes without saying that these structures should be in regions where the wind velocities are of interest. Another point against the current conceptions of larger VAWTs is the greater material expenditure per square meter of the surface covered in comparison to installations with a horizontal shaft. This feature mentioned here might result in a substantial additional cost factor that can hardly be compensated for by the theoretically better possible exploitation of stronger winds or gusts.

The main disadvantage of VAWTs is their low efficiency relative to HAWTs, which is a result of the variable torque produced by dynamic stall caused by each blade as it passes around the azimuth. The blades of a HAWT, on the other hand, generate constant torque around the azimuth. A further disadvantage is an inability to VAWTs for self-starting. By improving the torque, and, therefore, power produced by VAWTs, and enabling self-start, the two main roadblocks to their adoption would be removed. This treatment would enable the widespread adoption of VAWTs for low-wattage power generation in the residential wind power markets. This will increase renewable energy production in the locations where most power is consumed, reducing the losses and cost associated with transmission. Furthermore, it will reduce reliance on fossil fuel-based electricity and relieve consumers from the price variations that result from this reliance.

Dossena et al. [ 9 ] reported that the experimental thrust and power curves of the H-type VAWT, developed from basic measurements, exhibit the expected trends with a peak power coefficient of about 0.28 at a tip-speed ratio with 2.5. Wind velocity measurements for several tip-speed ratio demonstrate the full three-dimensional character of the wake, especially, in the tip region where a skew-symmetrical wake and tip vortex are observed. The aerodynamic loading on the wake unsteadiness shows the time-dependent character of the tip vortex and the onset of the dynamic stall for tip-speed ratio lower than 2.

VAWT Advantage.

Several benefits of VAWTs over HAWTs are:

As VAWTs operate with a smaller tip speed than HAWTs, they cause less noise. VAWTs also have a better esthetic due to their three-dimensional nature, making them popular with architects. HAWTs are sensitive to yaw and skew, experiencing decrease in torque and power due to the aerodynamic asymmetry on the rotor disk under such flow conditions.

Due to the complexity of airflow in the urban environment, the wind direction is not perpendicular to the vertical, making this issue a significant problem in this environment. VAWTs, however, are less sensitive to both yaw and skew [ 10 ].

Vertical axis wind turbines have the feature of the simplicity of the mechanical design and maintainability of the turbine system.

Vertical axis wind turbines can be designed with all significant components located at ground level, except a single bearing. This advantage is of particular interest to the residential power generation market, in which ease of repair is critical.

Of the various configurations of VAWT that exist, the simplest is the H-Darrieus VAWT, illustrated in Fig. 4 . One of its major advantages is its ease of manufacture since the blades can be extruded. It makes the H-Darrieus VAWT, in particular, less expensive to build. Even the curved and helical blades of the more common “egg-beater” and helical VAWT designs are economical to manufacture than the tapered blades of HAWTs. By reducing the manufacturing cost of wind turbines for the home market, the installation cost is reduced, as is the return time on investment. This time is one of the main barriers to most renewable energy system in the home power market. On the other hand, the situation with smaller installations with a nominal output up to approximately 10 kW can be considered to be substantially different. At this level of the production, there are very many applications which up until now could only be insufficiently covered with horizontal systems. In particular, horizontal installations come up against their limits when located in high mountain areas, in regions with extremely strong or gusty winds, or in urban areas. But also in areas with relatively constant winds, that is, where the conditions are ideal for systems with a horizontal axis, a VAWT can have its advantages, at minimum if the neighbors complain about the annoyance of the noise. There have already been reports of enraged neighbors who have settled the acoustical problems with firearms.

H-Darrieus VAWT

H-Darrieus VAWT

Structural Consideration.

Research on implementation of structural re-enforcement of mechanism in the polymer matrix composite materials has been increasing [ 11 ]. Matt et al. [ 11 ] reported a study toward development of a feasible method to supply the healing agent throughout woven fiberglass reinforced epoxy composite. Huang et al. [ 12 ] investigated the influence of embedded circular hollow vascules on the structural performance of a fiber-reinforced polymer (FRP) composite laminate. The presence of these off-axis vascules caused resin-rich regions in the FRP laminates. In-plane and out-of-plane fiber alignments were changed due to the inclusion of these vascules. A proportional correlation between the cross-sectional diameter of vascules to that of resin-rich region area, pocket length, and fiber disturbance height was found in their experimental study. The study revealed that the compressive strength of the FRP composites decreases with the inclusion of vascules and further decreases with the increase in cross-sectional vascular diameter.

Motuku et al. [ 13 ] used hollow glass fibers along with borosilicate glass microcapillary pipettes (1.15 mm outer diameter), flint glass Pasteur pipettes, copper tubing, and aluminum tubing in addition to plain weave glass 2 fiber fabrics in polymer composites to study and choose an optimal material to supply the healing agent for low velocity impact. Borosilicate glass microcapillary tubes were selected as the best material to provide the healing agent with equal impact strength to that of conventional composite material. This concept is supported by the study made by Matt et al. [ 11 ] on hollow borosilicate tubes (1 mm outer diameter) embedded in glass fiber-reinforced-polymer composites. Equivalent tensile strength was observed in the samples with and without borosilicate tubes in their study. The microtubes are used both as reinforcements and to supply the healing agent (see Fig. 5 ). This study is a continuation of study made by Matt et al. [ 11 , 14 ] using smaller, hollow borosilicate tubes of 500  μ m outer diameter and 250  μ m inner diameter to supply the healing agent. Microscopic analysis of fiberglass reinforced polymer composite with smaller circular tubes is made in this study along with the effect of these embedded microtubes on the tensile and flexural strength of the composite. It is anticipated that such healing technology might advance the structural problem that is facing to an enormous size wind turbines.

Microstructure of vascular tube reinforcement [11]

Microstructure of vascular tube reinforcement [ 11 ]

Smart Blades.

Franco et al. [ 15 ] present a technique for optimizing wind turbine blade designs in smart rotors. The objective was to maximize power regardless of wind conditions. An extensive analysis of what is known as “smart blades” from aeronautical solutions and helicopter rotors is provided. The authors show that the analysis of the primary components such as sensors, mechanisms of actuation, and materials is included. Advance research in this technology is presented as a potential solution for more efficient blade designs, and methods for reducing aerodynamic loads are discussed.

Other Consideration.

Many different wind turbine blades are modeled by designers, scientists, and researchers such as slotted turbine blade, which is a biomimetic model of layered bird wind, that increases power efficiencies [ 16 , 17 ], a tubercle wind turbine blade, similar to whale fin, [ 18 ] for controlling turbulence wake and suppress drag power reductions (Fig. 6 ).

Tubercle wind turbine blade [18]

Tubercle wind turbine blade [ 18 ]

Wind farm (Fig. 7 ) efficiency is a function of many variables including atmospheric conditions, geographic terrain, wind turbine design, turbine spacing, and electrical transmission. Understanding the behavior of turbulence generated from wind turbines and wind turbine wake dynamics can lead to more robust wind turbine designs and aid engineers in wind farm layout and lead to increased wind farm efficiency.

Wind farm photo in Edwards Air Force Base

Wind farm photo in Edwards Air Force Base

Periodically, researchers have summarized advances and state-of-the-art approaches to wind turbine modeling or wake simulations [ 19 – 22 ]. Several of these reviews are noteworthy in that they are comprehensive, relevant, and detailed. In 1999, Crespo et al. [ 23 ] provided an overview of the different modeling methods used to predict velocity deficit in the wake of single and multiple wind turbines. Their review included discussions on kinematic wake models, field models, terrain effects, and wind farm modeling. Kinematic models express the velocity deficit by an analytical expression developed from theoretical work on coflowing jets and experimental data. Field models are much like today's computational fluid dynamics models in that they calculate the velocity at every point of the flow field and rely on a numerical solution of turbulent momentum and continuity equations. Early kinematic and field models are still incorporated into software used for wind farm analysis. Vermeer et al. [ 24 ] followed in 2003 with an overview of computational methods relative to horizontal axis wind turbines and included further discussion of kinematic and field models. The unique aspect of the Vermeer paper was their segregation of experimental and analytical research based on near and far wake studies. They also included a thorough review of experiments that had been performed on a variety of wind turbines. Hansen et al. [ 25 ] studied computational methods for wind turbine analysis including blade element momentum methods, panel methods, vortex methods, and actuator disk methods. Aeroelastic methods for predicting the dynamic response of the turbine blades from time-dependent aerodynamic loads were also presented. In 2011, Sanderse et al. [ 26 ] provided a state-of-the-art review of computational fluid dynamics methods for simulating wind turbines. They classified different numerical methods used and distinguished between models specific for simulating the rotor versus affecting the wake.

Two turbine integration on a complex terrain was studied by Hyvärinen and Segalini that is aligned in the streamwise direction [ 27 ] as shown in Fig. 8 . This phenomenon was more prominent at homogeneous inflow conditions than regular conditions, while with a turbulent boundary layer inflow the diffusion of the front-row turbine wake decreased this effect.

Wind farm on complex terrain [27]

Wind farm on complex terrain [ 27 ]

The dependency of the atmospheric boundary layer characteristics on the boundary layer height is investigated by using large-eddy simulations [ 22 , 28 , 29 ]. These researchers investigated the impacts of atmospheric boundary layer's height on the wind turbine power production by simulating two subsequent wind turbines using the actuator line method [ 22 ].

According to the National Health and Medical Research Council, there is no direct evidence that wind farms affect health in humans [ 30 ]. This report is very encouraging for the wind energy scientists and manufacturers for exploring further wind farm siting [ 31 ].

Lens Wind Turbines.

The researchers at Kyushu University, Fukuoka, Japan, developed a lens type wind turbines [ 32 ]. The blades are installed in a shroud, which is known as diffuser-augmented wind turbines, which can considerably increase the performance of the rotor. In wind tunnel experiments, the power output and aerodynamics characteristics diffuser-augmented wind turbines installed, in side-by-side arrangements, demonstrated an increase of up to 12% in total power output. The results can be explained by observing the bluff body flow phenomena in the wake interference around the multiple circular disks. An airfoil section of the turbine blade gives the best performance in a low-tip to wind speed ratio range. Since the shroud suppresses vortices generated from the turbine blades within the diffuser shroud, the aerodynamic performance is effectively enhanced, and noise is reduced (see Fig. 9 ).

Lens wind turbine of Kyushu University

Lens wind turbine of Kyushu University

Floating Wind Turbines.

Offshore wind power has a keen interest in the development of next-generation alternative energy. This interest stems primarily from several advantages that offshore wind energy has over traditional onshore technology. Specifically, the marine environment provides substantial, steady, and relatively uniform wind conditions that constitute a rich and robust energy resource. Current off-shore turbine technology focuses on designs for shallow water scenarios. Wind turbines sited in shallow water are fixed to the bedrock with sturdy foundations. Although these models are proven the technology, there has been a recent interest in the wind energy community to move further offshore to exploit stronger winds [ 33 ]. However, at present deep water designs remain purely conceptual and in prototype development.

Offshore wind needs consideration by taking into account the experience of offshore wind farms in current operation, a new design based on dimensionless wave height parameter is proposed by Esteban et al. The authors presented improving a preliminary design of scour protection systems taking into account climatic parameters as the wave height or the wave period.

The U.S. National Renewable Energy Laboratory has provided concepts of deep water designs that consist of the tower and rotor mounted on a floating platform, which is attached to a mooring/tension line station-keeping system [ 33 ]. Despite the progress made in these conceptual designs, there still exist many engineering challenges associated with the employment and commercialization of these floating offshore wind turbines. For instance, even though station-keeping systems are used to keep wind turbines from drifting away, floating offshore wind turbines are free to move about six degrees-of-freedom. It is anticipated that the off-shore environment will actively force surge and pitch motions.

The Swedish company Hexicon (Stockholm, Sweden) has developed an innovative floating wind farm (see Fig. 10 ). 2 These types may soon supply Malta with 9% of its electricity needs via one of these floating wind farms.

Floating wind turbine station [34]. Permission granted by GWEC.

Floating wind turbine station [ 34 ]. Permission granted by GWEC.

The hexagonal structure of floating wind turbine platform made of concrete carries lens turbines developed by Kyushu University. The floating structure consists of six-cylinder floating bodies that are constructed by truss members. The cylinder floating bodies and the truss members are made of prestressed concrete with high durability [ 35 , 36 ] (see Fig. 11 ).

Floating wind turbines developed by Professor Y. Ohya's group at Kyushu University [36]

Floating wind turbines developed by Professor Y. Ohya's group at Kyushu University [ 36 ]

Magenn Power Air Rotor System is developing a “lighter-than-air” helium-lifted wind turbine, which may serve as a future land- and sea-based windmills. Magenn Power proposes an air rotor that operates with Magnus effect. The company is on schedule to put the first units into production shortly. This type of floating wind turbines can generate roughly 4 kW residential systems, costing just USD 10,000. They claim that the floating turbine operates either straight lift from the helium or the Magnus effect as wind speed increases; as this turbine uses the Magnus effect of rotation, lift increases, drag can be minimized because of reduced leaning, and stability increases. The future versions will dwarf even some legendary airships (Fig. 12 ).

Floating wind turbine [1]

Floating wind turbine [ 1 ]

De-Icing Problem.

Wind turbines operating in cold areas or at high altitudes often face icing conditions during winter operation (Fig. 13 ). At the same time, the best sites for wind farm installation are located at higher elevations, as wind speed increases by 1 m/s per 1 km of altitude for the first 1 km. In regions with northern climate, available wind power is approximately 10% higher than other areas due to increased air density at lower temperatures. Under icing conditions, thermal management is one of the treatments for wind energy systems, regarding de-icing and anti-icing of the turbine blades in cold climates and cooling of the heat-generating components. Nowadays, large wind farms or wind power projects are more often implemented in cold climates and at higher altitudes mainly due to their attracting wind energy potential and helpful wind power resources [ 36 , 37 ]. Ice accretion is detrimental to turbine performance, durability, and the safety of those in the vicinity of operating iced turbines. For example, the ice buildup on the turbine blades, even for slight icing, disturbs the flows which attribute to the decrease in the power generation, overloads stall-regulated turbines, and deteriorates the performance. Besides, the added ice mass increases the loads on all the turbine components and causes a mass imbalance among turbine blades, which might cause mechanical failure and thus the turbine lifetime. Therefore, icing mitigation techniques are essential for the operation of wind turbines in cold climates. Icing reduction techniques include anti-icing and de-icing methods. Anti-icing prevents ice to accrete on the blade while de-icing removes the accreted ice layer from the rotor blade surface. Ice reduction techniques can also be divided into passive and active techniques. Passive methods take advantage of the physical properties of the blade surface to eliminate or prevent ice, including ice-phobic coatings, or blades sprayed with a chemical or painted while effective methods use external energy such as thermal, mechanical, chemical reacting, or pneumatic energies [ 38 ] (Fig. 14 ). 2

De-icing technology. Permission granted by GWEC.

De-icing technology. Permission granted by GWEC.

Heating element for de-icing [39,40]

Heating element for de-icing [ 39 , 40 ]

The use of wind power was an old practice since thousands of years ago. However, this technology has revived due to the shortage of fuels and the environmental problems generated by the traditional energy resources. During the last decade, there is a rapid increase in wind turbine generated electricity worldwide and is widely recognized with an extensive industry manufacturing and installing a large amount of electric power of new capacity every year. Despite the exciting new technology implements arose, particularly for large wind turbines, and many challenges remain, there is a considerable rate of established knowledge concerning the science and technology of wind turbines. This book is intended to present some of this knowledge and to present it in a form suitable for use by students and by those involved in the design, manufacture, or operation of wind turbines.

The research on this project was funded by the U.S. National Science Foundation (NSF) under CBET 1236312 and CBET 1539857.

http://en.wikipedia.org/wiki/Wind_power_in_the_United_States

http://en.wikipedia.org/wiki/Wind_power_by_country

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Review article, wind energy-harvesting technologies and recent research progresses in wind farm control models.

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  • 1 Bahir Dar Energy Center, Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, Ethiopia
  • 2 Department of Physics, College of Natural and Computational Science, Wolaita Sodo University, Sodo, Ethiopia
  • 3 Department of Physics, Addis Ababa University, Addis Ababa, Ethiopia

In order to sustain the overall competitiveness of the wind power industry, unrelenting focus is required on working toward the advancement of enabling technologies and research studies that are associated with wind farm systems. First, wind farm technologies that include various turbine generator systems coupled with different power transmission configurations have enormous impact in determining the quality of wind power production. In addition, modern wind farms are expected to implement robust power control algorithms to meet more advanced requirements of electricity generation. Accordingly, this study explores the statuses of wind energy harvesting technologies and wind farm control strategies by discussing their recent and future impact on transforming the wind power industry. Doubly fed induction generator (DFIG)-based wind energy harvesting technology is well-matured and has exhibited an excellent track-record in past and recent experiences, but its capability of being further scalable for large-scale power production is limited as it is largely incompatible with high-voltage power transmission networks. On the other hand, permanent magnet synchronous generator (PMSG)-based technology is making significant advancements to attain the maximum possible efficiency level in greatly facilitating larger scale power generation, although the construction of bulky and costly power transmission systems is required. In this regard, future technological advances in the wind farm industry are expected to reasonably optimize the design and cost of high-voltage power transmission systems. Similarly, an increasing number of research studies are introducing a number of power optimization-based control models to create an ideal integration of the aforementioned wind farm technologies so as to ultimately enhance the reliability of electricity production by maintaining the systems’ safety. Yet, additional work is still expected to be undertaken in the future for a more extended evaluation of the performances of many different control models under a similar environment.

1 Introduction

As a part of ensuring successful improvements in global cumulative installations of wind power as shown in Figure 1 , various systems of wind power technologies were proposed, developed, and used by researchers, manufacturers, and wind farm industries as the solutions for enhancing the extraction and transportation of onshore and offshore wind energy. In this regard, generator technologies and wind farm transmission systems have a considerable cumulative effect on onshore and offshore power production. More interestingly, wind generators and power transmission systems provide researchers and engineers with the options required for the significant achievement of wind power generation objectives, including a reduction in energy costs and maximization in wind power production. Consequently, the scale and cost of wind power production largely rely on the efficiencies, reliabilities, and configurations of the generators and power transmission systems ( Cheng and Zhu, 2014 ; Biswas et al., 2021 ). In the case of generator systems, two main technologies are well-proven to be the leading candidates for onshore and offshore wind power applications: a partial-scale converter-based doubly fed induction generator (DFIG) system is widely popular for its better compatibility with onshore power generation ( Mwaniki et al., 2017a ), whereas a full-scale converter-based permanent magnet synchronous generator (PMSG)-based system is regarded as an attractive solution for offshore and multi-mega-scale wind power generation ( Mohan and Vittal, 2018 ). Moreover, compared to the partial-scale converter DFIG-based wind farm, the reactive power capability of a full-scale converter PMSG-based wind farm can be significantly extended, and the grid-side converter in each configuration unit can provide the required reactive power locally. Regardless of its higher cost, full-scale converter-based wind energy conversion technology nowadays receives increasing recognition because of its superior performing efficiency and reliability, particularly in offshore wind farm applications ( Chaithanya et al., 2019 ; Yaramasu and Wu, 2016 ).

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FIGURE 1 . Global onshore and offshore wind power installation trends (in GW) from 2012 to 2021 ( I. Renewable Energy Agency, 2022 ).

Two options of electric power transmission systems can be implemented in interconnecting power generation systems (electric generators and power electronics converters) with wind farm substations, which are high-voltage alternating current (HVAC) and high-voltage direct current (HVDC). The HVAC transmission option is efficient and economical for the wind power industries for which the power substations can be built closer to the power-generating points. More specifically, when the distances between power-generating points and substations can be limited to shorter ranges (usually less than 50 km), the HVAC system shows excellent power transmission capability and economic benefits compared with the HVDC system for both onshore and offshore power applications ( Wei et al., 2017 ). Nevertheless, substations are usually commissioned at points very far away from power-generating sites, particularly in the case of multi-mega-scale offshore wind power generation. Under this situation (for longer transmission lines and multi-scale offshore power generation), the HVAC option is not compatible with the power transmission application. The reason is that HVAC transmission cables are inherently characterized by a large capacitance per length, and thus, in addition to electrical current delivery, power transmission via extended cables of HVAC could also cause generation of capacitive currents. These capacitive currents are regularly rippling, and this significantly affects the transmission capability of electrical currents, and hence, the quality of power production. Consequently, excessive reactive power is required for the HVAC extended power transmission cables. This reactive power requirement can be met by making use of reactive shunt compensation; however, this results in the addition of extra expenses related to capital and operating costs ( Machado et al., 2015 ).

Unlike HVAC-based configuration, the HVDC system is the standard solution in terms of power transmission capability and cost-effectiveness for applications of offshore wind farms that are commissioned far away (usually greater than 140 km) from power load centers. Moreover, the operation of HVDC-based power transmission configurations can be effectively scalable and no reactive power compensators are required at longer ranges. Different configurations of AC and DC power converter-connected HVDC transmission systems can be used for offshore wind farm applications ( Kalair et al., 2016 ; Ryndzionek and Sienkiewicz, 2020 ). Currently, modular multilevel voltage source converter (MM VSC)-connected HVDC in AC parallel configuration, with 864 MW capacity, a voltage of ± 320   k V , and transmission cable length 160 km, is in operation ( Li et al., 2021 ). Additional MM converter models with varying capacities, voltages, and cable lengths are also under construction for applications in the near future. Another AC converter topology that makes a parallel connection with an HVDC system is a cascaded rectifier. Cascaded rectifier-based HVDC configuration was reported to increase energy conversion efficiency by 20% and reduce power system complexity by 65% ( Blaabjerg and Ma, 2017 ).

Furthermore, DC-based converter-HVDC parallel-connected transmission networks including three-level neutral point clamped (3L NPC) HVDC; DC converter-HVDC series connected transmission networks, such as solid-state transformer (SST)-HVDC; and pulse width modulator current source converter (PWM CSC)-HVDC were presented as potential alternatives for high-power transmission applications in recent literature reports. The DC-HVDC/3L NPC-HVDC design in parallel connection was proposed to simplify the complexity of the configuration of offshore power transmission substations by offering the benefit of cost. Series DC-connected designs (SST-HVDC and PWM CSC-HVDC) are also expected to further reduce the costs of bulky offshore wind power transmission substations ( Yaramasu and Wu, 2016 ; Wei et al., 2017 ; Ryndzionek and Sienkiewicz, 2020 ; Peng et al., 2021 ).

On the other hand, research advances have achieved promising milestones in introducing potential strategies that could be practically implemented to enhance the operation of wind farms for maximizing and securing wind power production. These strategies mainly involve the application of optimization-based algorithms for wind farm control. In this context, wind farm control provides a cooperative strategy for the design and operation of the wind power plants, and it is a crucial development to alleviate the losses resulting from the turbine-to-turbine interaction within the plant. Many recent research works have revealed the improvement in wind farm performances, particularly in terms of power production, by using different optimization-based algorithms and models. In this work, the research results from implementing optimization-based wind farm control strategies that use feedforward, model-based closed-loop, and model-free closed-loop algorithms have been presented. For example, under the feedforward control approach, gradient-based optimization algorithms that include sequential quadratic programming, steepest descent, and conjugate gradient and heuristic algorithms that include genetic algorithm, particle swarm optimization (PSO) ( Dursun et al., 2021 ), and artificial bee colony were implemented by using different optimization and evaluation models to increase wind farm power production, and varying results were reported.

With the model-based closed-loop approach, a model predictive optimization strategy was implemented by different researchers including Fontanella et al. (2021) , and the output power of wind farms was claimed to increase with varying levels; the implementation of data-driven optimization methods, such as Bayesian optimization, knowledge-assisted deep deterministic policy gradient algorithm, and support vector machine (SVM) algorithm, also supported the achievability of increments in wind farm power productions. Furthermore, based on the model-free closed-loop approach, different optimization algorithms including simultaneous perturbation stochastic algorithm and nested extremum-seeking controller (NESC) were shown to have capabilities of maximizing wind farms’ output power. The model-based closed-loop approach is generally popular because it would help develop robust wind farm control designs with reduced complexity, cost, etc., in comparison to the other control strategies ( Jain et al., 2021 ).

According to recent research trends, the increase in wind farms’ power production could be generally achieved by the implementation of different optimization-based algorithms along with various design optimization and evaluation models. The amount of increase in power production differs depending on various factors, which include the type of optimization algorithm that could be implemented, wind farm input control parameter that could be optimized, and models that could be used for optimization and evaluation. On the other hand, evaluating the performances of multiple wind farm optimization strategies under a single model environment is challenging; this could affect the acceptance of research reports and their practical implementation on real-world wind farms. In response to this challenge, the benchmark named FarmConners was recently introduced, and a project was also launched, aiming to determine the practical effect of wind farm control on loads of power systems. In its most recent report, the National Renewable Energy Laboratory (NREL) ( Engaging Autopilot, 2021 ) revealed that it tested the practical implementation of the study model analyzed by Martínez-Tossas (2021 ), through the Composites Manufacturing Education and Technology (CoMET) facility, and validated that the ultimate objective of this study (to reduce the turbines’ cost) can be applied to real-world wind operation. Yet, more advanced works are still underway on the practical achievements of further wind farm control objectives that include design optimization, power reliability enhancement, and cost reduction.

Finally, more extended discussions of this work are organized under the sections to follow. Accordingly, Section 2 presents the two most popular wind turbine generator technologies (DFIG and PMSG systems); Section 3 explores technological trends in high-power transmission systems; Section 4 provides summaries of previous studies on different strategies of optimization-based wind farm control models; Section 5 presents brief assessments on the recent and future prospects of wind power systems control; and Section 6 summarizes the impact of relevant technologies and research studies on wind power production.

2 Popular generator technologies for wind energy harvesting

The most common device components used for energy conversion from wind to electrical power in a modern wind energy conversion system comprise a rotor with turbine blades, optionally a gearbox (it can be removed in gearless technologies), an electric generator, a power electronic converter, and a transformer, as shown in Figure 2 . Designs of wind energy conversion systems can be classified into various notions on the basis of the type of generator, speed regulation capability, and strategy by which the aerodynamic power is restricted. In these notions of wind energy conversion systems, the power electronics plays entirely special roles and contributes to power ratings of the system with varying capacities. Two main wind energy harvesting technologies are widely adopted in modern wind energy industries. In the past decade, the DFIG technology designed with partial-scale power electronic converters was a prime choice in wind energy industries; however, the PMSG topology developed with a full-scale power electronic converter is recently receiving prominence due to the fact that it involves full power-regulation capability.

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FIGURE 2 . Energy conversion levels in a modern wind energy conversion system.

2.1 Doubly fed induction generator-based turbine technologies

The DFIG (its configuration is shown in Figure 3 ) is the most recognized technology yet, and it has been installed widely since 2000. This wind energy harvesting technology uses both multiple- and single-gearbox systems; but the system with multiple gearboxes had widely gained acceptance until recent years, while single-gearbox technology is currently reported to have outstanding features in several research studies. The stator windings in a DFIG are directly tied to the grid by means of a transformer, and the rotor windings are tied to the power grid via a power electronic converter with approximately 30% power rating of the generator ( Cheng and Zhu, 2014 ; Desalegn et al., 11912 ). In this technology, the frequency and the current in the rotor of the generator can be smoothly regulated by the power electronic converter, and hence, the rotational speed of rotor blades can be adjusted in an acceptable range to increase energy harvesting and minimize the mechanical stress. The comparatively lower rating of the power converter makes this technology preferable in terms of cost. Yet, the major limitations of this technology are the application of slip rings with poor reliability and inadequate power regulation capability with regard to grid or generator power fluctuations. This technology has a globally dominant share in onshore wind power generation, and it is less suitable for offshore wind power application.

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FIGURE 3 . Topology of DFIG-based wind energy conversion technology.

Due to the fact that the power rating capacity for the power electronics converter in DFIG-based wind energy conversion technology is comparatively small, the two-stage voltage source converter (VSC) topology is widely recognized in this technology. Conventionally, two-stage VSCs are developed with a back-to-back design along with a common direct current (DC) link. A special feature of this back-to-back design is that it can help to implement complete power regulation under system operation. Moreover, this design has comparatively low structure complexity with a low component number, and this contributes to excellent efficiency and reduced cost of the DFIG-based system.

2.2 Permanent magnet synchronous generator-based turbine technologies

PMSG-based wind energy conversion technology ( Figure 4 ) is another interesting system, and it is highly recognized in the most recently installed wind farm industries. By developing a full-scale power electronics converter and transformer to couple the power grid and the stator windings of the machine, the energy harvested by this wind energy conversion system can be entirely handled ( Le et al., 12023 ). In comparison to the DFIG-based wind energy conversion design, the most important features that can be recognized are the absence of slip rings, uncomplicated or even unneeded gearbox, enhanced power and speed regulation on the broader scale, and superior grid compensation efficiency. However, increased stress and high cost of power electronics devices and increased power loss in the converter phase are the major disadvantages ( Yaramasu et al., 2017 ). This design is usually not preferable in recently installed onshore wind energy conversion systems. On the other hand, PMSG-based wind power technology has been reported in multiple recent studies as a highly attractive candidate for recent and future offshore wind power applications because its converter device can be scalable to large-scale MW power.

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FIGURE 4 . Topology of PMSG-based wind energy conversion technology.

Since the power electronics converter in the PMSG-based wind energy harvesting technology needs to embrace the full power harvested at large-scale megawatts, the two-stage VSC topology may be prone to maximum loss at this power scale. In addition, the cabling in the instance of low-voltage scales below 1 kV with increased current is a design limitation. In order to get along with increasing power rating, various multi-cell converter configurations have been developed for different synchronous generator technology-based systems. The multi-cell converter topology generally has the benefits of standard and robust low-voltage converter technologies. For instance, multi-cell two-stage VSC in parallel connection is the state-of-the-art option for PMSG-based wind energy-harvesting technologies exceeding 3 MW ( Yaramasu and Wu, 2016 ). The advanced configurations of power electronics converters for offshore wind farm applications are presented in Section 3 .

2.3 Comparison of performances of DFIG- and PMSG-based wind energy-harvesting technologies

The performances of DFIG- and PMSG-based wind electric power generation systems are qualitatively summarized and compared in Table 1 . Based on their design types of mechanical and electrical system alignments, different configurations of DFIG- and PMSG-based wind power technologies have been introduced to wind farms for harnessing onshore and offshore wind power. In some cases, the nature of the alignments between mechanical and electrical devices can serve to generally characterize these technologies as geared and direct-drive systems. For example, DFIG-based wind turbines are generally geared technologies as they may depend on either three-stage gearbox or single-stage gearbox design systems, while PMSG-based wind turbines can depend on either single-stage gearbox design or they are direct-drive (gearless) technologies. However, the recent state-of-the-art wind power plants generally rely on gearbox-based technologies, such as three-stage gearbox-based DFIG, single-stage gearbox-based DFIG, and single-stage gearbox-based PMSG. Direct-drive PMSG technology has recently been undergoing research and technological advancements, and reports have claimed its promising development for the application of offshore wind multi-mega scale power generation.

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TABLE 1 . Comparison of DFIG- and PMSG-based wind energy harvesting technologies.

Table 1 provides comparative summaries pertinent to the performances and operation characteristics of DFIG- and PMSG-based wind power systems based on multiple requirements. These summaries are designed such that the listed requirements for comparisons generally apply to all configurations of DFIG-based systems (three-stage and single-stage gearbox technologies) and PMSG-based systems (single-stage gearbox and direct-drive technologies). In consideration of the cost of the system as one of the requirements for the comparisons, an average of the costs of two configurations (three-stage and single-stage gearbox) of DFIG technologies is compared with an average of the costs of two configurations (single-stage and direct-drive) of PMSG technologies. As shown in Table 1 , DFIG-based systems are desirable for their lower general cost than PMSG-based systems, while PMSG-based systems are generally attractive solutions due to their suitability for multi-scale offshore wind power application.

The operational characteristics of DFIG- and PMSG-based wind power systems can be quantitatively evaluated based on research findings. Most research works were focused on the studies of DFIG- and PMG-based systems’ reliabilities, active and reactive power performances, etc. Here, the capability of power generation reliability for both generator systems is analyzed and compared based on the reports that were presented in the research works. In addition, active and reactive power performances and energy harvesting ranges of these two generator-based systems are considered for comparative discussion.

The junction temperature of power devices that correspond to DFIG- and PMSG-based systems of a 2-MW rated power capacity is shown in Figures 5A, B comparatively. For these two generator-based wind energy-harvesting technologies, the power devices’ thermal cycling is demonstrated within 0.2 s. According to Figure 5A , the performance of a partial-scale power converter in the DFIG-based system could deteriorate due to its thermal cycle that is characterized by a larger amplitude than the performance of a full-scale power converter in the PMSG-based system, whose thermal cycles are characterized by a smaller amplitude as shown in Figure 5B . This indicates that the reliability performance of the power device in a DFIG-based system could be severely affected due to a larger amplitude, which is associated with the system’s thermal characteristics. Consequently, advanced modeling and testing approaches should be proposed in helping adjust the reliability by establishing the power devices’ thermal behavior more effectively based on the wind power converter’s mission profile ( Blaabjerg and Ma, 2017 ). A robust strategy has been presented by Ma et al. (2015) , which resembles lenses with varying focal lengths used in photography. The wind power converter’s loading analysis and modeling are partitioned subject to some given time constants and various modeling methods and tools.

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FIGURE 5 . Junction temperature of power devices with generator systems of a 2-MW wind power converter: (A) DFIG system’s rotor side converter and (B) PMSG system’s machine side converter ( Blaabjerg and Ma, 2017 ).

Furthermore, Figures 6A, B show active power control performances for DFIG- and PMSG-based marine current wind energy-harvesting systems under the variable speed control strategy. As shown in Figure 6A , the major advantage of the DFIG-based marine current system is its capability to supply fixed voltage and frequency within the range of ± 30 % speed change with respect to normal synchronous speed. An additional option for the range of speed variation (30%–50%) can also possibly be used. A minimum power rating of the rotor converter is directly associated with the range of 30% speed variation. Similarly, in Figure 6B , the active power control of the PMSG-based marine current system also indicates good power-tracking performance. In both systems, the differences between the estimated and simulated power are negligible. These negligible differences primarily result from the implementation of variable speed control rather than a direct power control.

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FIGURE 6 . Active power performances of (A) DFIG-based wind energy harvesting technology and (B) PMSG-based wind energy harvesting technology ( Benelghali et al., 2010 ).

As noted, the comparison of DFIG- and PMSG-based wind energy-harvesting systems is qualitatively shown in Table 1 by considering their annual power production capability as one of the criteria. Herein, Figures 7A, B show the annual energy-harvesting performances of DFIG- and PMSG-based marine current turbine technologies for the given ranges of tidal velocities, based on the research findings by Benelghali et al. (2010) . Accordingly, Figure 7A represents the annual energy harvested by a DFIG-based marine current wind power system; the annual harvested energy corresponding to different tidal velocities, with DFIG technology, is calculated to be around 1,530 MWh/year according to the study. On the other hand, by the application of a PMSG-based marine current wind power system ( Figure 7B ), the cumulative generated power under the similar ranges of tidal velocities is reported to be about 1,916 MWh/year by the same study. As noticed, there is nearly a 25% difference in the power produced over a year between these two power systems, and this difference can be further extended when using a larger turbine system. The DFIG-based power system is characterized by its restricted speeds, and this is the reason for the reduction in its annual power production compared to the full-scale PMSG-based system. Meanwhile, according to a study by Fischereit et al. (2015) , the relationship between tidal currents and wind speed was quantified such that the wind speed over a marine was observed to increase or decrease by around 0.2 m/s depending on the direction of the tidal flow with respect to wind direction.

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FIGURE 7 . Comparison of energy ranges harvested by (A) DFIG-based system and (B) PMSG-based system ( Benelghali et al., 2010 ).

3 High-power transmission systems for wind farms: Technological advances

Under this section, the general comparison of power transmission systems of wind farms that are based on HVAC and HVDC technologies is shown in Table 2 based on capital expenditure. Parallel AC-connected HVAC, parallel AC-connected HVDC, parallel DC-connected HVDC, and series DC-connected HVDC transmission configurations are also comparatively shown in Table 3 based on main energy criteria, which include the operational condition of the system, cost of the system, and system’s capability for power scalability. Moreover, different HVDC-based state-of-the-art and more advanced technologies for the transmissions of offshore wind farms are shown in Table 4 on the basis of important requirements: the technology’s energy conversion quality, range of applications for recent offshore wind power generation, and the possibility of the development for future offshore wind power application.

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TABLE 2 . Comparison of HVAC and HVDC in terms of their capital costs ( Li et al., 2021 ).

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TABLE 3 . Comparison of different configurations of HVAC and HVDC converter systems.

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TABLE 4 . Comparison of different converter–HVDC networks for offshore power transmission applications.

3.1 Applications of HVAC and HVDC in wind farms

The general structure of offshore wind farm transmission with the application of HVDC and HVAC systems that include the components, such as power converter, transformer, offshore substation, and onshore substation, is shown in Figure 8 . Here, the main focus is to evaluate the economic efficiencies of HVAC- and HVDC-based transmission systems by comparing the capital expenditures required for their respective components in commissioning offshore wind farms. Accordingly, the outlines of capital costs for the electrical components of HVAC and HVDC systems are shown in Table 2 based on the most recent study by Li et al. (2021) . According to this study, the required capital expenditures are mainly allotted to cover the costs of transformers, onshore GIS switchgear, offshore substations, cable trench, shunt reactors, and submarine cable in the case of the HVAC system. In the case of the HVDC system, the capital expenditures are divided among the costs of cable trench, HVDC cable, onshore inverter, offshore rectifier, transformers, and additional offshore facilities. Based on the outlines of these costs for the components of HVAC and HVDC systems, it can be deduced that at a bigger power capacity and extended transmission line, HVDC presents more attractive cost benefits.

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FIGURE 8 . General scheme of an offshore wind farm.

The HVAC system produces a huge amount of capacitive current that corresponds to the length of the transmission line. Here, the real power transmission capability may be determined with specified and reactive currents of a specific HVAC cable. With 50 Hz HVAC, the highest transmission length is around 140 km without compensation for reactive power. The HVDC system is characterized by considerable capability of power transmission compared to the HVAC system, particularly over an extended distance.

Moreover, the HVDC system offers additional advantages without being limited to cost and transmission capability by outperforming the HVAC system over an extended distance; for the vast offshore wind farm power transmission, the HVDC system also demonstrates lower transmission losses and its cables are commercially available (for larger voltages, HVAC cables are not available), and therefore, the HVDC system helps control excessive load as its power can be inherently adjusted. In general, HVDC is a more desirable solution than HVAC in the transmission of multi-mega-scale offshore wind power that is commissioned far away from the shore.

3.2 Advanced configurations of HVAC and HVDC for wind farms

Different configurations of HVAC and HVDC systems are given in Table 3 based on various factors, which include operational condition, overall cost, and capability of the system’s configuration. The configurations include parallel AC-connected HVAC system, parallel AC-connected HVDC system, parallel DC-connected HVDC system, and series DC-connected HVDC system. Parallel AC-connected HVAC system-based configuration is characterized by its low complexity under the condition that the transmission distance between offshore and onshore sites is not long and is economically efficient under the same condition. However, this configuration shows serious disadvantages when the distance between offshore power and onshore substations is extended.

Parallel AC-connected HVDC configuration, which is based on the modular multilevel converter (MMC) topology, is the state-of-the-art solution to offshore wind farms with larger power capacity and longer transmission lines. However, the main disadvantage of this configuration is the high overall cost of power conversion components and the requirement of a bulk offshore substation. On the other hand, this configuration is characterized by its high capability of power scalability, and MMC topology with a power capacity of 864 MW, voltage of ± 320   k V , and cable length 160/45 km is currently in operation. In addition, MMC topology with a power capacity of 900 MW, voltage of ± 320   k V , and cable length of 45/45 km is under construction for offshore wind farm commercial application by 2023; MMC topology with 900 MW, ± 320   k V , and 100/30 km is under development for application by 2024 ( Li et al., 2021 ).

Furthermore, parallel and series DC-connected HVDC configurations are designed to have reduced size and weight, and are recently under development for commercial use in offshore wind farms. The disadvantages that are being posed with the applications of other AC-connected HVDC configurations could be eliminated by DC-connected HVDC configurations. The discussions for different power converter–HVDC networks for offshore wind power transmission are given in Table 4 based on the criteria mainly related to energy conversion quality and range of applications in recent wind farms and possibility for future developments. The power converter–HVDC networks that are considered for discussions here include line-commutated current source converter (LC CSC)-based parallel medium voltage-alternating current (MVAC)-connected HVDC, two-level voltage source converter (2L-VSC)-based parallel MVAC-connected HVDC, multi-modular voltage source converter (MM VSC)-based parallel MVAC-connected HVDC, cascaded rectifier (diode)-based parallel MVAC-connected HVDC, three-level neutral point clamped (3L-NPC)-based parallel MVDC-connected HVDC, solid-state transformer (SST)-based series medium voltage direct current (MVDC)-connected HVDC, and pulse width modulator current source converter (PWM CSC)-based series MVDC-connected HVDC. Each of these HVDC-based power transmission networks is discussed in the following paragraph.

The application of LC CSC-based parallel MVAC connection with HVDC has shown well-proven performance in onshore wind power generation for about the last five decades. Nevertheless, this LC CSC-based network is inapplicable for offshore wind power, because it is incompatible with huge-scale power generation. On the other hand, the application of 2L back-to-back (BTB) VSC topology that is based on parallel MVAC connection with an HVDC system has been dominating over the onshore wind farm, and is less suitable for offshore wind farm due to its compatibility issue with multi-scale wind power generation. Modular multilevel (MM) VSC, based on parallel MVAC connection with an HVDC system, has been applied to offshore wind power transmission with various power capacity, voltage, and cable length levels. Currently, MM VSC is under further development for further improvement so that it would help ensure power maximization from offshore wind farms. At a wind power scale of 200 MW, cascaded rectifier (diode)-based parallel MVAC connection with HVDC was implemented, and it improved power production by 20% and reduced weight by 60% against traditional 2L-VSC-based parallel connection with HVDC according to study assessment by Blaabjerg and Ma (2017) .

Furthermore, various offshore wind power transmission networks that are based on different converter technologies have been proposed in studies to enhance wind power conversion. For example, a three-level (3L) NPC-HVDC parallel MVDC-connected power transmission network has been developed in literature reports in response to the limitations posed while using a power transmission network that is based on the parallel MVAC-connected 2L VSC-HVDC. Series MVDC-connected transmission networks can also be implemented with a (3L) NPC converter ( Peng et al., 2021 ). A series MVDC-connected (3L) NPC-HVDC system can be configured to have a relatively uncomplicated structure, higher power density, and lower cost than a parallel DC-connected system. In addition, SST- and PWM CSC-based series MVDC-connected HVDC configurations are undergoing promising development with attractive features, such as lower costs and weight than parallel MVAC and MVDC-connected networks such as 2L VSC-HVDC, MM VSC-HVDC, and (3L) NPC-HVDC ( Wei et al., 2017 ). Further discussions on this section are given in Tables 3 , 4 .

4 Optimization-based wind farm control models: Research perspectives

Wind farms with larger capacities are required to perform under grid integration in order to ensure more reliable and efficient ways of generating wind power, which include reduced energy losses, maximized power production, lowered energy costs, enhanced power quality, and minimized loads on power systems as the ultimate objectives. Multiple recent studies have indicated that implementing control to individual wind turbines is not an effective strategy to achieve these objectives due to the fact that this strategy is unable to emphasize the complicated aerodynamic interactions across different turbines. Consequently, the strategy for wind farm control design has been aimed at enhancing controllers, which regulate and oversee the performance of a group of wind turbines from a supervisory level as shown in Figure 9 with a hierarchical method. The wind farm controller operates as the supervisory control system and comprises control levels that supervise production of power, system operation and maintenance, and power system services. The supervisory control system uses power grid requirement, energy costs, and turbine condition inputs to deliver the reference inputs to all turbines so that the desired operation can be ultimately met by the wind farm. The hierarchical control structure ( Figure 9 ) enables robust and efficient control of turbines and wind farms by handling the turbines’ power output and affecting the power transfer in the electrical devices so as to achieve the aforementioned objectives.

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FIGURE 9 . Scheme of the model-based closed-loop wind farm control approach.

The purpose of the discussion under this particular section is to assess the recent research results that have been reported in claiming to achieve the improvements in wind turbines/wind farm performances as per the aforementioned objectives by using different control approaches. In the research practices, two types of universal control approaches are usually proposed for the implementation of the required control objectives. A number of control strategies that rely on non-optimization- and optimization-based approaches are introduced by different researchers in aiming to achieve various wind farm control objectives, such as power maximization, power system load reduction, and grid services provision. In the most recent works, the optimization-based approach, which encompasses standard feedforward, model-based closed-loop, and model-free closed-loop control strategies has been consistently underlined to show outstanding performances compared with the non-optimization-based approach that uses conventional feedback control and feedforward control strategies. Therefore, this study summarizes research findings that have been recently reported in the efforts to realize some wind farm control objectives (primarily power gain/maximization) based on standard feedforward, model-based closed-loop, and model-free closed-loop control algorithms with the implementation of different wind farm optimization strategies and optimization and evaluation models while considering various input parameters, such as yaw angle, blade pitch angle, tip–speed ratio, and axial induction factor. Summaries of the studies are given in Sections 4.1 – 4.3 and Tables 5–7 .

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TABLE 5 . Performances of feedforward control strategies with different optimization and evaluation models.

4.1 Standard feedforward control

The research studies on wind farm optimization using the concepts of standard feedforward control are summarized in Table 5 . This approach uses different control algorithms, optimization strategies, input parameters, and wind farm design optimization and evaluation models in enhancing wind farm performances so as to achieve the desired wind farm control objectives. Accordingly, the gradient-based control algorithms with different optimization strategies, such as sequential quadratic programming, steepest descent, and conjugate gradient, were proposed ( Table 5 ) to maximize wind farm power production by adjusting wind turbines’ yaw angles and induction factors. For instance, Annoni et al. (2018) used sequential quadratic programing by implementing the Gaussian wake concept as both the optimization and evaluation model in increasing power production by adjusting the wind turbine’s yaw angle. Under this framework, power production was claimed to increase by 17% compared with the greedy control algorithm. On the other hand, Thomas et al. (2017) , with a similar strategy (sequential quadratic programming), used the Jensen–Park model to optimize the yaw angle and layout of the wind farm, and power production was reported to increase up to 24% compared with the greedy control algorithm. Moreover, steepest descent- and conjugate gradient-based optimizations were proposed by Park and Law (2017 ) and Thøgersen et al. (2017 ) to adjust the axial induction factor and yaw angle (in the case of the steepest descent strategy) and the yaw angle (in the case of the conjugate gradient strategy) of wind farms, and the results indicated that the power production could be maximized by 7% and 7.5%, respectively.

As shown in Table 5 , further standard feedforward optimization strategies that are based on heuristic algorithms (genetic algorithm, particle swarm optimization, and artificial bee colony) and game theory optimization were proposed by different researchers for optimizing various input parameters of wind farms. For instance, a genetic algorithm was proposed by Serrano González et al. (2015) to optimize wind turbines’ tip–speed ratio and blade pitch angle based on the Jensen–Park evaluation model, and it resulted in 1.5% increase in power production. The genetic algorithm was also used by Wang et al. (2018) , aimed at maximizing power production by adjusting the axial induction factor and layout of the wind power system based on the Jensen–Park optimization and evaluation model, and it ended up increasing wind power production by 1%–2%. Another powerful heuristic algorithm-based standard feedforward strategy includes particle swarm optimization, which has been widely proposed in recent research works to achieve various wind farm control objectives. In Table 5 , the results of several studies that implemented the particle swarm algorithm in optimizing wind farm performance are given. Accordingly, in Behnood et al. (2014) , the tip–speed ratio and blade pitch angle were adjusted for a wind farm of 16 turbines based on the Jensen–Park model, and approximation of the power coefficient (as a function of the turbines’ tip–speed ratio and blade pitch angle) and 10.6% increase in power production were reported. The study results that were presented by Bo et al. (2016 ), Hou et al. (2016 ), and Gionfra et al. (2019) also indicated the robustness of particle swarm optimization in helping to maximize wind farm output power. In addition, the heuristic algorithm-based wind farm optimization strategy, artificial bee colony, was proposed to adjust the tip–speed ratio and yaw angle of wind turbines based on the Jensen–Park model and FLORIS for design optimization and evaluation in Abbes and Allagui (2016 ) and Quick et al. (2017 ) and output power increment of 4%–6% and 3% were, respectively, reported to be achieved.

The algorithmic game theory-based wind farm optimization strategy can be implemented to minimize load fluctuations of power systems in addition to maximizing output power by facilitating the adjustments of the yaw angle and axial induction factor. By using this strategy along with different wind farm performance optimization and evaluation models, some research works were introduced by researchers. Accordingly, in Gebraad et al. (2016) , the yaw angle was optimized by using the FLORIS model, whereas output power performance was evaluated by using both SOWFA and FLORIS models, and the wind farm power production was reported to be improved by 1% with FLORIS and SOWFA optimization and evaluation models, and by 13% when the FLORIS platform was used as both the optimization and evaluation model. In addition, in van Dijk et al. (2021) , the FLORIS model was used as both a single platform and in combination with the CC-Blade model for optimizing wind turbines’ yaw angle and evaluating wind farm output performance, where power production was reported to be maximized by 3.7% wind and a single model (FLORIS) and load fluctuation of power systems decreased by 18.7% with combined modes (FLORIS + CC-Blade). More work was also introduced in Herp et al. (2015) , where the axial induction factor was optimized with the Jensen–Park model under different settings and resulted in 1.4%–5.4% increase in wind farm power production.

Additional works that are based on standard feedforward control strategies involving different algorithms and optimization and evaluation models for the design and performance of wind farms are given in Table 5 as extended summary from Table 5 . A dynamic programming strategy was proposed by Rotea (2014) to optimize axial induction of a wind farm by applying the actuator disk model as the design optimization and evaluation standard. The result of this study shows that the power production was maximized by around 5% and wind farm load fluctuation was reduced up to 38% compared to the greedy algorithm strategy. In addition, a dynamic programming optimization strategy was implemented by Santhanagopalan et al. (2018 ) and Dar et al. (2016) by adjusting the tip–speed ratio through the RANS model and the yaw angle and axial induction factor through the enhanced Jensen–Park model to achieve output power increment by 0.8% and 4.5%–26.5%, respectively.

Additional standard feedforward optimization strategies that are based on different wind farm control objectives and with various wind farm design optimization and evaluation models were also presented in many research works, and the summaries of some of their parts are given in Table 5 . Accordingly, the Nelden–Mead simplex algorithm was implemented with the simplified Ainslie model for optimization and evaluation power reference of wind farms in Kim et al. (2017) , and the results showed an increase in the output power by 2.4% and reduction in the load fluctuation at upstream wind by 16.7%. According to a study presented in Bossanyi (2018) , the generator torque, blade pitch angle, and yaw angle were adjusted and evaluated in aiming at minimizing the overall power losses and power system stress, and power production was saved by 2%. In addition, the research works based on field test optimization were discussed by Fleming et al. (2019 ) and Fleming et al. (2020 ) claiming a power gain of 2% and minimization of wake losses by 6.6%, respectively, by using FLORIS as the optimization model in adjusting the yaw angle. In addition, some studies that are specific to floating offshore wind farms were conducted based on blade pitch angle adjustments, and the results of these studies are given in the last row of Table 5 . For instance, in the study by Kheirabadi and Nagamune (2019) , the FLORIS benchmark was used as the optimization and evaluation model for maximizing power production by adjusting the floating offshore wind farm blade pitch angle under different settings, and 16%–54% increase in output power was reported. In addition, the adjustment of floating offshore wind farms under different design optimization and evaluation benchmarks: deep neural learning optimization model and LSTM evaluation model by Sierra-Garcia and Santos (2021) and the Jensen–Park optimization model and FarmFlow evaluation model by Rodrigues et al. (2015) were implemented; power production growth of 7%–21% and 4.4% were, respectively, reported.

4.2 Model-based closed-loop control

Under this control approach, the research works that are based on model predictive optimization and data-driven optimization strategies are summarized in Table 6 . Accordingly, a model predictive optimization algorithm was implemented by Heer et al. (2014) , using the Jensen–Park platform as the optimization model and the SimWindFarm platform as evaluation in aiming to increase wind farm power production with adjustments of the blade pitch angle and tip–speed ratio. Under these two adjustments, the output power showed an increment of 0.4%–1.4%. On the other hand, the axial induction factor was adjusted in the study by Vali et al. (2016) , making use of another wind farm performance optimization and evaluation platform (WFSim) based on the model predictive optimization strategy to achieve a power production growth of 3.8%. Furthermore, with the implementation of the model predictive optimization strategy through the application of the WFSim model for optimizing the axial induction factor and evaluating wind farm output power performance, additional works were presented by Vali et al. (2017 ) and Vali et al. (2019 ), reporting an improvement in power production by 2%–8% and 4%, respectively. On the other hand, larger increments of power production were reported to be achieved while using the SP-wind benchmark as the optimization and evaluation model for adjusting thrust coefficients ( Goit and Meyers, 2015 ; Munters and Meyers, 2016 ; Munters and Meyers, 2017 ), and thrust coefficients and yaw angle rates ( Munters and Meyers, 2018a ; Munters and Meyers, 2018b ) of wind farms under different control settings.

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TABLE 6 . Performances of model-based closed-loop control strategies with different optimization and evaluation models.

Different wind farm performance optimization and evaluation models (FLORIS and SOWFA, deep neural learning model using LES and CNN + LSTM, and DB-PC and extended Kalman filter model) were used to adjust the yaw angle ( Doekemeijer et al., 2019a ), tip–speed ratio and blade pitch angle ( Yin and Zhao, 2021 ), and rotor rotational speed and axial induction factor ( Abdelrahem et al., 1596 ); output power growths of 7%–11%, up to 38%, and 20%, respectively, were claimed to be achieved. On the other hand, various data-driven model-based optimization strategies (Bayesian ascent, Bayesian optimization, etc.) were implemented by researchers such as Park and Law (2016 ), Zhao et al. (2020 ), and Yin et al. (2020) by making use of different wind farm optimization models (Gaussian regression model, FLORIS, deep reinforcement learning, and support vector machine, respectively), and evaluation models (wind tunnel, SOWFA, WFSim, and FLORIS, respectively) in adjusting input parameters (yaw angle and blade pitch angle, yaw angle, axial induction factor, and yaw angle, respectively) to determine the levels of increments in the resulted power productions and reliability improvement. Accordingly, output power growths were reported to be achieved by 30.4%–33.2%, 4.4%, and 10%, and reliability was enhanced by 1.7%, respectively.

4.3 Model-free closed-loop control

As shown in Table 7 , model-free closed-loop control algorithm-based wind farm optimization strategies, which include multiple resolution-based simultaneous perturbation stochastic algorithm, game theory, gradient descent, and nested extremum controller, were implemented in optimizing wind turbines’ input parameters, such as axial induction factors and generator torque gains in order to evaluate the possible outcomes in generated power. Axial induction factors were optimized under different settings [simultaneous perturbation stochastic algorithm ( Ahmad et al., 2014) , game theory ( Marden et al., 2013) , and gradient descent ( Gebraad et al., 2013) ], and the outcomes were evaluated with the same model (Jensen–Park), and different levels of increments (32%, up to 25%, and 1%) in the output power were reported to be achieved. On the other hand, generator torque gains and yaw angle were adjusted by implementing the same optimization strategy (nested extremum-seeking controller) and using different evaluation models [SimWindFarm ( Yang et al., 2015) , UTD-WF ( Ciri et al., 2016) ; Ciri et al., 2017) , and wind tunnel ( Campagnolo et al., 2016) ]; varying levels of increase (1.3%, 10%/7.8%, and 15%) in the power production were claimed to be achieved.

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TABLE 7 . Performances of model-free closed-loop control strategies with evaluation models.

5 Recent challenges and future prospects of research studies on wind farm control

Standardizing the effect of wind farm optimization strategies on loads of wind power plants would be highly helpful in applying the results of the research studies for their practical implementation in real-world wind farm industries. This is because the optimization of wind farm performance through research studies is modeled and evaluated by using different strategies (as the summaries of multiple studies in Table 5 – 7 indicate), and this still raises challenges in evaluating the performances of different optimization models under a unified environment. As a result, the technological approach for collaborated wind farm control is presently under advancement in multiple research institutions and industries. In particular, the FarmConners environment was introduced recently for the purpose of producing datasets as the basis for the evaluation of different control models in aiming to alleviate the barriers for wind farm control acceptance ( Göcmen et al., 2020 ). In addition, the project was started by FarmConners to eliminate the challenges associated with the commercial application of wind farm control by collaborating a genuine assessment of the state-of-the-art of wind farm control in the FarmConners environment. Thus, a reliable evaluation of the efficiency of wind farm control models should be conducted so as to accurately determine the performance of wind farm controllers. Correspondingly, in order to maximize the acceptance of the wind farm control models and, ultimately, the FarmConners environment, FarmConners presents an extensive validation framework for wind farm control-oriented flow and load models, at which high-fidelity simulation generates (imitative dataset), wind tunnel experiments, and the field data from a fully operating wind farm in natural environmental conditions are drawn together (as shown in Figure 10 ).

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FIGURE 10 . Data texture of the FarmConners environment.

Regardless of this technological progress by FarmConners, and even though recent studies demonstrated through simulations that the power production by wind farms can be maximized with collaborative control strategies, the practical confirmation on a real-world wind farm is still lacking. For instance, it raises the question of whether axial induction control can effectively result in increasing the wind farm’s annual power generation. Even for the more promising approach in simulation, wake-steering wind farm control, its direct practical applicability for a real-world wind farm has not been implemented yet. The considerable measurement noise and unpredictability is a challenge in wake-steering control, making it hard to evaluate the outcomes of measurement campaigns. Because of the unpredictability in models such as wind direction and aerodynamics, collaborative control strategies can become counterproductive over a number of periods, and this may not lead to enhancing the annual power output. However, even if these control concepts are unable to maximize the production of power, they may still be effective in reducing wind farms loads.

In general, wake steering is assumed to be a favorable control strategy for wind farm optimization. Yet, it is not fully convincing whether wake steering can practically be effective in helping to minimize the cost of energy. Turbines are not oriented to being yawed into the wind all the time. These will result in the addition of dynamic fatigue on various components of the turbines, which could result in increased costs for maintenance. Reports by several simulation studies demonstrated that the fatigue load on the blade and drive-train can be significantly improved. However, more advanced studies supported with extended experiments are required to accurately determine the effects of the wake-steering control strategy on the lifespan of the components of the turbines. If this control strategy proves to be effective, it could probably even help redesign various components of wind power systems ( Andersson et al., 2021 ). On the other hand, the most recently published report by the National Renewable Energy Laboratory (NREL) has revealed that it has practically validated that the wake-steering wind farm control, which was based on the study model in Martínez-Tossas (2021) , can be implemented to minimize the costs of wind turbines.

Many related studies were carried out to explore these problems and they came up with various indications. The study by Howland et al. (2019) proposed a wake-steering approach based on yaw misalignment that deflects wakes away from downstream turbines in order to maximize wind farm power production. The evaluation of this approach was conducted with site-specific analytic gradient ascent by using historical operational data; power increases of 7%–13% for wind speeds near the site average and 28%–47% for low wind speeds were observed. This study also reported that the wake steering minimized the power production variability of the wind farm by up to 72%. This work finally indicated that even though the wake-steering results demonstrated the potential to enhance the efficiency and predictability of power production, the resulting gains in annual power production were insignificant at the wind farm. Another study that focused on wind turbines under yawed conditions is a work by Rahimi et al. (2018) , which claimed to present a significantly improved engineering model for the prediction of the loads in yawed flow based on the skewed wake effect. This work put particular emphasis on the contribution of the root vorticity to the azimuthal variation of induced velocity for the prediction of fatigue loads and determining the yawing moment; the new model was derived from computational fluid dynamics of three multi-megawatt wind turbines (each rated 5 MW) and two 10-MW turbines. Simulations were conducted by means of an actuator line model, whereas the proposed model was evaluated based on results from a free vortex wake code and actuator line model simulations for all wind turbines and different yaw angles. The results of this study finally indicated that the proposed model significantly improved the estimation of the azimuthal variation of the axial induction factor, and also considerably improved the prediction of the resulting variation in blade loads.

Furthermore, a dynamic wind farm wake modeling approach that was based on a bilateral convolutional neural network and high-fidelity LES data was proposed by Li et al. (2022) ; another approach that relied on a point vortex transportation model for yawed wind turbine wakes was introduced by Zong and Porté-Agel (2020) . The former ( Li et al., 2022 ) used a novel deep learning method, named the bilateral convolutional neural network (BiCNN), for accurate modeling of dynamic wind farm wakes based on flow field data generated by high-fidelity simulations; its discussion indicated that the developed machine learning-based wake model would capture the spatial variations of the dynamic wakes closely as high-fidelity wake models and would run as fast as low-fidelity static wake models. In addition, this model was shown to outperform high-fidelity numerical models that would be used for the same scenario. On the other hand, the latter ( Zong and Porté-Agel, 2020 ) performed stereo particle imaging velocimetry measurements at multiple stream-wise locations behind a yawed wind turbine in order to study the formation mechanisms of the counter-rotating vortex pair; the results of this study showed that the counter-rotating vortex pair formed behind a yawed wind turbine would originate from the complex interactions between the hub vortex and stream-wise components of the blade tip vortices, which was observed to be essentially different from a yawed drag disk case where the hub vortex would be absent. Furthermore, the model by this study was considered to be the first model that would be capable of accurately simulating the wake deformation behind a yawed wind turbine.

As it has been generally indicated, the popular collaborative wind farm control approaches are based on optimization, which involves model-based closed-loop control, etc. In order to implement a model-based optimizer in a closed-loop, a state estimator needs to be used. However, the utilization of a state estimator will reduce the capability of the optimization approach in some way compared to the non-optimization-based noise-free full state feedback strategy. Recently, a very limited number of studies have been conducted by proposing the enhanced state estimators in optimizing input parameters of wind farms for enabling the provision of grid services.

6 Conclusion

This work presents a comprehensive review on the wind energy engineering approaches by incorporating research and technological issues in order to reflect the recent and future advances, challenges, and opportunities in wind power industry development. Wind power research and technological advancements have greatly contributed to the progress achieved by the wind power industry so far, and the future fate of this industry will also largely rely on the potential of wind power-related studies and technologies. Evidently, the energy-related policies and projections would be directed based on the status of ongoing research studies and technological innovations in the given energy field, which can also be a usual trend in the case of wind power installation. Based on this fundamental premise, advanced research studies and technological progresses that could potentially impact the recent and future onshore and offshore wind power developments have been discussed in this study in line with the directions (zero-emission goals) set by the United Nations Convention on Climate Change (UNCCC).

Numerous wind energy system-related recent research studies are largely inclined toward the technological developments of offshore wind power due to the reason that offshore power conversion technologies can be further scalable for high-power generation applications. Many HVDC-power transmission system-based converters are currently undergoing promising developments with various designs to achieve the weight reduction, cost optimization, and further enhancement of power conversion efficiency of offshore systems in the future. In general, based on the recent advances in the power transmission technologies, the future of the wind power industry seems be more dependent on the offshore wind farms than those onshore.

Moreover, the current advancements in automation-based research projects indicated a very promising future about the objective implementation of some optimization theories on real-world farms. Challenges with wind farm control modeling and evaluation tools were generally projected to be addressed toward the realization of fundamental wind farm optimization objectives. One of the objectives (wind turbines’ cost minimization) has already been reported to be practically validated based on the results of the recently proposed research model. These recent developments and more enhanced works that may be subsequently conducted by researchers and engineers on the wind farm optimal control are of great importance, which could partly contribute to enabling the wind power industry to see grand transition in the not-too-distant future.

Author contributions

All authors listed made a substantial, direct, and intellectual contribution to the manuscript and approved it for publication.

This work was supported by Bahir Dar University Institute of Technology, Bahir Dar Energy Center, and Wolaita Sodo University.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Abbreviations

CC, component certification; CoMET, composites manufacturing education and technology; DFIG, doubly fed induction generator; FLORIS, flow redirection and induction in steady state; HVAC, high-voltage alternating current; HVDC, high-voltage direct current; MM VSC, modular multilevel - voltage source converter; NREL, national renewable energy laboratory; PMSG, permanent magnet synchronous generator; PSO, particle swarm optimization; 3L NPC, three-level neural point clamped; SST, solid-state transformer; PWM CSC, pulse width modulator-current source converter; SVM, support vector machine; NESC, nested extremum-seeking controller; SOWFA, simulator for wind farm applications; RANS, reynolds-averaged navier–stokes; LSTM, long short-term memory; WFSim, wind farm simulator.

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Keywords: wind energy harvesting technologies, doubly fed induction generator system, permanent magnet synchronous generator system, high-voltage power transmission systems, optimization-based wind farm control models

Citation: Desalegn B, Gebeyehu D, Tamrat B and Tadiwose T (2023) Wind energy-harvesting technologies and recent research progresses in wind farm control models. Front. Energy Res. 11:1124203. doi: 10.3389/fenrg.2023.1124203

Received: 14 December 2022; Accepted: 26 January 2023; Published: 15 February 2023.

Reviewed by:

Copyright © 2023 Desalegn, Gebeyehu, Tamrat and Tadiwose. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Belachew Desalegn, [email protected]

This article is part of the Research Topic

Climate Change Challenge: A Wind Energy Perspective

  • Original Research Article
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  • Published: 17 August 2020

Wind turbine performance analysis for energy cost minimization

  • Yassine Charabi   ORCID: orcid.org/0000-0003-2054-688X 1 &
  • Sabah Abdul-Wahab 2  

Renewables: Wind, Water, and Solar volume  7 , Article number:  5 ( 2020 ) Cite this article

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The use of wind energy worldwide has overgrown in recent years to reduce greenhouse gas emissions. Wind power is free, but the installation and maintenance of wind turbines remain very costly. The size of the installation of the wind turbine is not only determined by wind statistics at a given location, but also by turbine infrastructure and maintenance costs. The payback time of the turbine is dependent on turbine energy costs. This study estimates the wind power generation capacity of Northern and Southern Oman and discusses the selection of the most economical, efficient and reliable wind turbines in Oman. HOMER Pro Software was used in this paper to evaluate the wind energy data in the north and south of Oman and to provide well-informed guidance on the most suitable turbines for the power needs of each area. Six different standard wind turbines were measured and compared in terms of the cost of energy and performance. The simulation analysis reveals that the DW54 turbine is the best possible turbine to generate electricity in northern Oman at $0.119/kW. Due to the difference in the wind regime between the north and the south of Oman, the simulation showed that the Hummer H25.0–200 kW turbine is the best option for south Oman with power generation at $0.070/kW. The northern wind turbine plant can efficiently contribute to decarbonization of the energy sector in Oman, with a potential reduction of CO 2 emission approximately 19,000 tons/year in comparison to natural gas and 28,000 tons/year in comparison to diesel. In the Southern Power Plant, carbon emissions are reduced by 18,000 and 12,000 tons/year compared to diesel and natural gas.

Introduction

The rise in global temperature and severe climate change worldwide has increased environmental concerns. Nowadays, more than 90% of the world’s electricity comes from fossil fuels (World-Bank 2015 ), and that energy production plays a vital role in global warming. Any changes in this field can have a significant impact on the environment. Numerous researchers, therefore, have attempted to change or alleviate the negative impacts of global warming, with much of this effort coming from the energy sector (Ghodsi et al. 2019 ; Khare et al. 2016 ; Sahu et al. 2018 ). In comparison to fossil fuels, the impact of renewable energy sources on the environment is negligible. These sources, for example, have no direct CO 2 or NOx emissions. From solar panels to wind turbine generators, a wide range of devices can convert ambient energy into a more useful form, like electricity (Charabi et al. 2019 ). Among these devices, wind turbines are some of the most popular and accessible methods of converting ambient energy to electricity (Yang et al. 2018 ). However, wind energy, like most other sources of renewable energy, has high capital costs, but during the past decade, this trend has changed tremendously. Statics show that the cost of wind production has dropped enormously in recent years, from two million dollars per M.W. to one million in the last decade (Moné 2017 ). This achievement has made it possible to see wind power plants with increasing frequency in both developed and developing countries (Sahu 2018 ).

As a Middle Eastern, oil-dependent country, Oman has started in a new direction on its path of development. The country is trying to change its electricity production industry from one that is entirely oil-based to one that is more reliant on sustainable “greener” energy sources (Abdul-Wahab et al. 2019a ; Al-Suleiman et al. 2019 ). The main two options for this plan are solar and wind energy. Although Oman’s sunny weather provides a unique opportunity for solar energy generation, the country’s wind power potential must not be neglected. As of this article’s writing, Oman has no industrial wind power stations, and the country’s wind turbines are mainly used for research purposes. However, this situation is changing, beginning with developing an understanding of the country’s wind power potential. An incorrect estimation of wind energy needs or the use of low-performance equipment not only reduces the benefits of the project, but also might lead to economic disaster (Dolatabadi et al. 2017 ).

Over the last decade, considerable information on wind resource mapping across Oman has been accumulated to stimulate the deployment of wind power (Al-Yayai and Charabi 2015 ; Al-Yahyai et al. 2012 , 2013 ; Charabi et al. 2011 ; Al Yahyai el al. 2010). Despite the availability of wind mapping information, the deployment of wind energy across Oman is still lagging due to the lack of accurate information on turbine energy cost. Without access to sound information on the cost of wind power technology, it is difficult for decision-makers, if not impossible; to evaluate which wind turbine technologies will most fit their national circumstances. The fast growth and cost reductions in the installed wind energy technologies mean that even data aged one or 2 years will substantially overestimate the cost of power from wind energy technologies. There is also a significant amount of perceived knowledge about the cost and performance of wind power generation technologies that are not accurate or is misleading. Significant knowledge of the cost and performance of wind generation technologies is also viewed that is not right or misleading. This paper fills a significant information gap because there is a lack of precise, comparable, and the latest data on the costs and performance of wind turbines in Oman.

Studies on the viability and economic potential of wind energy have recently spread worldwide.

Kumar and Gaddada ( 2015 ) have explored the outputs of four statistical methods to evaluate Weibull parameters for wind energy applications in four selected sites, located in northern Ethiopia. Gaddada and Kodicherla ( 2016 ) have evaluated wind power capacity and wind energy cost estimates for electricity generation systems in eight selected locations in Tigray (Ethiopia). Kodicherla et al. ( 2017 ) explored the potential of wind energy and developed an economic assessment of the water pumping system in various wind power conversion systems. In three selected Fiji Island stations, Kodicherla et al. ( 2018 ) have investigated the potential of wind power-assisted wind hydrogen production using different types of turbines. The literature also reflects different foci around wind turbines. Many researchers have worked on defining the shape and structure of wind turbines and their effects on aerodynamics (Cai 2019 ; Nema et al. 2009 ; Akpinar and Akpinar 2006 ). Others have tried to improve the performance of current turbines by optimizing placement and hub height (Abdul-Wahab et al. 2019b ; Elkinton et al. 2008 ).

Despite these efforts, the stochastic nature of wind speed makes wind energy generation difficult for some places (Padrón et al. 2019 ). A deep understanding of the specifications of each wind turbine and complete statistical data on wind velocity in any given location can begin to address this problem. These data must be processed and matched to a potential turbine to give a realistic and feasible answer to the suitability of any given piece of wind power equipment. In this paper, HOMER Pro software (HOMER Energy L.L.C., Boulder, Colorado, U.S.A.) was used to analyze wind data for the north and south of Oman and make a well-informed recommendation on the most suitable turbines for each region’s power needs. HOMER Pro software can combine data associated with wind regime, the specifications of wind turbines, and the power demands of consumers to estimate the cost of producing energy using different generators.

In this study, the researchers tried to estimate the potential for wind energy production in Oman’s north and south and suggest the feasibility of using wind turbines in the country. To this end, the performance of six different popular wind turbines was calculated and compared. By considering the performance and cost of energy (C.O.E.), suggestions on the best possible turbines for the north and south of Oman are provided.

Study areas

As has been mentioned previously, two locations were selected for the wind power plants. The northern site was located in Al Batinah North Governorate (24° 42′ 23″ N 56° 28′ 48″ E). The southern site was Mirbat, Dhofar Governorate (16° 58′ 22″ N 54° 42′ 56″ E) (Fig.  1 ). Both plants are located in rural areas with low populations and, therefore, low power demands. Population, power consumption per capita and power consumption patterns change power demands in an area. Demand also changes daily, hourly, and even in the summer and winter. The last reported data from Oman show that each Omani annually consumes around 6550 kWh on average (S.A.O.C 2017 ). Based on this information and the population of the area, the size of the wind power plant is considered at 10 MW. This size can cover current electricity consumption and any possible future growth. Even with a highly accurate prediction, real conditions can have unexpected variations. In order to consider this variation, the monthly 2% day-to-day random variability and 2% time-to-time step of random variability was considered. Figure  2 shows the power consumption patterns in Oman’s households. As can be seen in the figure, April to October is Oman’s summer season and has high electricity demand, while in wintertime, November to March, the power demand decreases significantly. The high demand for energy by cooling systems in the long summer of Oman is the main reason for this trend.

figure 1

The location of the wind farms in north and south of Oman

figure 2

Monthly average of power demand (MW)

HOMER software

Wind turbine performance analysis.

A realistic estimation of power production requires accurate statistical data on wind velocity for an extended period, like a year or more, if possible. The accuracy of the output results entirely depends on the accuracy of this information. Wind velocity is usually measured on an hourly basis. Due to the high number of measurements in a calendar year, however, further processing for such an extended period would be time-consuming and difficult. Therefore, when making calculations based on such large data sets, the average wind velocity is usually used to reduce the processing load. Although using the monthly average seems practical, such a simple average can be misleading. For instance, by using a wind velocity of 0 m/s for 50% of the time and using a velocity of 6 m/s for the rest of the time, the simple average of the wind velocity would be 3 m/s.

Considering a wind turbine with a maximum output power of 3 m/s, the output performance would be wrongly calculated at 100% all day long. Such a system would have 100% output at 50% of the time at best. In order to address such miscalculations, in this research, the two-parameter Weibull distribution was used (Wang et al. 2018 ). In this method, both wind velocity and its probability over time are considered, and the distribution of the wind velocity is used for the following calculations (Moein et al. 2018 ). The probability density (f) and cumulative distribution (F) of the wind based on Weibull distribution are:

where c is the Weibull scale (m/s), and k is the Weibull shape factor.

The different wind turbines on the market have very different specifications. Considering and analyzing all of these turbines in this paper is not possible. Six of the most popular turbines on the market were selected and analyzed in order to make the article descriptive, rational, and practical. In some countries, other brands and models of turbines might be more popular, but the present approach can be used in those countries, too. In making this comparison, the C.O.E. production for each turbine must be calculated and compared carefully. Moreover, the whole system of a wind power plant consisting of one or more turbines must be able to handle the load demand of consumers with no or limited access to the main power line, for such a scenario where there is no access to the power grid, the power generation system has to be equipped with a sufficiently sized battery bank or a fossil fuel generator to cover non-windy hours or days. In order to simplify the problem and eliminate the calculation of fossil fuel generators, the system under consideration was conceptualized as having up to a 10% deficiency in a limited number of days. In real conditions, this amount of energy can be obtained from the main power lines (if accessible) or local generators. However, in this article, further calculations based on these generators were not considered.

Wind speed calculations represent the first phase of the HOMER Pro simulation. The wind velocity was measured and recorded every hour for 1 year. The system measured wind speed at a 10-m height above the sea level, which is the standard height for the measurement. Table  1 shows a sample of the measurements from the northern site for 1 week. For the calculation of the velocity at a different height (based on the height of each wind turbine), the measured values must be modified as in Eq. ( 3 ):

where \(V_{\text{Turbine}}\) and \(V\) show the wind velocity at the turbine and standard anemometer height, \(Z_{\text{Turbine}}\) and \(Z_{\text{anm}}\) are the height of the turbine and the anemometer (m) and \(Z_{0}\) is the surface roughness (m). Surface roughness characterizes the roughness of the field around the turbine. In this project, based on the local properties of the site location, \(Z_{0}\) was considered 0.03 m, which indicates a smooth field with some crops and no trees or buildings in the surrounding area (Homer-Energy 2016 ).

By combining the Weibull equation and Eq. ( 3 ), the average wind velocity can be written as:

And the output power in a wind turbine can be written in the form:

where \(\tau\) is the time, \(C_{p}\) is the turbine’s nominal capacity, and \(f_{v}\) is the wind velocity distribution.

The producers also provide the power curve of each turbine by testing different wind velocities. The power curve shows the real output power of the system in different ranges of wind velocity. Figure  3 shows the power curves of the six selected turbines with data extracted from the producers’ datasheet for the following turbine models:

figure 3

The power curves of the selected turbines

GE 1.5 SLE (GE Power, Schenectady, New York, USA).

Enercon E44 (Enercon, Aurich, Germany).

Enercon E53 (Enercon, Aurich, Germany).

FD21-100 (Enercon, Aurich, Germany).

EWT DW54 (Emergya Wind Turbines Pvt. Ltd., Amersfoort, The Netherlands);

Hummer H25.0–200 kW (Anhui Hummer Dynamo Co., Ltd., Hefei, Anhui, People’s Republic of China).

Economic analysis

In project planning, economic analysis is the most critical factor in decision-making. In this study, an economic analysis was the only indicator considered to show the feasibility of wind projects. Economic feasibility incorporates long-term performance, pointing to the best possible option among the wind turbines. In order to make an accurate estimation of economic feasibility, the total cost of the project must be calculated, including the capital cost (initial cost of the construction and devices), replacement cost as necessary, and maintenance costs. Operation costs should also be considered for the whole project. However, due to the low cost of operation in wind turbines, the operation cost can be considered part of maintenance costs. By accurately estimating these costs, the price of power generation per kW can be estimated. This price is a suitable indicator for choosing the best possible turbine for a wind power plant. In this research, the cost of energy (C.O.E.) per kW was the distinguishing feature considered among the turbines studied. HOMER sensitivity and optimization algorithms were used to select the best wind turbine (Pahlavan et al. 2018 ; Vahdatpour et al. 2017 ). The equations of the method of optimal system measuring, which has a minimum amount of total net present cost (N.P.C.), are as follows:

where C ann,total , C.R.F. i and R proj are the total annual cost, cost recovery factor, real interest rate and lifetime of the project, respectively.

All costs and incomes are evaluated at a constant interest rate over the year. The actual interest rate resulting from inflation is calculated and the effect of the change in interest rate on final N.P.C. is applied to purpose of influencing inflation in calculations. The cost recovery factor (C.R.F.), which indicates the cost recovery over the N  years, is calculated as follows:

Software is able to calculate the annual interest rate through the following equation:

Also, the cost of per kW of energy during the lifetime of the project is obtained by software from the following equation:

In the above equation, E Load served is the real electric load in the hybrid system by unit kW/year.

Table  2 shows all costs associated with the selected turbines and which include:

The Capital cost is the initial purchase price,

The Replacement cost is the cost of replacing the generator at the end of its lifetime, the O&M cost is the annual cost of operating and maintaining the generator.

No energy battery storage system storage was taken into consideration for the current simulation focusing on the selection of the best wind turbine, and an annual interest rate of 6% was taken into account.

Results and discussion

Comparison between the proposed wind turbines.

Implementing big data associated with turbine measurements and specifications is difficult. HOMER Pro helps analyze this data and simulate plans for 20 years. The results of the simulation for each turbine are presented in Table  3 .

The main findings from the turbines simulation were as follows:

G.E. Energy 1.5 SLE This turbine is designed and manufactured by G.E. Power, a subsidiary of the General Electric Energy Company, and is a 1500-kW-rated power producer. This model has the highest power output among the selected turbines. It has a three-blade rotor with a 77-m diameter and 85-m hub height. The cut-in wind velocity for this model is 3 m/s, and the cut-off speed is 25 m/s. Cut-in and cut-off velocities can have a significant impact on the performance of the turbine. A turbine with a lower cut-off speed has the advantage of generating power in lower wind speed locations, like the north of Oman. The results of the simulation show that the C.O.E. for this turbine is USD$0.171 for each kW of energy in the north and USD$0.089 in the south. This cost contains the USD$1.75 million dollar maintenance cost for 20 years of operation and a capital cost of USD$3.38 million.

Enercon E44 This turbine, produced in Germany, has the second-highest power output of those considered, with a 900-kW-rated generator, 55-m hub height, and 44-m blade size. This Enercon production has a minimum cut-off wind velocity of 3 m/s, and a 28 m/s maximum cut-off. The HOMER Pro results showed that, by considering the capital cost of USD$2.34m and a maintenance cost of around USD$1 million, the C.O.E. would be USD$0.303 for each kW of energy in the north and USD$0.135/kW in the south.

Enercon E53 This turbine has a 53-m rotor diameter and 800 kW power production potential. Due to the lower power output, this model has lower capital and maintenance costs. Considering all of the costs of the turbine, the system would be able to generate power at USD$0.163/kW and USD$0.088/kW in the north and south, respectively.

FD21 - 100 This Enercon model uses GHREPOWER production with 100-kW output power. The lower output power makes it suitable for smaller wind power plants. FD21-100 has a 3–25 m/s range of working speed, and its highest possible hub height is 42 m. The HOMER Pro software simulation for this turbine showed that the C.O.E. would reach up to USD$0.290 per kW in the north and USD$0.144 kW in the south. In comparison to other turbines, this model has the highest cost of power generation for both locations.

DW54 This turbine is a 500-kW generator designed and produced by Energy Wind Technology (E.W.T.) in Amersfoort, The Netherlands. It has a 54-m rotor diameter and a working velocity between 3 and 10 m/s. With a USD$1.2 million capital cost and USD$750,000 maintenance cost over 20 years, the power generation cost would be USD$0.119/kW. This cost is the lowest possible for generating power in the north of Oman. However, the simulation showed that, due to differences in the wind regime in the north and south, this model is not the best possible option for the south. Each kW of energy produced in the south would cost USD$0.071. However, with its C.O.E., this model is the second best possible turbine for Oman’s north.

Hummer H25.0 – 200   K.W. This model is a 200-kW-rated wind turbine produced by the Anhui Hummer Dynamo Company of Hefei, China. In comparison to other analyzed turbines, this model has a lower cut-in wind velocity by 2.5 m/s and a smaller blade size (12 m). The simulation showed that while the capital cost of the turbine could be as low as USD$300,000, this model’s C.O.E. is not the best for all situations. In the north, power production would cost USD$0.132/kW. While this price is not the best possible option for the north, the results for the south are different. The simulation showed that the turbine would have the best possible results in the south among the selected models, generating power at USD$0.070/kW.

Considering the above-mentioned findings, the DW54 turbine is the best possible turbine for the north of Oman. On the other hand, the Hummer H25.0–200 KW turbine is the best option for Oman’s south. These models can generate electricity at the lowest possible cost. Figure  4 shows the graph of energy production cost for each turbine in the northern and southern sites.

figure 4

Cost of electricity for different turbines

Advantages of provided wind turbines over natural gas and diesel generators

The current power plants in Oman mostly use natural gas for electricity production. On the other hand, for off-grid consumers (some rural regions), the diesel generators are the primary source of electricity. It is clear that fossil fuel generators emit pollutant gases into the atmosphere and have negative impacts on the environment. In short, the diesel generator’s gas emission is calculated using the same energy production as the best wind turbines. For comparison, the unmet electrical load of wind turbines is considered (Fig.  5 ). Table  4 shows the emitted pollutant gases over one year of use. As it can be seen in Table  3 , the wind turbine power plant in the north can stop the CO 2 emission approximately 19,000 ton/year in comparison to natural gas and 28,000 ton/year in comparison to diesel. In the southern power plant, the reduced gas emission in comparison to diesel and natural gas are 18,000 and 12,000 ton/year, respectively.

figure 5

Unmet electrical loads for different turbines

In this study, the feasibility of using wind energy as a source of power production was calculated by collecting and analyzing hourly data on wind regimes over a 1-year period. HOMER Pro software was used to calculate the C.O.E. production of six different wind turbines, in order to select the most suitable wind turbine for two distinct locations in the north and south of Oman. The study’s main findings can be summarized as follows:

DW54 turbine produced by Energy Wind Technology in Amersfoort, The Netherlands, would have the best performance for Oman’s northern regions and can generate the cheapest possible energy from wind at $0.119/kW.

H25.0–200 kW turbine manufactured by the Anhui Hummer Dynamo Company of Hefeit, China, gives the best C.O.E. production for the southern regions of Oman and the lowest possible wind energy can be produced at $0.70/KW.

The difference of the wind regime between the northern and southern parts of Oman and the power curves of the turbines are the main reasons for the selection of two different wind turbines form different manufacturers.

The northern wind turbine plant is estimated to decrease CO 2 emissions by around 19,000 tons per year, compared to natural gas, while diesel emissions by around by 28,000 tons per year.

The southern wind turbines have a potential carbon emission reduction of about 18,000 and 12,000 tons per year compared to diesel and natural gas.

The application of the turbine selection using the HOMER Model described in this paper determined that the H25.0–200 kW turbine selected for the southern parts of Oman has a C.O.E. that is 58.8% lower than the DW54 turbine that was selected for the northern parts of the country. The application of the method followed in this research by developers during the planning stage could significantly improve the financial performance of their investment. Similarly, such techniques could be added to tools such as WAsP to improve decision-making during the initial planning stage.

Availability of data and materials

Data are openly available with HOMER software. HOMER uses the monthly average wind speeds, plus four parameters (Weibull k, 1-h autocorrelation factor, Diurnal pattern strength and Hour of peak wind speed) to synthesize wind data for simulation.

Change history

17 january 2021.

An amendment to this paper has been published and can be accessed via the original article.

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Study explores how wind turbine visibility affects property values across the US

by CMCC Foundation

wind turbine

Renewable energy sources are essential for transitioning towards a decarbonized energy system and making the electricity grid more environmentally sustainable. Clean energy alternatives like wind power can effectively replace fossil fuels, contributing to reduced air pollution and slowing down climate change.

Wind power has emerged as the fastest-growing non-hydro renewable energy source worldwide. However, the implementation of wind energy infrastructure, including windmills, faces significant challenges. One major obstacle is the opposition from local communities.

Wind turbines, the primary components of wind power generation, can be noisy, obstruct sunlight, produce flickering lights, and disrupt scenic views. These concerns can lead to conflicts between renewable energy development and environmental preservation, potentially exacerbating existing social inequalities.

A study published in the journal Proceedings of the National Academy of Sciences ( PNAS ) by an international team of scientists, including researchers from CMCC, the Potsdam Institute for Climate Impact Research (PIK), and University of California at Berkeley, offers a comprehensive perspective on this issue, crucial for evaluating the trade-offs between the benefits and costs of renewable energy sources, and for gaining a thorough understanding of their impacts.

This study represents a unique evaluation of the externality costs of wind power generation, specifically focusing on the impact of visibility on property values across the United States.

"This situation is a classic 'Not In My Backyard' problem, which leads to extensive policy debates on renewable energy growth," says Wei Guo, researcher at CMCC and EIEE, the European Institute on Economics and the Environment, and first author of the study. "In the big picture, the economic solution is about finding a balance between the global environmental benefits of renewable energy and the local impacts on communities nearby."

The research focuses on the impact of wind power generation on local communities, which is usually overlooked. Specifically, the study addresses how wind turbines, when integrated into the landscape, influence the perceived value of homes by residents.

The main aim is to contribute to the benefit and cost analysis of renewable energy development, facilitating more informed decision-making for both policymakers and the public regarding new projects.

In pursuit of this objective, the researchers have meticulously compiled a database on wind turbine visibility, incorporating details on the location and height of each utility-scale turbine that has contributed power to the U.S. grid. The database is complemented by a high-resolution elevation map, which accounts for the underlying topography of the landscape.

Grounded in hedonic valuation theory, the researchers conducted statistical estimations to discern the impact of wind turbine visibility on home values. These estimations draw on data sourced from a comprehensive dataset covering the majority of home sales in the U.S.A. since 1997.

The study reveals that, on average, the visibility of wind turbines has a negative and economically significant impact on home values within proximity of less than 8 km. However, this effect becomes indistinguishable from zero at larger distances. Moreover, the impact is notably smaller for recently installed turbines and diminishes significantly over time following their installation.

The findings shed light on the nuanced dynamics between renewable energy infrastructure and local property values, providing valuable insights for sustainable and community-friendly energy development.

The results of the study show that seeing a windmill closer than 2 kilometers away can lower a house's value by up to 8%. "To picture this, imagine holding a golf ball at arm's length—that is roughly how big a wind turbine looks from that distance," says Guo.

"However, as one moves further from the windmill, its impact on house values drops off quickly. From 8 kilometers away, a wind turbine looks about as big as an aspirin tablet at arm's length, and at this distance, it doesn't really affect what people think their homes are worth."

The total loss in values across all US houses with a view of windmills adds up to a drop of US $24.5 billion. Although this is a significant loss, it amounts to a relatively small fraction when comparing it to the total value of all homes in the US—over $45 trillion in 2022.

"We conclude that although houses close to wind turbines can lose some value due to the disrupted view, the impacts are just a small part in the grand theme of all houses, and we expect it to become even less an issue in the future," says Guo.

"This project stands at the cutting edge of understanding how renewable energy affects local communities . It is like putting on a new pair of glasses to look at how wind power impacts people's lives and homes."

This research pioneered a comprehensive nationwide evaluation of the external costs of wind power generation, but it also marks a significant advancement in quantitative precision by considering not only proximity but also actual visibility of wind turbines from homes.

The creation of an extensive database utilizing advanced techniques from geography and cartography sciences is another innovative element of this research. By applying these methods to every utility-scale turbine and high-resolution elevation maps, the study integrates interdisciplinary areas, representing a substantial step forward in environmental economics and policy research.

"Personally, living in northern California for the past five years, I have seen firsthand how local people can be hesitant or opposed to new wind turbines projects. This sparked my interest in this field of research," said Guo. "For me, this project is more than just an academic study. It's about addressing a real-world issue that I've observed, and using my expertise to shed light on a topic that affects many people's lives."

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