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• Original Research Article
• Open access
• 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.

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

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:

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.

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.

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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Acknowledgements

The authors would like to thank the anonymous reviewers for their insightful suggestions and careful reading of the manuscript.

The authors received no specific funding for this work.

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Yassine Charabi

Department of Mechanical and Industrial Engineering, Sultan Qaboos University, Muscat, Oman

Sabah Abdul-Wahab

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Correspondence to Yassine Charabi .

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Charabi, Y., Abdul-Wahab, S. Wind turbine performance analysis for energy cost minimization. Renewables 7 , 5 (2020). https://doi.org/10.1186/s40807-020-00062-7

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DOI : https://doi.org/10.1186/s40807-020-00062-7

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Research on energy scheduling optimization strategy with compressed air energy storage.

1. Introduction

• A hierarchical scheduling model for CAES systems is constructed and transformed into a Markov decision process. The coordinated scheduling problem of wind farms and energy storage is balanced using the DRL algorithm.
• In order to achieve the efficient learning of intelligences, a deterministic policy gradient-based DDPG algorithm is used. The algorithm effectively improves the learning ability of the intelligent body in continuous action space to adapt to its complex environment in the power system.
• This paper introduces a combined algorithm that merges the NEAT algorithm with the DDPG algorithm to enhance the effectiveness of the algorithm. By utilizing the adaptive network structure of NEAT, the combined approach improves adaptability in complex environments and efficiency in renewable energy utilization.

2. Integrated Energy Framework

2.1. aa-caes structure, 2.2. hierarchical energy optimization strategy, 2.3. drl description, 3. cooperative control framework of source–storage–grid system, 3.1. wind farm model, 3.2. aa-caes model.

• It is assumed that air is an ideal gas and satisfies the ideal gas equation of state;
• The reservoir is modeled using an isothermal constant volume model, where the temperature of the air in the reservoir is equal to the ambient temperature, and the volume of the reservoir is exploded to be constant;
• The compressor and expander are modeled adiabatically;
• Heat loss from the heat storage tank and heat loss from the heat exchange process are excluded.

3.3. Energy Scheduling Model

3.4. markov model, 3.4.1. state space s t, 3.4.2. action space a t, 3.4.3. reward r t, 4. deep reinforcement learning algorithms, 4.1. actor–critic algorithm, 4.2. deep deterministic policy gradient, 4.3. neuroevolution of augmenting topologies.

5. Case Studies and Results

6. discussion, 7. conclusions.

• Deep reinforcement learning algorithms can play an important role in the intelligent scheduling of power systems containing AA-CAES.
• The effectiveness of the algorithm is verified by analyzing the simulation results. The algorithm realizes the cooperative scheduling in the source–storage network and ensures the safe operation of the power grid. Even in the case of unstable wind power generation, the system operation can be made smoother by scheduling AA-CAES.
• The experimental results also show the better performance of the improved DDPG algorithm with DDPG-NEAT compared to the other two DRL algorithms. The comparison of the power scheduling data of the three algorithms shows that the DDPG-NEAT algorithm can perform the scheduling task better and improve the energy utilization efficiency.

Author Contributions

Institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest, abbreviations.

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Share and Cite

Wang, R.; Zhang, Z.; Meng, K.; Lei, P.; Wang, K.; Yang, W.; Liu, Y.; Lin, Z. Research on Energy Scheduling Optimization Strategy with Compressed Air Energy Storage. Sustainability 2024 , 16 , 8008. https://doi.org/10.3390/su16188008

Wang R, Zhang Z, Meng K, Lei P, Wang K, Yang W, Liu Y, Lin Z. Research on Energy Scheduling Optimization Strategy with Compressed Air Energy Storage. Sustainability . 2024; 16(18):8008. https://doi.org/10.3390/su16188008

Wang, Rui, Zhanqiang Zhang, Keqilao Meng, Pengbing Lei, Kuo Wang, Wenlu Yang, Yong Liu, and Zhihua Lin. 2024. "Research on Energy Scheduling Optimization Strategy with Compressed Air Energy Storage" Sustainability 16, no. 18: 8008. https://doi.org/10.3390/su16188008

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Recyclable wind turbine blades are possible, new research claims

Figuring out what to do with decommissioned blades is a major drawback of wind energy.

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Gabriel C. Pérez

Windmills on a wind turbine farm in Nolan County, Texas.

In recent years, wind energy has become a bigger and bigger part of Texas’ power portfolio . And as it’s become more common, so have questions about what happens to those big turbine blades that catch the wind and spin to produce power.

The blades are good for about 20 years, but after that, most of them get piled up in turbine graveyards with no clear re-use. They’re not biodegradable.

A team at the National Renewable Energy Laboratory in Colorado may have a solution, however: a turbine blade that can be recycled . Nicholas Rorrer , senior researcher at the laboratory, spoke to the Texas Standard about how it works.

This transcript has been edited lightly for clarity:

Texas Standard: You and your team developed a wind turbine blade that, under the right circumstances, will break down. Explain what it’s made of and how that works.

Nicholas Rorrer:  So in a lot of our work, our approach was to look at kind of how wind turbines are made today and enable kind of a material that could undergo all the same manufacturing conditions but come from possibly bio derived resources and really be recyclable at the end of life through kind of known chemical linkages.

And so in our work, that’s effectively what we did, is we started with the application. We said, Hey, we want to make wind turbine blades. How are those made? They’re made through this process called vacuum assisted resin transfer molding, where you effectively pull kind of a thick viscous resin like honey through a bunch of fibers, and then you heat it up and you cure it and it makes a hard part.

And so what we did is we looked through our kind of entire chemistry textbook and our resources that we knew of what we could get bio derived. And we found specific materials that we could get from bio derived resources that were non food competitive that could undergo all the same manufacturing conditions, yet have these recycling linkages that we knew how to break down at the end of life.

How much more expensive might this be to make than a conventional turbine blade?

I mean, it’s a really hard question to answer. But when we look at our resins themselves, they’re near cost competitive to what’s made kind of today from epoxy amine materials. So today’s wind blades are made out of these epoxy amine resins. And for our stuff, we use kind of a bio derivable epoxy and hydride resin. And the costs are, you know, maybe plus or minus 10%.

But, you know, when you look at a wind turbine blade, you also have to consider that right now today’s manufacturers of wind turbine blades consider end-of-life disposal also in their cost. So if we actually have something that enables us to get around end-of-life disposal, you might end up saving costs.

So I would really say like maybe the initial blade could be up to 10% more expensive. But when you actually consider all the factors across the life of these materials, it might be very cost competitive, if not cheaper.

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Are there any questions about the long-term durability of these blades?

That’s one of my favorite kind of questions about the work that we did that was recently published in Science. Right now, kind of in the academic literature or even kind of a cultural perception, we sometimes think that something made out of recycled material or something that can be recyclable is like less efficient than something that’s durable.

But really, we did the analysis and we kind of made a nine meter wind turbine blade and we showed that actually our materials themselves creep less. So they kind of deform less over time than today’s standard materials. So for all intents and purposes, these materials from like a mechanical performance should last just as long, if not longer.

And when we did advanced weathering studies – so kind of exposing these things to harsh UV light or other conditions – we saw no detriment in performance compared to today’s incumbent materials. So everything really points to the fact that these should perform at least as long as today’s materials, if not better.

Very interesting. How would manufacturers have to change their current processes to accommodate this new technology?

I think that the big thing is when we set forth in our approaches, we made sure that today’s wind manufacturers could actually still use their current technology for doing so. We made sure we developed something that actually met all the requirements.

To some extent, that’s kind of a unique approach to our science that people don’t always do. But we were making sure from day one that we were meeting those manufacturing requirements in doing so. And so honestly, nothing. I think the hardest part they would have to do is find suppliers for these materials.

What’s the next step in your research?

I think composite structures themselves, we often think about them in wind, but they’re materials that can also be used in vehicles and beyond that. So I think our work itself is going to understand like where else can we apply these fundamentals to enable kind of circularity in more applications beyond wind – or even when we think of wind energy, we often sometimes also think of water power because water power has a lot of similar requirements but different.

So we’ll probably look at things saying, Hey, can we make a water power turbine – that, you know, we might not see when driving across the country, but certainly exist – recyclable by design itself. So I think it’s broadened into our application scope. And then science itself is always an iterative process. So really making sure that these materials are performing best as we go into longer and bigger scales.

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Broken Blades, Angry Fishermen and Rising Costs Slow Offshore Wind

Accidents involving blades made by GE Vernova have delayed projects off the coasts of Massachusetts and England and could imperil climate goals.

A wind turbine blade more than 300 feet long collapsed in July at Vineyard Wind 1, a wind farm off the coast of Massachusetts. Credit... Randi Baird for The New York Times

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By Stanley Reed and Ivan Penn

• Sept. 12, 2024

The collapse of a giant wind turbine blade off the Massachusetts coast confirmed Peter Kaizer’s worst fears about the dangers a new clean energy business could pose to fishermen like him.

Jagged pieces of fiberglass and other materials from the shattered blade drifted with the tide, forcing officials to close beaches on Nantucket and leaving Mr. Kaizer worried about the threat the fragments might pose to his vessel and other fishing boats, especially at night when the debris would be harder to avoid.

“All these small boats could be subject to damage,” Mr. Kaizer said. “Everyone wants this green legacy, but at the cost of what?”

The blade, which was more than 300 feet long, failed in July, but the repercussions are still unfolding at the $4 billion project that it came from — Vineyard Wind 1. Developers had hoped to finish the project this summer, making it the first large-scale wind farm completed in U.S. waters, but now that goal will take a lot longer than expected.

The blade failure is the latest problem slowing the fledgling U.S. offshore wind industry, which the Biden administration and East Coast states are counting on to deliver emission-free energy to millions of people from Virginia to Maine. President Biden and governors of those states had hoped to follow the examples of European countries like Britain and Denmark, which have plunked down thousands of wind turbines around the North Sea.

But the American offshore wind business has struggled to get going because of cost overruns, delays in issuing permits, and opposition from local residents and fishing groups. Several large projects were canceled or postponed even before the blade failure in Massachusetts because their costs increased sharply and developers did not anticipate supply chain problems and higher interest rates.

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What Maine hopes to learn from its offshore wind research array

VolturnUS is UMaine's patented floating concrete hull technology that has been awarded 43 patents in the U.S. and abroad. (University of Maine image)

Maine has big goals for adding offshore wind to its energy repertoire with hopes that it will not only be a friendlier option for the planet, but help revitalize communities through its economic and labor opportunities. But before those benefits can be realized, there are still a number of outstanding questions. 

Last month, the state and the federal government reached an agreement on a lease for an offshore wind research array that will sit about 30 miles southeast of Portland. It will take up about 15 square miles in federal waters and include up to 12 floating turbines that will help inform how floating offshore wind operates and interacts with ecosystems in the water. 

Just last week, the Federal Bureau of Ocean Energy Management released its final environmental assessment that showed leasing activities such as surveys and installing meteorological buoys in the Gulf of Maine won’t harm the surrounding environment. 

And while that assessment did not look at the impact of the offshore turbines, the goal of the research array is to better understand how they will interact with the Gulf of Maine ecosystems. 

“The only way we really can answer those questions is to have this type of a program and this kind of a unique in-water opportunity to actually answer those questions,” said Stephanie Watson, offshore wind program manager for the state.

Research has been a throughline of Maine’s offshore wind efforts, Watson said, especially when thinking about the pioneering research and development from the University of Maine for semi-submersible floating turbines . 

The next step in that process is to understand how to responsibly deploy the budding industry and actually transmit the energy back to shore, all while reducing impacts to the fisheries that are vital to the economy and culture of Maine, Watson said. 

The state is partnering with Boston-based Diamond Offshore Wind to develop the research array; however, there will also be a state-coordinated research program that includes the departments of Marine Resources and Inland Fisheries and Wildlife. 

The state’s Offshore Wind Research Consortium , a 26-person advisory board that was organized in 2021 to understand the local and regional effects of offshore wind power projects in the Gulf of Maine, will also contribute to the work. 

Offshore wind research priorities

As part of its work over the past few years, the consortium identified priority areas to guide the research conducted on the array.

Among those priorities is improving seafloor mapping of the Gulf of Maine because it’s not currently well understood, Watson said. 

“The Gulf of Maine is relatively understudied, in general,” Watson said, especially when compared to other marine areas of the United States. 

She said this could be a result of the Gulf of Maine seeing less activity and shipping than other, larger regions. 

Additionally, the consortium hopes to better understand fishing communities and how their socioeconomics could be affected by offshore wind. Relatedly, it wants the research array to look at various fishing technology and consider how best it can coexist with offshore wind.

Finding a positive working relationship between Maine’s heritage fishing industry and clean energy has been an ongoing conversation that the governor and congressional delegation have weighed in on. Gov. Janet Mills and Maine’s members of Congress praised a decision last March by the federal Bureau of Ocean Energy Management to exclude a vital lobster fishing area from the offshore wind leasing map.

One additional research priority for the arrays is exploring how different avian species, such as bats and birds, could be affected by the presence of wind turbines in the water. 

“We have a long list, so we have a lot of work to do,” Watson said.

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June 2024 summary

On this page.

This is the web version of the New Zealand Energy Quarterly June 2024 Summary.

Download the PDF and word versions:

New Zealand Energy Quarterly June 2024 summary [PDF, 401 KB]

New Zealand Energy Quarterly June 2024 summary [DOCX, 944 KB]

Low hydro lake levels drive increase in non-renewable electricity generation

New data from the Ministry of Business Innovation & Employment shows between April and June this year, extra coal and gas were needed to generate enough power for New Zealand homes and businesses.

Low hydro lake levels meant electricity generated from renewable sources dropped to 81.3% of total generation, an 8.6% drop compared to same time last year.

For the first time since June 2021, and despite a 50% increase in wind capacity, coal generated more electricity than wind.

Coal-based electricity generation increased to 883 GWh for the quarter, compared to 144 GWh of generation in the same quarter last year. Electricity generation from natural gas increased 43.9% on the same quarter last year, contributing 10.7% of overall electricity generation for the quarter, but due to limitations in natural gas supply, it wasn't able to fully cover the drop in hydro generation for the quarter. Contributing 7.9% of total generation for the quarter, coal-fired generation was required to support gas-fired generation in making up for the reduction in generation from hydro.

On the supply side, net production of gas was down 19%. Decreasing gas supply resulted in a drop in gas use, with large users responding by continuing to operate at lower levels.

Electricity generation from geothermal was the highest on record for a quarterly basis with 2143 GWh generated, contributing 19.1% of total generation for the quarter.

This record was achieved by the new Tauhara geothermal station near Taupō coming online and supply being at normal levels compared to the same time last year when geothermal generation was lower than usual due to outages at a number of plants.

Increased electricity generation from non-renewable sources saw emissions from electricity generation reach the highest level on a quarterly basis since June 2021 with 1,431 kt CO2-e, representing a 169% increase from the previous June quarter. 

Alongside Energy Quarterly, MBIE is also releasing Energy in New Zealand, which compiles data from each Energy Quarterly from 2023, and its regular Oil and Gas statistics, current to July 2024.

Read more information on energy use in New Zealand

Summary charts

Electricity generation from hydro.

Text description of graph

A time series chart showing electricity generation from hydro generation, from 2000 until the second quarter of 2024. Generation fluctuates from about 5,000 to 7, 000 GWh, but this quarter's generation was the lowest in two years at 5866 GWh.

Electricity generation from coal and wind sources

A time series chart showing electricity generation from coal and wind sources, from 2000 until the second quarter of 2024. Coal generation fluctuates wildly but is overall in decline. Wind is steadily increasing. This quarter, coal was used to generate more electricity than wind for the first time since the June quarter of 2021.

Natural gas production

A time series chart showing natural gas production from 2000 until the second quarter of 2024. Production dropped sharply in 2003, slowly trended upwards up until 2015, then steadily declined.

Electricity generation from geothermal sources

A time series chart showing electricity generation from geothermal sources, from 2000 until the second quarter of 2024. Electricity generation from geothermal trends upward from 2000 and peaks in the second quarter of 2024. 

Electricity generation from renewable vs non-renewable sources

A area chart showing electricity generation from renewable and non-renewable sources, from 2000 until the second quarter of 2024. Electricity generation from renewable sources has trended upwards over the time series. 

CO2-e emissions from electricity generation

A time series chart showing carbon emissions from electricity generation, from 2000 until the second quarter of 2024. Emissions increased from 2000 until a peak in 2009, then trended downwards with significant fluctuations. This quarter saw an increase that nearly reached June quarter of 2021 levels.

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https://www.mbie.govt.nz/building-and-energy/energy-and-natural-resources/energy-statistics-and-modelling/energy-publications-and-technical-papers/new-zealand-energy-quarterly/june-2024-summary Please note: This content will change over time and can go out of date.