Installed Wind Power Capacity Research Articles

Multi-timescale available flexibility assessment of integrated energy systems considering different proportions of wind power installations

The available flexibility capacity of the integrated energy system can be used as one of the indicators of proportions of system wind power installations, which, in turn, affects the maximum installed capacity of the system wind power, and this paper proposes a method for assessing the available flexibility of the integrated energy system at multiple timescales considering different proportions of system wind power installations. First, the framework of the integrated energy system is constructed, and based on the coupling relationship between the electrical and thermal systems, the mathematical models of the P2G, combined heat and power (CHP), energy storage equipment, and wind power generation equipment within the integrated energy system are established, and the Monte Carlo method is used to predict the wind power output in a typical scenario. Second, an integrated energy system optimization model is constructed to obtain the optimal dispatch operation of the system; the empirical mode decomposition (EMD) algorithm is used to decompose the flexibility demand curve of the system in multiple timescales. The flexibility supply capacity model of different types of flexibility resources in the system at different timescales is established, and through the comparative analysis of flexibility supply and demand at the same timescale, the upward and downward flexibility shortage probability and shortage expectation indexes at each timescale can be intuitively calculated and then weighted to constitute a comprehensive index of system flexibility assessment. Finally, the available flexibility analysis of the integrated energy system under different installed wind power capacities shows that the proposed methodology can more comprehensively assess the available flexibility capacity of the integrated energy system under different timescales, and the maximum installed wind power capacity that the system can withstand can be obtained while guaranteeing sufficient available flexibility capacity.

A hybrid model with combined feature selection based on optimized VMD and improved multi-objective coati optimization algorithm for short-term wind power prediction

With the continuous global increase in installed wind power capacity and subsequent surge in power generation, the contradiction between the safe operation of the grid and the efficient consumption of new energy after large-scale grid connection has become increasingly prominent. This paper presents a hybrid model prediction method to further improve the stability and accuracy of wind power prediction. Firstly, variational modal decomposition (VMD) optimized by the coati optimization algorithm (COA) is employed to decompose original wind power, mitigating the non-stationary characteristics of the power sequence. Subsequently, permutation entropy (PE) is used to recombine the decomposed components, and the combined feature selection method is achieved by integrating the Spearman correlation coefficient (SCC) and the autocorrelation function (ACF). Then, the multivariate combined model is constructed, and the improved multi-objective coati optimization algorithm (IMOCOA) determines the weight coefficients of each model to enhance the performance of the hybrid model. Finally, research and analysis are conducted from multiple scenarios and time scales using historical operating data from a wind farm in Xinjiang. The experimental results show that the proposed prediction model effectively improves the accuracy and stability of the wind power prediction compared with other popular prediction models.

Cost analysis of onshore wind power in China based on learning curve

As installed wind power capacity continues to rise, the cost of onshore wind power generation in China has fallen, far exceeding the world average. The purpose of this study is to explore the main factors affecting onshore wind power in China and to identify ways to reduce costs. So as to reduce the cost of wind power and promote the large-scale grid-connected use of wind power. Therefore, this paper summarizes various factors that affect the development of onshore wind power. We also provide a comprehensive and repeatable MFLC (multi-factor learning curve) method to evaluate the factors affecting the cost of onshore wind power. Subsequently, the learning rates of various factors are calculated using this method. The results show that empirical learning by increasing installed capacity/generation, improving technology and selecting sites which are rich in natural resources, and appropriately reducing capital investment and material prices can significantly reduce the LCOE (levelized cost of electricity) of onshore wind power in China. Compared with wind power giants of the United States and Germany, the reduction in the cost of onshore wind power generation in China is more dependent on inputs such as capital investment and raw materials, while experience plays a relatively minor role. The ability to make full use of natural resources and to convert wind energy into electricity more efficiently is crucial to reducing the cost of wind power.

EL FUTURO DE LOS AEROGENERADORES OFFSHORE: EVOLUCIÓN Y PERSPECTIVA DE LAS TECNOLOGÍAS DIRECT-DRIVE Y CON CAJA DE CAMBIOS TRADICIONAL

Currently, the installed wind power capacity around the world in offshore installations is more than 60 GW, with a much higher growth rate than traditional onshore installations. This is due to the lack of barriers in the high seas, which means that air flows at a higher and more regular speed, allowing a greater amount of energy to be generated than in traditional onshore installations, which can even double it.

Low impact siting for wind power facilities in the Southeast United States

AbstractAlthough installed wind power generation capacity in the United States reached 132 GW in 2021, more than quadruple the capacity in 2008, a noticeable void exists in the Southeast. Scant wind power development in this region is due to relatively poorer wind resources, other competitive energy sources, and political opposition. However, the dramatic increases in wind turbine hub height, which allow harvesting the faster wind speeds that occur farther from the ground, combined with a growing sense of urgency to develop renewable energy, point to a near future with significant wind development everywhere, including the Southeast. Nevertheless, the enthusiasm for replacing fossil fuels with renewable sources is tempered by fears that the vast land requirements of utility‐scale wind farms may disrupt valuable ecosystems. In this paper, we identify the areas where installed wind power capacity is least likely to disrupt wildlife and sensitive natural areas in the southeastern United States. The generated maps exclude geographic areas unsuitable for wind power development due to environmental concerns or technical considerations corresponding to five categories. The resulting geospatial product suggests that even after removing sizable areas from consideration, there is significant land for wind development to meet the Southeast's energy needs and clean energy goals.

Capacity investment decisions of energy storage power stations supporting wind power projects

PurposeRapidly increasing the proportion of installed wind power capacity with zero carbon emission characteristics will help adjust the energy structure and support the realization of carbon neutrality targets. The intermittency of wind resources and fluctuations in electricity demand has exacerbated the contradiction between power supply and demand. The time-of-use pricing and supply-side allocation of energy storage power stations will help “peak shaving and valley filling” and reduce the gap between power supply and demand. To this end, this paper constructs a decision-making model for the capacity investment of energy storage power stations under time-of-use pricing, which is intended to provide a reference for scientific decision-making on electricity prices and energy storage power station capacity.Design/methodology/approachBased on the research framework of time-of-use pricing, this paper constructs a profit-maximizing electricity price and capacity investment decision model of energy storage power station for flat pricing and time-of-use pricing respectively. In the process, this study considers the dual uncertain scenarios of intermittency of wind resources and random fluctuations in power demand.Findings(1) Investment in energy storage power stations is the optimal decision. Time-of-use pricing will reduce the optimal capacity of the energy storage power station. (2) The optimal capacity of the energy storage power station and optimal electricity price are related to factors such as the intermittency of wind resources, the unit investment cost, the price sensitivities of the demand, the proportion of time-of-use pricing and the thermal power price. (3) The carbon emission level is affected by the intermittency of wind resources, price sensitivities of the demand and the proportion of time-of-use pricing. Incentive policies can always reduce carbon emission levels.Originality/valueThis paper creatively introduced the research framework of time-of-use pricing into the capacity decision-making of energy storage power stations, and considering the influence of wind power intermittentness and power demand fluctuations, constructed the capacity investment decision model of energy storage power stations under different pricing methods, and compared the impact of pricing methods on optimal energy storage power station capacity and carbon emissions.HighlightsElectricity pricing and capacity of energy storage power stations in an uncertain electricity market.Investment strategy of energy storage power stations on the supply side of wind power generators.Impact of pricing method on the investment decisions of energy storage power stations.Impact of pricing method, energy storage investment and incentive policies on carbon emissions.A two-stage wind power supply chain including energy storage power stations.

RETRACTED: Supervisory Control and Data Acquisition for Fault Diagnosis of Wind Turbines via Deep Transfer Learning

The installed wind power capacity is growing worldwide. Remote condition monitoring of wind turbines is employed to achieve higher up-times and lower maintenance costs. Machine learning approaches can be used for detecting developing faults in wind turbines in their earlier occurrence. However, training fault detection models may require large amounts of past and present data. These data are often not available or not representative of the current operation behaviour. These data can be acquired with supervisory control and data acquisition systems. Note also that newly commissioned wind farms lack data from previous operation, whilst older installations may also lack representative working condition data as a result of control software updates or component replacements. After such events, a turbine’s operation behaviour can change significantly so its data are no longer representative of its current behaviour. Therefore, this paper shows that cross–turbine transfer learning can improve the accuracy of fault detection models in turbines with scarce data from supervisory control and data acquisition systems. In particular, it highlights that combining the knowledge from turbines with scarce data and turbines with plentiful data enables earlier detection of faults than prior art methods. In this way, the reuse and the knowledge transfer across wind turbines allows us to overcome this lack of data, thus enabling accurate fault detection in wind turbines.

Suitability analysis for wind farm constructions in Yukon area

The development of science and technology has made people's quality of life better and better, and the mature use of electricity has made people's lives more convenient and faster. However, this has also led to a drastic increase in people's use of non-renewable energy such as coal and oil. In 2021, about 4,108 billion kilowatt-hours (kWh) (or about 4.11 trillion kWh) of electricity were generated at utility-scale electricity generation facilities in the United States, about 546 million short tons (MMst) of coal were consumed. (EIA). In such situation, the exploitation and use of renewable energy becomes significantly urgent and necessary. From 2008 to 2018, global installed wind power capacity grew by an average of 17.2% per year (BP Statistical Review of World Energy 2020). On IRENA's (2019) transformation roadmap, to stay on the pathway of 1.5 °C warming, wind energy is projected to generate 35% of the total electricity demand by 2050. My study area for my research is in Yukon, it is in south part of Canada and contains 4.7 percent of total area. The aim of my project is to determine the suitability of Wind Farm Expansion and its Electric Applications Radiation for Yukon area. The method for the project is using reclassify analysis tool in order to find the suitability site for constructing the wind farms. 1. How Much of Each Energy Source Does It Take to Power Your Home. (2017, September 29). McGinley Support Services. https://www.mcginley.co.uk/news/how-much-of-eachenergy-source-does-it-taketo-power-your-home/bp254/2. CER – Provincial and Territorial Energy Profiles – Yukon. (n.d.).https://www.cerrec.gc.ca/en/data-analysis/energy-markets/provincial-territorialenergyprofiles/provincial-territorial-energy-profiles-yukon.html3. Just a moment. . . (n.d.). https://www.researchgate.net/figure/Flow-Diagram-of-aWindTurbine-System-Here-1-Wind-Turbine-Converts-wind-energy- into_fig3_307906589

Routes for the wind power generation in Brazil

Most of the electricity generated in Brazil still comes from hydroelectric plants. In 2021, the country reached a critical hydrological scenario due to low capacities in the reservoirs. Although the installed wind power capacity in Brazil has expanded considerably in recent years, wind generation in the country is limited to the onshore type. This study aims to evaluate the feasibility of a new wind farm to be installed in Brazil, comparing several possible conditions and technologies, for installing a wind farm on a predetermined location in the northeast region of the country, the most promising region for wind farms. Based on the literature review, different technology routes that characterize wind farms around the world were identified and, through expert evaluation, the best technology route in terms of cost-effectiveness was defined. The results show that, under current conditions, the onshore route with higher unit power of the wind turbine and tubular tower proved to be the preferred one. However, it was shown that in the medium term, offshore wind energy shall become a reality in the country, as long as favorable regulations and licensing models, political and economic stability, and long-term public policies in terms of incentives and subsidies to attract investments for this type of energy generation are present.

Transfer Learning for Fault Detection with Application to Wind Turbine SCADA Data

The installed wind power capacity is growing worldwide. Remote condition monitoring of wind turbines is employed to achieve higher uptimes and lower maintenance costs. Machine learning models can detect developing damages in wind turbines. Therefore, this paper demonstrates that cross–turbine transfer learning can drastically improve the accuracy of fault detection models in turbines with scarce SCADA data. In particular, it shows that combining the knowledge from turbines with scarce and turbines with plentiful data enables earlier detection of faults than prior art methods. Training fault detection models require large amounts of past and present SCADA data but these data are often unavailable or not representative of the current operation behavior. Newly commissioned wind farms lack SCADA data from the previous operation. Due to control software updates or hardware replacements, older turbines may also lack representative SCADA data. After such events, a turbine’s operation behavior can change significantly so its SCADA data no longer represent its current behavior. Therefore, the work highlights how to reuse and transfer knowledge across wind turbines to overcome this lack of data and enable the earlier detection of faults in wind turbines.

Quantifying the importance of feed-in tariffs to wind power development in China.

Governments worldwide often provide subsidies to renewable energy for reasons such as climate change mitigation and environmental pollution reduction. However, the importance of such subsides is not well understood and much debated. In this study, 109 monthly observations of the installed wind power capacity at the provincial level were used to assess the most important wind power subsidy policy in China-feed-in tariffs, while controlling for other confounding factors, such as technological change, local energy mix, and wind curtailment. The long panel regression results indicate that with other factors unchanged, an increase of 0.1 yuan/kWh in feed-in tariffs added 7.4-9.6 GW of wind capacity to China's national wind power market annually, higher than most of the estimates in the literature, but more consistent with the fast wind capacity development in China. Without the FIT subsidy, China's current wind power market size would likely be approximately 80% smaller. Our findings can be used to predict the impact of future cost reduction of wind technologies, and examine the interconnected relationships between wind capacity development, subsidy burden, and wind curtailment issues.

Wind energy and assessment of wind energy potential in Turkey: A case study for Mersin province

Today, decrease in fossil resources and the ever increasing need for energy increases the importance of renewable energy resources. Wind power; It has an important potential and usage area in clean and renewable energy sources. The measurement of Wind speed and direction values is very important in terms of evaluating the data of Wind power plant (WPP) project and estimating the amount of energy that can be produced. Wind energy is a good example of clean energy source due to its advantages such as cleanless fast commissioning, and reliability. There has been a significant increase in recent years especially in terms of utilizing wind energy. By the end of 2016, the installed wind power capacity in the world is 486 749 MW, Turkey takes the 11th place with 6,081 MW of installed capacity. In this study, the use of wind power in Turkey's Mersin province were studied. Mersin province in terms of installed wind power capacity has an important place in Turkey. As of 2016, the installed wind power capacity throughout the province is 364 MW, but the usable wind energy potential in the region is about 3414 MW.

Optimization of wind farm portfolios for minimizing overall power fluctuations at selective frequencies – a case study of the Faroe Islands

Abstract. Hourly modeled wind turbine power output time series – modeled based on outputs from the mesoscale numerical weather prediction system Weather Research and Forecasting Model (WRF) – are used to examine the spatial smoothing of various wind farm portfolios located on a complex isolated island group with a surface area of 1400 km2. Power spectral densities (PSDs), hourly step-change functions, and duration curves are generated, and the 5th and 95th percentiles and the standard deviations of the hourly step-change functions are calculated. The spatial smoothing is identified from smaller high-frequency PSD amplitudes, lower hourly fluctuations, and more flat duration curves per installed wind power capacity, compared with single wind turbine outputs. A discussion on the limitation of the spatial smoothing for the region is included, where a smoothing effect is observed for periods of up to 1–2 d, although it is most evident at higher frequencies. By maximizing the smoothing effect, optimal wind farm portfolios are presented with the intention of minimizing overall wind power fluctuations. The focus is mainly on the smoothing effect on the 1–3 h timescale, during which the coherency between wind farm power outputs is expected to be dependent on how the regional weather travels between local sites, thereby making optimizations of wind farm portfolios relevant – in contrast to a focus on either lower or higher frequencies on the scale of days or minutes, respectively, during which wind farm power output time series are expected to be either close to fully coherent due to the same weather conditions covering a small region or not coherent as the turbulences in separate wind farm locations are expected to be uncorrelated. Results show that an optimization of the wind farm capacities at 14 pre-defined wind farm site locations has a minimal improvement on the hourly fluctuations compared with a portfolio with equally weighted wind farm capacities. However, choosing optimized combinations of individual wind farm site locations decreases the 1–3 h fluctuations considerably. For example, selecting a portfolio with four wind farms (out of the 14 pre-defined wind farm site locations) results in 15 % lower 5th and 95th percentiles of the hourly step-change function when choosing optimal wind farm combinations compared with choosing the worst wind farm combinations. For an optimized wind farm portfolio of seven wind farms, this number is 13 %. Optimized wind farm portfolios consist of distant wind farms, while the worst portfolios consist of clustered wind farms.

Design Simulation and Review of Solar PV Power Forecasting Using Computing Techniques

The field of renewable energies provides solutions to the sustainable energy challenges of developing countries. Various renewable energy options are used to solve the power shortage in India. In recent years, power generation has increased significantly, but the market is promising for domestic organisations, distribution networks and transmission networks, and the financial situation is sluggish and influential. India has 450,000 kilowatts of hydroelectric power, has an installed wind power capacity of 230,000 kilowatts, but has almost no great potential for renewable energy. However, India is currently very high in this region, 2022 (not including large hydropower), the target is to raise the current installed capacity from 37 GW of renewable energy to 1.75 million kilowatts. Solar energy is a key part of the government’s extension policy. The demonstration of solar PV Systems is highly advantageous for geography and structure. For efficient structure, we need effective design and forecasting tools. PV system is a popular tool to optimise and schedule the design and construction of independent photovoltaic solar systems connected to the grid.
 The objective of this research is to introduce the equivalent design model of the photo voltaic solar power plant and to analyses the impact of power forecasting on performance assessment of solar photo voltaic system. Mathematical model of solar photovoltaic system has been implemented using and performance is analysed using PV and IV characteristics of solar photovoltaic system. Modified prediction technique was implemented for optimum forecasting in the specified scenario of complex operating condition.

Assessment of different end-of-life strategies for wind power plants under uncertainty

The enormous rise in installed wind power capacity worldwide over the last twenty years means most of the wind power plants will soon reach their end-of-life of service. The attention of owners, governments and investors is being drawn to strategies aimed at increasing the useful life of these facilities or the repowering of old wind farms, with the additional benefit of increasing generated power by using more efficient wind turbines. With the aim of evaluating ways to address an actual case of the end of life of a wind power plant in Spain, three strategies are studied to assess the most suitable solutions, allowing the useful life of the current wind turbines to be extended for another ten years, or adopting a repowering strategy, in which the old wind turbines are replaced by new ones with a service life of twenty years. A stochastic techno-economic model is proposed, and the Monte Carlo simulation technique is used, allowing uncertainties and risks associated with the key technical and economic input variables to be considered, also revealing those with the greatest impact on the economic performance of the project. Results provided by the stochastic model are more realistic than the deterministic analysis, revealing an increase in the annual energy production ranging from 9.53% to 56.93%, depending on the strategy adopted, with repowering being the most advisable project. Valuable, practical outcomes for making tactical decisions in optimizing wind energy resources are obtained, considering different levels of improvements to be implemented in wind power plants according to the available resources and the expected return on investments.

Evaluation of priority strategies for the expansion of installed wind power capacity in Turkey using a fuzzy analytic network process analysis

Given its widespread potential and technological advancements, it is critical to use the greatest amount of wind power in reaching the 100% renewable energy target in power grids. SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis is frequently used to evaluate alternative strategies in multi-criteria decision issues. This paper employs a hybrid Fuzzy Analytic Network Process (FANP) approach based on SWOT to assess the future development of wind power capacities in Turkey in light of the sectoral effects of the Covid-19 outbreak in 2020. To validate the proposed approach, the results are compared to the results of the SWOT-based Analytic Network Process (ANP), Analytic Hierarchy Process (AHP), and fuzzy AHP (FAHP). According to the findings, “Development of domestic and efficient technologies (ST1)" and “Sustaining support mechanisms in investments and technological research (SO2)" are the best alternative strategies in all analysis models. While the priority ranks of other alternative strategies are the same in AHP and FAHP hierarchical techniques, FANP differs from ANP when the linguistic assessment process is taken into account. This study proposes long-term strategies for increasing wind power capacity and sector sustainability, and it demonstrates that FANP could be an appropriate approach for prioritizing these strategies in current scenarios.

The mathematical model of a wind power plant and a gas power plant

To enable conduction of quality research of power system dynamics using computer simulation software, appropriate mathematical and simulation models have to be developed. In the last two decades, an exponential increase of installed wind power capacity can be observed, while the gas power plant capacity and energy production has also seen an increase in the recent years. Consequently, their impact on power system dynamics is no longer negligible. Furthermore, the increased penetration of wind power has led to a lot of research concerning frequency support capabilities from wind power plants (WPPs). In this paper, after a brief theoretical introduction, simplified system frequency response (SFR) simulation models of a generic wind power plant and a gas power plant have been developed in MATLAB. These generic models have been integrated with existing SFR models of a steam and hydro unit. The model contains a representation of under-frequency load shedding (UFLS) and demand response, as well. Graphical user interface (GUI) has been developed to control this expanded model.

Design Simulation and Review of Solar PV Power Forecasting Using Computing Techniques

The field of renewable energies provides solutions to the sustainable energy challenges of developing countries. Various renewable energy options are used to solve the power shortage in India. In recent years, power generation has increased significantly, but the market is promising for domestic organisations, distribution networks and transmission networks, and the financial situation is sluggish and influential. India has 450,000 kilowatts of hydroelectric power, has an installed wind power capacity of 230,000 kilowatts, but has almost no great potential for renewable energy. However, India is currently very high in this region, 2022 (not including large hydropower), the target is to raise the current installed capacity from 37 GW of renewable energy to 1.75 million kilowatts. Solar energy is a key part of the government’s extension policy. The demonstration of solar PV Systems is highly advantageous for geography and structure. For efficient structure, we need effective design and forecasting tools. PV system is a popular tool to optimise and schedule the design and construction of independent photovoltaic solar systems connected to the grid.
 The objective of this research is to introduce the equivalent design model of the photo voltaic solar power plant and to analyses the impact of power forecasting on performance assessment of solar photo voltaic system. Mathematical model of solar photovoltaic system has been implemented using and performance is analysed using PV and IV characteristics of solar photovoltaic system. Modified prediction technique was implemented for optimum forecasting in the specified scenario of complex operating condition.

Empirical evidence based effectiveness assessment of policy regimes for wind power development in China

As a renewable energy, wind power has been improving energy structures and assisting in the maintenance of strategic energy security. Based on provincial level panel data from 2000 to 2018, this paper developed models to evaluate the effects of different policies on newly installed wind power capacity and investigated the market reform influences on wind development in eight spot market pilots. It was found that on a national level, while the pricing and government guideline policies significantly promoted wind power growth, present market reforms were having an opposite effect. The results also suggested that from a resource endowment heterogeneity perspective, there was a significant difference in the wind capacity promotion policy performances. Additionally, of the eight spot market pilots, market designs had led to a significant reduction in new wind power installations in Gansu and Inner Mongolia, and no significant wind power development effects were found in the other pilots. Based on the research results, a discussion on policy use and suggestions on policy improvements are given.

Stories From China

Under the goal of “Emission Peak” and “Carbon Neutrality,” the number of new energy resources is increasing quickly in China. By the end of November 2021, the total installed power-generating capacity had increased 9% to about 2,320 GW. The installed wind power capacity had increased 29% to about 300 GW, which is 2 times the number at the end of 2016. According to the 2021 global offshore wind power report released by GWEC, more than 3 GW of new offshore wind power was connected to the grid in China during 2020, and China is the world's largest offshore wind power market for the third consecutive year.