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Brazil is a case of the achievement of a high share of wind energy integration to an interconnected power system in an emerging market. A series of successful energy supply programmes and a strong renewable-oriented finance framework implemented since 2002, have promoted alternative renewables – wind installed capacity increased over 22-fold from 927 MW in 2011 to 21 GW in 2022, reaching 11.77% of total procured power. From this, about 90% are concentrated in Brazil's least developed region, the Northeast, where wind investment should create jobs and propel socioeconomic development. Despite that, there is no studies specific assessing the regional and economy-wide effects of those programmes, gap that this paper aims to fill by applying the regional Computable General Equilibrium (CGE) model TERM-BR10. We assess that by modelling an alternative scenario for the period 2010-19 based on an official electricity matrix built in 2010, that projected 3.4 additional GW of wind power while not considering all the success of renewable policies, against the observed installed capacity expansion, where wind power was 3.7 times higher than what had been planned. Our results show that in the absence of renewable policies, Brazil would have faced economic losses such as GDP (−0.27%) and investment (−4.76%). The Northeast region would have been the most impacted in terms of GDP (−1.3%), real wage (−2.99%) and employment creation (−0.1%). We propose that such socioeconomic co-benefits are incorporated in energy planning, which could promote regional equality through renewable energy expansion. • Wind power capacity increased over 20-fold from 2011 to 2022. • 90% are in the poorest region where policies propel development and job creation. • Policy absence would entail a GDP loss of 0.27% nationally and of 1.3% in the region. • Negative impacts to labour market are mostly over skilled jobs. [ABSTRACT FROM AUTHOR]

This is a discussion on the paper titled “Assessing feed-in tariffs on wind power installation and industry development in Taiwan” published by Hsu et al. In the discussed paper, there were several missing variables in the historical test of the system dynamics (SD) model, and thus the test was not integrated and valid. Additionally, the simulated results of the missing variables were used as important indexes to observe the behavior of the SD model. Therefore, it was highly likely that the solution and related policy analysis based on the simulated results were incorrect. Accordingly, the present study illustrated the structure characteristics of the SD model with respect to the fore-mentioned problems, discussed the reason for missing variables and proposed a method to assess whether or not the historical test was integrated. Finally, after a careful analysis, this study offered suggestions to perfect the historical test proposed in the discussed paper. [ABSTRACT FROM AUTHOR]

Recently China's wind power industry is challenged by many problems such as wind power integration and wind curtailment, which seriously hinders the development of onshore wind power. Since China's policy has a direct and significant influence on wind power industry, it is vital to analyze and evaluate published policies. This paper explores in detail 134 China's onshore wind power policies from 2005 to 2015 so as to provide analytical support for future formulation and implementation of wind power policy. Based on policy summary from equipment manufacturing industry, wind farms and power grid industry, this paper evaluates the policies from the perspective of overall plan, support policy and policy implementation using fitting method, game theory and empirical analysis. The results indicate that China's wind policy is gradually becoming perfected, for example, there are more reasonable objectives and improved support policies. However, many problems remain to be solved, like unreasonable planning, imperfect support policies, immature trading systems and uncoordinated actions among interest related parties. Furthermore, combined with international experiences, suggestions on improving China's wind power policy are proposed from seven aspects. [ABSTRACT FROM AUTHOR]

The application of VSC-HVDC (Voltage Source Converter Based High Voltage Direct Current Transmission) is one of the proven solutions to the transmission problems of offshore wind power. As AC and DC networks become more complex, VSC-MTDC (Multi-terminal High Voltage Direct Current Transmission Based on VSC) is developed based on two-terminal HVDC to offer technology support for the accessing among multi-supplier center, multi-loads center and distributed renewable powers. This paper reviews the MTDC and its implementation in China, mainly discusses the topologies of DC network of VSC-MTDC system and the control strategies of VSC-MTDC which includes converter station control and coordinated control. In most cases, double closed-loop space vector control (DCSVC) strategy applied in the Zhoushan project, which includes five-terminals and has been put into operation, is adopted for the converter station control. But the method can not work under the unbalanced voltage situation, thus an improved double closed-loop space vector control strategy, which includes positive and negative sequence components, is proposed in this paper. As for coordinated control, master-slave control, DC voltage droop control and DC voltage margin control are presented. As for the part of implementation of MTDC in China, Zhoushan project is presented in detail. This paper provides good theoretical foundation for the grid integration and the transmission of large offshore wind farms. [ABSTRACT FROM AUTHOR]

Currently, wind energy is one of the most important sources of renewable energy. Offshore locations for wind turbines are increasingly exploited because of their numerous advantages. However, offshore wind farms require high investment in maintenance service. Due to its complexity and special requirements, maintenance service is usually outsourced by wind farm owners. In this paper, we propose a novel approach to determine, quantify, and reduce the possible conflicts of interest between owners and maintenance suppliers. We created a complete techno-economic model to address this problem from an impartial point of view. An iterative process was developed to obtain statistical results that can help stakeholders negotiate the terms of the contract, in which the availability of the wind farm is the reference parameter by which to determine penalisations and incentives. Moreover, a multi-objective programming problem was addressed that maximises the profits of both parties without losing the alignment of their interests. The main scientific contribution of this paper is the maintenance analysis of offshore wind farms from two perspectives: that of the owner and the maintenance supplier. This analysis evaluates the conflicts of interest of both parties. In addition, we demonstrate that proper adjustment of some parameters, such as penalisation, incentives, and resources, and adequate control of availability can help reduce this conflict of interests. • Reliability of OWF analysis for owners and maintenance suppliers. • Consequences of outsourced maintenance for owners and maintenance suppliers. • Costs and profits analysis of OWF owners and maintenance suppliers. • A novel approach to reduce the conflict of interests between both parties. [ABSTRACT FROM AUTHOR]

Reliable and efficient power modeling from meteorological wind data is vital for optimal implementation and monitoring of wind energy, and it is important for understanding turbine control, farm operational optimization, and grid load balance. Based on the idea that similar wind conditions lead to similar wind powers; this paper constructs a modeling scheme that orderly integrates three types of ensemble learning algorithms—bagging, boosting, and stacking—and clustering approaches to achieve wind power modeling from multiple wind-based meteorological factors in a wind farm. The paper also investigates the applications of different clustering algorithms and methodologies to determine cluster numbers in the modeling. The results reveal that all ensemble models with clustering exploit the intrinsic information in wind data and thus outperform models without clustering by approximately 15% on average in modeling wind power. The model with the best-performing Farthest First clustering is computationally rapid and with an improvement of around 30% compared with the baselines. Given the diversity introduced by clustering algorithms, the power modeling performance is further boosted by about 5% by introducing stacking that fuses ensembles with varying clusters. The proposed modeling framework thus demonstrates promise by delivering efficient and robust performance on the targeted problem. • Systematic demonstration of wind power modeling from weather data. • Construction and comparison of various clustering methods for classifying wind data. • Combination of bagging, boosting, and stacking for modeling. • Multiple wind meteorological characteristics are considered in the modeling. [ABSTRACT FROM AUTHOR]

Given the abundant availability of resources, the market potential, and their cost competitiveness, onshore wind farms and photovoltaic units are expected to drive the overall growth of renewable energy sources in the next decade. However, Europe is a small and densely populated continent, which results in many countries experiencing a severe shortage of suitable land sites for installing new wind and photovoltaic facilities. This, combined with the fact that many existing wind turbines and photovoltaic units reach the end of their operational lifetime, has laid the groundwork for 'repowering'. Repowering includes upgrading or retrofitting renewable energy sources' components and exploits existing land sites, in which renewables are operating for years. This paper intends to emphasise the multiple dimensions of repowering, also delving in its surrounding socio-economic context in various European countries, to identify its barriers and allies as well as gather best practices and lessons learnt from its implementation. Considering data availability, extent of spatial use and bulk equipment, and consequent attention in literature and practice, the analysis mainly orbits around repowering of wind projects, rather than solar photovoltaics. Finally, the paper presents key directions toward wider future exploitation of repowering. • Land scarcity problem with high renewable energy potential addressed by repowering. • In-depth socio-economic extensions of repowering are discussed. • Semi-quantified evaluation highlighting main implementation pillars of repowering. • Best practices and lessons learnt from the implementation of repowering. • Legislative barriers against the wider implementation of repowering. [ABSTRACT FROM AUTHOR]

In recent years, an increasing number of Chinese wind power firms have been going international in search of global market for development channels. By employing qualitative research and case study methods, this paper examines the export scale and markets of China’s wind turbines, the market entry modes and location choices of China’s outward foreign direct investment (FDI) in the wind power sector, identifies the determinants and prospects of Chinese wind firms’ internationalization, as well as provides policy implications. It is found that Chinese wind power firms has chosen to internationalize very differently from the Uppsala model in that the exports and FDI of Chinese wind power firms both took place around 2008, and the exports and FDI of Chinese wind power firms are closely linked. The determinants of Chinese wind firms’ internationalization are the competitive advantages of leading Chinese wind firms and the intensified domestic market competition, followed by Chinese government support for internationalization and the attractive overseas market. The prospects of Chinese wind firms’ internationalization are bright but challenging. Drawn on this analysis, conclusions and three policy implications are provided at the end of the paper. This paper contributes to the study of China’s wind power sector from a new perspective. [ABSTRACT FROM AUTHOR]

The determination of maintenance strategies is subject to complexity and uncertainty arising from variable offshore wind farm states and inaccuracies in model parameters. The most common method in the existing studies is to adopt an open-loop approach to optimize a maintenance strategy. However, this approach lacks the ability to capture periodic operational state of the wind farm and the awareness of eliminating uncertainty. Consequently, the determined strategy is inadequate to instruct maintenance activities, inducing excessive revenue losses. In this paper, a closed-loop maintenance strategy optimization method is proposed for decision-makers to identify a more profitable manner of wind farm maintenance management. The life-cycle maintenance optimization problem is decomposed into a sequence of sub-optimization problems covering multiple time periods by using a rolling-horizon approach. Each sub-optimization problem is intentionally designed based on the monitored state of the wind farm and the available reliability, availability, and maintainability (RAM) database. Meanwhile, the decision maker consciously mitigates the parameter uncertainty in the maintenance model gradually by updating the current database. Compared to conventional strategies covering the entire lifetime of wind farms, the proposed maintenance strategy is periodically adjusted to provide a series of sub-strategies. The proposed approach was applied in a simulation experiment, a generic small-scale offshore wind farm, to assess its performance. Computational results show that adapting maintenance strategies based on the current state of the wind farm can reduce revenue losses in comparison to conventional open-loop strategies. In addition, the benefits of updating the RAM database in decreasing revenue losses is revealed. • Proposing a maintenance strategy with a closed-loop decision-making process. • Dividing the entire maintenance optimization problem into sub-problems covering multiple time periods. • Gradually mitigating model parameter uncertainty over time with new operational data. • Adjusting the entire maintenance strategy into a series of sub-strategies based on wind farm states and updated parameters. [ABSTRACT FROM AUTHOR]

Abstract Wind speed and power forecast is an essential component to ensure grid stability and reliability. The traditional forecasting methods fail to address the non-linearity in the wind speed time-series, thus paving way for machine intelligent algorithms. This paper discusses a hybrid machine intelligent wind forecasting model utilizing different variants of Support Vector Regression (SVR) built on wavelet transform. Various performance indices are evaluated to identify the possible best one among four different machine learning regressors for wind forecasting application. Apart from standard ε-SVR and LS-SVR, two new regression models, namely, ε-Twin Support vector regression (ε-TSVR) and Twin Support vector regression (TSVR) are used to forecast short-term wind speed, and are compared with Persistence model for four wind farm sites. The effect of larger dataset on forecasting performance is evaluated for two wind farm sites from USA and India. Further, wind power ramp events are investigated at different hub heights and the forecasting performance of different variants of SVR is compared for five wind farm sites. Highlights • Machine intelligent SVR variants are discussed for short-term wind forecasting. • Overfitting and underfitting scenarios of variants of SVR are assessed. • Forecasting performance of SVR variants is assessed during wind power ramp event. • Wind power ramp events are analyzed at different hub heights for 5 wind farm sites. [ABSTRACT FROM AUTHOR]

Abstract Subsynchronous control interaction (SSCI) associated with wind farms has become one of the major challenges for maintaining the stability and reliability of modern power systems. SSCI is mainly caused by the active participation of fast-acting wind turbine converter (WTC) controls in the phenomenon. It can damage the system equipment, reduce the amount of power generation, and degrade the power quality. Thus, it is urgent to develop practical mitigation techniques for the SSCI in order to achieve a smooth and reliable operation of grid interfaced wind farms. This study presents an overview of potential SSCI mitigation techniques at various stages of the power system, including system planning, operation, control, and protection phases at both network and generation sides. In particular, this paper reviews the active damping controls utilizing flexible ac transmission systems (FACTS) and WTC controls. Finally, it outlines the challenges and future work in studying SSCI mitigation techniques in practical wind power systems. Highlights • SSCI mitigation techniques: planning, operation, control and protection stages. • Review of active damping control techniques in network and generation sides. • Challenges and limitations of various SSCI mitigation techniques are discussed. • WTC control modifications and shunt-VSC using filters are recommended approaches. [ABSTRACT FROM AUTHOR]

The most important single event of the last years in wind energy technology is the reduction in the cost of producing wind electricity offshore, a reduction that can reach 75%, depending on the system boundary considered, for installations commissioned by 2024. Surprisingly, there is very little scientific literature showing how this reduction is being achieved. The objective of this paper is to analyse the evidence behind cost reduction in one of the most significant cost elements of offshore wind farms, the installation of foundations and turbines. This cost is directly dependent on the daily rates of the installation vessels and on the days it takes to install those wind farm elements. Therefore, we collected installation data from 87 wind farms installed from 2000 to 2017, to establish the exact time for installation in each. The results show that advances have reached 70% reduction in installation times throughout the period for the whole set, turbine plus foundation. Most of these improvements (and the corresponding impact in reducing costs) relate to the larger size of turbines installed nowadays. There is, therefore, not any leap forward in the installation process, but only incremental improvements applied to turbines that are now four times as large as in 2000. [ABSTRACT FROM AUTHOR]

Spatial determination planning is at an initial and very sensitive step in the development of wind farms. On one hand, it is important to maximize the potential of wind in a particular area, and on the other hand, it is important to achieve environmental protection (including the human population and natural heritage) in the same space at the same time. With this in mind, it is important to balance all the requirements that are relevant for reaching the optimal solution when determining the micro-location of wind turbines at the earliest stages of the planning and development of wind power projects. In this context, planning is a key stage in finding sustainable solutions for the implementation of such projects, and an indispensable instrument in the planning process, offering support and control is Strategic Environmental Assessment (SEA). This paper presents the role of SEA in the planning process for wind farms. The place of the SEA process is identified in relation to other environmental impact assessment instruments, such as Environmental Impact Assessment (EIA) and Environmental Social Impact Assessment (ESIA), and a comparative analysis of these instruments is made, including their advantages and disadvantages. The results of the paper indicate the importance of applying SEA in the earliest stage of wind farm planning, so as to formally apply the precautionary principle and avoid problems, which in the later stages of the project, when EIA is usually used, can be disadvantageous both economically and in terms of environmental impact. • Various environmental instruments are used in the development of wind farm (WF) projects. • The Strategic Environmental Assessment (SEA) provides preventive environmental protection at the earliest phase of theWF project development. • The need to apply the so-called partial assessments within the SEA process is emphasized. [ABSTRACT FROM AUTHOR]

The continuing increase of wind energy penetration into power systems, in combination with the retirement of conventional generation, raises new challenges for the maintenance of power system stability. This paper presents a comprehensive review of wind power plant capabilities to provide frequency support and the corresponding methods available in the published literature are thoroughly analysed and compared. The topic is covered from different perspectives giving a comprehensive overview on the work carried out in this field. In addition, the integration of energy storage technologies and dispatching of wind farms during frequency deviations are thoroughly discussed. Finally, technical challenges, future research lines and general recommendations are provided. [ABSTRACT FROM AUTHOR]

Congestion management is imperative for reliable and secure system operation in restructured power systems. Since the installation of wind farms at proper locations offers the possibility of congestion relief, this paper investigates congestion management in power systems with specific consideration of wind energy sources. The optimal location of a wind farm is determined by the Bus Sensitivity Factor and the Wind Availability Factor. Differential Evolution is a population-based heuristics algorithm used for solving non-linear optimization problems. We propose an Improved Differential Evolution based approach to ease congestion in transmission lines by generator rescheduling and installation of new wind farms. In this approach, an enhanced mutation operator is introduced to improve the performance of the Differential Evolution algorithm. A standard IEEE-30 bus system is used to evaluate the proposed algorithm under critical line outages. The simulation results show that the proposed approach is more effective than other approaches. [ABSTRACT FROM AUTHOR]

The subsynchronous control interaction (SSCI) occurs when the wind turbine converter (WTC) controls interact with the series-compensated or weak AC network. The mechanism and attributes of the emerging interaction phenomenon invilving wind turbine generators (WTGs) are quite different from the traditional subsynchronous resonance or oscillation (SSR/SSO) phenomenon in steam turbine-generators (STGs). The SSCI is characterized by various system-wide parameters, including the wind speed and its uneven distribution in a wind power plant, varying number of WTGs connected to the grid, type of the WTGs, parameters of the WTC controls, level of series compensation, stiffness of the grid, etc. Such system-wide parameters change over time and thus make the modeling and analysis rather challenging to conduct SSCI studies. An ideal modeling approach for the SSCI is expected to preserve the system topology as well as valid for a wide range of operating conditions and parameters of the system. On the other hand, an ideal stability analysis method is supposed to give key quantitative information, such as the magnitude, frequency, and origin of the oscillation, as well as the component level participation indices. This paper provides a comparative insight into the application of existing and emerging modeling and stability analysis approaches for SSCI investigations in large-scale wind farms. It highlights the strengths and weaknesses of various modeling methods and analysis criteria. Finally, the paper underlines the recent advancements and points out towards the research directions in small/large-signal impedance modeling and stability approaches for accurate and quantitative investigation of the SSCI. • SSCI is a system-wide phenomenon thus the modeling and analysis is challenging. • Strengths and weaknesses of the existing modeling and stability analysis methods in the time and frequency-domain are discussed. • An modeling method should preserve system topology and remains valid under a wide range of operating conditions. • An ideal stability analysis method should give key quantitative information: magnitude, frequency and source. [ABSTRACT FROM AUTHOR]

The wind farm development is very slow in the developing countries particularly in the Middle East. This is attributed to many factors including economy of these countries which is mainly rely on fossil fuels and hence yields to low cost of energy, political and technological barriers, lack of wind potential studies or lack of high wind regions and more. This paper attempts to address these issues and foresees opportunities for developing household or small communities wind farms which can be rely on individual or small communities’ funds as well as creating job opportunities for them. As the case study, the province of Sistan-Baluchestan in Iran is considered to study how the economy of local communities or households can be strengthen by exploring small wind turbine developments for them. An economic and statistical analysis is conducted based on wind data for cities of Chabahar, Dehak, and Dalgan in the province. Wind energy characteristics and patterns are extensively explored for these cities from 473,040 wind data samples. This is resulted in the average annual wind power density of 181 W / m 2 , 158 W / m 2 , and 146 W / m 2 , for Chabahar, Dalgan, and Dehak, respectively at the height of 40 m. By considering five different commercial wind turbines, from 1.6 to 30 kW capacities, the total annual electrical power production, profits and payback time for each wind turbine is presented. It is concluded that nearly 90,000 households can benefit from profit of annual energy production of 225 GWh. [ABSTRACT FROM AUTHOR]

Wind power curves play important roles in wind power forecasting, wind turbine condition monitoring, estimation of wind energy potential and wind turbine selection. In practice, it is a challenging task to produce reliable wind power curves from raw wind data due to the presence of outliers formed in unexpected conditions, e.g., wind curtailment and blade damage. This paper comprehensively reviews wind power curve modeling techniques from the perspective of modeling processes, i.e., wind data analyses, wind data preprocessing and various wind power curve models. Moreover, the performances of many popular power curve models are studied in different seasons and different wind farms. The results show that no universal wind power curve model can always perform better than other models under any environmental conditions. In general, there are three factors that affect the final wind power curves: data filtering approaches; wind power curve models; and choice of optimization strategies (especially the method applied to construct objective functions). However, there is no guarantee that all outliers will be removed from the raw wind data. Consequently, designing robust regression models or constructing robust objective functions may be two effective ways to obtain accurate power curves in the presence of outliers. The above two strategies depend largely on the error characteristics of power curve modeling. While it is often observed that the error distribution of the power curve modeling may be asymmetric, few researchers have considered this trait when building wind power curves. Therefore, this paper proposes several strategies that focus on designing asymmetric loss functions and developing robust regression models with asymmetric error distributions. Models that benefit from these characteristics may be more suitable for power curve modeling tasks and are more likely to produce better wind power curves. • The different roles of wind power curve models in the utilization of wind energy are reviewed. • The paper discusses the classification of preprocessing approaches for the uncertainties in raw wind data in detail. • The paper provides a comprehensive review of the probabilistic and deterministic wind power curve models. • The performances of many power curve models are comprehensively compared under different environmental conditions. • The asymmetric error characteristic of power curve modeling is analyzed and considered for accuracy improvement. [ABSTRACT FROM AUTHOR]

Traditionally, inertia in power systems has been determined by considering all the rotating masses directly connected to the grid. During the last decade, the integration of renewable energy sources, mainly photovoltaic installations and wind power plants, has led to a significant dynamic characteristic change in power systems. This change is mainly due to the fact that most renewables have power electronics at the grid interface. The overall impact on stability and reliability analysis of power systems is very significant. The power systems become more dynamic and require a new set of strategies modifying traditional generation control algorithms. Indeed, renewable generation units are decoupled from the grid by electronic converters, decreasing the overall inertia of the grid. 'Hidden inertia', 'synthetic inertia' or 'virtual inertia' are terms currently used to represent artificial inertia created by converter control of the renewable sources. Alternative spinning reserves are then needed in the new power system with high penetration renewables, where the lack of rotating masses directly connected to the grid must be emulated to maintain an acceptable power system reliability. This paper reviews the inertia concept in terms of values and their evolution in the last decades, as well as the damping factor values. A comparison of the rotational grid inertia for traditional and current averaged generation mix scenarios is also carried out. In addition, an extensive discussion on wind and photovoltaic power plants and their contributions to inertia in terms of frequency control strategies is included in the paper. • Extensive review about inertia values for power systems and wind power plants. • Wind turbine and photovoltaic installation frequency control: inertial response. • New proposal to estimate the averaged inertia constant of power systems. • Estimation and comparison of averaged inertia constant in regions and countries. • Relevant information for grid frequency studies with high renewable integration. [ABSTRACT FROM AUTHOR]

Operations and maintenance (O&M) is a key contributor to wind farm expenditures. To increase competitiveness, wind farm operators are increasingly looking into leveraging real-time sensor data from condition monitoring (CM) systems. CM provides significant insights on evolving asset failure risks for wind turbines. To date, these insights have not been fully leveraged in wind farm O&M due to ad-hoc connections to decision-making. Specifically, CM applications in wind farms have been limited to detection of turbines with imminent failure risks that require immediate replacement. In reality, wind farm maintenance requires a careful proactive orchestration of O&M dependencies across turbines along with multiple sources of uncertainty associated with asset availability, operational and market conditions. This paper proposes a unified condition-based maintenance and operations scheduling approach for wind farms that models uncertainties related to turbine availability, wind power output and market price. The proposed formulation explicitly considers the turbine-to-turbine dependencies in operations and maintenance, such as opportunistic maintenance, to identify the O&M decisions that are optimal for multiple wind farms. The problem is formulated as a chance-constrained stochastic programming model to maximize operational revenue while ensuring high levels of turbine availability and generation. To make the chance constraints tractable, two approximations are proposed with a focus on sample average approximation (SAA) and prominent tail inequalities such as Markov's inequality and Chernoff bound. Our results on a comprehensive set of experiments demonstrate that the proposed approach provides significant improvements in asset availability, market revenue and maintenance costs in large scale wind farms. • A stochastic condition-based optimization model is proposed for wind farm O&M. • Condition-based chance constraints are formulated to model farm-level availability. • Tractable approximations are developed for the proposed chance constraints. • An extensive experimental framework is built using degradation and operational data. • Proposed model significantly improves availability, revenue and maintenance outcomes. [ABSTRACT FROM AUTHOR]

The use of wind turbines can help progress towards economic and technological development, lower rates of fossil fuel consumption, decreased greenhouse emissions, and reduced side-effects of climate change. A successful mechanism for developing renewable energy worldwide is the guaranteed purchase of electricity generated from renewable energy sources. Accordingly, this study aims to integrate Geographic Information System-based Multi-criteria Evaluation (GIS-MCE) models with economic frameworks to estimate the optimal purchasing price for electricity produced by wind turbines. A total of 13 criteria maps were used and integrated using Ordered Weighted Averaging (OWA) as a type of MCE model. The criteria were initially normalized based on the minimum, and maximum values and weights were assigned to each criterion, using the Best-Worst method. The OWA model identified optimal site locations at various decision risk levels. The economic efficiency of wind turbines and the potential purchasing price of electricity from turbines were also assessed in terms of Net Present Value (NPV). The results show that Ardabil and Southern Khorasan provinces had the most significant areas in the very-suitable class for wind turbine installation (small/large scale). The purchasing prices for wind-generated electricity ranged from 0.047 to 0.182 US$ for large wind farms and 0.074 to 0.384 US$ for small wind plants. The highest electricity produced from large wind farms was found in Maragheh. • This paper presents a GIS-MCE model to assess the suitability of locations for wind power plants. • It shows a GIS-MCE-based economic model for price estimation of wind energy generated electricity. • The study integrates expert weights, criteria maps, and risk degrees in the price estimation model. [ABSTRACT FROM AUTHOR]

This paper describes the development of a data set mapping all commercial wind turbines in Canada: the 9th largest onshore wind energy fleet in the world. Details contained in the data enable the analysis of the evolution of commercially deployed wind turbines as well as the illustrating the effects of the introduction and cessation of different policies affected historical deployment. In total, over 6700 turbines spanning more than 265 projects and nearly 14.0 GW of generating capacity were indexed including coordinates, model, manufacturer, owner, tower height and commissioning date. Data were compiled from publicly available sources including planning documents, technical reports, environmental impact studies and acoustic emission reports. In 2021, average rotor diameters were 140 m with 108 m tall towers, more than triple the average diameter and double the average tower height from 20 years ago. The analysis of this data found that a typical wind farm in Canada consists of 0.97 turbines/km 2 , while the typical installed capacity density has recently jumped from around 1 to 2.94 MW/km 2 in 2021. Competitive procurement policies have often trended towards larger installed capacities, whereas smaller wind farms emerged more often when policies directed at community ownership were in place. Policies that encouraged local manufacturing developed regional clusters of particular manufacturers, but generally ceased after the local manufacturing policies ended. This work serves as a benchmark for the evolution of wind energy in Canada and a framework for similar data sets in other jurisdictions, and provides a resource for academic researchers, wind farm developers, government bodies and planning agencies as well as the general public. • Details of the Canadian wind fleet database mapping over 6700 individual turbines. • Discussion of the major policies that have influenced the evolution of wind in Canada. • Evolution of wind turbine and wind farm size and capacity density. [ABSTRACT FROM AUTHOR]

Wind turbine (WT) experiments in wind tunnels can benefit the efficient utilization of wind energy in many aspects, such as the testing of new products, the validation of numerical models, and the exploration of underlying mechanisms of WT-induced flow field. However, there is a lack of comprehensive and critical review on this topic. In this paper, necessary pre-experimental works are presented first in terms of the design process of miniature WT, scaling criteria, blockage correction, and devices and methods for measurement. In addition, the wind tunnel tests on flat and complex terrains are discussed for in-depth understanding. The dynamic wake behaviors in both near wake and far wake are reviewed with the clarification of two hypotheses of wake meandering. Careful microsite selections of WTs on complex terrain can be beneficial for the total power yields. Furthermore, special attention is paid to active yaw control to verify the potential to increase power output while mitigating structural loads. Finally, discussion on research limitations and prospects are given as avenues for future research. This study provides a comprehensive overview and in-depth discussion on WT experiments in wind tunnels, which is expected to facilitate the solution of theoretical problems in wind energy and enable more practical research. • Experimental setup of wind tunnel tests for wind turbines are reviewed. • Dynamic flow in near wake and far wake is discussed with theoretical analysis. • Typical terrain models and terrain effects on wind turbine performance are discussed. • The underlying physics and effect of active yaw control are experimentally verified. • Research challenges and prospects are summarized. [ABSTRACT FROM AUTHOR]

The selection of training data for establishing a model directly affects the prediction precision. Wind power has the characteristic of daily similarity. The corresponding meteorological data also has the characteristic of daily similarity. This paper proposes a new model with cluster analysis of the numerical weather prediction information. The similar day with the predicted day is searched as training sample to a generalized regression neural network model. The numerical weather prediction data and actual wind power data from a wind farm are used in this study to test the model. The prediction results show that correct cluster analysis method is a useful tool in day-ahead wind power prediction. [ABSTRACT FROM AUTHOR]

We investigate the importance of taking the spatial interaction of turbines inside a wind park into account for power forecasting. This paper provides two tests that check for spatial interdependence such as wake effects. Those effects are suspected to have a negative influence on wind power production. After that, we introduce a new modeling approach that is based on the generalized wind power prediction tool (GWPPT) and therefore respect both-sided censoring of the data. The new model makes use of a spatial lag model (SLM) specification and allows for random effects in the panel data. Finally, we provide a short empirical study that compares the forecasting accuracy of our model to the established models WPPT, GWPPT, and the naïve persistence predictor. We show that our new model provides significantly better forecasts than the established models. [ABSTRACT FROM AUTHOR]

At the end of January 2012, the Spanish government suspended the economic incentives for electricity generation facilities using renewable energy sources, including wind energy plants. Spain maintains a high level of energy dependence that can only be reduced by applying measures to increase energy efficiency and using massive amounts of renewable sources. In addition, the target assumed by Spain, i.e., to have at least 20% of the primary energy to be supplied by renewable sources by 2020, has not yet been reached. In Spain, wind farms, a number of which have been in commercial operation for over 15 years, offer a broad market appropriate for repowering. The use of more efficient wind turbines by means of repowering provides benefits to the electricity sector as a whole by optimizing the use of natural resources and facilitating the grid integration of the energy generated. This paper analyses existing wind farms to quantify and characterize the market suitable for repowering. We discuss whether repowering is a valid alternative from the point of view of feasibility to enable the continuation of the integration of wind energy in the Spanish energy mix and whether this feasibility is sufficient when the energy generated is charged at the electricity market price in terms of grid parity. The results support that repowering is a profitable alternative and is often even better than the construction of new wind farms under certain conditions. [ABSTRACT FROM AUTHOR]

In this paper the harmonization of new wind turbine rotor blades development is given as well as the analysis of behavior by verification testing for a wind turbine rotor blade of composite materials. The design, fabrication, the status of wind energy standards and the analysis of behavior by full-scale verification testing for wind turbine rotor blades of composite laminated materials is given, too. The experimental methodology of static, vibratory and fatigue tests for the wind turbine rotor blade of composite laminated materials is presented. These verification tests were performed after the rotor blade development had been completed. The development of the rotor blade was performed using the PC computer with the CATIA designing system and the Gerber Garment cutter system. The blade was fabricated from composite laminated materials. The contour of airfoil was formed by a continuous structural pocket and a fiberglass skin. [ABSTRACT FROM AUTHOR]

To reduce the release of CO 2 from traditional energy, wind energy is developed very fast all over the world. This means that more large wind turbines will be installed on land and more environmental impacts will be created from wind turbines. Noise generated from rotating wind turbines in wind farm propagates through a complex flow-field including a varying atmospheric flow and varying wakes created by the wind turbines, which can greatly influence the sound propagation. This paper presents a new multi-fidelity flow-acoustics simulation framework to predict the sound propagation in both near and far fields of wind turbines in wind farm, which has the potential for wind farm application with good accuracy and acceptable computing time (i.e. 8 min with 6 CPUs for the centre frequencies between 20 Hz and 1000 Hz in 1/3 octave bands). The new multi-fidelity flow-acoustics simulation framework (named WindSTAR) consists of a three-dimensional wind farm engineering flow model, an engineering sound source model, and a high-fidelity sound propagation model. To validate the new framework, a comprehensive measurement strategy was designed and performed to measure simultaneously the turbine operations, inflow conditions, wake flow fields and acoustic fields from near to far fields of a 3.9/4.1 MW SGRE wind turbine in Drantum, Denmark. Comparisons between measurements and computations in various flow conditions from stable to unstable atmospheres show good agreements with an averaged absolute difference of 0.8 dBA. Based on its good accuracy and acceptable computational efficiency, the new multi-fidelity framework is able to be used for designing and controlling wind farms. • A new multi-fidelity flow-acoustics simulation framework is developed. • A new 3D engineering wind farm flow model is developed for studying sound propagation. • A comprehensive flow-acoustic measurement is done for a 3.9/4.1 MW wind turbine. • Validations against measurement show good agreement in stable, neutral and unstable atmospheric flows. • The framework has potential for use in wind farm design and control application. [ABSTRACT FROM AUTHOR]

Large-scale hydro-photovoltaic-wind hybrid systems have the potential to improve flexibility with multiple renewable energy sources. However, few studies have investigated the optimal configuration of hybrid systems, especially on a global scale. This paper examines the regulation capacity of global reservoirs and the characteristics of wind and solar resources, thereby configuring hybrid energy systems at 3080 selected sites around the world. The operation schemes of each hybrid system are simulated, and the optimal sizes of wind and PV power plants are determined considering the risks and benefits of systems. The results show that the total potential installed capacity is 1699 GW with an electricity generation of 4348 TW-hours per year. Hydropower, PV and wind power account for 67%, 20% and 13% of the total electricity generation, respectively, and the largest potential is found in the Asia-Pacific region (40%). The fluctuation ratios of hybrid systems are 78–99% lower than those of independent systems, and integration of wind and PV power into hydropower results in an increase in the average utilization efficiency of transmission networks from 50% to 72%. It is expected that 3900 GW of additional PV and wind power will be produced by 2040, 26% of which could be provided by hybrid systems. The results indicate that large-scale hydro-PV-wind hybrid systems could make important contributions to the global transition to low-carbon energy systems. [Display omitted] • Global potential assessment of large-scale hydro-PV-wind hybrid systems is provided. • Size optimization considering the risks and benefits is performed on 3080 selected sites across the globe. • The total potential installed capacity is 1699 GW and annual yield is 4348 TW-hours worldwide. • Such hybrid systems have the potential to provide 26% of future wind and PV installed capacity additions by 2040. [ABSTRACT FROM AUTHOR]

The development of the wind power market has led various countries to begin shifting the construction of wind farms from land to offshore. Accurately predicting the short-term wind power of an offshore wind farm is significant for preventive control and scheduling. This paper proposes a novel two-stage hybrid model to predict short-term wind power. First, complete ensemble empirical mode decomposition with adaptive noise is used to preprocess the raw data and make it smoother. Second, an improved grey-box model is utilized to predict the wind power, where a multi-objective grey wolf optimizer is used to optimize the kernel-based nonlinear extension of the Arps decline model to ensure both prediction stability and accuracy. The historical wind power data for offshore wind farms in Belgium are taken as a case study, and the results indicate that the proposed model has higher prediction accuracy and stability than those of six benchmark models. Three critical issues related to this work are also considered. The following conclusions are reached: (1) Utilizing a denoising method and multi-objective optimizer effectively improves the prediction accuracy and stability of the original model. Specifically, the multi-objective optimizer improves the prediction performance; (2) The proposed model performs best at predicting wind power in autumn; (3) Because of the great variation in dimensions for the wind power time series, the mean absolute percentage error and root mean squared percentage error are not suitable as error evaluation indicators. • Two-stage hybrid model for predicting wind power of offshore wind farms. • Multi-objective algorithm optimizes the traditional kernel-based model. • Decomposition algorithm preprocesses raw data. • Rationality of the error indicators for the prediction results discussed. [ABSTRACT FROM AUTHOR]

Local governments will play an important role in the transition towards fully renewable energy systems. In order to develop new roles for themselves in climate and energy planning, they have to be creative and find new ways of acting within existing regulatory frameworks. The objective of this paper is to explore the kinds of arguments and discussions local key actors use and encounter when developing innovative activities. We study two exemplary cases using a qualitative research approach based on interviews with key informants: a municipality-owned nearshore wind farm, and a local funding scheme for energy-efficiency refurbishment in single-family houses. Our findings indicate that i) innovative projects grow out of the momentum of previous visions and experiences; ii) key actors balance 'green' and economic arguments in order to gain project approval; and iii) innovative projects can sometimes only be realized by drawing on resources from outside the municipal sphere. Even though committed key actors may be successful, it is worth considering how the grip of narrow economic motives can be loosened in order to support the local experimentation and innovation that will become more and more important in green energy transitions. • Local authorities need to experiment towards smart energy systems. • Unprecedented local energy action is risky and needs convincing arguments. • Key actors balance wide and narrow societal commitments to promote new initiatives. • Space is needed for wider societal commitments in local experiments in the future. [ABSTRACT FROM AUTHOR]