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Abstract The main aim of this study was to analyse scientific and energy production, funding, collaboration among countries and most cited papers on the renewable energies topic through bibliometric and social network study of articles included in the Web of Science database. 12,167 papers were recovered from 2007 to 2016. Wind power is the energy from which a greater number of articles have been published (n = 3,930), followed by solar energy (n = 2,570) and ocean energy (n = 1,565). The United States leads the world production of articles (n = 2,320), followed by China (n = 1,629), the United Kingdom (n = 1007), Germany (n = 730) and Spain (n = 729). China is the leading country in financed works (80%), followed by South Korea (77%), Spain (61%) and the United States (59%). An important level of international collaboration has been identified, notably popping up the triangle made by United States, China and the European Union. Highlights • Scientific study based in the analysis of the energy production, funding and collaboration among countries. • Wind power is the energy from which a greater number of articles have been published. • The United States leads the world production of articles. • China is the leading country in financed works. • The triangle made by United States, China and the European Union have the most important level of collaboration. [ABSTRACT FROM AUTHOR]

Global warming has focused attention on how the world produces the energy required to power the planet. It has driven a major need to move away from using fossil fuels for energy production toward cleaner and more sustainable methods of producing renewable energy. The development of offshore windfarms, which harness the power of the wind, is seen as a viable approach to creating renewable energy but they can be difficult to design efficiently. The complexity of their design can benefit significantly from the use of computational optimisation. The windfarm optimisation problem typically consists of two smaller optimisation problems: turbine placement and cable routing, which are generally solved separately. This paper aims to utilise selection hyper-heuristics to optimise both turbine placement and cable routing simultaneously within one optimisation problem. This paper identifies and confirms the feasibility of using selection hyper-heuristics within windfarm optimisation to consider both cabling and turbine positioning within the same single optimisation problem. Key results could not identify a conclusive advantage to combining this into one optimisation problem as opposed to considering both as two sequential optimisation problems. [ABSTRACT FROM AUTHOR]

Renewable energy is regarded as an effective method to alleviate environmental pollution and accelerate the green transition in China. This paper systematically investigates the impact of financial inclusion on renewable energy demand by using S-GMM model based on provincial data in the green finance era. This paper also analyzes the asymmetry, heterogeneity, and mediating mechanism in the nexus between financial inclusion and renewable energy demand, and highlights the following findings: (1) Improved financial inclusion can promote renewable energy development in China and present the characteristics of green finance; (2) the impact of financial inclusion on renewable energy demand is asymmetric and heterogeneous; specifically, this impact is much higher in northern China and the regions with higher renewable energy development; and (3) wind and photovoltaic power generation are effective mediators between financial inclusion and renewable energy industry, while the mediation effects of hydropower and nuclear power are not significant. New evidence in this study is presented for the achievement of green transition in China, as well as practical policy implications for improving financial inclusion and increasing renewable energy consumption. For instance, fiscal and tax incentives and green investment subsidies in the renewable energy sector need to be further improved. • This study explores the impact of financial inclusion (FI) on renewable energy demand (RED) in China. • Asymmetry, heterogeneity, and the mediating mechanism are further discussed. • Increased FI can promote RED , which presents the characteristics of green finance. • The impact of FI on RED is asymmetric and heterogeneous. • Wind and photovoltaic power are effective mediators between FI and RED. [ABSTRACT FROM AUTHOR]

Significant increases in renewable energy are needed in electricity systems in order to reduce greenhouse gas emissions, particularly in the face of increasing electricity demand arising from the electrification of heat and transport. Greater flexibility is required in the electricity system to facilitate this, using proven flexibility providers such as batteries and interconnection or new technologies yet to be proven. This paper investigates how the relationship between battery and interconnection development and carbon price can impact carbon dioxide emissions and renewable energy curtailment. The study simulates twenty-eight scenarios and sensitivities using a unit commitment and economic dispatch model of a 2030 European power system that acknowledges that coal will not be fully eliminated from the 2030 generation mix. The results show that interconnection and battery deployment can alleviate renewable energy curtailment by over 2.4 TWh for the basecase, but a high carbon price is critical to ensuring their deployment plays a complementary role in reducing emissions. The paper also examines the impact of batteries and interconnection on solar and wind energy curtailment. It reveals that battery development nearly doubled the solar energy benefits achieved by interconnection development in some cases, while wind energy benefited more from interconnection development. • A high CO 2 price is critical to IC and batteries achieving emissions reductions. • IC can facilitate production of cheaper higher emitting fuels when CO 2 price is low. • Solar energy benefits more from deployment of batteries due to similar timescales. • Diversity of wind profiles can be leveraged with increased interconnection. • Regardless of CO 2 price IC and batteries alleviate renewable energy curtailment. [ABSTRACT FROM AUTHOR]

In recent studies, the effectiveness of different so-called wake mixing strategies has been assessed in terms of wind farm power maximization. These studies show that by dynamically varying the pitch angles of a wind turbine, wake mixing can be enhanced to increase the overall power production of a wind farm. However, such strategies also increase the loads experienced by the turbine, which may disqualify such methods as viable wind farm control strategies. In this paper, an extensive analysis of the load effects of two specific wake mixing strategies, Dynamic Induction Control (DIC) and the helix approach, is presented. The damage equivalent load of critical components such as the turbine blades and tower is assessed, and the risk of fatigue damage on the blade pitch bearings is determined. This paper therefore contributes to determining the implementability of such wake mixing strategies in wind farms of the future. [ABSTRACT FROM AUTHOR]

Renewable portfolio standard is an effective administrative tool to guide the behavior of electricity producers and impact the renewable energy diffusion. However, currently the renewable energy quota is generally set to be static and the efficiency is limited. In this paper, we proposed a dynamic renewable portfolio standard policy and we aimed to analyze how it will impact the renewable energy diffusion in China. The connections between electricity producers were simulated under both the perfectly competitive market and monopoly market. And a networked evolutionary game approach is applied considering dynamic linking between the electricity producers. The results show that dynamic quota policy is effective in promoting the proportion of wind power generation from 20% to 59.17% and promoting the proportion of solar power generation from 20% to 79.25%. With the sensitivity analysis, we found that renewable energy quota and the on-grid price are the most influential parameters. The proportion will reach 76.47% if quota is 0.7 and 68.1% if on-grid price is 630 Yuan/MWh. And the influence of tradable green certificate prices and generation cost is limited in promoting renewable energy diffusion. This paper offers the Chinese government a potential implementation for the perfection of national renewable energy diffusion. [Display omitted] • Propose a novel dynamic renewable portfolio standard scheme. • Analyze the influences of different kinds of the electricity market. • Investigate parameters affecting the wind power and solar power generation. • Establish dynamic networked evolutionary game models. • Dynamic linking is considered when the connections of players change. [ABSTRACT FROM AUTHOR]

The dispatch-down of excess wind energy is a growing concern, especially for countries integrating high levels of variable renewable energy. Demand flexibility presents an opportunity to move consumers loads to periods of excess wind energy, which could provide numerous values to the system. While previous research has focused on managing wind energy curtailment (a system-wide issue), much wind energy is rejected due to constraint (a local issue) and hence can only be resolved by local load-on-demand. This paper provides a framework to assess the value of demand flexibility for managing wind energy constraint and curtailment. A methodology to determine the optimal number of subscribers to yield sufficient reduction in excess wind energy while ensuring reasonable cost savings for the subscribers is developed. Analysis shows that this optimal number of subscribers could provide a 67% reduction in constraint and a 74% reduction in curtailment. Consumers can save up to £220 per year, depending on their priority in the dispatch process. A 10-MW wind farm could earn £19,400 annually from avoided curtailments. System operators could save up to 78% on constraint payments. The paper also assesses the network impact of flexible loads and provides a methodology for calculating the heat-pump hosting capacity of the grid. • A framework for utilizing wasted wind energy for heating homes is investigated. • Currently wasted wind energy could save fuel poor households up to £220 per year. • A 10 MW wind farm could earn about £20,000 annually from avoided curtailment. • System Operators could save up to 78% on constraint payments. • Mathematical model to determine heat pump hosting capacity of a grid is developed. [ABSTRACT FROM AUTHOR]

The existing decentralized wind farm control incorporating the wind turbine wake interaction has almost all concentrated on the condition of fixed wind speed/direction without considering the variability of wind in nature. Meanwhile, most of the current wake models cannot meet the requirement for wind turbine control optimization research with the high demand of wake calculation speed and accuracy. In this paper, a novel decentralized control strategy, named the wind-interval-based (WIB) control which adopts the uniform operation among the same interval of variable wind speeds/directions, is proposed for the control optimization study of in-line two scaling wind turbines for demonstration. To prove the effectiveness of the new control mechanism, the ideal instantaneous control is introduced for the comparative study. At the same time, a wake model incorporating the two directly controllable parameters (i.e., the tip speed ratio λ and pitch angle γ) is established based on artificial neural network (ANN). The comparative results show that the total power output applying the instantaneous control and the new WIB control mechanisms are increased by 0.1%–4.1% and 0.45%–3.9% depending on the tested wind scenarios, respectively. By comparing the optimized power output with the two controls, it is found that the error between them is generally lower than 3%, while the proposed WIB control reduces the operational difficulty to a large extent facilitating its application to the real wind turbine operation in reality. In summary, this paper shows that the new proposed WIB control not only reduces the difficulties of the wind turbine control mechanism, but maintains the control effectiveness by achieving a comparable total power output with respect to the traditional instantaneous control, which is of great significance to the wind farm developer. [ABSTRACT FROM AUTHOR]

Global economy is developing rapidly and consequently energy requirements has been focused, particularly in developing countries. There are potentials of using renewable energies in the cities and Islands located in Iranian Seas. In this study, five locations in the Persian Gulf and the Gulf of Oman, combining of wind and solar energies have been considered. This paper evaluates the best locations for using different renewable energies in some important cities in the south of Iran. The paper presents a comparative study among different hybrid renewable energies (Wind and Solar) in five cities of Iran located in the coast of the Persian Gulf and the Gulf of Oman namely, Kish Island, Qeshm Island, Chabahar, Jask, and Genaveh. To evaluate the feasibility of using renewable energies in the above-mentioned locations, four types of building namely; hospital, large hotel, medium office, and strip mall have been analyzed. HOMER Software has been used to analyze and optimize different configurations. Considering the load profiles of chosen cases, the optimum solution for different configuration along with cost summary is obtained and analyzed. The simulation results presents that Kish Island owns the most load profile among other cities and PV-Diesel-Battery-Converter is the most suitable hybrid system, which possesses the optimum cost for the considered buildings. Moreover, Chabahar has mostly the most amount of carbon emission for the proposed case studies and owns the maximum lowest cost system among all considered cities except in the case of large hotel where Jask has the maximum initial capital cost. [ABSTRACT FROM AUTHOR]

The promotion of wind power is an important strategic measure to promote energy transformation and achieve carbon neutralization in China. Driven by a series of industrial policies, China's wind power industry is still faced with the dilemma of "large quantity yet poor quality". Therefore, the paper considers as research object the wind power industry policies issued by the government of China between 2008 and 2019, and evaluates their impact on the innovation quality of enterprises from the perspective of financing modes. The results show that different categories of industry innovation policies and their combination have an important positive influence on enterprise innovation quality. There are differences in the regulatory effects of different financing modes on wind power industrial policy and enterprise innovation quality: internal financing inhibits the effect of industrial policy on enterprise innovation quality, while external financing promotes the impact of industrial policy on enterprise innovation quality. Further analysis shows that different categories of innovation policies and their combinations have impacts on the innovation quality of both private and state-owned enterprises, and in many regions. Finally, based on the research results, there are recommendations for policy managers and micro enterprises to stimulate the innovation quality of wind power plants. • The paper evaluates wind power industry policies and their combination impact on the innovation quality of enterprises from the perspective of financing modes. • Different categories of wind power industry policies and their combinations have an important positive influence on enterprise innovation quality. • There are conspicuous differences in the regulatory effects of different financing modes on industry policy and enterprise innovation quality. • Different types of industry policies and their combinations have impacts on the innovation quality of both private and state-owned enterprises, and in many regions. • There are differences in the regulatory effects of different financing modes on industry policy and innovation quality of both private and state-owned enterprises, and in many regions. [ABSTRACT FROM AUTHOR]

Wind turbine power curve cleaning, by way of removing curtailment, stoppage, and other anomalies, is an essential step in making raw data useable for further analysis, such as determining turbine performance, site characteristics, or improving forecasting models. Typically, data comes as SCADA (Supervisory Control and Data Acquisition) data, so contains not only environmental and turbine performance data but also the control action imposed on the turbine by the operator. Many different anomaly detection (AD) methods have been proposed to clean power curves; however, few papers have explored filtering explicit and obvious anomalies from the SCADA prior to running AD. This paper actively explores this filtering impact by comparing the performances of 4 different AD methods with/without filtering. These are: iForest, Local Outlier Factor, Gaussian Mixture Models, and k-Nearest Neighbours. Each approach is evaluated in terms of prediction error, data removal rates, and ability to maintain the underlying wind statistical characteristics. The results show the effectiveness of filtering with every technique showing improvement compared to its unfiltered counterpart. Furthermore, Gaussian Mixture Models are shown to provide favourable accuracy whilst maintaining wind variability, however, with the wide range of performances of methods, a user's choice may be different depending on their needs. [ABSTRACT FROM AUTHOR]

In this paper, the day-ahead optimal dispatching model of power system that is combined by wind-photovoltaic-hydropower-thermal-pumped storage is established. Firstly, according to the characteristics of the short-term hydropower system dispatching problem, a new mathematical model of the cascade hydropower group system with pumped storage power stations is proposed. The coordinated optimized dispatching model of the hydropower group system formed by the cascading of pumped storage power plants and conventional hydropower in the power system is studied. Secondly, the opportunity constraint programming model of forecast error reserve is used to deal with the output uncertainty of wind power and photovoltaic. The randomness and intermittency of renewable energy on the stability of the power system are overcame by the combination of wind-photovoltaic-pumped storage. Thirdly, the model for the joint optimal dispatch of short-term wind, photovoltaic, hydropower and thermal power systems with pumped storage is developed with system economics as the goal. Fourthly, the operation volatility coefficient of thermal power units is proposed to study the impact of renewable energy on the operation of thermal power. Finally, an example system is used to verify the correctness of the proposed dispatching optimal scheduling model, and the results prove that the daily dispatch optimization model proposed in this paper can increase the economic efficiency of the power system by 5%, reduce the start-stop times of thermal power units by 36.55%, and reduce the fluctuation coefficient of the unit by 2.8. • A new mathematical model of the cascade hydropower group system is proposed. • The integral flow constraint of lag time in the daily scheduling is considered. • The operation volatility coefficient of thermal power units is proposed. [ABSTRACT FROM AUTHOR]

To fully extract the potential of offshore wind power, the wind farms are built further from mainland and the capacity is growing larger. Low Frequency AC (LFAC) transmission System is a promising solution to the integration of large offshore wind farms in long distance. This paper conducts a comparative economic analysis of LFAC. Compared to 50 Hz High Voltage AC (HVAC) transmission, LFAC can reduce charging current in AC cables to enhance the transmission ability in long distance. Compared to High Voltage DC (HVDC) transmission, LFAC eliminates the large expense of offshore converter station. In addition, the topologies of AC/AC converter in LFAC also influences the economic performance significantly. By designing typical AC/AC converters based on modular multilevel technique in a unified way, this paper performs a full-scale economic comparison in different voltage level, active power and reactive power cases. Furthermore, the economic comparison based on capital cost of HVAC, HVDC and LFAC is comprehensively studied considering different transmission capacity and the AC/AC converter topologies. The economic range of HVAC, LFAC and HVDC application is obtained, and results show that LFAC is suitable for medium distance circumstances (around 80–200 km). The economic range of LFAC expands when power rating increases. • Economic range of HVAC, HVDC and LFAC is analyzed according to their capital cost. • Converter topologies and transmission capacity is considered in the cost of LFAC. • Comprehensive comparison of four AC/AC converter topologies for LFAC is conducted. • A unified method is introduced to design four AC/AC converter topologies. [ABSTRACT FROM AUTHOR]

This paper considers off-grid microgrids (MGs) from the Philippine archipelago and analyses their energy generation in differents aspects. Seven different energy clusters are used, representing realistic configurations and renewable energy shares. A Robust Model Predictive Control (MPC) framework is used for the energy management and coordination task of these island MGs. The MPC is based on a min./max. optimization procedure, which takes into account the whole uncertainty set. The reliability of the MG operations are analysed with respect to the different clusters; this evaluation is conducted using μ -analysis, performed with respect to the baseline model and the uncertainty set. The demand-side compliance of the MG is also investigated, with respect to stochastic behaviours of the demands and of the renewable sources (wind and solar). Numerical simulation results are presented in order to demonstrate that reliable power outlets are produced despite variation in renewables and of the demands. This paper offers a thorough analysis of simple energy system coordinated via MPC, showing how this method can indeed be used for renewable MG management, offering robustness and ensuring reliability. • A generalized model is presented for Off-Grid Microgrids with multiple carriers. • The Philippine Off-grid islands are considered, with diesel, solar, and wind energy. • A Robust Model Predictive Control scheme is proposed to manage these Microgrids. • Demand compliance and operation reliability are assessed through robustness analysis. • Numerical simulation illustrate reliability under multiple scenarios. [ABSTRACT FROM AUTHOR]

Accurate prediction of renewable energy generation acts as a critical role which not only provides short-term power generation in the future, but also facilitates scheduling and pre-configuration of energy storage systems. More importantly, the power generation prediction is of great significance to the demand response management (DRM) of renewable energy to participate in the electricity spot market. Therefore, DRM helps improve the stability and reliability of renewable energy systems. This paper presents a novel prediction-smoothing based methodology to reduce and eliminate the influence caused by the uncertainty of renewable energy output. Firstly, the Whale Optimization Algorithm (WOA) is combined with Long Short-Term Memory (LSTM) to predict short-term wind power output. Then, the Hampel-Butterworth-SG filtering strategy with outlier regression and specific risk band elimination is introduced. After that, according to the short-term output forecast results, the scheduling and pre-configuration scheme of peak-filling type energy storage is developed. Finally, based on the predicted renewable energy output and electricity price, the dynamic changes of demand response (DR) are calculated based on Logistics function, optimistic response and pessimistic response factors. Through extensive case studies, it is demonstrated that the assessment deviations in different scenarios is less than 5%, which are 0.64% for Scenario 1 and 3.64% for Scenario 2, and no additional penalty is required at this time. In addition, the proposed Demand Response Load Adjustment Rate (DRLAR) help compare the differences between predicted and actual DR, which are DRLAR = 0.03% in Scenario 1 and DRLAR = 0.01% in Scenario 2. Users are able to adjust the DR dynamically according to the electricity price to realize the optimal scheduling of their renewable resources. The proposed methodology creates a special connection between DRM and renewable energy prediction, which provides a reliable reference for future work. [ABSTRACT FROM AUTHOR]

The world's key renewable power markets are generally challenged by wind and solar power curtailment. Research on the influencing factors of curtailment improvement can provide a reference for the further penetration of renewable energy (RE) into the power system. Based on the logarithmic mean Divisia index (LMDI) method, this paper analyzes the factors influencing the wind and solar power consumption rate in China from 2015 to 2018, and four typical provinces are discussed in detail. By decomposing the consumption rate into six factors (transmission structure, generation proportion, power self-sufficiency, resource development, resource utilization, and power consumption), and focusing on the transmission structure factors, this paper further clarifies the contributions of local use, ultra-high-voltage (UHV) transmission, and conventional transmission to the improvement of curtailment. The results reveal the following. (1) At the national level, the power mixing effect and the resource development effect have played major roles in the change of the consumption rate, but the influences of these two effects have been decreasing. (2) The UHV power lines provide a robust resource for the inter-provincial deployment of renewable power (such as the RE power transfer in Inner Mongolia and Gansu), but the role of UHV transmission has not yet been fully utilized. (3) The impact of the electricity consumption effect on renewable power consumption is increasing year by year, but the local consumption capacity remains insufficient (both at the national level and in the four typical provinces analyzed), so it is necessary to further increase the share of renewable electricity in UHV power transmission and the local consumption capacity. (4) The expanding development of wind and solar power may be the reason for the curtailment in Shaanxi, and a reasonable renewable power development plan must be further formulated. [ABSTRACT FROM AUTHOR]

Accurate and stable ultra-short-term wind speed prediction is very valuable for the dispatch planning and operational security for the wind power system, however it's very difficult to obtain satisfactory forecasting results in the wind power system due to the complexity and non-linearity of the wind speed series. In this paper, a novel hybrid model combined multi-objective optimization, data preprocessing technology and Elman neural network was proposed to forecast ultra-short-term wind speed, including 30min and 10min wind speed. To obtain better forecasting results with high accuracy and strong stability, multi-objective optimization target was utilized to balance the variance and bias of the forecasted series. Complementary ensemble empirical mode decomposition was used to remove the noise in the original data and several IMFs were obtained. This paper proposed a new optimization algorithm combined adaptive wind driven optimization and modified simulated annealing to optimize initial weights and thresholds of ENN. Wind speed data from two observation sites in China was involved in this paper to verify the forecasting performance of the proposed model. The simulation results illustrate that the proposed hybrid model has the best forecasting results at all step among all related models. • A new hybrid is proposed to forecast wind speed in this paper. • Data preprocessing technology is introduced to improve the forecasting performance. • Multi-objective optimization target is utilized to obtain stability and accuracy forecasting results. • The results are validated well in China. [ABSTRACT FROM AUTHOR]

Wind prediction technology has been the focus on the national research with the basis of the power system planning, a reference for power dispatch, and the optimal power flow distribution. Prediction technology is moving into the direction of controlling refinement with the development of the information technology, the artificial intelligence technology, and the improvement of edge computing devices. The wind farms can use real-time wind power prediction to improve their overall efficiency through advanced planning of the wind turbine adjustment and the pre-setting of the yaw, pitch, and generation excitation control systems. This paper proposes an innovative autoregressive dynamic adaptive (ARDA) model based on the improvement of the autoregressive (AR) model. The fixed parameter estimation method of the AR model is improved in the proposed model to a dynamically adaptive stepwise parameter estimation method. Meanwhile, the coefficients of the model are updated adaptively based on the characteristics of wind power data, which improves the accuracy of the proposed model. The prediction accuracy of the proposed model is further improved by the residual function. It was observed that the model adapts well to wind power data with different degrees of volatility. The ARDA model and two other models were tested by using stationary and fluctuating wind power data (unit: seconds), and the wind power prediction results at different forecasting step lengths were compared. It was observed that the ARDA model is more accurate, with faster calculation rate, and better dynamic adaptability to data fluctuations than the ARIMA and LSTM models. This paper proposes an important method for real-time power prediction that can be employed for the advanced control and improved the power generation of wind farms. • We develop a novel model called the ARDA model based on AR model. • The proposed model is self-learning and dynamically adaptive to data fluctuations. • Parameter estimation highlights neighbouring data and forgets distant data. • The residual function corrects the prediction result based on data fluctuations. [ABSTRACT FROM AUTHOR]

With over 200 wind farm projects underway in 33 states in America, now more than ever, innovation and precision are necessary in wind farm design. The Wind Farm Layout Optimization Problem (WFLOP) calls for optimally positioning turbines within a wind farm so that a particular objective function is optimized in the presence of wake effect. To make the WFLOP tractable, many solution methods begin by modeling the wind farm as a n x n square grid where the centers of each of the n 2 cells serve as potential locations for wind turbines. After making this modeling assumption, there are 2 n 2 potential layouts to consider, and so heuristic algorithms are often employed in order to search the solution space and generate a near-optimal layout. In this paper, we propose a local, continuous refinement technique that seeks to improve the layouts generated by these heuristic algorithms. In particular, we consider the objective function Levelized Cost of Energy (LCOE) and capture wake using the simple, yet effective, Jensen Model. Given the anemometer data for two potential wind farm sites, we begin by generating initial layouts using a specific heuristic algorithm (the Distributed Genetic Algorithm) and then refine these layouts using our proposed continuous, deterministic refinement scheme. We compare the performance of this deterministic refinement technique to another deterministic refinement technique in the space: a Heuristic Hill-climbing approach. Results suggest that our refinement technique outperforms the compared refinement technique by generating layouts that increase overall energy production, consequently reducing the LCOE. To our knowledge, this is the first paper to compare refinement techniques in wind farm layout optimization , and in doing so, we employ a simulation framework that examines the energy production of the generated wind farm layouts for 10 min intervals. • Refinement algorithm. • Derivative-free optimization. • Wind farm layout. • Simulation framework. [ABSTRACT FROM AUTHOR]

Ethiopia has one of the most ambitious energy expansion programmes but to achieve these goals, policies must be aimed at localizing the development and manufacturing of energy technologies. However, the country is dependent on importing solar and wind energy technologies. The aim of this paper is therefore to explore the challenges of locally developing and manufacturing solar and wind energy technologies, the necessary assets and policy instruments that can facilitate localization of energy technologies. This was achieved through survey questionnaire and interview from different stakeholders. The results show that the region has very limited capability of developing and manufacturing of solar and wind energy technologies because of lack of investment capital, under developed solar and wind supply chain, lack of skilled workforce and others. To overcome these challenges, financial incentives for research and development, solar and wind based power generation, manufacturing are considered as the top policy instruments with other policies included in this paper. Therefore, a proper implementation of the internationally proven policy instruments recommended in this paper are urgently needed in the region in order to achieve one of the sustainable development goals that stipulate facilitation of the implementation of modern, affordable and sustainable energy to the society. • Ethiopia has very limited capability of developing solar and wind energy technologies. • The results show that Ethiopia has low level of local industrial assets. • Lack of investment capital is the top challenge for the technologies development. • Different financial incentives are key policy instruments for local capacity development. • Effective implementation of the recommended policies can have significant co-benefits. [ABSTRACT FROM AUTHOR]

In the pursuit of sustainable energy solutions, the integration of renewable energy sources and hydrogen technologies has emerged as a promising avenue. This paper introduces the Integrated Renewable Energy-Driven Hydrogen System as a holistic approach to achieve energy independence and self-sufficiency. Seamlessly integrating renewable energy sources, hydrogen production, storage, and utilization, this system enables diverse applications across various sectors. By harnessing solar and/or wind energy, the Integrated Renewable Energy-Driven Hydrogen System optimizes energy generation, distribution, and storage. Employing a systematic methodology, the paper thoroughly examines the advantages of this integrated system over other alternatives, emphasizing its zero greenhouse gas emissions, versatility, energy resilience, and potential for large-scale hydrogen production. Thus, the proposed system sets our study apart, offering a distinct and efficient alternative compared to conventional approaches. Recent advancements and challenges in hydrogen energy are also discussed, highlighting increasing public awareness and technological progress. Findings reveal a payback period ranging from 2.8 to 6.7 years, depending on the renewable energy configuration, emphasizing the economic attractiveness and potential return on investment. This research significantly contributes to the ongoing discourse on renewable energy integration and underscores the viability of the Integrated Renewable Energy-Driven Hydrogen System as a transformative solution for achieving energy independence. The employed model is innovative and transferable to other contexts. [Display omitted] • Integrating renewable energy and hydrogen for energy independence. • Advantages of Integrated Renewable Energy-Driven Hydrogen System: Zero emissions, resilient energy. • Economic viability: Payback period of 2.8–6.7 years. • Technological progress in hydrogen energy. • A novel and transferable system dynamics model for sustainable solutions is proposed. [ABSTRACT FROM AUTHOR]

In the pursuit of sustainable energy solutions, the integration of renewable energy sources and hydrogen technologies has emerged as a promising avenue. This paper introduces the Integrated Renewable Energy-Driven Hydrogen System as a holistic approach to achieve energy independence and self-sufficiency. Seamlessly integrating renewable energy sources, hydrogen production, storage, and utilization, this system enables diverse applications across various sectors. By harnessing solar and/or wind energy, the Integrated Renewable Energy-Driven Hydrogen System optimizes energy generation, distribution, and storage. Employing a systematic methodology, the paper thoroughly examines the advantages of this integrated system over other alternatives, emphasizing its zero greenhouse gas emissions, versatility, energy resilience, and potential for large-scale hydrogen production. Thus, the proposed system sets our study apart, offering a distinct and efficient alternative compared to conventional approaches. Recent advancements and challenges in hydrogen energy are also discussed, highlighting increasing public awareness and technological progress. Findings reveal a payback period ranging from 2.8 to 6.7 years, depending on the renewable energy configuration, emphasizing the economic attractiveness and potential return on investment. This research significantly contributes to the ongoing discourse on renewable energy integration and underscores the viability of the Integrated Renewable Energy-Driven Hydrogen System as a transformative solution for achieving energy independence. The employed model is innovative and transferable to other contexts. [Display omitted] • Integrating renewable energy and hydrogen for energy independence. • Advantages of Integrated Renewable Energy-Driven Hydrogen System: Zero emissions, resilient energy. • Economic viability: Payback period of 2.8–6.7 years. • Technological progress in hydrogen energy. • A novel and transferable system dynamics model for sustainable solutions is proposed. [ABSTRACT FROM AUTHOR]

In order to improve the reliability of wind turbine grid connection and reduce the cost of wind power generation, this paper studies a counter-rotating dual rotor wind turbine (DRWT) which consists of decoupled driven-train subsystems, a triple-terminal converter and a dual rotor generator. For this new-type wind energy conversion system, its modeling and control strategy need to be studied. Firstly, mechanism model of the DRWT with a new structure is constructed. By investigating the effects of pitch angles and rotor speeds on output power, respectively, the maximum power output condition of the DRWT is obtained. Considering the overall energy conversion efficiency and mechanical load constraints, the optimal regime of the DRWT is established. Based on the optimal torque algorithm and cross-coupling control strategy, this paper presents a novel cross-coupling control strategy for the DRWT to track maximum power. The proposed strategy can regulate the rotor speed of the front and rear rotors, enabling DRWT to operate on the optimal speed combination curve. Finally, the hardware-in-the-loop experiment is built to test the performance of the proposed strategy under various wind conditions. The simulation results verify that the proposed strategy can improve power generation, and analysis shows that the proposed strategy fundamentally alters the energy capture ratio between the front and rear wind turbines to optimize total output power. • Influence of pitch angles and rotor speeds on power of dual rotor wind turbine. • A cross-coupling control strategy for dual rotor wind turbine system is proposed. • The shaft power of the front rotor should be reduced for the maximum total power. [ABSTRACT FROM AUTHOR]

Wind power integration and carbon reduction cooperation, as low-carbon approaches, are introduced into the current power dispatching process under carbon reduction requirements. In order to measure the environmental performance of power dispatch influenced by the above approaches, this paper firstly presents a low-carbon power dispatch model for wind power integrated system imported with carbon reduction cooperation. Next, the methods related to wind speed prediction, optimization algorithm and efficiency evaluation are fully discussed. Finally, empirical analysis shows that: (1) the overall effect of ARIMA-NARX model is superior to that of ARIMA model for wind speed prediction, whose MSE is reduced by 79.73%, (2) compared with GA and PSO, GA-PSO has better effect of optimization, which can effectively jump out of the local optimal value, and its convergence speed keeps improving, (3) the two low-carbon approaches can facilitate the achievement of system carbon reduction targets with good environmental performance of power dispatch, and (4) low-carbon power dispatching strategies taking full account of these two approaches are developed, which proves to be of reliability, economy and environment. • A power dispatching model integrated with two low-carbon approaches is proposed. • ARIMA-NARX hybrid model with good effect of wind speed prediction is constructed. • GA-PSO with better optimization effect is designed. • Environmental performance of low-carbon power dispatch is measured. • Power dispatching strategies developed are of reliability, economy and environment. [ABSTRACT FROM AUTHOR]

Wind power ramp is a destructive event accompanied with a sharp change of wind power. Offshore wind power is expected to receive more attention for it can harvest consistent and strong winds. In this paper, a ramp detection framework based on the swinging door algorithm (SDA) and the mergence of correct ramps is proposed. Meanwhile, a trend-based prediction method (T-Method) is proposed to predict offshore wind power ramps. The ramp detection framework is utilized to identify wind power ramp events (WPREs) and label original data according to the detection results. After that, the labeled data is selected as the input of recurrent neural network to produce wind power prediction results. Finally, the prediction results are detected by the ramp detection framework to produce WPREs prediction results. Four recurrent neural network models and two traditional methods are applied to two offshore wind power datasets for corroborating the effectiveness of our method. Comparative experiments show that our proposed method performs excellent in all evaluation indicators. The proposed WPREs prediction method improves the performance of WPREs prediction under the premise of low false alarm rate and missing alarm rate. It provides early warning for power system operators to reduce the harm of WPREs. • This is a novel offshore wind power ramp events (WPREs) forecasting method. • The method can improve the forecasting accuracy of offshore WPREs. • Results are evaluated by the metrics of classification. [ABSTRACT FROM AUTHOR]

This paper investigates conversion of a Nordic oil-heated townhouse into carbon-neutral by different energy efficiency (EE) improvements and an off-grid system including solar photovoltaics (PV), wind power, and battery and hydrogen energy storage systems (BESS and HESS). A heat-pump-based heating system including waste heat recovery (WHR) from the HESS and an off-grid electrical system are dimensioned for the building by applying models developed in MATLAB and Microsoft Excel to study the life cycle costs (LCC). The work uses a measured electrical load profile, and the heat generation of the new heating system and the power generation are simulated by commercial software. It is shown that the EE improvements and WHR from the HESS have a positive effect on the dimensioning of the off-grid system, and the LCC can be reduced by up to €2 million. With the EE improvements and WHR, the component dimensioning can be reduced by 22%–41% and 13%–51% on average, respectively. WHR can cover up to 57% of the building's annual heat demand, and full-power dimensioning of the heat pump is not reasonable when WHR is applied. Wind power was found to be very relevant in the Nordic conditions, reducing the LCC by 32%. [Display omitted] • Energy efficiency improvements notably reduce dimensioning of system components. • Waste heat from FC and electrolyzer covers up to 57% of the annual heat demand. • A full-power-dimensioned GSHP does not reduce the fuel cell dimensioning. • Seasonal storage on a small scale makes the system not economically viable. • Small-scale wind power is expensive, but it significantly reduces the system LCC. [ABSTRACT FROM AUTHOR]

Wind tunnel experiments provide worthwhile insights for designing efficient micro wind energy harvesters and large-scale wind turbines. As wind tunnel tests with large-scale wind turbines are expensive and not always feasible, most experiments are conducted with geometrically scaled rotors. Furthermore, micro-scale runners used for wind energy harvesting face the issue of lower efficiency than large turbines. A better understanding of Reynolds number effects induced by the downsizing of a turbine would help to design more efficient wind energy harvesters and more faithfully scaled experiments. This paper reports on Reynolds number effects on the performance and wake of micro-scale wind turbines. Wind turbines' power and torque coefficients are measured in a wind tunnel for a wide range of Reynolds numbers. The wake axial velocity fields and the vortex core locations are collected for three Reynolds numbers using phase-averaged and phase-locked particle image velocimetry technique. The results emphasize that an increase in the Reynolds number leads to larger power coefficients, torque coefficients, and optimum tip-speed ratios. Higher Reynolds numbers induce wider wake expansion and a larger axial velocity defect. This quantitative analysis will contribute to a clearer understanding of the scaling effects and help to design more efficient wind energy harvesters. [ABSTRACT FROM AUTHOR]

Obtaining reliable and high-quality wind speed probability forecast results is of great significance to wind energy utilization and power system management. In this paper, multi-network deep ensemble method, which combines the intelligent optimization algorithm and deep ensemble method, is proposed to deal with the probabilistic prediction problems. This method can effectively integrate a variety of different deep learning neural networks and provide reliable uncertainty estimates for prediction. Furthermore, spatiotemporal multi-network deep ensemble model, which employs multi-network deep ensemble method, is proposed to deal with the probabilistic spatiotemporal wind speed forecasting problems. In the model, three advanced convolutional recurrent neural networks are integrated to capture spatiotemporal information from the underlying meteorological variables. Intelligent optimization algorithm is used to assign weights to each network in the ensemble. In addition, an uncertainty quantification method, which quantify the uncertainty by adjusting the network structure and optimize the uncertainty by utilizing the truncated negative log-likelihood scoring rule, is introduced to provide reliable probability forecasts. The proposed model is applied to a real-world case in the United States. The test results demonstrate that spatiotemporal multi-network deep ensemble model can not only provide high-precision point prediction results, but also provide suitable interval predictions and reliable probability prediction results. Moreover, the impact of input features on model prediction results is also evaluated. [ABSTRACT FROM AUTHOR]

Accurate and credible operation data sets of wind and solar power stations are the basis of many research works. However, such data sets often contain abnormal data due to failure, maintenance, energy curtailment, etc. The existing identification methods fail to consider the operating characteristics of power stations and the forms of abnormal data, resulting in low identification ability. Therefore, an adaptive identification method of abnormal data (AIMAD) in the wind and solar power stations is proposed in this paper, including the bidirectional one-sided quartile method and double DBSCAN method to deal with unevenly distributed abnormal data; the improved K-means clustering method based on the distance between the cluster center and benchmark power curve to process the abnormal data that are densely accumulated and closely connected with normal data in the power scatter diagram. The proposed method can adjust adaptively according to the forms of abnormal data to realize accurate identification and has strong robustness for power stations. The operation data of 30 wind farms and 8 solar plants in China are taken as examples to verify the effectiveness and superiority of the proposed method. • BOQ and d-DBSCAN are proposed to deal with unevenly distributed abnormal data. • Improved K-means method is proposed for identifying stacked abnormal data. • AIMAD is proposed to realize accurate identification of abnormal data. • Operation data in 38 stations are used to verify the effectiveness of AIMAD. [ABSTRACT FROM AUTHOR]

To cooperate with new energy sources such as wind and solar, pumped storage units are required to frequently change conditions and operate under part loads, leading to the problem of violent pressure fluctuation and unit vibration. To meet this challenge, the next generation of pump-turbine runners will therefore have to be designed differently. The goal of this article is to construct a framework for broadening the operating range of pump-turbine to deep-part loads. In this study, a multi-objective optimization strategy for the runner with wider output range is suggested, wherein the blade control parameters, specifically the blade loading and blade lean angle, are optimized to enhance the efficiency, anti-cavitation, and stability of the pump-turbine runner. A runner with turbine output range of 40%–100% is optimized for a specific power station. Next, the impact of runner control parameters on runner performance is evaluated, and the runner geometric characteristics for different optimizing priorities such as the pump peak efficiency, cavitation performance, turbine rated efficiency, and turbine partial load efficiency, are concluded. The mutual restriction mechanism between the pump peak efficiency and cavitation performance of pump mode, as well as the turbine-rated efficiency and partial load efficiency of turbine mode, is revealed. The main contributions of this paper include putting forward an effective optimization design process for wide output pump-turbine, and pointing out the relationship between runner geometric characteristics and performance. The results indicate that the present approach can significantly improve the efficiency and stability of runners operating in deep-part loads. The blade loading pattern with hub section aft-loaded and shroud section fore-loaded, combined with negative blade lean angle, is recommended for such runners. Blades with a smaller wrap angle and lower arch are conducive to improving the pump peak efficiency and turbine-rated efficiency, while the opposite geometrical features are advantageous for improving the runner cavitation performance and efficiency performance under part load conditions. These findings can serve as an important guide for the development of runners with a wider output range. [ABSTRACT FROM AUTHOR]

Renewable energy attracts increasing attention from both industry and academia under the context of carbon neutrality. For wind and solar energy, the strong dependence on natural processes results in the imbalance between energy production and real demands. Energy storage technologies, e.g., Compressed Air Energy Storage (CAES), are promising solutions to increase the renewable energy penetration. However, the CAES system is a multi-component structure with multiple energy forms involved in the process subject to high temperature and high-pressure working conditions. The CAES system is a complex process flowsheet consisting of charging and discharging process. The process should be optimized to achieve the best thermodynamic and economic performance. Under the optimal design conditions, it might lead to severe consequences once a failure occurs, e.g., harm to humans, the environment, and assets. Limited attention and scarce available information have been paid to the CAES system risk management yet. Hence, this paper applies the System-Theoretic Process Analysis (STPA), which is a top-down method based on system theory, to identify the CAES system safety hazards. The results are expected to provide a preliminary guideline for practitioners regarding the safety and reliability of the CAES system. As a result, a more reliable CAES system can contribute to a more flexible energy system with more efficient and economic utilization of fluctuating renewable energy. [ABSTRACT FROM AUTHOR]

Climate change and scarcity of energy sources have put developing countries in a situation that demands emergency future planning for matured renewable technologies in order to continue sustainable progress and provide clean and cheap energy to masses. This paper provides a comprehensive structure of site suitability for utility scale solar PV and wind energy projects considering criteria critical to each technology while only using criteria with absolute necessity for varying scenarios incorporating Multi Criteria Decision Analysis (MCDA) along with Geographic Information System (GIS). A novel approach is also considered for far-off areas potentially deprived of electricity access for small scale solar PV implementation. A total of eight sub-criteria were considered in site selection process regarding wind and solar energy, analyzed using Analytical Hierarchy Process (AHP) in order to rank criteria depending on their importance to selection process. Resulting methodology was applied to case study of Pakistan, a developing country with growing climate change emergency and ongoing power crisis. End results showed 25.28% area suitable for utility scale solar PV farms, 5.93% area suitable for utility scale wind farms and 40.02% suitable area for small scale solar PV farms. [ABSTRACT FROM AUTHOR]

This paper investigates a novel control strategy that reduces the fatigue load of wind turbines during active power control. Based on the analysis of the reference power tracking of wind turbines, the proposed new control strategy considers simultaneous control of rotor speed and pitch angle. For this goal, a two-degree-of-freedom wind turbine active power control model based on the small-signal method is established, and the linearized fatigue load calculation is also completed in the model. For the effective control under this two-degree-of-freedom control framework, a model predictive control method suitable for multi-objective optimization is applied. Simultaneously, with the help of simulation tools, adaptive weight selection for multi-objective optimization is discussed and implemented. Simulation results show that the two-degree-of-freedom control system proposed in this paper can accurately implement reference power tracking and significantly reduce the fatigue load of wind turbines. The proposed method is valid not only on a single wind turbine but also in the wind farm's active power dispatching process. • Two-degree-of-freedom wind turbine control model is studied. • Fatigue load of wind turbines is modeled linearly. • Optimized multi-input multi-output MPC optimization control is adopted. • Fatigue load of wind turbine operation is significantly reduced. [ABSTRACT FROM AUTHOR]

During the last 10 years Brazil has accelerated the incorporation of new wind capacity into its power matrix. This situation has generated the need for increased transport capacity. However, there have been delays in the planning process for new transmission line construction, thus inducing trapped electricity in the system; in other words, electricity that cannot be dispatched to the interconnected system. Using system dynamics modelling, this paper assesses energy policy alternatives for improving the synchronisation between the construction of both power generation and transmission capacities. Simulation results show that policy synchronisation within the wind industry may provide important opportunities regarding security of supply. This paper concludes that in Brazil's case it is possible to attain a balance between wind power expansion and interconnection capacity through synchronised and systemic interventions. • Power systems require a sustainable balance between supply and demand. • Wind power industry could be affected by energy policy fragmentation. • This article assesses alternative policy options to contribute to the existing literature on grid congestion. • New insights on the synchronisation of energy policies for the wind industry expansion is offered. [ABSTRACT FROM AUTHOR]

This paper presents analytic equations modelling the main physics and cost of a generic Airborne Wind Energy System flying crosswind. The power equation of a system that can handle ground and on-board generation is presented, under the assumption of steady-state flight. A structural and a take-off model are used to derive the key equations for the flying mass estimation of a hard-wing kite. A modification of the power equation is carried out to show how to include gravitation forces in the power estimation. Finally, an economic model is introduced. The model presented in this paper is intended to describe the key behavior of the system based on basic principles. It is suitable to be coupled with system design tools. • Unified physical modelling of a crosswind Airborne Wind Energy System with ground and on board power generation. • Power losses due to gravity. • Mass model, on board turbines design, take off model. • Cost model of crosswind Airborne Wind Energy Systems. [ABSTRACT FROM AUTHOR]

The growing energy demand places its focus on renewable energy resources as a sustainable energy resource. Wind Power is one of the important sources of renewable energy. However, it exhibits challenges in the form of stability and control when integrating with the conventional grid. Though the connection of wind power plants does not require any major redesign, it is, however, necessary to ensure that issues are taken care of through control and compensative devices, to enable the grid to recover from grid faults and system disturbances. One such proposal is discussed here. This paper analyses the design and implementation of Proportional-Integral (PI) controlled Static Synchronous Compensator (STATCOM) controllers and is compared with Fuzzy Logic Controlled(FLC) STATCOM controllers. These controllers are primarily used to stabilize the voltage in wind power plants by preventing tripping of Wind Generators (WG) in the event of a grid side disturbance or fault and can recover from the fault by injecting reactive power in the grid. This paper applies STATCOM for restoring the voltage levels at the terminals of the wind power plants and simulated using MATLAB Simulink. It is observed from the simulation results, that STATCOM with fuzzy logic controls tends to reduce the voltage fluctuation and ensure the stability of the system as compared to STATCOM with PI. • STATCOM with PI controller improves voltage regulation. • Enhanced performance with the usage of STATCOM with Fuzzy Logic controller. • Enhanced gain stability in the event of network disturbances. • Improves transient performance. [ABSTRACT FROM AUTHOR]

This paper assesses the effects of inserting solar and wind power in a hydro-dominated electricity system that has been at risk of outages in the short term. The extent to which renewables could contribute to overcoming potential critical conditions has not received much attention given the intermittency of the sources. In this context, this paper proposes a robust optimization model that, under different scenarios, estimates the implications of building new power plants based on renewables and traditional technologies. It considers costs, average construction times, and blackout events. Hidroituango, a mega hydroelectric project – prone to non-completion due to failures during its construction phase – is considered here because it threatens Colombia's future energy security of supply. Model results indicate that investing in hydro or thermal power units could be risky and twice as expensive as investing in renewables. Findings show that solar and wind technologies, in a ratio of 2.5 to 1, make part of the optimal and secure expansion plan for the Colombian power system. Besides reducing the CO 2 emissions of the electricity sector, in the scenarios analyzed, these renewables show fewer blackouts (between 15 and 40% less) than the traditional technologies and more water savings in reservoirs. • Solar and wind power (RES) are the best options to face the Hidroituango crisis. • RES should replace Hidroituango's missing capacity rather than hydro, coal or gas. • RES scenarios could be twice as cheaper as the business as usual scenarios. • RES would allow more water savings in reservoirs than traditional technologies. • RES scenarios show lower blackout probabilities than traditional technologies. [ABSTRACT FROM AUTHOR]

Wind energy is an important renewable clean energy resource. However, the stochastic and volatile nature of wind power brings significant challenges to the power system's reliable and secure operation. Accurate and reliable wind power prediction is critical for the integration of wind power into the grid. The existing wind power forecasting (WPF) methods lack an assessment of the reliability of the predicted results, which may result in a financial penalty for the wind energy producers. An accurate prediction with reliability measurement is urgently needed to encounter the intricate nature of the problem. In this paper, a Bayesian framework-based bidirectional gated logic unit (BiGRU) method was proposed for ultra-short-term wind power forecasting. First, an encoder-decoder (ED) architecture was combined with a BiGRU time series modeling and feature–temporal attention (FT-Attention) to improve the accuracy of wind power prediction. Then, two uncertainty losses were applied to improve the model's performance further. The proposed method obtains the uncertainty of forecast results, which effectively eliminates the untrusted results. Our proposed method demonstrated promising results for ultra-short-term wind power forecasting due to its competitive performance compared with traditional forecasting methods. • A novel ultra-short-term WPF method is proposed with a deep Bayesian model. • Quantifying the uncertainty of WPF is a unique ability of our proposed framework. • Aleatoric uncertainty loss and epistemic uncertainty loss are introduced. • Temporal attention and feature attention are designed to enhance the accuracy of WPF. [Display omitted] [ABSTRACT FROM AUTHOR]

In the growing wind energy sector, as in other high investment sectors, the need to make assets profitable has put the spotlight on maintenance. Efficient solutions which leverage from condition or performance based maintenance policies have been proposed during the last decades, but the proposed methods generally focus on individual components or stand for specific application areas. This paper aims to contribute to the development of performance based maintenance strategies within the wind energy sector by providing a condition monitoring based generic methodology for wind turbine performance assessment at system level. The proposed methodology is based on the detection of critical periods in which low performance is detected repeatedly. Multiple machine learning methods and models are applied to assess the wind turbine performance. This methodology has been applied in a case study with SCADA data of eight wind turbines. An analyst could benefit from the implementation of the methodology and the easy-to-interpret results shown in the proposed control chart, especially in cases in which there is less know-how about which variables have higher impact on systems performance. • A methodology for performance-based maintenance of wind turbines is proposed. • The methodology has been applied in a case study with eight wind turbines. • Machine learning methods are applied to assess the wind turbine performance. [ABSTRACT FROM AUTHOR]

This paper explores the effect of having a nuclear baseload in a 100% carbon-free electricity system The study analyses numerous scenarios based on different penetrations of conventional nuclear, wind and solar PV power, different levels of overgeneration and different combinations between medium and long duration energy stores (hydrogen and compressed air, respectively) to determine the configuration that achieves the lowest total cost of electricity (TCoE). At their current cost, new baseload nuclear power plants are too expensive. Results indicate the TCoE is minimised when demand is supplied entirely by renewables with no contribution from conventional nuclear. However, small modular reactors may achieve costs of ∼£60/MWh (1.5× current wind cost) in the future. With such costs, supplying ∼80% of the country's electricity demand with nuclear power could minimise the TCoE. In this scenario, wind provides the remaining 20% plus a small percentage of overgeneration (∼2.5%). Hydrogen in underground caverns provides ∼30.5 TWh (81 days) of long-duration energy storage while CAES systems provide 2.8 TWh (∼8 days) of medium-duration storage. This configuration achieves costs of ∼65.8 £/MWh. Batteries (required for short duration imbalances) are not included in the figure. The TCoE achieved will be higher once short duration storage is accounted for. [ABSTRACT FROM AUTHOR]

With the proposal of the "double carbon" goal, the large-scale development of new energy has spawned the development of green card market. It is urgent to study and explore the formation mechanism of on grid electricity price suitable for new energy power generation under the "double carbon" goal. Therefore, this paper studies the on grid price mechanism of new energy power stations considering the market environment. Firstly, the cost structure of photovoltaic power generation and wind power generation is analyzed, and the least squares support vector mechanism (LS-SVM) of quantum particle swarm optimization (QPSO) is used to build the cost prediction model of new energy power generation. Then, considering the coupling relationship of carbon market, electricity market and green card market, the income model of new energy power stations is constructed. Furthermore, the price mechanism under incomplete competition based on Bayesian game and the price mechanism under perfect competition based on bargaining game are designed. Then, the price of the designed mechanism is adjusted combined with the cost sharing of ancillary services. Finally, an example is given to verify the effectiveness of the model. [Display omitted] [ABSTRACT FROM AUTHOR]

As renewable energy sources such as wind farms become dominant, new challenges emerge for operating and controlling them. Traditionally, wind farm control aims to dispatch power set-points to individual turbines to maximize energy extraction and, thus, their usage as assets. Yet, grid balance and frequency support are fundamental in presence of high renewable penetration and volatility of energy prices and demand. This requires a paradigm change, moving from power maximization to revenue maximization. In this paper, three active power control strategies pushing this shift of paradigm are investigated, namely: wake-loss compensation, thrust balancing, and load-limiting control. The findings of large eddy simulations of a reference wind farm show that wake-loss compensation indeed improves the power generation on waked wind farms, but at the price of increased structural loads on certain turbines. The addition of a thrust balancing can equalize the stresses of individual turbines and their wear in the long term, while still attaining the required power output at the farm level. Furthermore, load-limiting controllers could potentially aid by allowing maintenance to be scheduled in a single time window, thus reducing operation and maintenance costs. • Wind farms can provide ancillary services while considering instantaneous loads. • A three-pronged approach enhances power reliability and improves structure safety. • Less is more: derating allows extended service life by reducing structure damage. • The profitability of wind farms can be increased via power tracking strategies. [ABSTRACT FROM AUTHOR]

Accurate estimation of wind power curves using field data is instrumental to several wind farm operations including productivity assessment, power output estimation, operations and maintenance, among others. Existing methods for estimating wind power curves mainly rely on environmental variables (e.g., wind speed, direction, density) as inputs to construct the wind-to-power relationship. This paper attempts to integrate yaw misalignment as an additional input to power curve models, constructing what is referred to hereinafter as "yaw-adjusted wind power curves." Our analysis shows that integrating yaw misalignment into power curves is non-trivial, largely due to the overwhelming impact of environmental variables (mainly wind speed) on a turbine's power output, which obscures the secondary effect of yaw errors on power production. In response, we propose a local-regression-based method which reconstructs the yaw-to-power relationship conditional on an effective neighborhood of environmental variables. Tested on operational data from two onshore wind turbines in France, our proposed approach achieves significant improvements, in terms of power estimation accuracy, relative to a set of prevalent statistical- and machine-learning-based power curve models. • A local regression approach is proposed for yaw-adjusted wind power curve modeling. • Yaw-to-power relation is locally learnt conditional on a set of environmental factors. • Tested on actual data, large gains in power curve estimation accuracy are achieved. [ABSTRACT FROM AUTHOR]

With the increasing utilization of offshore wind power, accurate prediction of offshore wind power is crucial for preventive control and scheduling. In this paper, a new hybrid probability density model is proposed for multi-step offshore wind power prediction, including time varying filter based empirical mode decomposition (TVFEMD), approximate entropy (AE), Yeo–Johnson transform quantile regression (YJQR) and gaussian approximation of quantile (GAQ). Firstly, TVFEMD decomposition and AE theory are used to preprocess the original data for reducing the complexity and modeling workload. Secondly, the 16-step ahead offshore wind power is predicted using YJQR, in which the model structures established for each component are selected by grid search for comprehensive optimization to ensure the best prediction performance. Finally, the GAQ method is adopted to construct probability density curves for the 16-step cumulative quantile prediction results. The variance of the probability density curves in each step is adjusted to optimize the interval prediction results, resulting in more robust and integrated prediction results. Taking the historical offshore wind power data collected by German transmission system operator 50 Hertz as an example, the model has higher prediction accuracy and stability on the basis of obtaining reasonable quantile estimation results in multi-step offshore wind power probabilistic prediction. • Propose a TAYG multi-step probability density method to predict offshore wind power. • TVFEMD and AE theory are adopted to preprocess the original offshore wind data. • YJQR is utilized to forecast 16 steps ahead offshore wind power. • GAQ is used to adjust the variance of the probability density curve. [ABSTRACT FROM AUTHOR]

With the liberation of the electricity market, a growing number of investors participate in market bidding. However, due to the inaccurate prediction of wind power, the interest of the investors can be damaged. In order to solve such problem, a distributionally robust chance-constrained (DRCC) scheduling for a wind-battery hybrid system in the day-ahead electricity market is developed by considering the uncertain wind power. The overall objectives of this paper contain revenue calculation from the electricity market, curtailment penalty caused by the wind power, and degradation cost of the battery. When selling/buying electricity is to/from the electricity market, the available power is limited by the capacity of the transmission line. This paper develops a chance constraint for the transmission line and introduces the moment ambiguity set to capture the uncertain wind power generation. The chance constraint can be reformulated into a standard second-order conic programming problem (SOCP) via a distributionally robust optimization method. The model is tested with a case study and the results indicate that the battery plays an important role in wind power scheduling in the electricity market. In the end, comparison with the stochastic optimization with normal distribution (SND) is conducted to prove the performance and robustness of the proposed model based on a distributionally robust optimization (DRO) method. • A chance constraint for the transmission line of a wind-battery system is proposed. • Multi-objective function is considered including revenue, penalty and cost. • The chance constraint problem is addressed by the distributionally robust optimization. • DRO method is 15.36% more reliable than stochastic optimization method. [ABSTRACT FROM AUTHOR]

Wind power, as a potential new energy generation technology, is gradually developing towards to the mainstream energy in the world. However, the inherent random volatility of wind brings severe challenges to the safe operation of the grid and the reliability of power supply, one of the effective ways to solve the problem is to improve the accuracy of wind speed prediction. However, most of wind speed prediction model cannot well mine the inherent regularity of wind speed data. Therefore, this paper introduces variational mode decomposition (VMD) algorithm. And the Short-term Wind Speed Prediction Model based on GA-ANN improved by VMD is proposed, which can effectively improve the accuracy of wind speed prediction. Firstly, hierarchical cluster method in this paper is employed to extract the historical data with high similarity to the predicted day. And then the appropriate number of decompositions K is selected by judging the value of sample entropy, so that the extracted historical data is decomposed into K subsequences by the variational mode decomposition. Next, with the global optimization ability of genetic algorithm, the artificial neural network is optimized to improve the forecasting performance. Finally, the short-term wind speed forecasting model based on GA-ANN improved by VMD is employed to predict the wind speed of each subsequence and superimposed them to obtain the final wind speed prediction sequence. The results in this paper show that (1) the model can find the periodic fluctuation of wind speed through historical data by hierarchical cluster method, so that significantly improving the accuracy of short-term wind speed prediction; (2) for the wind speed prediction, the error value of GA-ANN model is smaller than that of BP neural network; (3) in view of the inherent nature of the wind, the model proposed in this paper can use VMD to decompose the wind speed signal to obtain different scale fluctuations or trends, so as to fully exploit the potential information of wind speed, and obtain more accurate prediction results. The research work can help the relevant departments of the power system to accurately assess the risk of power grid operation, make a reasonable generation plan, effectively reduce the cost of power operation, and then greatly promote the development of green energy. • Proposing a new windspeedprediction model named HC-VMD-GA-BPmodel. • Using hierarchical cluster methodtoselect the historical wind speed data. • VMD is introduced to decomposethe original wind speed sequence. • Artificial neural networkisoptimized by GA to improve the prediction performance. • The HC-VMD-GA-BP model is comparedwith other models to verify itseffectiveness and high prediction accuracy. [ABSTRACT FROM AUTHOR]

The electric vehicle to grid (V2G) interaction technology can improve the utilization of renewable energy and stabilize its grid connection. At the same time, renewable energy can be used for a microgrid nearby, or incorporated into a large grid, to effectively address the volatility of renewable energy sources. Motivated by the increasing number of electric vehicles (EVs) and the randomness of renewable energy output, this paper proposes an effective strategy to improve the security and economy of the microgrid system. The uncertainty of wind power and EV's state of charge (SOC) is modeled as uncertainty prediction sets. And considering the worst-case scenario, this proposed strategy can increase the absorption ratio of renewable energy while orderly guiding the charging and discharging of EVs in peak-load reduction and valley filling and thus, lower operating costs under various practical constraints. To solve the problem of over-conservatism of the robust optimization, this paper introduces a dispatch interval coefficient to adjust the degree of conservatism, while improving the economy of microgrids system. The robustness and feasibility of the proposed dispatch strategy are demonstrated by numerical case studies. • Electric vehicle is introduced as both load and source (V2G mode) to help the consumption of wind power. • Operation stability and economic performance of microgrid is improved with the adjustable robust optimization (RO). • The robustness and economy of the system has been quantified. • Sensitivity analysis of uncertain variables to system total cost is carried out. [ABSTRACT FROM AUTHOR]

The electric vehicle to grid (V2G) interaction technology can improve the utilization of renewable energy and stabilize its grid connection. At the same time, renewable energy can be used for a microgrid nearby, or incorporated into a large grid, to effectively address the volatility of renewable energy sources. Motivated by the increasing number of electric vehicles (EVs) and the randomness of renewable energy output, this paper proposes an effective strategy to improve the security and economy of the microgrid system. The uncertainty of wind power and EV's state of charge (SOC) is modeled as uncertainty prediction sets. And considering the worst-case scenario, this proposed strategy can increase the absorption ratio of renewable energy while orderly guiding the charging and discharging of EVs in peak-load reduction and valley filling and thus, lower operating costs under various practical constraints. To solve the problem of over-conservatism of the robust optimization, this paper introduces a dispatch interval coefficient to adjust the degree of conservatism, while improving the economy of microgrids system. The robustness and feasibility of the proposed dispatch strategy are demonstrated by numerical case studies. • Electric vehicle is introduced as both load and source (V2G mode) to help the consumption of wind power. • Operation stability and economic performance of microgrid is improved with the adjustable robust optimization (RO). • The robustness and economy of the system has been quantified. • Sensitivity analysis of uncertain variables to system total cost is carried out. [ABSTRACT FROM AUTHOR]

This paper presents the first comprehensive hourly-resolution scenario study of the Indian electricity system with a view to investigate the transition from fossil to renewable energy-based power generation. Currently, India is 76% dependent on coal for power generation, however ambitious goals exist. Energy organisations including International Renewable Energy Agency and The Energy and Resources Institute, have presented scenarios but neither with support of dynamic simulation nor with assessment of optimal renewable energy contributions. In this paper, the optimal electricity system is analyzed in comparison with these. Results show that an optimal scenario could supply the hourly electricity by (i) increasing production capacity to about 615 GW, which is a 98.3% increase compared to the 310 GW in 2016, (ii) improving capacity factor of wind power and photovoltaic to 27% and 21% respectively, (iii) increasing wind power production to 433 TWh/yr (from 46 GWh/yr in 2016), solar photovoltaic to 290 TWh/yr (12 GWh/yr in 2016), river hydro to 42 TWh/yr (7.7 GWh/yr in 2016), biomass and nuclear power of 15 GW each. The costs of the identified optimal scenario are lower than of the previous scenarios. Finally, simulations show that inclusion of optimal proportion of biomass and nuclear could avoid any import dependency. • Design and analysis of renewable energy scenarios for India. • Evaluation of the cost structure of renewable energy scenarios. • Comparison with official Indian scenarios. • Proposes the Renewable Nuclear Dependency Index (RNDI) for evaluating scenarios. [ABSTRACT FROM AUTHOR]

Economic nonlinear model predictive control (ENMPC) is a strong candidate for controlling wind turbines (WTs). In the model predictive control (MPC) group, the model is the crucial component for the true controller performance. It is common to use simplified models to reduce the problem complexity. These models neglect some of the underlying dynamic responses of real wind turbines. This paper simulates the case in which high accuracy nonlinear models describe both the plant and the controller. The results will be compared to reduced-order models in order to extract conclusions and decide the most appropriate model for WT control. On the other hand, one of the main features of MPC and ENMPC is the concept of receding prediction horizon, which considers the future evolution of the plant to compute the control action. The error of prediction will drastically reduce MPC performance. Also, rapid variation in wind speed can cause problems since wind turbines cannot easily follow these sudden variations due to their high inertia and aerodynamic characteristics. This paper provides an advanced control approach to improve the energy extraction from turbulent wind and enhance wind turbine durability. By implementing this method, the wind speed forecasting is done with a combination of artificial neural networks (ANN) and dynamic equations applied in ENMPC. The results show a significant enhancement of the control performance. • Enhancing the net energy of variable speed wind turbines (WTs). • Considering high accuracy nonlinear models for wind turbine simulation. • Comparing reduced-order models to choose the most appropriate model for WT control. • Suggesting an advanced control approach combining wind speed forecasting with ENMPC. • Improving ENMPC performance, and thereby increasing the power output of WT. [ABSTRACT FROM AUTHOR]