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This paper investigates whether renewable energy influences economic growth and happiness. Using panel data from 25 European Union countries for the period 2012–2022, this study employs a panel model for estimation with fixed and random effects, and robust HAC standard errors. According to the research results, in general, the growing share of renewable energy in the energy mix has a positive impact on economic growth and the happiness of citizens. However, detailed research has shown that this effect depends on the type of energy; a significant positive impact was recorded only in solar share energy, wind share energy and economic growth. However, almost all types of renewable energy were included, i.e., biofuel, hydro, solar and other renewable share energy, and all had a significantly positive impact on the level of happiness. The exception was wind share energy, which showed a significant negative impact. The research findings of this paper provide empirical support for promoting renewable energy, which is positive both for economies and the happiness of citizens. It is one of the main aspects of sustainable economic growth. [ABSTRACT FROM AUTHOR]

At present, most studies use the direct method to analyze the oscillation problem of modern power systems. However, these studies often only simplify the external characteristics of the wind turbine and lack an in-depth understanding of its internal refined energy structure. In this paper, based on the direct-drive permanent magnetic synchronous generator's detailed model (D-PMSG), combined with the dynamic energy of its port, layers of analysis are performed on the wind turbine's internal connections, and a detailed model of the energy structure is created. Then, the interaction mechanism of each control link in the wind turbine is analyzed by combining the energy function of the wind turbine with the improved perturbation method. Finally, this paper constructs a sub-synchronous oscillation (SSO) scenario of weak damping and a forcing type and proves the accuracy and effectiveness of the traceability method based on the refined energy of D-PMSG. This traceability method based on refined energy is expected to provide a new solution to the stability problem caused by the integration of new energy. [ABSTRACT FROM AUTHOR]

Recently, there has been a significant increase in the integration of distributed energy resources (DERs) such as small-scale photovoltaic systems and wind turbines in power distribution systems. When the aggregated outputs of DERs are combined, excessive reverse current may occur in distribution lines, leading to overvoltage issues and exceeding thermal limits of the distribution lines. To address these issues, it is necessary to limit the output of DERs to a certain level, which results in constraining the hosting capacity of DERs in the distribution system. In this paper, coordination control methodologies of DERs are developed and executed to mitigate the overvoltage and overcurrent induced by DERs, thereby increasing the hosting capacity for DERs of the distribution system. This paper proposes three coordinated approaches of active and reactive power control of DERs, namely Var Precedence, Watt Precedence, and Integrated Watt and Var Control. The Var and Watt Precedence prioritizes reactive power for voltage (Q–V) and active power for current (P–I) to address network congestion, thereby enhancing hosting capacity. Conversely, the Integrated Var and Watt Precedence propose a novel algorithm that combines four control indices (Q–V, P–V, Q–I, and P–I) to solve network problems while maximizing hosting capacity. The three proposed methods are based on the sensitivity analysis of voltage and current to the active and reactive power outputs at the DER installation locations on the distribution lines, aiming to minimize DER active power curtailment. Each sensitivity is derived from linearized power equations at the operating points of the distribution system. To minimize the computation burden of iterative computation, each proposed method decouples active and reactive power and proceeds with sequential control in its own unique way, iteratively determining the precise output control of distributed power sources to reduce linearization errors. The three proposed algorithms are verified via case studies, evaluating their performance compared to conventional approaches. The case studies exhibit superior control effectiveness of the proposed DER power control methods compared to conventional methods when issues such as overvoltage and overcurrent occur simultaneously in the distribution line so that the DER hosting capacity of the system can be improved. [ABSTRACT FROM AUTHOR]

There is limited research focused on achieving optimal torque control performance of Switched Reluctance Generators (SRGs). The majority of existing studies tend to favor voltage or power control strategies. However, a significant drawback of SRGs is their susceptibility to high torque ripple. In power generation systems, torque ripple implicates fluctuations in the generated power of the generator. Moreover, high torque ripple can lead to mechanical vibrations and noise in the powertrain, impacting the overall system performance. In this paper, a Torque Sharing Function (TSF) with Indirect Instantaneous Torque Control (IITC) for SRG applied to Wind Energy Conversion Systems (WECS) is proposed to minimize torque ripple. The proposed method adjusts the shared reference torque function between the phases based on instantaneous torque, rather than the existing TSF methods formulated with a mathematical expression. Additionally, this paper introduces an innovative speed control scheme for SRG drive using a Fuzzy Super-Twisting Sliding Mode Command (FSTSMC) method. Notably robust against parameter uncertainties and payload disturbances, the proposed scheme ensures finite-time convergence even in the presence of external disturbances, while effectively reducing chattering. To assess the effectiveness of the proposed methods, comprehensive comparisons are made with traditional control techniques, including Proportional–Integral (PI), Integral Sliding Mode Control (ISMC), and Super-Twisting Sliding Mode Control (STSMC). The simulation results, obtained using MATLAB®/SIMULINK® under various speeds and mechanical torque conditions, demonstrate the superior performance and robustness of the proposed approaches. This study presents a thorough experimental analysis of a 250 W four-phase 8/6 SRG. The generator was connected to a DC resistive load, and the analysis focuses on assessing its performance and operational characteristics across different rotational speeds. The primary objective is to validate and confirm the efficacy of the SRG under varying conditions. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy resources, their variable, intermittent and unpredictable characteristics bring new challenges to the power system. These challenges require micro-meteorological data and techniques to provide more support for the power systems, including planning, dispatching, operation, and so on. This paper aims to provide readers with insights into the effects of micro-meteorology on power systems, as well as the actual improvement brought by micro-meteorology in some power system scenarios. This paper provides a review including the relevant micro-meteorological techniques such as observation, assimilation and numerical techniques, as well as artificial intelligence, presenting a relatively complete overview of the most recent and relevant micro-meteorology-related literature associated with power systems. The impact of micro-meteorology on power systems is analyzed in six different forms of power generation and three typical scenarios of different stages in the power system, as well as integrated energy systems and disaster prevention and reduction. Finally, a case study in China is provided. This case takes wind power prediction as an example in a power system to compare the performance when applying micro-meteorological data or not. The experimental results demonstrated that using the micro-meteorological reanalysis dataset with high spatial--temporal resolution for wind power prediction performed better, verifying the improvement of micro-meteorology to the power system to some extent. [ABSTRACT FROM AUTHOR]

With a large proportion of wind farms connected to the power grid, it brings more pressure on the stable operation of power systems in shorter time scales. Efficient and accurate scheduling, operation control and decision making require high time resolution power forecasting algorithms with higher accuracy and real-time performance. In this paper, we innovatively propose a high temporal resolution wind power forecasting method based on a light convolutional architecture—DC_LCNN. The method starts from the source data and novelly designs the dual-channel data input mode to provide different combinations of feature data for the model, thus improving the upper limit of the learning ability of the whole model. The dual-channel convolutional neural network (CNN) structure extracts different spatial and temporal constraints of the input features. The light global maximum pooling method replaces the flat operation combined with the fully connected (FC) forecasting method in the traditional CNN, extracts the most significant features of the global method, and directly performs data downscaling at the same time, which significantly improves the forecasting accuracy and efficiency of the model. In this paper, the experiments are carried out on the 1 s resolution data of the actual wind field, and the single-step forecasting task with 1 s ahead of time and the multi-step forecasting task with 1~10 s ahead of time are executed, respectively. Comparing the experimental results with the classical deep learning models in the current field, the proposed model shows absolute accuracy advantages on both forecasting tasks. This also shows that the light architecture design based on simple deep learning models is also a good solution in performing high time resolution wind power forecasting tasks. [ABSTRACT FROM AUTHOR]

Offshore wind energy is considered as one of the most promising resources of clean and renewable energy to replace fossil fuels. Additionally, its cost is expected to be lower than onshore wind energy as the technology matures. Offshore wind turbines (OWTs) normally operate in harsh ocean environments, which could impact their structural integrity. Scour erosion around foundations of OWTs can substantially change the overall stiffness of these structures and shorten their lifetime. Currently, there are a limited number of studies on countermeasures and their engineering requirements for decreasing the scouring effect; this is due to their different hydraulic circumstances, such as their stability, reliability, and resistance capacity. To this end, advancements in scour protection measures in the offshore energy sector are evaluated in this paper through a thorough and critical review following the PRISMA systematic literature mapping approach. This includes 68 papers on scour protection and over 30 scour protection designs for various types of wind turbine foundations. Here, we aim to provide an overview of the latest scouring protection measures and their comprehensive assessment, as well as their prospects and future challenges. The findings of this study will provide key insights into scour protection measures for OWTs and will subsequently contribute to the future growth of the offshore renewable energy sector. [ABSTRACT FROM AUTHOR]

Reduction or elimination of reliance on traditional fossil fuels and of the emission of greenhouse gases and pollutants into the environment are affecting energy technologies, systems, and applications. In this context, one potential approach to achieving sustainability, decarbonization, and ensuring the energy and economic viability of existing and future energy systems involves adopting one or more renewable sources. The presented paper concentrates on examining the performance of a small-scale hybrid renewable polygeneration system. This system utilizes biogas produced through anaerobic digestion, which is then supplied to an internal combustion engine, along with solar energy converted into electrical energy by photovoltaic modules and wind energy harnessed through a wind turbine. A small-scale user, represented by residential buildings and a zootechnical farm with heating, cooling, and electrical energy demands, serves as the case study. TRNSYS software is employed to design and model the system, considering realistic assumptions about technical aspects and user energy requirements. The investigation involves analyzing the system's operation, considering both energy and economic perspectives. The paper discusses the pros and cons of combining biogas, solar, and wind energy in the proposed hybrid system under the considered case study. Despite non-satisfactory economic profitability without incentives, the proposed system allows one to save significant amounts of primary energy and carbon dioxide equivalent emissions. [ABSTRACT FROM AUTHOR]

With the shortage of fossil energy and the increasingly serious environmental problems, renewable energy based on wind and solar power generation has been gradually developed. For the problem of wind power uncertainty and the low-carbon economic optimization problem of an integrated energy system with power to gas (P2G) and carbon capture and storage (CCS), this paper proposes an economic optimization scheduling strategy of an integrated energy system considering wind power uncertainty and P2G-CCS technology. Firstly, the mathematical model of the park integrated energy system with P2G-CCS technology is established. Secondly, to address the wind power uncertainty problem, Latin hypercube sampling (LHS) is used to generate a large number of wind power scenarios, and the fast antecedent elimination technique is used to reduce the scenarios. Then, to establish a mixed integer linear programming model, the branch and bound algorithm is employed to develop an economic optimal scheduling model with the lowest operating cost of the system as the optimization objective, taking into account the ladder-type carbon trading mechanism, and the sensitivity of the scale parameters of P2G-CCS construction is analyzed. Finally, the scheduling scheme is introduced into a typical industrial park model for simulation. The simulation result shows that the consideration of the wind uncertainty problem can further reduce the system's operating cost, and the introduction of P2G-CCS can effectively help the park's integrated energy system to reduce carbon emissions and solve the problem of wind and solar power consumption. Moreover, it can more effectively reduce the system's operating costs and improve the economic benefits of the park. [ABSTRACT FROM AUTHOR]

The development of wind energy has been observed for many years. Both construction firms and the scientific world are analyzing new design solutions, atmospheric conditions and the technical performance achieved. The main goal of this research is to evaluate the requirements that have to be met to design wind power stations that would be an optimal fit for the climatic conditions in Poland. This study combines the results of empirical studies on wind velocity distributions with the physical fundamentals of wind power station design. This paper presents modelling of the relationships between wind velocity distributions observed in Poland and technical requirements for wind power stations design. The wind velocities distributions for various locations in Poland are determined and expressed in Weibull distribution parameters. Theoretical computations concerning the dependence of wind power stations as function of wind speed and air's physical properties are presented. Conclusions important for the design of power stations fitted to the atmospheric conditions in Poland are given. LabVIEW 2021 was used for computer modeling. [ABSTRACT FROM AUTHOR]

The massive grid integration of renewable energy necessitates frequent and rapid response of hydropower output, which has brought enormous challenges to the hydropower operation and new opportunities for hydropower development. To investigate feasible solutions for complementary systems to cope with the energy transition in the context of the constantly changing role of the hydropower plant and the rapid evolution of wind and solar power, the short-term coordinated scheduling model is developed for the wind–solar–hydro hybrid pumped storage (WSHPS) system with peak shaving operation. The effects of different reservoir inflow conditions, different wind and solar power forecast output, and installed capacity of pumping station on the performance of WSHPS system are analyzed. The results show that compared with the wind–solar–hydro hybrid (WSH) system, the total power generation of the WSHPS system in the dry, normal, and wet year increased by 10.69%, 11.40%, and 11.27% respectively. The solar curtailment decreased by 68.97%, 61.61%, and 48.43%, respectively, and the wind curtailment decreased by 76.14%, 58.48%, and 50.91%, respectively. The high proportion of wind and solar energy connected to the grid in summer leads to large net load fluctuations and serious energy curtailment. The increase in the installed capacity of the pumping station will promote the consumption of wind and solar energy in the WSHPS system. The model proposed in this paper can improve the operational flexibility of hydropower station and promote the consumption of wind and solar energy, which provides a reference for the research of cascade hydropower energy storage system. [ABSTRACT FROM AUTHOR]

While it is well known that the Weibull distribution is a good model for wind-speed measurements and can be explained through simple statistical arguments, how such a model holds for shorter time periods is still an open question. In this paper, we present a systematic investigation of the accuracy of the Weibull distribution to wind-speed measurements, in comparison with other possible "cousin" distributions. In particular, we show that the Gaussian distribution enables one to predict wind-speed histograms with higher accuracy than the Weibull distribution. Two other good candidates are the Nakagami and the Rice distributions, which can be interpreted as particular cases of the Weibull distribution for particular choices of the shape and scale parameters. These findings hold not only when predicting next-point values of the wind speed but also when predicting the wind energy values. Finally, we discuss such findings in the context of wind power forecasting and monitoring for power-grid assessment. [ABSTRACT FROM AUTHOR]

Offshore wind power has attracted significant attention due to its high potential, capability for large-scale farms, and high capacity factor. However, it faces high investment costs and issues with subsea power transmission. Conventional high-voltage AC (HVAC) methods are limited by charging current, while high-voltage DC (HVDC) methods suffer from the high cost of power conversion stations. The low-frequency AC (LFAC) method mitigates the charging current through low-frequency operation and can reduce power conversion station costs. This paper aims to identify the economically optimal frequency by comparing the investment costs of LFAC systems at various frequencies. The components of LFAC, including transformers, offshore platforms, and cables, exhibit frequency-dependent characteristics. Lower frequencies result in an increased size and volume of transformers, leading to higher investment costs for offshore platforms. In contrast, cable charging currents and losses are proportional to frequency, causing the total cost to reach a minimum at a specific frequency. To determine the optimal frequency, simulations of investment costs for varying capacities and distances were conducted. [ABSTRACT FROM AUTHOR]

Over the past few decades, many universities have turned to using microgrid systems because of their dependability, security, flexibility, and less reliance on the primary grid. Microgrids on campuses face challenges in the instability of power production due to meteorological conditions, as the output of renewable sources such as solar and wind power relies entirely on the weather and determining the optimal size of microgrids. Therefore, this paper comprehensively reviews the university campuses' microgrids. Some renewable energy sources, such as geothermal (GE), wind turbine (WT), and photovoltaic (PV), are compared in terms of installation costs, availability, weather conditions, efficiency, environmental impact, and maintenance. Furthermore, a description of microgrid systems and their components, including distributed generation (DG), energy storage system (ESS), and microgrid load, is presented. As a result, the most common optimization models for analyzing the performance of campus microgrids are discussed. Hybrid microgrid system configurations are introduced and compared to find the optimal configuration in terms of energy production and flexibility. Therefore, configuration A (Hybrid PV- grid-connected) is the most common configuration compared to the others due to its simplicity and free-charge operation. [ABSTRACT FROM AUTHOR]

Integrating wind power plants into the electricity grid poses challenges due to the intermittent nature of wind energy generation. Energy storage systems (ESSs) have shown promise in mitigating the intermittent variability associated with wind power. This paper presents a distributionally robust optimization (DRO) model for sizing energy storage systems to dispatch wind power plants. The variable wind power is formulated as a moment-based ambiguity set. Dispatchability is described by the expected value of the insufficient power of wind power relative to the dispatch command, which is a sum of nonlinear functions and is taken as the optimal index. A deterministic semi-definite positive model is derived to solve the problem effectively. Numerical studies are conducted to demonstrate the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

The stability of offshore floating wind turbine foundation platforms is a fundamental requirement for the efficiency and safety of wind power generation systems. This paper proposes a novel small-diameter float-type semi-submersible platform to improve system stability. To evaluate the superior motion stability of the proposed floating platform, a comprehensive frequency–domain response analysis and experimental study were conducted in comparison with the OC4-DeepCwind platform developed by the National Renewable Energy Laboratory (NREL). The respective comparison of the frequency–domain response analysis and the experimental results demonstrated that the proposed floating wind turbine platform shows better hydrodynamic characteristics and resonance avoidance capability. This not only reduces the Response Amplitude Operators (RAOs), but also enhances the system stability, namely, effectively avoiding the regions of concentrated wave loading and low-frequency ranges. Furthermore, the proposed small-diameter semi-submersible platform has the potential to reduce manufacturing costs, providing valuable insights for the manufacturing of offshore floating wind turbine systems. [ABSTRACT FROM AUTHOR]

With the continuous development of large-scale wind and photovoltaic power worldwide, the net load fluctuation of systems is increasing, thereby imposing higher demands for power supply assurance and new energy consumption capacity within emerging power systems. It is imperative to establish a quantifiable and efficient model for planning new power systems, to propose an analytical approach for determining optimal evolutionary paths, and to advance research on flexible resource planning across wide areas. In this paper, based on the simplified operating characteristics of multi-type flexible resources, a source-grid-load-storage collaborative planning and evolution analysis framework is established. Secondly, the lowest total cost of the whole life cycle of the system is taken as the optimization goal, the multiple flexible resource investment decisions and production operation constraints of various flexible resources on all sides of the system are considered, and the source-grid-load-storage planning model is established. Then, through the investment decision-making strategy setting of the system in different planning level years, the evolutionary path analysis method of the whole life cycle economy and weighted environmental protection benefit of the system is given. Finally, by taking the sending-end power grid in Gansu Province as an example, a case study is carried out. Calculations of new energy, key channels within the province, energy storage capacity, and load-side response capacity requirements for 2025, 2030, and 2060 are optimized. Based on the above analysis, the optimal evolution path of the power grid is proposed. When considering the weighted benefits of economy and environmental protection, the greater the weight of environmental protection benefits, the greater the possibility of choosing a radical scheme. The model and method proposed in this paper can provide technical reference for the future development planning and evolution analysis of new power systems. [ABSTRACT FROM AUTHOR]

Short-term wind power forecasting has difficult problems due to the very large variety of speeds of the wind, which is a key factor in producing energy. From the point of view of the whole country, an important problem is predicting the total impact of wind power's contribution to the country's energy demands for succeeding days. Accordingly, efficient planning of classical power sources may be made for the next day. This paper will investigate this direction of research. Based on historical data, a few neural network predictors will be combined into an ensemble that is responsible for the next day's wind power generation. The problem is difficult since wind farms are distributed in large regions of the country, where different wind conditions exist. Moreover, the information on wind speed is not available. This paper proposes and compares different structures of an ensemble combined from three neural networks. The best accuracy has been obtained with the application of an MLP combiner. The results of numerical experiments have shown a significant reduction in prediction errors compared to the naïve approach. The improvement in results with this naïve solution is close to two in the one-day-ahead prediction task. [ABSTRACT FROM AUTHOR]

The government has set an ambitious target of renewable energy development in Malaysia. As a promising renewable energy source, wind energy plays an important role in the Malaysia renewable energy roadmap. Compared to onshore wind energy, offshore wind resources with better quality can be provided in the areas away from the coast, which has greater potential to contribute to electricity generation. Wind and bathymetry conditions are two important factors that determine the feasibility and economics of offshore wind turbines. In this paper, an investigation is conducted on wind and bathymetry conditions around Malaysia. The data source mainly originates from the Global Wind Atlas. The conditions of the coastal areas in different states and federal territories of both Peninsular Malaysia and East Malaysia are analysed, with a specific focus on wind speed, wind energy density, and bathymetry conditions in high-wind-speed regions. The data and survey are verified and compared with the past published literature. This paper aims to investigate the wind and bathymetry conditions around Malaysia, assess the potential of offshore wind energy, discuss the feasibility of offshore wind turbines, and provide references for offshore wind development in Malaysia. [ABSTRACT FROM AUTHOR]

The ageing of coal-fired power stations in South Africa has led to regular power outages. Therefore, the country will need to urgently increase its electricity capacity to meet further energy demand from growing urbanization and population growth. This challenge has increased interest in alternative energy sources, such as renewable energy (RE). South Africa is gradually implementing appropriate renewable energy practices, reducing carbon emissions, cleansing the air, and assuring a more sustainable future. This paper summarizes the availability, current state, and future potential of renewable energy choices in South Africa. This paper also evaluates specific policy measures and government actions aimed at eliminating barriers and increasing renewable energy deployment in the future. It also considers the South African network's specialized techno-economic analysis. The findings show that South Africa is still heavily reliant on coal, with 78% of the current installed capacity coming from coal power in 2022, compared to 9.3% for solar and wind energy. [ABSTRACT FROM AUTHOR]

To face the growing electricity demand, several countries have adopted the solution of clean energy and use renewable energy sources (e.g., wind and solar) to reinforce the stability of the power network, especially during peak demand periods. Forecasting wind power generation is one of the important tasks for the network regulator. This paper deals with the probabilistic forecasting of hourly wind speed time series. In this approach, instead of evaluating a single-point forecast, an intraday interval prediction is provided, which allows modeling the probability distribution of the wind speed process at any specific hour. Practically, the quantification of uncertainty might be of particular interest for risk management purposes associated with wind power generation. The definition of interval prediction is based on the notion of conditional quantiles. In this paper, we introduce a new statistical approach, which deals with the nonstationarity behavior of the wind speed process, to define the conditional quantile predictor. The proposed approach was applied and evaluated on hourly wind speed processes. The suggested methodology provides accurate single-point forecasts using the conditional median as the predictor. Furthermore, the obtained hourly interval predictions are small and well adapted to the shape of the daily wind speed curves, which confirms the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

The renewable-dominant hybrid generation systems (HGSs) are increasingly important to the electric power system worldwide. However, influenced by uncertain meteorological factors, the operational robustness of HGSs must be evaluated to inform the associated decision-making. Additionally, the main factors affecting the HGS's robustness should be urgently identified under deep uncertainties, as this provides valuable guidance for HGS capacity configuration. In this paper, a multivariate stochastic simulation method is developed and used to generate uncertain resource scenarios of runoff, photovoltaic power, and wind power. Subsequently, a long-term stochastic optimization model of the HGS is employed to derive the optimal operating rules. Finally, these operating rules are used to simulate the long-term operation of an HGS, and the results are used to evaluate the HGS's robustness and identify its main sensitivities. A clean energy base located in the Upper Yellow River Basin, China, is selected as a case study. The results show that the HGS achieves greater operational robustness than an individual hydropower system, and the robustness becomes weaker as the total capacity of photovoltaic and wind power increases. Additionally, the operational robustness of the HGS is found to be more sensitive to the total capacity than to the capacity ratio between photovoltaic and wind power. [ABSTRACT FROM AUTHOR]

Large-scale fluctuating and intermittent new energy power generation in a new power system is gradually connected to the grid. In view of the impact of the uncertainty of wind power on the spinning reserve capacity of thermal power units in the new power system's day-ahead dispatching and reserve auxiliary service market, the original dispatching mode and intensity can no longer meet the system demand. To address this problem, the establishment of a wind power grid-connected new power system's standby auxiliary service market reward and punishment assessment mechanism is undertaken to fundamentally reduce the demand for auxiliary services of the new power system pressure. In the first part of this paper, a two-stage optimal scheduling strategy is proposed for the first day of the year that takes into account the operational risk and standby economics. First, a data-driven method is used to generate the forecast value of the wind power interval before the day, and a unit start–stop optimization model (the first-stage optimization model) is established by taking into account the CvaR (conditional value at risk) theory to optimize the risk loss of wind abandonment and loss of load and the fuel cost of each unit, and an optimization algorithm is used to carry out the three scenarios and the corresponding four scenarios to optimize the configuration of the start–stop state and power output of each unit. The optimization algorithm is used to optimize the starting and stopping status and output of each unit for three circumstances and four corresponding scenarios. Then, in the second stage, a standby auxiliary service market incentive and penalty assessment model is established to effectively coordinate the sharing of rotating standby capacity and cost among thermal power units through the incentive and penalty mechanism so as to make a reasonable and efficient allocation of wind power output, curtailable load, and synchronized standby capacity. The new power system with improved IEEE30 nodes is simulated and verified, and it is found that the two-stage optimization model obtains a scheduling strategy that takes into account the system operating cost, standby economy, and reliability, and at the same time, through the standby auxiliary service market incentive and penalty assessment mechanism, the extra cost caused by standby cost mismatch can be avoided. This evaluation model provides a reference for the safe, efficient, flexible, and nimble operation of the new power system, improves the economic efficiency and improves the auxiliary service market mechanism. [ABSTRACT FROM AUTHOR]

Utilizing renewable energy (RE) sources can provide a realistic answer to the problem of many nations' energy needs. This paper reviews the current status of using RE to produce electricity in the Kingdom of Saudi Arabia (KSA). The primary aim of the review is to identify and analyze the solar- and wind-energy utilization, problems, and future trends in KSA while taking into account all relevant aspects, associated development hurdles, and suggested solutions. All involved parties will receive useful results and conclusions from a thorough examination and analysis of the pertinent literature that can assist them in understanding the potential of solar and wind energy, considering all the linked issues. [ABSTRACT FROM AUTHOR]

With the greatly increased penetration rate of wind power, photovoltaic, and other new energy sources in the power system, the proportion of controllable units gradually decreased, resulting in increased system uncertainty. The biogas power generation system can effectively alleviate the pressure caused by source-load uncertainty in such high-permeability systems of new energy sources such as wind power and photovoltaic. Hence, from the perspective of the power system, this paper introduces a capacity demand analysis method for a rural biogas power generation system capable of independent operation amidst source-load uncertainty. To enhance the depiction of pure load demand uncertainty, a scene set generation method is proposed, leveraging quantile regression analysis and Gaussian mixture model clustering. Each scene's data and probability of occurrence elucidate the uncertainty of pure load demand. An integrated optimal operation model for new energy and biogas-generating units, free from energy storage capacity constraints, is established based on the generated scenario set. Addressing considerations such as biogas utilization rate and system operation cost, a biogas storage correction model, utilizing the gas storage deviation degree index and the cost growth rate index, is developed to determine biogas demand and capacity. The example results demonstrate the significant reduction in gas storage construction costs and charging and discharging imbalances achieved by the proposed model while ensuring systemic operational cost effectiveness. [ABSTRACT FROM AUTHOR]

With the large-scale use of wind turbines, the ability of the power system to resist frequency fluctuations has been greatly weakened, making the contradiction between frequency regulation supply and demand in the power system increasingly prominent. In order to ensure the frequency security of the power system, this paper conducts research on the primary frequency regulation (PR) backup configuration problem of a power system containing a high proportion of wind power. First, according to the dynamic properties of the speed regulator, a system frequency response model considering the limiting link is established. And the system frequency response model is transformed into a time-domain analytical function of PR reserve capacity and frequency maximum value. On this basis, a PR backup configuration model of a power system containing a high proportion of wind power is constructed with the optimization goal of minimizing system operating costs and taking into account the limiting link constraints. It is proposed to use the L-shape algorithm to decompose the model into main problems and sub-problems, which effectively reduces the solution complexity of the model. Finally, the correctness and effectiveness of this model are verified based on the improved IEEE 39-bus system. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamic patterns in the small-signal behavior of power systems with wind power generation. The interactions between synchronous generators and wind generators are investigated. In addition, the impact of increased wind generation penetration on the damping and frequency of the synchronous generator's electromechanical oscillations is addressed. Wind generators of three different technologies are considered throughout this study. Very detailed dynamic models of wind generators are used and detailed. [ABSTRACT FROM AUTHOR]

To meet future load projection with the integration of renewable sources, the transmission system must be planned optimally. Thus, this paper introduces a comparative analysis and comprehensive methodology for transmission expansion planning (TEP), incorporating the combined effects of wind power, losses, N-1 contingency, a FACTS, and storage in a flexible environment. Specifically, the optimal placement of the FACTS, known as series capacitive compensation (SCC) devices, is used. The intraday constraints associated with wind power and energy storage are represented by the methodology of typical days jointly with the load scenarios light, heavy, and medium. The TEP problem is formulated as a mixed-integer nonlinear programming (MINLP) problem through a DC model and is solved using a specialized genetic algorithm. This algorithm is also used to determine the optimal placement of SCC devices and storage systems in expansion planning. The proposed methodology is then used to perform a comparison of the effect of the different technologies on the robustness and cost of the final solution of the TEP problem. Three test systems were used to perform the comparative analyses, namely the Garver system, the IEEE-24 system, and a real-world Colombian power system of 93 buses. The results indicate that energy storage and SCC devices lead to a decrease in transmission requirements and overall investment, enabling the effective integration of wind farms. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to estimating short-term production of wind farms, which are made up of numerous turbine generators. It harnesses the power of big data through a blend of data-driven and model-based methods. Specifically, it combines an Artificial Neural Network (ANN) for immediate future predictions of wind turbine power output with a stochastic model for dependability, using Hybrid Reliability Block Diagrams. A thorough state-of-the-art review has been conducted in order to demonstrate the applicability of an ANN for non-linear stochastic problems of energy or power forecast estimation. The study leverages an innovative cluster analysis to group wind turbines and reduce the computational effort of the ANN, with a dependability model that improves the accuracy of the data-driven output estimation. Therefore, the main novelty is the employment of a hybrid model that combines an ANN with a dependability stochastic model that accounts for the realistic operational scenarios of wind turbines, including their susceptibility to random shutdowns This approach marks a significant advancement in the field, introducing a methodology which can aid the design and the power production forecast. The research has been applied to a case study of a 24 MW wind farm located in the south of Italy, characterized by 28 turbines. The findings demonstrate that the integrated model significantly enhances short-term wind-energy production estimation, achieving a 480% improvement in accuracy over the solo-clustering approach. [ABSTRACT FROM AUTHOR]

With the continuous warming of the global climate, expanding the use of renewable energy has become one of the main social responsibilities. However, as the number of installed wind turbines and their physical dimensions continue to increase, the issue of generated noise has become increasingly significant in influencing the acceptance and endorsement of wind power projects by neighboring communities. In this paper, we investigated the noise generated by two wind turbine units with rated powers of 1.5 MW and 4.5 MW and analyzed the variations in low-frequency noise during their operation and shutdown periods. This research shows that the power of a single unit has a significant impact on the low-frequency noise emitted into the environment. Compared with 1.5 MW wind turbines, 4.5 MW wind turbines generate more low-frequency noise when operating at the same wind speed. Further analysis of the narrowband frequency spectra and one-third octave spectra of the measured noise indicates that the low-frequency noise from the 4.5 MW wind turbine increases significantly in the range of approximately 80 Hz to 300 Hz, with more pronounced variations below 250 Hz corresponding to changes in wind speed. However, the overall variations in low-frequency noise with wind speed are not as notable as those observed for the 1.5 MW wind turbine. Due to the relatively weak attenuation of low-frequency noise in the atmosphere, the higher low-frequency content of large wind turbines may cause more distress to residents near wind farms. The result of this study emphasizes that in the planning and design of wind power projects, in addition to considering the efficiency of single-unit power generation and the contribution of renewable energy, it is also necessary to pay full attention to noise emission issues to ensure that the project is widely supported and accepted in the community. [ABSTRACT FROM AUTHOR]

Scouring around the fixed foundations of offshore wind turbines (OWTs) remains a pressing concern within the offshore wind industry, significantly jeopardizing OWT safety. Despite previous efforts, the current understanding of this phenomenon remains incomplete, as various factors related to offshore wind farms and OWT operations can exert intricate influences. To further explore this matter, this paper undertakes a new study to further understand the interplay between monopile vibrations, tidal currents, and sea waves, elucidating their combined impact on scour surrounding an OWT's foundation. It was found that, besides pile vibration amplitude and frequency, the pile vibration direction can also notably influence the scour around the monopile. Specifically, the most pronounced scour mostly transpires when the monopile undergoes vibrations along the 135° direction relative to the inflow direction. The mildest scour mostly occurs when the pile vibrates along the 90° direction. Additionally, this study reveals that different types of waves, indicated by the ratio of water depth, H, to wavelength, L, also engender varying scour around the monopile. The influence of waves on scour increases with the decrease in the H/L ratio, implying that OWT foundations are more threatened by shallow water waves than by deep water waves. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative and eco-friendly computational methodology to assess the wind potential of a location with the aid of high-resolution simulations with a mesoscale numerical weather prediction model (WRF), coupled with the statistical "10% sampling condition". The proposed methodology is tested for a location with complex terrain on the Greek island of Crete, where moderate to strong winds prevail for most of the year. The results are promising, indicating that this method has great potential for studying and assessing areas of interest. Adverse effects and challenges associated with wind energy production may be mitigated with methods such as the proposed one. Mitigating such effects should constitute the main focus and priority in research concerning wind energy production. [ABSTRACT FROM AUTHOR]

Offshore wind farms are great options for addressing the world's energy and climate change challenges, as well as meeting rising energy demand while taking environmental and economic impacts into account. Floating wind turbines, in specific, depict the next horizon in the sustainable renewable energy industry. In this study, a life-cycle cost analysis for floating offshore wind turbines is developed by combining the most recent data and parametric formulas from databases and relevant papers. The cost analysis models focused on cost minimization with special emphasis on Operation and Maintenance Cost (OPEX), Decommissioning Cost (DECOM), and Levelized Cost of Energy (LCOE), which are important factors in wind power economy. Given that floating wind energy is still developing, the presented scenarios should be beneficial in making future decisions. The cost analysis scenarios include on-site and off-site maintenance scenarios for OPEX. In addition, four alternative scenarios for DECOM have been examined: mechanical recycling, mechanical-incineration, incineration processes, and landfill. According to the findings of these scenarios, OPEX varies from 16.89 to 19.93 £/MWh and DECOM between 3.47 and 3.65 £/MWh, whilst the total LCOE varied from 50.67 to 66.73 £/MWh. [ABSTRACT FROM AUTHOR]

To enhance the economic viability of wind energy in cold regions and ensure the safe operational management of wind farms, this paper proposes a short-term wind turbine blade icing wind power prediction method that combines principal component analysis (PCA) and fractional Lévy stable motion (fLsm). By applying supervisory control and data acquisition (SCADA) data from wind turbines experiencing icing in a mountainous area of Yunnan Province, China, the model comprehensively considers long-range dependence (LRD) and self-similar features. Adopting a combined pattern of previous-day predictions and actual measurement data, the model predicts the power under near-icing conditions, thereby enhancing the credibility and accuracy of icing forecasts. After validation and comparison with other prediction models (fBm, CNN-Attention-GRU, XGBoost), the model demonstrates a remarkable advantage in accuracy, achieving an accuracy rate and F1 score of 96.86% and 97.13%, respectively. This study proves the feasibility and wide applicability of the proposed model, providing robust data support for reducing wind turbine efficiency losses and minimizing operational risks. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for optimizing wind farm control through the utilization of a combined model predictive control method. In contrast to conventional methods of controlling active and reactive power in wind farms, the suggested approach integrates a wind power prediction model driven by a neural network and a state-space model for wind turbines. This combination facilitates a more precise forecast of active power, thereby enabling the dynamic prediction of the range of reactive power output from the wind turbines. When combined with the equation of state in wind farm space, it is possible to accurately optimize the reactive power of a wind farm. Furthermore, the impact of active power on voltage fluctuations in the wind farm collector system was examined. The utilization of model predictive control enhances voltage regulation, optimizes system redundancy, and increases the reactive capacity. Sensitivity coefficients were calculated using analytical methods to enhance computational efficiency and to resolve issues related to convergence. In order to validate the proposed methodology and control scheme, a wind farm simulation model comprising 20 turbines was developed to assess the feasibility of the scheme. [ABSTRACT FROM AUTHOR]

Wind prediction has consistently been in the spotlight as a crucial element in achieving efficient wind power generation and reducing operational costs. In recent years, with the rapid advancement of artificial intelligence (AI) technology, its application in the field of wind prediction has made significant strides. Focusing on the process of AI-based wind prediction modeling, this paper provides a comprehensive summary and discussion of key techniques and models in data preprocessing, feature extraction, relationship learning, and parameter optimization. Building upon this, three major challenges are identified in AI-based wind prediction: the uncertainty of wind data, the incompleteness of feature extraction, and the complexity of relationship learning. In response to these challenges, targeted suggestions are proposed for future research directions, aiming to promote the effective application of AI technology in the field of wind prediction and address the crucial issues therein. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative framework for wind power prediction that focuses on the future of energy forecasting utilizing intelligent deep learning and strategic feature engineering. This research investigates the application of a state-of-the-art deep learning model for wind energy prediction to make extremely short-term forecasts using real-time data on wind generation from New South Wales, Australia. In contrast with typical approaches to wind energy forecasting, this model relies entirely on historical data and strategic feature engineering to make predictions, rather than relying on meteorological parameters. A hybrid feature engineering strategy that integrates features from several feature generation techniques to obtain the optimal input parameters is a significant contribution to this work. The model's performance is assessed using key metrics, yielding optimal results with a Mean Absolute Error (MAE) of 8.76, Mean Squared Error (MSE) of 139.49, Root Mean Squared Error (RMSE) of 11.81, R-squared score of 0.997, and Mean Absolute Percentage Error (MAPE) of 4.85%. Additionally, the proposed framework outperforms six other deep learning and hybrid deep learning models in terms of wind energy prediction accuracy. These findings highlight the importance of advanced data analysis for feature generation in data processing, pointing to its key role in boosting the precision of forecasting applications. [ABSTRACT FROM AUTHOR]

Beyond solar and wind energy, wave energy is gaining great interest due to its very high theoretical potential, although its stochastic nature causes intermittent and fluctuating power production. Energy storage system (ESS) integration to wave energy converter (WEC) plants represents a promising solution to mitigate this issue. To overcome the technological limits of the single storage devices, the hybridization of complementary ESSs represents an effective solution, extending the operating range over different timeframes. This paper analyzes the benefits of Li-ion battery–supercapacitor hybrid ESS integration into a grid-connected WEC, aiming at smoothing the produced power oscillations. The hybridization concept involves coupling a power-intensive technology, such as a supercapacitor devoted to managing fluctuations at higher frequency, with a battery technology exploited to manage power variations over longer timeframes to mitigate degradation issues. In this study, a multi-objective data-driven power management strategy, based on the simultaneous perturbation stochastic approximation (SPSA) algorithm, is implemented to minimize power fluctuations in terms of power ramp (representing the power variation between two consecutive values with a 1 s time step), both at the Point of Common Coupling (PCC) and the Li-ion battery terminals, thanks to the supercapacitor peak-shaving function. The SPSA management strategy, together with a suitable sizing procedure, allows a reduction of more than 70% in the power oscillations at the PCC with respect to those at the WEC terminals, while decreasing battery stress by more than 25% if compared to a non-hybrid ESS consisting of a Li-ion battery. This shows how supercapacitor features can extend battery lifespan when integrated in a hybrid ESS. [ABSTRACT FROM AUTHOR]

This research delineates a pivotal advancement in the domain of sustainable energy systems, with a focused emphasis on the integration of renewable energy sources—predominantly wind and solar power—into the hydrogen production paradigm. At the core of this scientific endeavor is the formulation and implementation of a deep-learning-based framework for short-term localized weather forecasting, specifically designed to enhance the efficiency of hydrogen production derived from renewable energy sources. The study presents a comprehensive evaluation of the efficacy of fully connected neural networks (FCNs) and convolutional neural networks (CNNs) within the realm of deep learning, aimed at refining the accuracy of renewable energy forecasts. These methodologies have demonstrated remarkable proficiency in navigating the inherent complexities and variabilities associated with renewable energy systems, thereby significantly improving the reliability and precision of predictions pertaining to energy output. The cornerstone of this investigation is the deployment of an artificial intelligence (AI)-driven weather forecasting system, which meticulously analyzes data procured from 25 distinct weather monitoring stations across Latvia. This system is specifically tailored to deliver short-term (1 h ahead) forecasts, employing a comprehensive sensor fusion approach to accurately predicting wind and solar power outputs. A major finding of this research is the achievement of a mean squared error (MSE) of 1.36 in the forecasting model, underscoring the potential of this approach in optimizing renewable energy utilization for hydrogen production. Furthermore, the paper elucidates the construction of the forecasting model, revealing that the integration of sensor fusion significantly enhances the model's predictive capabilities by leveraging data from multiple sources to generate a more accurate and robust forecast. The entire codebase developed during this research endeavor has been made available on an open access GIT server. [ABSTRACT FROM AUTHOR]

Galloping-based piezoelectric energy harvesting systems are being used to supply renewable electricity for low-power wireless sensor network nodes. In this paper, a W-shaped bluff body is proposed as the core component of a piezoelectric wind energy harvester. Experiments and simulations have shown that the W-shaped bluff body can improve harvesting efficiency at low wind speeds. For the W-shaped structure, the finite element simulation results indicate that the structure can help improve the aerodynamic performance to obtain high aerodynamic force. The experimental results demonstrate that compared with the traditional bluff bodies, the piezoelectric wind energy harvester with the W-shaped bluff body (WEHW) can generate higher output voltages and has a lower cut-in speed. When the length L is 30 mm and the rear groove angle β is 30°, the W-shaped structure can induce the best harvesting performance. When an external load resistance of 820 KΩ is connected and the wind speed is 5 m/s, the WEHW generates an average output power of 0.28 mW. [ABSTRACT FROM AUTHOR]

This paper performs a comprehensive analysis of the wind energy potential of onshore regions in Greece with emphasis on quantifying the volume risk and the spatial covariance structure. Optimization techniques are employed to derive efficient wind capacity allocation plans (also known as generation portfolios) incorporating different yield aspirations. The generation profile of minimum variance and other optimal portfolios along the efficient frontier are subject to rigorous evaluation using a fusion of descriptive and statistical methods. In particular, principal component analysis is employed to estimate factor models and investigate the spatiotemporal properties of wind power generation, providing valuable insights into the persistence of volume risk. The overarching goal of the study is to employ a set of statistical and mathematical programming tools guiding investors, aggregators and policy makers in their selection of wind energy generating assets. The findings of this research challenge the effectiveness of current policies and industry practices, offering a new perspective on wind energy harvesting with a focus on the management of volume risk. [ABSTRACT FROM AUTHOR]

Demand response (DR) can improve the accommodation of renewable energy and further affect the distributed generation (DG) allocation strategy. In this context, this paper proposes a stochastic optimal allocation model of DG, considering DR. Firstly, to address the uncertainty of wind and solar power outputs, a large number of scenarios of wind and solar power are generated based on the scenario method, which are then clustered into 10 typical scenarios by the k-means method. Secondly, with the goal of maximizing the total cost, the DR cost and corresponding constraints are introduced. Then, the stochastic planning model for DG is established, where the planning level aims to minimize the investment cost while the operation level minimizes the total operation expectation cost. For the non-linear term in the DR cost and power flow constraint, the Taylor expansion method and second-order conic relaxation method are both adopted to transform the original mixed-integer non-linear model to the mixed-integer second-order conic planning model. Finally, the whole planning model for DG is solved by CPLEX 12.6.0. The results show that DR can reduce the total cost and improve the accommodation of renewable energy in the DG planning process, which should be paid more attention to in the DG planning model. [ABSTRACT FROM AUTHOR]

In an electricity and heat integrated energy system, the transmission of thermal energy encounters significant delays, and the delays are often not integer multiples of the dispatch interval. This mismatch poses challenges for achieving coordinated dispatch with the electric power system. To address this problem, the fictitious node method is proposed in this paper, offering a novel approach to calculating the quasi-dynamic characteristics of the heating network. Furthermore, to enhance the local consumption capacity of wind power, the heat storage capacity of the heat supply network was taken into consideration in this study, and a combined energy supply model equipped with electric boilers, incorporating combined heat and power (CHP) units and gas turbine units, was developed. This model effectively expands the operational range of CHP units and enables the decoupling of electricity and heat operations in gas turbine units. The analysis conducted demonstrated the effectiveness of the proposed method and model in achieving the coordinated dispatch of electricity and heat. Moreover, it highlighted the positive impact on the overall economy of system operation and the promotion of wind power consumption. The optimal configuration presented in this paper resulted in an 8.2% improvement in system operating economics and a 38.3% enhancement in wind power integration. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the development of a floating artificial sustainable energy island at a conceptual design level that would enhance the energy independence of islands focusing on a case study on the island of Crete. This paper provides a baseline assessment showing the immense potential of wind and solar energy in and around Crete integrating the third significant renewable energy source (RES) of ocean waves into the energy island. The selection of the best location for the floating offshore platforms that compose the energy island is addressed through exploiting the great potential of the above-mentioned RES, taking into consideration criteria with regard to several significant human activities. To this end, the concept of an innovative floating modular energy island (FMEI) that integrates different renewable energy resources is proposed; in addition, a case study that focuses on the energy independency of a big island illustrates the concept referring to the substitution of the local thermal power plants that are currently in operation in Crete with sustainable energy power. Although focused on the renewable energy resources around Crete, the work of this paper provides a basis for a systematic offshore renewable energy assessment as it proposes a new methodology that could be used anywhere around the globe. [ABSTRACT FROM AUTHOR]

Obtaining wind energy for the production of electric energy plays a key role in overcoming the problems associated with climate change and the dwindling reserves of traditional types of energy resources. The purpose of this work is to analyze current methods of energy estimation and forecasting, to consider the main classifications of forecasts and methods used in their construction and to review the main types of mathematical distributions used to calculate the speed and power of wind flow, depending on specific geographical conditions. In recent years, there has been an increase in the capacity of modern wind generators, which has significantly improved the efficiency of wind energy parks. The initial stage in determining the feasibility of involving a particular energy source in the overall energy system of the region is a preliminary assessment of the energy potential, allowing one to determine the possible percentage of substitution of traditional energy. To solve such a problem, it is necessary to use models of energy supply. Evaluation of wind as a resource creates certain difficulties in modeling because this resource is stochastic and variable. In this regard, this paper proposes to consider various models for estimating wind energy potential, which can be classified into empirical models and models based on the application of modern intelligent data analysis technologies. The paper presents an analysis of the existing models for estimating the amount of energy, which can be used in a system designed to determine the most optimal configuration of the energy system based on the use of different conversion technologies most relevant to the case under study, and it also serves as the basis for creating digital twins designed to model and optimize the operation of the projected energy complex. [ABSTRACT FROM AUTHOR]

As the penetration of renewable energy sources into a power system increases, the significance of precise short-term forecasts for wind power generation becomes paramount. However, the erratic and non-periodic nature of wind poses challenges in accurately predicting the output. This paper presents a comprehensive investigation into forecasting wind power generation for the following day, using three machine learning models: long short-term memory (LSTM), convolutional neural network-bidirectional LSTM (CNN-biLSTM), and light gradient boosting machine (LGBM). In addition, this paper proposes a method to improve the prediction performance of LGBM by separating data according to the distribution of features, and training and testing each separated dataset with a distinct model. This study includes a comparative analysis of the performance of the proposed models in predicting wind turbine output, offering valuable insights into their respective efficiencies. The results of this investigation were analyzed for two geographically distinct wind farms (Korea and the UK). The findings of this study are expected to facilitate the selection of efficient prediction models within the forecast accuracy auxiliary service market and assist grid operators in ensuring reliable power supply for the grid. [ABSTRACT FROM AUTHOR]

Since hybrid power plants (HPPs) play an intensive role in the energy supply balance of future energy systems, there is today increased attention on co-located wind–battery HPPs both in industry and academia. This paper proposes an energy management system (EMS) methodology for wind–battery plants participating in two sequential electricity markets, namely in the spot market (SM) and the balancing market (BM). The proposed and implemented EMS consists of day-ahead (DA) spot market optimization, hour-ahead (HA) balancing market optimization, and intra-hour re-dispatch optimization to allow HPPs to achieve energy arbitrage, to offer regulation power at the HA stage, and to reduce real-time imbalances. The optimization models used in the EMS incorporate an accurate battery degradation model and grid connection constraints. This paper presents a detailed case analysis of the profitability of HPPs in markets towards 2030 based on the proposed EMS. Furthermore, the value of intra-hour re-dispatch optimization in improving the feasibility of generation plans, as well as the impacts of overplanting on wind energy curtailment and battery degradation, is also investigated based on the proposed EMS. [ABSTRACT FROM AUTHOR]

This paper publishes the experimental performance results of a novel small dual−stage wind turbine at different speeds under wind tunnel testing. The results confirm its superiority at low wind speeds <7 m/s, which is consistent with natural wind conditions in most countries around the world. At lower wind speeds, the efficiency of the small, dual−stage wind turbine is higher than that of a conventional single−stage turbine. At wind speeds of <6 m/s, the novel dual−stage wind turbine always produces approximately 2.5 times more power than a single−stage wind turbine; the power coefficient is greater than 0.4. Thanks to this feature, the novel small, dual−stage wind turbine is effective in practical applications. For windy areas in Vietnam, the power generated over the same amount of time by the novel dual−stage wind turbine can be more than double that of a single−stage turbine. Next, a standard CFD model is established and validated with the above experimental measurements. This method ensures the objectivity and authenticity of the computational model. This standard CFD model is applicable to a horizontal double−stage wind turbine with any configuration and blade profile. The URANS simulation method, which uses sliding meshes and the k−omega SST turbulence model, was implemented with rotational domains of the minimum thicknesses of rotational domains (in this paper, 0.09Rfront and 0.06Rrear, accordingly). This method predicts higher mechanical work for the turbine than the actual value by an interval of <10% in the same speed range as in the experiment. This is an acceptable deviation. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review on the employment of wide bandgap (WBG) semiconductor power devices in wind energy conversion systems (WECSs). Silicon-carbide- (SiC) and gallium-nitride (GaN)-based power devices are highlighted and studied in this review, focusing on their application in the wind energy system. This is due to their premium characteristics such as the operation at high switching frequency, which can reduce the switching losses, and the capability to operate at high temperatures compared with silicon (Si)-based devices. These advantages promote the replacement of the conventional Si-based devices with the WBG semiconductor devices in the new modular converter topologies due to the persistent demand for a more-efficient power converter topology with lower losses and smaller sizes. The main objective of this paper was to provide a comprehensive overview of the WBG power devices commercially available on the market and employed in the modular converter topologies for renewable energy systems. The paper also provides a comparison between the WBG power technologies and the traditional ones based on the Si devices. The paper starts from the conventional modular power converter topology circuits, and then, it discusses the opportunities for integrating the SiC and WBG devices in the modular power converters to improve and enhance the system's performance. [ABSTRACT FROM AUTHOR]

The open-winding permanent magnet synchronous machines (OW-PMSMs) have recently been gaining more attention because of their fault-tolerant capability and power quality comparable to a 3-level converter-driven system. This paper reviews the common configurations of OW-PMSM when used as a generator, highlighting its shortcomings and benefits. The OW-PMSM with a common DC bus was found to be a promising direct-drive generator solution for wind energy conversion (WEC) systems considering fault tolerance, DC bus utilization, and power quality when appropriate control algorithms are in place. The presence of the zero-sequence current is the key disadvantage of the common DC bus configuration. The review highlights the algorithms that have been proposed to suppress the zero-sequence current of the OW-PMSM under healthy and various fault conditions, especially the open-circuit fault of semiconductor switch. Shutting down remotely located wind turbines because of faults, until they can be repaired, may not make economic sense. The OW-PMSM can offer the opportunity, to run a WEC system even under fault conditions albeit with low output power. This paper will assess the literature gaps in the existing control techniques that prevent the extension via a comprehensive review. [ABSTRACT FROM AUTHOR]