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This paper presents a detailed model of low-frequency oscillations and their damping within the Electric Power System (EPS) of Bosnia and Herzegovina (B&H). The system is modeled using MATLAB software, analysing the steady state and dynamic responses. This research highlights the challenges and impacts of integrating renewable energy sources, such as wind farms, on grid stability and oscillation damping. The paper utilizes eigenvalue analysis to investigate the dynamic characteristics of the system, emphasizing the need for efficient damping strategies to maintain system stability. The methodology includes a comprehensive review of existing literature, the creation of a detailed EPS model of B&H, and the application of eigenvalue and oscillation amplitude analysis to determine damping ratios. The dynamic responses of hydro power plants, HPP Mostar and HPP Jablanica, to transient disturbances are analysed to validate the model and refine damping strategies. The results indicate that the B&H EPS is well-damped, with all eigenvalues possessing negative real parts, and demonstrate the system's resilience to small disturbances. The results are compared with the technical report on the integration of the wind power plant WPP Podveležje. This comparative analysis shows consistent patterns between the modeled calculations and empirical data, confirming the robustness of the EPS model. This alignment underscores the effectiveness of current damping mechanisms and provides a foundational strategy for enhancing system stability with increasing renewable energy penetration. The findings highlight the importance of developing advanced control strategies to sustain system stability as the integration of variable renewable energy sources continues to grow. [ABSTRACT FROM AUTHOR]

10.3390/en13143519 15 Yang R., Yuan Y., Ying R., Shen B., Long T. A novel energy management strategy for a ship's hybrid solar energy generation system using a particle swarm optimization algorithm. Unfortunately, the paper does not take into account the fragility of the European Energy System with respect to the Energy suppliers. Moreover, the Energy Plant is strongly constrained by the primary energy providers and the energy distribution system must provide the difficult tasks related to implement dispatching and energy saving systems. 10.3390/en13040894 10 Nikolaou T., Stavrakakis G.S., Tsamoudalis K. Modeling and optimal dimensioning of a pumped hydro energy storage system for the exploitation of the rejected wind energy in the non-interconnected electrical power system of the Crete island, Greece. [Extracted from the article]

Optimal power flow (OPF) calculation methods are important for the power system operation and mainly focus on the deterministic power flow calculation, neglecting the impact of demand response on online security calculation of power systems with renewable energy sources. Therefore, this paper proposes an OPF calculation method that considers the uncertainties of wind power, photovoltaic (PV) power generation and demand-side response. Firstly, the research focuses on the renewable energy grid, considering the uncertainties of wind power and PV power generation, and establishes uncertainty models for wind power and PV output. Secondly, based on cloud model theory, an uncertainty model for demand response is established. According to the established models, an efficient OPF model is constructed with a linearized submodels considering multiple uncertainties. By testing on the IEEE 30-bus system as a typical example, we found the effectiveness and superiority of the proposed OPF calculation method can benefit the power system economic operation and demand side resource utilization. [ABSTRACT FROM AUTHOR]

Renewable energy is becoming more attractive as traditional fossil fuels are rapidly depleted and expensive, and their use would release pollutants. Power systems that use both wind and solar energy are more reliable and efficient than those that utilize only one energy. Hybrid renewable energy systems (HRES) are viable for remote areas operating in standalone mode. This paper aims to present the state-of-the-art research on off-grid solar-wind hybrid energy systems over the last two decades. More than 1500 published articles extracted from the Web of Science are analyzed by bibliometric methods and processed by CiteSpace to present the results with figures and tables. Productive countries and highly cited authors are identified, and hot topics with hotspot articles are shown in landscape and timeline views. Emerging trends and new developments related to techno-economic analysis and microgrids, as well as the application of HOMER software, are predicted based on the analysis of citation bursts. Furthermore, the opportunities of hybrid energy systems for sustainable development are discussed, and challenges and possible solutions are proposed. The study of this paper provides researchers with a comprehensive understanding and intuitive representation of standalone solar-wind hybrid energy systems. [ABSTRACT FROM AUTHOR]

Wind power is a rapidly developing energy source. Many nations use wind power to meet a considerable amount of their energy needs. Moreover, the technology of wind power has evolved over the period of time. As a result, the wind farm-incorporated power system has received more attention for its outstanding contributions. The purpose of this study is to review the research works published on four key topics within the theme of wind farm-incorporated power systems. We survey the research papers that are featured in the Web of Science database. We employ an approach called Methodi Ordinatio to filter the papers. The publication of papers related to wind farm-incorporated power system has increased significantly, especially between 2018 and 2022. Therefore, we conduct a database search during this period and select important papers. Then we review and describe the technical challenges and solutions of these papers. Furthermore, a bibliographic coupling analysis is presented. The analysis shows that the journals such as Energy, Energies, and Renewable Energy are the leading journals publishing papers on all four key topics. The analysis further demonstrates that the focus of the researchers is on wind power forecasting, followed by energy storage systems, and wind farm layout optimization. The least focus is on optimal power flow. [ABSTRACT FROM AUTHOR]

In recent years, various energy crisis and environmental considerations have prompted the use of renewable energy resources. Renewable energy resources like solar, wind, hydro, biomass, etc. have been a continuous source of clean energy. Wind energy is one of the renewable energy resources that has been widely used all over the world. The wind power is mainly dependent on wind speed which is a random variable and its unpredictable behavior creates various challenges for wind farm operators like energy dispatching and system scheduling. Hence, predicting wind power energy becomes crucial. This has led to the development of various forecasting models in the recent decades. The most commonly used deep learning algorithms for wind power prediction are- RNN (Recurrent Neural Network), LSTM (Long Short- Term Memory) and CNN (Convolutional Neural Network). This paper presents the working of these algorithms and provides a timeline review of the research papers that used these algorithms for wind power prediction. [ABSTRACT FROM AUTHOR]

With the increase in the proportion of photovoltaic and wind power access, the scale and form of distribution networks are becoming more and more complex. The traditional single distribution network vulnerability assessment method is difficult to use to identify the vulnerable links in the distribution network. Therefore, this paper proposes a method for identifying and evaluating vulnerable links in distribution networks based on minimum discriminant information. First, considering the influence of distributed grid connection, an improved probabilistic power flow calculation method is proposed, which improves the calculation efficiency and accuracy. Second, considering the correlation degree, transmission capacity, and voltage stability of branches in the distribution network, the identification index of vulnerable lines is defined. Based on power quality and operating state, the identification index of vulnerable nodes in a distribution network is defined. Finally, based on the indicators of vulnerable nodes and vulnerable lines, the vulnerable links in the distribution network are comprehensively evaluated based on the principle of minimum discriminant information, and the vulnerable links of the entire distribution network are evaluated according to different degrees of vulnerability. The rationality and effectiveness of the proposed method are verified via an example analysis of actual power grid data. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources (RES) into smart grids has been considered crucial for advancing towards a sustainable and resilient energy infrastructure. Their integration is vital for achieving energy sustainability among all clean energy sources, including wind, solar, and hydropower. This review paper provides a thoughtful analysis of the current status of the smart grid, focusing on integrating various RES, such as wind and solar, into the smart grid. This review highlights the significant role of RES in reducing greenhouse gas emissions and reducing traditional fossil fuel reliability, thereby contributing to environmental sustainability and empowering energy security. Moreover, key advancements in smart grid technologies, such as Advanced Metering Infrastructure (AMI), Distributed Control Systems (DCS), and Supervisory Control and Data Acquisition (SCADA) systems, are explored to clarify the related topics to the smart grid. The usage of various technologies enhances grid reliability, efficiency, and resilience are introduced. This paper also investigates the application of Machine Learning (ML) techniques in energy management optimization within smart grids with the usage of various optimization techniques. The findings emphasize the transformative impact of integrating RES and advanced smart grid technologies alongside the need for continued innovation and supportive policy frameworks to achieve a sustainable energy future. [ABSTRACT FROM AUTHOR]

As the proportion of new energy continues to increase, the safety and stability of the new power system are challenged, urgently requiring the allocation of new flexible resources. This paper proposes a two-stage robust capacity optimization model considering flexibility demand constraints. Firstly, the uncertain characteristics of new energy are described, and a model of flexible resource adjustment capacity is established. Then, uncertain parameters are introduced to construct a robust capacity optimization model considering supply-demand balance, solved by column constraint generation algorithm and KKT theorem. Finally, a power system in a certain region of China is selected as the simulation object for empirical analysis to verify the effectiveness of the constructed model. The results show that the two-stage robust configuration optimization model constructed in this paper can address the uncertainty of power system and the flexibility demand brought by new energy, and the planning results can achieve a balance between the safety and economy of the new power system. [ABSTRACT FROM AUTHOR]

Traditional horizontal-axis wind turbines (HAWTs) have limited efficiency in low-wind speed regions. In this paper, an integrated energy system (IES) incorporating vertical-axis wind turbines (VAWTs) is proposed; this IES is located in an oilfield management area, which can utilize the low-wind speed resources more efficiently and improve renewable energy consumption, and it also introduces a demand response model based on thermal inertia (TI), thus smoothing out the volatility caused by the VAWT. Typical output scenarios are obtained through stochastic optimization to deal with wind turbine and photovoltaic output uncertainties, and an optimal scheduling model is proposed to minimize the system operating cost. Finally, a simulation study was conducted in a micro-oilfield management area in Shandong Province, China, to demonstrate the performance of the proposed system. The results show that the IES using a VAWT and TI can increase the renewable energy consumption capacity by 87% over the conventional HAWT system, change the user behavior, increase the economic efficiency by 12%, and achieve the smoothing of load-side fluctuation of electric and thermal loads, peak shaving, and valley filling. This paper provides a feasible solution for an IES in low-wind speed areas. [ABSTRACT FROM AUTHOR]

The adaptation of renewable energy has been increasing in a very encouraging way all over the world. Among various renewable energy resources, wind and solar energy are the promising sources of alternative energy. Wind and solar photovoltaic (PV) have been employed in parallel as a hybrid system for better electricity service. This paper presents a case study and modeling of wind-solar hybrid system in Hriharpur Gadi village, Sindhuli District, Nepal. The hybrid system yields 110kWh of energy per day meeting the village's electricity demand of 87 kWh per day. Moreover, the hybrid power system with battery storage system is modeled using MATLAB simulator. Further, improvising in the existing modeling has been presented to enhance the efficiency and effectiveness of the system. [ABSTRACT FROM AUTHOR]

Technological innovation is the key to develop wind power and photovoltaic power industries. The feed-in tariff (FIT) policy, as a demand-pull policy, is important to support renewable energy technological innovation. Using the "difference-in-differences" method, this paper investigates the impact of FIT policy of wind power and the impact of the FIT policy designed according to differences in the distribution of resources on wind power technological innovation. The findings show that the FIT policy can drive patenting in wind power technologies during the implementation period, but may play a relatively weak promoting role in technological innovation in the latter term, and the FIT policy designed according to differences in the distribution of resources also stimulates more patent counts. Finally, based on the fixed effect negative binomial regression model, this paper finds that the higher feed-in tariffs can increase the patent counts in photovoltaic power technologies. [ABSTRACT FROM AUTHOR]

Overall, integrating EVs in power management proves to be a promising solution for enhancing green power generation, promoting green transport, and reducing grid pressure due to increasing demand. The integration of renewable energy sources (RESs) and advanced storage systems into existing power grids has become a pivotal approach to achieving this ambitious goal. This paper suggests future research directions, including the development of dynamic voltage stability analysis methods, online real-time techniques for assessing system voltage, and strategies for coping with increasing asynchronous generation from renewable energy sources to ensure stable and efficient power systems. [Extracted from the article]

Wind energy, as a key link in renewable energy, has seen its penetration in the power grid increase in recent years. In this context, accurate and reliable short-term wind power prediction is particularly important for the real-time scheduling and operation of power systems. However, many deep learning-based methods rely on the relationship between wind speed and wind power to build a prediction model. These methods tend to consider only the temporal features and ignore the spatial and frequency domain features of the wind power variables, resulting in poor prediction accuracy. In addition to this, existing power forecasts for wind farms are often based on the wind farm level, without considering the impact of individual turbines on the wind power forecast. Therefore, this paper proposes a wind power prediction model based on multi-feature domain learning (MFDnet). Firstly, the model captures the similarity between turbines using the latitude, longitude and wind speed of the turbines, and constructs a turbine group with similar features as input based on the nearest neighbor algorithm. On this basis, the Seq2Seq framework is utilized to achieve weighted fusion with temporal and spatial features in multi-feature domains through high-frequency feature extraction by DWT. Finally, the validity of the model is verified with data from a wind farm in the U.S. The results show that the overall performance of the model outperforms other wind farm power prediction algorithms, and reduces MAE by 25.5% and RMSE by 20.6% compared to the baseline persistence model in predicting the next hour of wind power. [ABSTRACT FROM AUTHOR]

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]

In recent years, frequent occurrence of extreme weather events has caused great losses to the power system. Meanwhile, the high penetration of renewable energy has brought new challenges to the safe operation of the system. Coordination of various measures against different stages of disaster can effectively reduce the overall loss and enhance system's resilience. This paper proposed a methodology to coordinate preventive and emergency dispatch stages based on the defender–attacker–defender model, in which various dispatching measures are modelled as defenders, whereas the extreme weather event and wind power's uncertainty are modelled as attackers. Specifically, a two‐stage three‐layer robust optimal dispatching model is established, in which conventional generator's output, wind farm's grid‐connected planning, and load shedding are taken as the main dispatching measures. To solve this model, the column and constraint generation algorithm is used to transform the three‐layer optimization model into a mixed integer linear problem. The effectiveness of the proposed model is verified in the modified IEEE RTS‐79 test system, and the results show that the coordination of preventive and emergency dispatch can effectively reduce the load outage cost under extreme weather, thus enhancing the ability of power system to cope with extreme scenarios. [ABSTRACT FROM AUTHOR]

The goal of increasing fuel efficiency and decreasing greenhouse gas (GHG) emissions has increased interest in the application of renewable energy sources and the usage of new technologies in the maritime industry. In order to implement the most suitable source, factors such as voyage duration, storage availability, and the condition of existing vessels as well as those that are still under construction should be taken into account. Propeller optimization is proposed as a long-term solution. This paper investigates the environmental aspects of propeller optimization, focusing on its potential to reduce ship vibrations fuel consumption, and, therefore, the ship's carbon footprint. The case study presents propeller optimization on a Ro-Ro passenger ship. The data collected during sea trials before and after propeller optimization will be compared. Expected fuel oil consumption will be correlated to the CO2 emission reduction. Besides propeller optimization, the paper performs a SWOT (strengths, weaknesses, opportunities, threats) analysis comparing it with solar and wind power applications on ships. [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]

In a power system with a high proportion of renewable energy, sudden increases in wind power or photovoltaic output can lead to huge challenges, such as difficulties in accommodating excess renewable energy and imbalances between supply and demand on the grid. As an important adjustable resource on the demand side, air conditioning load is a flexible load for realizing output consumption. In this paper, a heterogeneous air conditioning load regulation strategy for renewable energy consumption is proposed. Each air conditioning load regulation quantity is obtained based on the day-ahead dispatching mode. Then, the temperature setting value, rated power, and duty cycle are selected as the indexes. The load regulation sequence is obtained by the entropy weight method. Finally, the load regulation time of each air conditioning load is obtained based on the constraint of the quantity of loads during the possible adjustment time. The simulation analysis shows that the temperature regulation strategy presented in this paper can effectively reduce the power fluctuations of air conditioning loads, while ensuring that users with lower temperature settings are selected in the adjustment process. [ABSTRACT FROM AUTHOR]

Conventional energy resource problems made power system planners apply more renewable energy sources (RESs) in the power system. RESs have a significant impact on fulfilling the world's energy demand, protecting the environment at least in the exploration phase, and providing energy reliability as well as security for the new generation of power grids. In this regard, wind power generation is considered a promising solution. However, by increasing the share of wind power generation units in the power sector, some challenges, such as voltage problems, transient stability, increasing system losses, and line congestions can occur. Consequently, an alternative solution is required to accelerate wind power units' connections to the grid by preventing the problems caused by the penetration of wind power. In this regard, Flexible Alternating Current Transmission Systems (FACTS) devices can play an important role while they can improve power grid dynamic performance. This paper presents a review of the optimization techniques applied to accelerate the penetration of wind energy by exploiting FACTS devices. The topic identifies the objectives and optimization formulations, as well as schemes and models available in the published literature. This survey gives noteworthy insights to the researchers to cover available solutions in these regards. The literature in the field is summarized in different aspects, such as FACTS technology, objective functions, constraints, optimization methods, planning or operation purposes, and load flow types. At each aspect, a comparison is derived to recognize the most important issues. The result of the review is the finding of future work and contributions which can be done in the field of wind energy integration acceleration using FACTS devices and the output of the paper can be used as a guide for researchers to find a new path for research in this area. [ABSTRACT FROM AUTHOR]

Offshore wind power is a valuable renewable energy source. However, the development of offshore wind farms is facing many challenges, including understanding their impacts on ecosystems and society, as well as knowledge gaps in research. In this study, a bibliometric analysis was performed with the aim of providing a comprehensive understanding of current global progress in offshore wind farm research. Three stages of development were considered for the analysis and comparison of research characteristics and outcomes. Based on the total number of scientific publications and the international collaboration ratio (ICR), Europe has been leading research in and the development of offshore wind power since the early 1990s. However, a fast-growing period of offshore wind farm development occurred after 2000, during which Europe and Asia in particular showed increases in ICR. The installation of offshore wind turbines may have non-negligible adverse impacts on marine ecosystems, especially in ecologically vulnerable regions or developing countries. Research and relevant studies should be integrated to investigate and reduce the ecological and environmental damage that results from offshore wind farm development. This paper presents a systematic evaluation of the global developmental trends in offshore wind farm research, which could help to characterize and guide future trends within this field. [ABSTRACT FROM AUTHOR]

Wind power generation is a renewable energy source, and its power output is influenced by multiple factors such as wind speed, direction, meteorological conditions, and the characteristics of wind turbines. Therefore, accurately predicting wind power is crucial for the grid operation and maintenance management of wind power plants. This paper proposes a hybrid model to improve the accuracy of wind power prediction. Accurate wind power forecasting is critical for the safe operation of power systems. To improve the accuracy of wind power prediction, this paper proposes a hybrid model incorporating variational modal decomposition (VMD), a Sparrow Search Algorithm (SSA), and a temporal-convolutional-network-based bi-directional gated recurrent unit (TCN-BiGRU). The model first uses VMD to break down the raw power data into several modal components, and then it builds an SSA-TCN-BIGRU model for each component for prediction, and finally, it accumulates all the predicted components to obtain the wind power prediction results. The proposed short-term wind power prediction model was validated using measured data from a wind farm in China. The proposed VMD-SSA-TCN-BiGRU forecasting framework is compared with benchmark models to verify its practicability and reliability. Compared with the TCN-BiGRU, the symmetric mean absolute percentage error, the mean absolute error, and the root mean square error of the VMD-SSA-TCN-BiGRU model reduced by 34.36%, 49.14%, and 55.94%. [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]

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]

Developing photovoltaic (PV) and wind power is one of the most efficient approaches to reduce carbon emissions. Accumulating the PV and wind energy resources at different geographical locations can minimize total power output variance as injected into the power systems. To some extent, a low degree of the variance amplitude of the renewable resources can reduce the requirement of in-depth regulation and dispatch for the fossil fuel-based thermal power plants. Such an issue can alternatively reduce carbon emissions. Thus, the correlation problem by minimizing the variance of total PV and wind power plays a vital role in power system planning and operation. However, the synergistic effect of power output correlation is mainly considered on the generation side, and it is often neglected for the correlation relationship between the power grid components. To address this problem, this paper proposes a correlation coefficient analysis method for the power grid, which can quantify the relationship between energy storage and the probabilistic power flow (PPF) of the grid. Subsequently, to accelerate the mapping efficiency of power correlation coefficients, a novel deep neural network (DNN) optimized by multi-task learning and attention mechanism (MA-DNN) is developed to predict power flow fluctuations. Finally, the simulation results show that in IEEE 9-bus and IEEE14-bus systems, the strong correlation grouping percentage between the power correlation coefficients and power flow fluctuations reached 92% and 51%, respectively. The percentages of groups indicating weak correlation are 4% and 38%. In the modified IEEE 23-bus system, the computational accuracy of MA-DNN is improved by 37.35% compared to the PPF based on Latin hypercube sampling. Additionally, the MA-DNN regression prediction model exhibits a substantial improvement in assessing power flow fluctuations in the power grid, achieving a speed enhancement of 758.85 times compared to the conventional probability power flow algorithms. These findings provide the rapid selection of the grid access point with the minimum power flow fluctuations. [ABSTRACT FROM AUTHOR]

The floating offshore wind turbine provides a feasible solution for the development of renewable ocean energy. However, the sizeable rotor diameter of the wind turbine results in large wind heeling moments and pitch amplitude. It will increase the structural loads and cause safety problems. Additionally, the contradictory nature between the stability and the sea-keeping of the floating structure requires that the more flexible method should be adopted to reduce the motion response of the floating offshore wind turbine. Therefore, an innovative concept of a multibody anti-pitching semi-submersible floating offshore wind turbine, named the MBAPSF, is proposed in this paper. The MBAPSF consists of a 5 MW braceless semi-submersible wind turbine and three wave energy converters. The multibody coupled numerical model is established by using an F2A tool, and the dynamic performance of the MBAPSF is compared with that of the traditional semi-submersible wind turbine named the TSSF. The results show that the innovative concept proposed in this paper can reduce pitch motion up to approximately 27% under different load cases, and the maximum bending moment and shearing force at the tower base are also reduced by more than 10%. Meanwhile, WECs are beneficial for increases in the total power generation capacity. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive exploration of a hybrid energy system that integrates wind turbines with photovoltaics (PVs) to address the intermittent nature of electricity production from these sources. The necessity for such technology arises from the sporadic nature of electricity generated by PV cells and wind turbines. The envisioned outcome is an emissions-free, more efficient alternative to traditional energy sources. A variety of optimization techniques are utilized, specifically the Particle Swarm Optimization (PSO) algorithm and Electric Eel Foraging Optimization (EEFO), to achieve optimal power regulation and seamless integration with the public grid, as well as to mitigate anticipated loading issues. The employed mathematical modeling and simulation techniques are used to assess the effectiveness of EEFO in optimizing the operation of grid-connected PV and wind turbine hybrid systems. In this paper, the optimization methods applied to the system's architecture are described in detail, providing a clear understanding of the intricate nature of the approach. The efficacy of these optimization strategies is rigorously evaluated through simulations of diverse operating scenarios using MATLAB/SIMULINK. The results demonstrate that the proposed optimization strategies are not only capable of precisely and swiftly compensating for linked loads, but also effectively controlling the energy supply to maintain the load's power at the desired level. The findings underscore the potential of this hybrid energy system to offer a sustainable and reliable solution for meeting power demands, contributing to the advancement of clean and efficient energy technologies. The results demonstrate the capability of the proposed approach to improve system performance, maximize energy yield, and enhance grid integration, thereby contributing to the advancement of renewable energy technologies and sustainable energy systems. [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]

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]

With the rapid development of photovoltaic and wind power, the penetration rate of renewable energy in power system is gradually increasing. This escalation poses a challenge as it results in a continuous reduction in the inertia and damping of the power grid, accentuating the issue of frequency stability in power systems. It is one of the effective measures to deal with this risk that renewable energy sources such as wind turbines actively provide frequency support for power grid. This paper carries out research into the examination of wind turbines' capacity to contribute to system frequency support, considering two aspects: inertia support and primary frequency regulation capabilities. Subsequently, the frequency control method of the wind turbine support system is analyzed, emphasizing the roles of rotor kinetic energy control and power reserve control in facilitating frequency support. The evaluation of the transient frequency support capability of wind turbines is introduced, incorporating factors such as control methods, controller parameters, and the duration of transient frequency support. Key metrics, including accumulated energy and frequency change rate indices during the transient frequency support stage, are proposed to quantitatively assess the transient frequency support capability of wind turbines. These indices provide a comprehensive framework for the quantitative evaluation of wind turbine transient frequency support capabilities. [ABSTRACT FROM AUTHOR]

With the high penetration of renewable energy into power grids, frequency stability and oscillation have become big concerns due to the reduced system inertia. The application of the Battery Energy Storage System (BESS) is considered one of the options to deal with frequency stability and oscillation. This paper presents a strategy to size, locate, and operate the BESS within the power grid and, therefore, investigate how sizing capacity is related to renewable energy penetration levels. This paper proposes an identification method to determine the best location of the BESS using the Prony method based on system oscillation analysis, which is easy to implement based on measurements while actual physical system models are not required. The proposed methods for BESS size and location are applied using MATLAB/Simulink simulation software (version: R2023a) on the Kundur 2-area 11-bus test system with different renewable energy penetration levels, and the effectiveness of the applied method in enhancing frequency stability is illustrated in the study cases. The case studies showed a significant improvement in steady-state frequency deviation, frequency nadir, and Rate of Change of Frequency (ROCOF) after implementing BESS at the selected bus. The integration of BESS can help to avoid Under-frequency Load Shedding (UFLS) by proper selections of size, location, and operating strategy of the BESS within the power grid. [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]

Since the future grid needs to provide sufficient electrical energy with high quality, it is envisaged that most of the used energy will be supplied by grid-connected voltage source converters (VSCs). The VSCs are used for connecting renewable energy source to the grid, e.g., wind and solar power. The grid-connected VSC considered in this paper is a three-phase three-wire full-bridge converter in which a capacitor is connected to its dc side, while its ac-side is connected to the grid with current control. When operating under unbalanced voltages at the point of common coupling, either due to the presence of unbalanced loads or due to a grid fault condition, a voltage ripple appears at the dc side. The interaction between the VSC dc- side voltage ripple and the VSC ac-side currents is the main focus of this study. In this paper, we analyze the interaction between the VSC dc-side voltage ripple and the VSC ac-side currents under unbalanced voltages. Furthermore, the proposed model is used to determine in which conditions the stability of the VSC ac-side currents can be guaranteed even in the presence of the dc-side voltage ripple. Experimental results confirm that the system remains stable, although with distorted currents due to the dc voltage ripple. Furthermore, it is also shown that the injection of negative sequence currents can decrease the current distortion. [ABSTRACT FROM AUTHOR]

This paper presents the performance investigation of voltage and current for dynamic model of the wind turbine. In this study, the various numbers of wind turbine speed are applied in the simulation. This treatment is intended to see how much influence the turbine speed has on the voltage and current output. IEEE 14 bus system is integrated to the wind turbine in order to observe the impact of on-grid connection to the voltage and current performance. How to model wind turbine in PSCAD simulation software also discussed in this paper. The detail of supporting components in designing a wind turbine system and their functions are also explained. Several values of turbine speed are also considered in this paper as a study material in seeing the performance of wind turbines. The relationship between wind speed and pitch angle will also be discussed to ensure that the wind turbine is not damaged. In order to prove the accuracy of the simulation model, the obtained measurement generation of active power from the wind turbine is matched with the manual calculation. Based on the various wind speed values that have been tested, this can be the basis for the application of wind turbine (renewable energy) design development for further research. [ABSTRACT FROM AUTHOR]

The continuing transition to renewable energy will require more wind turbines to be installed and operated on land and offshore. On land, wind turbines will increasingly be deployed in hilly or mountainous regions, which are often described together as "complex terrain" in the wind energy industry. These areas can experience complex flows that are hard to model, as well as cold climate conditions that lead to instrument and blade icing and can further impact wind turbine operation. This paper -- a collaboration between several International Energy Agency (IEA) Wind Tasks and research groups based in mountainous countries -- sets out the research and development needed to improve the financial competitiveness and ease of integration of wind energy in hilly or mountainous regions. The focus of the paper is on the interaction between the atmosphere, terrain, land cover, and wind turbines, during all stages of a project life cycle. The key needs include collaborative research and development facilities, improved wind and weather models that can cope with mountainous terrain, frameworks for sharing data, and a common, quantitative definition of site complexity. Addressing these needs will be essential for the affordable and reliable large-scale deployment of wind energy in many countries across the globe. Because of the widespread nature of complex flow and icing conditions, addressing these challenges will have positive impacts on the risk and cost of energy from wind energy globally. [ABSTRACT FROM AUTHOR]

Repowering wind farms entails upgrading or replacing old turbines with more efficient, capable, and profitable ones. This technique has the potential to boost energy production, improve grid integration, and lower operational costs. Repowering also makes use of the most recent breakthroughs in wind energy technology, ensuring that wind farms stay economically viable and contribute to the growth of renewable energy. The majority of the wind farms that are located in India were constructed in early 2000, and their capacity ratings range from 200 kW to 800 kW. The lifespans of these wind farms have finally come to an end. Repowering wind farms is a viable alternative in these regions due to the significant wind capacity that exists there. In order to find the wind potential in the site wind resource assessment is needed. This paper proposes a repowering scheme for the existing wind farm located in Kayathar, Tamil Nadu. The reduction in power loss due to the wake effect in the existing wind farm is analyzed using WAsP software and repowering scheme is proposed to increase the Annual Energy Production (AEP), Capacity Utilisation Factor (CUF). The Wind Atlas Analysis and Application Program (WAsP) is utilized in order to carry out the site's wind resource evaluation. After the wind resource assessment, individual turbine wake loss is identified, and the viability of repowering the wind farm by raising the hub height of high wake-affected turbines was investigated. Another repowering study is also carried out by installing high-capacity turbines in place of the existing turbines. [ABSTRACT FROM AUTHOR]

The virtual power plant (VPP) has been recognized as an effective way to facilitate penetration of renewable and distributed energy resources in electricity markets. This paper introduces an adaptive curtailment strategy for a VPP comprising a wind power plant and uncertain demand, and explores the economic advantage of adaptively adjusting wind power curtailment. A two-stage stochastic model is proposed to deal with the uncertainties in wind power generation (WPG), load demand and market prices. In the model, the bid decision is made in the face of uncertainties in the first stage, while the control (curtailment) decision is made based on realized uncertain parameters in the second stage. This paper provides the closed-form optimal curtailment decision and characterizes the optimal bid decision. An efficient binary search algorithm is developed for optimizing the bid decision. By using a distribution-free approach, we show that as the prediction accuracy of WPG improves, the optimal bid decision converges toward the expected minimal power exchange, leading to a decrease in expected operational cost with diminishing marginal return. Numerical experiments based on real-world data demonstrate that compared with the existing greedy strategy and coordinated strategy, the proposed model can decrease the expected operational cost up to 16.9% and 11.0%, respectively. [ABSTRACT FROM AUTHOR]

With the rapid development of renewable energy sources such as wind and solar, the net load characteristics of power systems have undergone fundamental changes. This paper defines quantitative analysis indicators for net load characteristics and examines how these characteristics evolve as the proportion of wind and solar energy increases. By identifying inflection points in the system's adjustment capabilities, we categorize power systems into low, medium, and high renewable energy penetration. We then establish adjustment models that incorporate traditional coal power, hydropower, natural gas generation, adjustable loads, system interconnections, pumped-storage hydroelectricity, and new energy storage technologies. A genetic algorithm is employed to optimize and balance the net load curves under varying renewable energy proportions, analyzing the mechanism behind net load balance. A case study, based on real operational data from 2023 for a provincial power grid in western China, which is rich in renewable resources, conducts a quantitative analysis of the system's adjustment capability inflection point and net load balancing strategies. The results demonstrate that the proposed method effectively captures the evolution of the system's net load and reveals the mechanisms of net load balancing under different renewable energy penetration levels. [ABSTRACT FROM AUTHOR]

Summary: Nonagonal Modular Multilevel Converter (MMC) is a new type of three‐port AC‐AC converter suited for applications in FFTS‐based offshore wind power. It can accommodate three AC ports with only nine branches, greatly reducing the cost and size of energy conversion. However, the multiplexing structure of nonagonal MMC inevitably increases the peak branch current resulting in high semiconductor ratings, which weakens its advantage in cost and size. Since the control degrees of freedom are reduced, traditional current attenuation methods are not applicable. To address this issue, in this paper, a parameter adjustment method is proposed for nonagonal MMC to reduce the current stress. By establishing the mathematical model of nonagonal MMC, phase angle difference between ports of nonagonal MMC is proved to be a key influencing factor of peak branch current. By adjusting phase angle difference, the average current stress of nonagonal MMC can be reduced by as great as 66.31%. Finally, the correctness of the conclusions of this paper is verified by RT‐LAB results. [ABSTRACT FROM AUTHOR]

The rapid growth of renewable energy presents significant challenges for power grid operation, making the efficient integration of renewable energy crucial. This paper proposes a method to evaluate the power system's capacity to accommodate renewable energy based on the Gaussian mixture model (GMM) from a symmetry perspective, underscoring the symmetrical interplay between load and renewable energy sources and highlighting the balance necessary for enhancing grid stability. First, a 10th-order GMM is identified as the optimal model for analyzing power system load and wind power data, balancing accuracy with computational efficiency. The Metropolis–Hastings (M-H) algorithm is used to generate sample spaces, which are integrated into power flow calculations to determine the maximum renewable energy integration capacity while ensuring system stability. Short-circuit ratio calculations and N-1 fault simulations validate system robustness under high renewable energy integration. The consistency between the results from the M-H algorithm, Gibbs sampling, and Monte Carlo simulation (MCS) confirms the approach's accuracy. [ABSTRACT FROM AUTHOR]

Introduction. Weather changes lead to create oscillations in values of power extracted from renewable energy resources (RERs). These power oscillations pose significant challenges in RERs integration process with the power grid systems, through its effects on power system stability. Many studies have been performed in various methods to mitigate the output power fluctuation of wind power generation system (WPGS). Purpose. This study focuses on increasing the mitigation rate of the output power fluctuation of WPGS caused by the rapid wind speed changes during wind gusts. Superconducting magnetic energy storage (SMES) system through its properties represents an effective solution for the WPGS power fluctuation issue. WPGS and SMES systems are linked to power grid system through the point of common coupling (PCC). Methods. This paper proposes two robust controllers for controlling the SMES system. The first controller is a Fuzzy Logic Controller (FLC), which has been utilized for controlling the power exchange between the SMES coil and the PCC of the utility grid. While the second controller is a PI controller, which have been utilized to regulate the voltages between the two sides of the PCC and the DC link capacitor in the SMES system. The proposed controllers have been constructed so that can make the SMES system absorb/deliver the real power instantaneously from/toward PCC according the wind speed changes. MATLAB/Simulink has been utilized to simulate the system under study and test the performance of proposed controllers. In addition, two different wind speed scenarios have been used in the simulation. Practical value. Results of simulation have proven the effectiveness of proposed controllers so that the active power fluctuation delivered to utility grid can be reduced by up to 89 %. References 31, tables 4, figures 9. [ABSTRACT FROM AUTHOR]

Federal and state governments have recently expanded their regulatory mandates and incentives to promote renewable energy (RE) use in their planned new buildings. This requires planners to analyze and optimize their preliminary design decisions, such as building dimensions, orientation, location, and window-to-wall ratio, to maximize the use of RE in their buildings. To support designers in this critical task, this paper presents a novel model for optimizing preliminary building designs. The two optimization objectives of the optimization model focus on maximizing harvested RE and minimizing construction cost by identifying optimal building dimensions, orientation, window-to-wall ratio, and site layout. The optimization model complies with all design requirements, such as building dimensions, natural lighting, and cost-effectiveness constraints. The model performance is analyzed using two application examples to illustrate its capabilities in considering the impact of the surrounding environment on design decisions. The results of this analysis confirm the model contributions in identifying a set of nondominated optimal solutions that provide tradeoffs among the two optimization objectives of the developed model. These capabilities are expected to support building planners in identifying an optimal preliminary building design that maximizes the use of RE while minimizing all related construction costs. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of the most recent papers and research trends in the fields of wind energy and artificial intelligence. Our study aims to guide future research by identifying the potential application and research areas of artificial intelligence and machine learning techniques in the wind energy sector and the knowledge gaps in this field. Artificial intelligence techniques offer significant benefits and advantages in many sub‐areas, such as increasing the efficiency of wind energy facilities, estimating energy production, optimizing operation and maintenance, providing security and control, data analysis, and management. Our research focuses on studies indexed in the Web of Science library on wind energy between 2000 and 2023 using sub‐branches of artificial intelligence techniques such as artificial neural networks, other machine learning methods, data mining, fuzzy logic, meta‐heuristics, and statistical methods. In this way, current methods and techniques in the literature are examined to produce more efficient, sustainable, and reliable wind energy, and the findings are discussed for future studies. This comprehensive evaluation is designed to be helpful to academics and specialists interested in acquiring a current and broad perspective on the types of uses of artificial intelligence in wind energy and seeking what research subjects are needed in this field. [ABSTRACT FROM AUTHOR]

Recent research generally reports that the intermittent characteristics of sustainable energy sources pose great challenges to the efficiency and cost competitiveness of sustainable energy harvesting technologies. Hence, modern sustainable energy systems need to implement a stringent power management strategy to achieve the maximum possible green electricity production while reducing costs. Due to the above-mentioned characteristics of sustainable energy sources, power management systems have become increasingly sophisticated nowadays. For addressing the analysis, scheduling, and control problems of future sustainable power systems, conventional model-based methods are completely inefficient as they fail to handle irregular electric power disturbances in renewable energy generations. Consequently, with the advent of smart grids in recent years, power system operators have come to rely on smart metering and advanced sensing devices for collecting more extensive data. This, in turn, facilitates the application of advanced machine learning algorithms, which can ultimately cause the generation of useful information by learning from massive data without assumptions and simplifications in handling the most irregular operating behaviors of the power systems. This paper aims to explore various application objectives of some machine learning algorithms that primarily apply to wind energy conversion systems (WECSs). In addition, an enhanced proportional integral (PI) (2DoF) algorithm is particularly introduced and implemented in a doubly fed induction generator (DFIG)-based WECS to enhance the reliability of power production. The main contribution of this article is to leverage the superior qualities of the PI (2DoF) algorithm for enhanced performance, stability, and robustness of the WECS under uncertainties. Finally, the effectiveness of the study is demonstrated by developing a virtual reality in a MATLAB-Simulink environment. [ABSTRACT FROM AUTHOR]

The high uncertainty of wind power output greatly affects the rapid reactive power optimization of power systems. This paper proposes a neural network‐based comprehensive reactive power optimization method for large‐scale wind power grids, effectively addressing the challenges of rapid reactive power optimization in power systems. Firstly, by constructing typical wind‐power‐load scenarios, the generalization ability of the neural network is improved. Then, focusing on the comprehensive reactive power optimization problem after integrating typical wind‐power‐load scenarios into the system, the improved Harris hawks optimization algorithm (HHO) is compared with the particle swarm optimization algorithm and traditional HHO algorithm, highlighting its advantages. Finally, HHO is utilized for solving, thereby constructing a comprehensive reactive power optimization strategy tag set. Furthermore, through deep fitting of the neural network between the power grid operating state and the comprehensive reactive power optimization strategy, the computational complexity and decision‐making time of reactive power optimization are reduced. [ABSTRACT FROM AUTHOR]

This paper emphasizes the integration of wind and photovoltaic (PV) generation with battery energy storage systems (BESS) in distribution networks (DNs) to enhance grid sustainability, reliability, and flexibility. A novel multi‐objective optimization framework is introduced in this study to minimize energy supply costs, emissions, and energy losses while improving voltage deviation (VD) and voltage stability index (VSI). The proposed framework comprising normal boundary intersection (NBI) and decomposition‐based evolutionary algorithms (DBEA) determines the optimal siting and sizing of renewable‐based distributed resources, considering load demand variations and the intermittency of wind and solar outputs. The comparative analysis establishes that the proposed strategy performs better than many contemporary algorithms, specifically when all the objective functions are optimized simultaneously. The validation of the proposed framework was carried out on the standard IEEE‐33 bus test network, which demonstrates significant percentage savings in energy supply costs (49.6%), emission rate (62.2%), and energy loss (92.3%), along with enormous improvements in VSI (91.9%) and VD (99.8953%). The obtained results categorically underline the efficiency, reliability, and robustness of the proposed approach when employed on any complex distribution network comprising multiple renewable energy sources and battery storage systems. [ABSTRACT FROM AUTHOR]

This paper presents a comparative study between four techniques recently used to improve the wind energy conversion system (WECS) to water pumping systems. The WECS is a renewable energy source which has developed rapidly in recent years. The use of the WECS in the water pumping field is a free solution (economically) compared to the use of the electricity grid supply. The control of WECS, equipped with a permanent magnet synchronous generator, has the objective of carefully maximising power generation. A comparative study between the proposed Fuzzy Logic Control, optimised using a genetic algorithm and particle swarm optimisation algorithm, and the conventional Perturb and Observe MPPT method using Matlab/Simulink, is presented. The performance of the proposed system has been verified against the generated output voltage, current and power waveforms, intermediate circuit voltage waveform, and generator speed. The presented results demonstrate the effectiveness of the control strategy applied in this work. [ABSTRACT FROM AUTHOR]

Research and investigation into renewable energy sources is being sparked by the rapidly rising need for electricity, higher costs of fossil fuels, and increasing worries about the environment. Recent years have seen a tremendous increase in the use of wind energy (WE). In-depth study has been done to effectively produce power from WE. Nevertheless, it is exceedingly challenging and dangerous to set up wind turbines (WTs) for research and teaching uses due to constraints like space and upkeep. Numerous benefits come with a lab-scale WT emulator (WTE), such as freedom from space restrictions, an improved level of control, and independence from existing weather conditions. The design and execution of a low-power, lab-scale WTE are the focus of this study. The investigated experimental configuration is intended to precisely mimic the mechanical behavior of a real WT. Aerodynamics, blades, slow shafts, gearboxes, and controller elements, for example, are modeled in MATLAB/Simulink before they are assembled and implemented on a dSPACE 1104 board. A DC motor running under buck converter control is used to substitute the quick shaft. The WTE's functionality is evaluated in various wind speed conditions. The findings of comparing the WTE's dynamics with those offered by the manufacturer amply show the efficacy of the proposed WTE and its capacity to take the position of an actual WT. This paper will be a useful resource for investigators in helping them select the best WTE approach for their purposes. [ABSTRACT FROM AUTHOR]

In recent years, renewable energy has received extensive attention due to its advantages of sustainability, economy, and environmental protection. However, with the rapid development of renewable energy, the problem of curtailment is becoming increasingly serious. Studying the calculation method and establishing a quantitative evaluation system of renewable energy accommodation capacity are important means to solve this problem. This paper comprehensively considers the factors affecting the accommodation of renewable energy, establishes a accommodation calculation model with the maximum accommodation of renewable energy as the optimization target based on the time series production simulation method, and uses the hybrid particle swarm optimization (PSO) algorithm to solve it. The model is verified with historical data such as load, photovoltaic (PV), and wind power in a certain region throughout the year. The experimental results verify the rationality of the renewable-energy accommodation-capacity model proposed in this paper and the correctness of the theoretical analysis. The calculation results have important reference and guiding significance for the operation and control of power-grid planning and dispatching. [ABSTRACT FROM AUTHOR]