IET Renewable Power Generation最新文献

{"title":"Developing a cooperative approach under normal and contingency conditions for generation expansion planning of microgrids","authors":"Saeed Shahbazian,&nbsp;Saeed Kharrati,&nbsp;Abdollah Rastgou","doi":"10.1049/rpg2.13034","DOIUrl":"https://doi.org/10.1049/rpg2.13034","url":null,"abstract":"<p>The issue of generation expansion planning in microgrids has become a challenging issue in electricity industry for two reasons: load growth and uncertainties in renewables' generation. Therefore, this issue is considered here. Here, the modelling of generation expansion planning problem has been developed in a network of microgrids in a decentralized manner, considering normal and contingency conditions. On the other hand, in order to further develop the study considered, decentralized generation expansion planning model of microgrids by considering contingency conditions has been addressed in a cooperative approach to minimize total costs. In developed model, investment decisions are made at the higher level and operational constraints has been considered at lower one. Also, case studies are defined in three different scenarios: islanding operation of microgrids as first scenario and peer-to-peer trading of microgrids in non-cooperative and cooperative approaches as second and third scenarios, respectively. The results of simulations have shown that by facilitating the transactions between microgrids, their total costs are reduced. The costs of the whole set of microgrids in the non-cooperative scenario are reduced by 9.4% compared to the islanding scenario; and the costs are reduced by 7.5% in the cooperative scenario compared to the non-cooperative scenario.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 13","pages":"2065-2079"},"PeriodicalIF":2.6,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A day-ahead energy management strategy for electric vehicles in parking lots considering multi-scenario simulations and hydrogen storage system","authors":"Armin Mohajeri Avval,&nbsp;Abdolmajid Dejamkhooy","doi":"10.1049/rpg2.13047","DOIUrl":"https://doi.org/10.1049/rpg2.13047","url":null,"abstract":"<p>This article investigated the charge and discharge management structure of electric vehicles (EVs) in intelligent parking lots (IPLs). It seems that with the expansion of renewable energy sources (RESs) as clean energy and investigation of the effects of EVs on the operation and planning of future distribution networks around the way EVs exchange energy with each other and the upstream network operator, RESs such as solar and wind sources, along with hydrogen storage are essential. Therefore, a new stochastic multi-scenario approach for charge/discharge management of EVs parked in IPL was proposed, in which a newly developed model for the IPL with a hydrogen storage system (HSS) consisting of a fuel cell, an electrolyzer, and a hydrogen storage tank was presented. In the proposed model, the constraints of upstream network and power balance constraints, with the operating constraints related to the IPL, including EVs, renewable energy sources, and the hydrogen storage system, were formulated as the most important objectives of the optimization problem. The optimization algorithm, Competitive Swarm Optimizer (CSO), was formulated for implementation, and its results were compared with those of the PSO and GWO algorithms. The CSO must handle a variety of practical, large-scale optimization problems. Based on the obtained results, the excess energy purchased from the upstream network or renewable sources was used as hydrogen storage for consumption during peak hours. As expected, the technical constraints and financial goals of the system were met, and the proposed system was evaluated on a 33-bus network. As the expected benefits will be the most beneficial with the presence of renewable sources, the final profit will be reduced by taking into account uncertainty for charge/discharge management in EVs such as the uncertainty of electric load, market price, wind turbine, and photovoltaic cell sources. Nonetheless, the profit obtained from renewable resources is preferable to losses resulting from the uncertainty of the system, and according to expectation, the performance of the system for managing the optimal charging and discharging of EVs in the IPL will be acceptable with the maximum profit for the grid.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 13","pages":"2102-2127"},"PeriodicalIF":2.6,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal frequency response coordinated control strategy for hybrid wind-storage power plant based on state reconstruction","authors":"Xinshou Tian,&nbsp;Yongning Chi,&nbsp;Peng Cheng,&nbsp;Yihui Zhao,&nbsp;Hongzhi Liu","doi":"10.1049/rpg2.13049","DOIUrl":"10.1049/rpg2.13049","url":null,"abstract":"<p>When wind power and energy storage operate in tandem, their operational state undergoes continuous shifts during dynamic processes. Determining the frequency modulation capability of the combined wind and energy storage system during frequency modulation participation is challenging, often leading to a decline in power generation efficiency. To address this, the current study introduces an optimal frequency response coordinated control strategy for hybrid wind-storage power plants, anchored in state reconstruction. The frequency modulation capability is restructured based on the current state of charge (SOC) of the energy storage system. Subsequently, the equivalent active power reserve demand is evaluated for wind power, leading to the formulation of a wind power reserve active power level and its corresponding operational strategy. For swift active power support, an adaptive virtual inertia control is designed for the energy storage system, contingent on its current SOC. Concurrently, an adaptive virtual inertia control for wind power is developed, grounded in effective kinetic energy. The hybrid wind-storage power plant engages in primary frequency regulation, tailored to the nature of frequency disturbances and prevailing state characteristics. Simulation analyses affirm that this proposed strategy, which takes into account the SOC of the hybrid wind-storage power plant and the power grid's dynamics, offers robust active power frequency support during load disturbances.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 15","pages":"2892-2906"},"PeriodicalIF":2.6,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13049","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141801322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An optimizated additional damping control for suppressing ultra-low frequency oscillation suppression based on SVC","authors":"Huabo Shi,&nbsp;Chengwei Fan,&nbsp;Xueyang Zeng,&nbsp;Gang Chen,&nbsp;Baorui Chen,&nbsp;Zhen Chen","doi":"10.1049/rpg2.13059","DOIUrl":"10.1049/rpg2.13059","url":null,"abstract":"<p>The ultra-low frequency oscillation (ULFO) imposes an emerging stability challenge to the high proportion of hydropower grids. To suppress ULFO without reducing the primary frequency regulation of the hydro governor, a novel idea to exploit the voltage regulation effect of load is implemented. First, the additional damping controller is designed and configured in static var compensator (SVC), and the frequency analysis model considering the configuration of SVC as well as damping controller is established. Based on the model, the specific influence of SVC and controller on ULFO is analysed through eigenvalue calculation. In addition, the influence of various load models and SVC location on the damping level are further studied. Consequently, a parameter optimization design method for SVC additional damping control is proposed, it is modelled as the optimization problem of damping ratio in ULFO mode under multi-operation conditions, which is solved by a particle swarm optimization algorithm. Finally, the effectiveness of the designed SVC additional damping controller is verified in the improved four-machine two-area power system and the actual power grid in China.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 14","pages":"2238-2247"},"PeriodicalIF":2.6,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141810459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Grey wolf-based heuristic methods for accurate parameter extraction to optimize the performance of PV modules","authors":"Seyit Alperen Celtek,&nbsp;Seda Kul,&nbsp;Manish Kumar Singla,&nbsp;Jyoti Gupta,&nbsp;Murodbek Safaraliev,&nbsp;Hamed Zeinoddini-Meymand","doi":"10.1049/rpg2.13061","DOIUrl":"10.1049/rpg2.13061","url":null,"abstract":"<p>Parameter prediction for PV solar cells plays a crucial role in controlling and optimizing the performance of PV modules. In this study, the parameter prediction of a four-diode PV model was carried out using the Improved Grey Wolf Optimization (IGWO) algorithm, which builds upon the Grey Wolf Optimization (GWO) algorithm. The parameters required for the four-diode PV model were optimized based on a predefined objective function. Subsequently, the obtained data were compared with the data from RTCFrance Solar Cell to validate the accuracy and reliability of the optimization results. The evaluation of the optimization results revealed that the Sum Square Error (SSE) values for PSOGWO, AGWOCS, GWOCS, and GWO were 3.96E-05, while the MSE value for IGWO was 3.6309E-05. These findings clearly demonstrate that the proposed IGWO algorithm outperforms the other algorithms used in the study, based on the minimized SSE values. This study emphasizes the importance of parameter prediction in optimizing PV performance, and it contributes to thefield by introducing the novel IGWO algorithm for the four-diode PV model. The algorithm's superior performance, as demonstrated through extensive testing and comparison with existing algorithms, validates its efficacy in accurately predicting the parameters for the PV solar cell model.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 14","pages":"2248-2260"},"PeriodicalIF":2.6,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141812194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal adaptive coordination of overcurrent relays in power systems protection using a new hybrid metaheuristic algorithm","authors":"Samira Sadeghi,&nbsp;Ali Hesami Naghshbandy,&nbsp;Parham Moradi,&nbsp;Abed Bagheri","doi":"10.1049/rpg2.13041","DOIUrl":"10.1049/rpg2.13041","url":null,"abstract":"<p>Advent of distributed generation and progression towards an intelligent grid infrastructure within the domain of contemporary electrical power systems have created dynamic load profiles. Accompanying these developments, protective relays are faced with an evolving electrical load landscape and variable fault current conditions, resulting in disparate operational timings throughout the diurnal cycle. In light of these challenges, this paper delineates the formulation and simulation of a novel adaptive protection strategy for overcurrent relays, meticulously tailored to accommodate the fluctuations in electrical load. To construct a robust framework for this adaptive mechanism, a series of hypothetical fault current scenarios are meticulously crafted to activate the relays within the briefest time interval feasible. Further innovating within this sphere, this paper introduces a new hybrid algorithm, deftly amalgamating the strengths of three preeminent metaheuristic models: Improved Harmony Search, Particle Swarm Optimization, and Differential Evolution. Simulations and analyses substantiate the efficacy of the algorithm in optimizing the coordination among overcurrent relays aiming to uphold the overarching protective imperatives of the grid. For the IEEE 6-bus system, the mean value of the objective function during 24 h in Monte Carlo is 292.6607 and very close to 272.0758 in the simulation of eight stochastic scenarios, which contributes to the validity of the approach in practical settings. Also, in the IEEE 30-bus system, the results of the mean relay operation time set for the hours with the lowest and highest consumption load are 17.1297 and 14.8049 s, which reveals the increase in the operation speed of the relays.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 12","pages":"1948-1971"},"PeriodicalIF":2.6,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141818379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Concurrent PV production and consumption load forecasting using CT-Transformer deep learning to estimate energy system flexibility","authors":"Mohammad Zarghami,&nbsp;Taher Niknam,&nbsp;Jamshid Aghaei,&nbsp;Azita Hatami Nezhad","doi":"10.1049/rpg2.13050","DOIUrl":"10.1049/rpg2.13050","url":null,"abstract":"<p>The integration of renewable energy sources (RESs) into power systems has increased significantly due to technical, economic, and environmental factors, necessitating greater flexibility to manage variable consumption loads and renewable energy generation. Accurate forecasting of solar energy production and consumption load is critical for enhancing power system flexibility. This study introduces a novel deep learning model, a spatial-temporal hybrid convolutional-transformer (CT-Transformer) network with unique features and extended memory capacity. Additionally, a flexibility index (FI) is introduced to evaluate power system flexibility (PSF) based on the forecasting results. The CT-Transformer forecasts PSF for the next 24 and 168 hours, using the FI to evaluate PSF based on forecasting results. The input data includes meteorological, solar energy production, load demand, and pricing data from France, comprising hourly data from 2015 and 2016 (about 17,520 entries). Data preprocessing involves correcting incomplete and irrelevant segments. The CT-Transformer's performance is compared to other deep learning techniques, showing superior results with the lowest prediction error (2.5%) and a maximum error of 10.1% (MAE). It also achieved a prediction error of 0.08% for system flexibility, compared to the highest error of 0.96%. This research highlights the CT-Transformer's potential for accurate RES and load forecasting and PSF evaluation.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 13","pages":"2139-2161"},"PeriodicalIF":2.6,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141817727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized DBN-based control scheme for power quality enhancement in a microgrid cluster connected with renewable energy system","authors":"Narendiran Sivakumar,&nbsp;Jaisiva Selvaraj,&nbsp;Karthika Jayaprakash,&nbsp;Kinde Anlay Fante","doi":"10.1049/rpg2.13058","DOIUrl":"10.1049/rpg2.13058","url":null,"abstract":"<p>The increasing energy consumption and changing load variations place a significant burden on the sophisticated utility grid, which affects the source's dependability and quality. Researchers examining modern power networks must focus on those factors to prevent grid breakdowns. Superior power quality (PQ) is still intended to make sure everything runs smoothly under diverse organizations. The main cause of PQ problems, however, is now renewable energy used in power electronic converters that are integrated into the electrical grid. Even though new, better solutions are still being developed, adhering to international standards have been strongly advised. Consequently, microgrid clusters powered through renewable energy and incorporating multiple structures in an urban area has been proposed in this paper. This increases the dependability of the power sources by managing the energy that is available inside the cluster instead of having it focused around the utility grid. Additionally, a deep belief network model based on Improved Dwarf Mongoose Optimization is recommended for regulating the inverter by generating optimal pulse-width modulated signals that increases the quality of the power supplies. When compared to other conventional techniques, the suggested technique possesses less real power and reactive power setting duration of 0.8 ms and 0.75 ms, respectively.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 12","pages":"1926-1947"},"PeriodicalIF":2.6,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141818853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Equality-embedded augmented Lagrangian neural network for DC optimal power flow","authors":"Jiayu Han,&nbsp;Chao Yang,&nbsp;Lei Yan,&nbsp;Mengyang Niu,&nbsp;Yupeng Zhang,&nbsp;Cheng Yang","doi":"10.1049/rpg2.13048","DOIUrl":"10.1049/rpg2.13048","url":null,"abstract":"<p>Direct current optimal power flow (DC-OPF) problems need to be solved more frequently to maintain safety and economic power system operation. Traditional solvers take too much time to get optimal results. To overcome it, a new self-supervised augmented Lagrangian neural network (ALNN) is proposed to solve DC-OPF problem. The proposed ALNN consists of two neural networks: the control net and the penalty net. The control net predicts active power of generators; the penalty net updates the Lagrangian multipliers. The equality constraints are embedded into the control net to guarantee no equality violations. The generalized reduced gradient method is used to reduce theviolations of inequality constraint. The effectiveness of the proposed model is demonstrated on IEEE 118-bus. The results show that with the help of equality embedding, the equality constraints are always satisfied, which in turn improves the feasibility of ALNN. Compared to the state-of-art models, the proposed model has higher feasibility and less constraint violations without comprising optimality. What is more, most of the inactive constraints can be found during the training process and then they are used to speed up the post-processing part.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 13","pages":"2128-2138"},"PeriodicalIF":2.6,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141818668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of control strategies for optimized microgrid operations","authors":"Shaibu Ali Juma,&nbsp;Sarah Paul Ayeng'o,&nbsp;Cuthbert Z. M. Kimambo","doi":"10.1049/rpg2.13056","DOIUrl":"10.1049/rpg2.13056","url":null,"abstract":"<p>Microgrids (MGs) have emerged as a promising solution for providing reliable and sustainable electricity, particularly in underserved communities and remote areas. Integrating diverse renewable energy sources into the grid has further emphasized the need for effective management and sophisticated control strategies. This review explores the crucial role of control strategies in optimizing MG operations and ensuring efficient utilization of distributed energy resources, storage systems, networks, and loads. To maximize energy source utilization and overall system performance, various control strategies are implemented, including demand response, energy storage management, data management, and generation-load management. Employing artificial intelligence (AI) and optimization techniques further enhances these strategies, leading to improved energy management and performance in MGs. The review delves into the control strategies and their architectures, and highlights the significant contributions of AI and emerging technologies in advancing MG energy management.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 14","pages":"2785-2818"},"PeriodicalIF":2.6,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141824217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}