Renewable Energy Focus最新文献_第4页

Optimal Interval based tuning of 3DOF-PID controllers for power system stabilizers and dynamic performance in multi machine power systems 基于最优区间的3DOF-PID控制器在多机电力系统稳定器及动态性能中的整定

IF 4.2

Renewable Energy Focus Pub Date : 2025-06-06 DOI: 10.1016/j.ref.2025.100727

R. Ramamoorthi , M. Sai Veerraju , M.V. Ramana Rao , T. Himaja

{"title":"Optimal Interval based tuning of 3DOF-PID controllers for power system stabilizers and dynamic performance in multi machine power systems","authors":"R. Ramamoorthi , M. Sai Veerraju , M.V. Ramana Rao , T. Himaja","doi":"10.1016/j.ref.2025.100727","DOIUrl":"10.1016/j.ref.2025.100727","url":null,"abstract":"<div><div>The removal of inadequately damped oscillations is necessary to guarantee the dependability and security of electrical power systems. This is especially crucial in modern power grids, where increasing interconnections between systems amplify the importance of stability. This manuscript proposes a Snow Ablation Optimizer (SAO) for optimal interval-based tuning of three degrees of freedom proportional-integral-derivative tilted-integral (3DOF-PID-TI) Controller for power system stabilizers (PSS) and dynamic performance in multi-machine power systems. The main objective is to optimize the performance of the PSS to enhance stability and effectively damp oscillations in power systems. The 3DOF-PID-TI controller parameter is adjusted using the SOA method. By then, the proposed approach has been incorporated into the MATLAB working platform, and the execution is calculated using the current system. The proposed technique displays better results in all existing methods such as the Wolf Optimizer algorithm (GWO), the Mayfly Optimization Algorithm (MOA), Improved Whale Optimization Algorithm (IWOA). The proposed method achieves 97%, surpassing the existing techniques with GWO at 84%, MOA at 76%, and IWOA at 64%. Additionally, the proposed SAO method demonstrates a settling time of 1.312 seconds, while the existing methods have longer settling times: GWO at 1.811 seconds, MOA at 2.969 seconds, and IWOA at 2.572 seconds. This enhancement highlights the better performance and optimization capability of the proposed method, emphasizing its effectiveness in optimizing PSS in multi-machine power systems contrasted to conventional optimization approaches.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"55 ","pages":"Article 100727"},"PeriodicalIF":4.2,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Coordinated scheduling of renewable energy micro-grids through coupled energy flow and battery delivery logistics 基于能量流与电池配送物流耦合的可再生能源微电网协调调度

IF 4.2

Renewable Energy Focus Pub Date : 2025-05-29 DOI: 10.1016/j.ref.2025.100723

Wei Xu , Yufeng Guo , Yifei Liu , Xuechen Bai

{"title":"Coordinated scheduling of renewable energy micro-grids through coupled energy flow and battery delivery logistics","authors":"Wei Xu , Yufeng Guo , Yifei Liu , Xuechen Bai","doi":"10.1016/j.ref.2025.100723","DOIUrl":"10.1016/j.ref.2025.100723","url":null,"abstract":"<div><div>With the rapid penetration of new energy sources into urban distribution networks (DNs), the load curve of DNs is exhibiting a “valley” characteristic, placing higher grid connection requirements on market participants. This paper focuses on micro-grids (MGs) and, driven by the objectives of peak shaving and carbon reduction, proposes a joint scheduling model for multi-MGs based on battery distribution and circulation. The model aims to address the challenges of energy exchange and collaboration, which are caused by long distances between some MGs and the immature peer-to-peer trading mechanisms. Firstly, a dynamic electricity purchase and sale price mechanism is designed according to the next day’s peak shaving demand of the distribution grid, guiding the MG to enhance grid-friendly power integration. Secondly, we propose a joint optimization strategy for multiple MGs, achieving cross-regional energy exchange through the flow of batteries of MGs. To address the shortcomings in existing research regarding the coupling of energy scheduling and logistics scheduling, this paper considers the coupling relationship between the two in terms of energy and dispatch periods, optimizing both the day-ahead energy scheduling of each MG and the logistics strategy for battery delivery. Through case analysis, the proposed model is validated for its excellence in grid friendliness and carbon reduction.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"54 ","pages":"Article 100723"},"PeriodicalIF":4.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing grid-connected battery energy storage systems: a comprehensive evaluation methodology 优化并网电池储能系统：一种综合评价方法

IF 4.2

Renewable Energy Focus Pub Date : 2025-05-28 DOI: 10.1016/j.ref.2025.100724

Mohammad Zain Ul Abideen, Abdulrahman Alassi, Santiago Bañales

引用次数: 0

Comprehensive methodology for assessing the impact of vehicle-to-grid integration in power system expansion planning 评估电力系统扩展规划中车辆与电网整合影响的综合方法

IF 4.2

Renewable Energy Focus Pub Date : 2025-05-24 DOI: 10.1016/j.ref.2025.100718

Leonardo Bitencourt , Walquiria N. Silva , Bruno H. Dias , Tiago P. Abud , Bruno Borba , Pedro Peters

{"title":"Comprehensive methodology for assessing the impact of vehicle-to-grid integration in power system expansion planning","authors":"Leonardo Bitencourt , Walquiria N. Silva , Bruno H. Dias , Tiago P. Abud , Bruno Borba , Pedro Peters","doi":"10.1016/j.ref.2025.100718","DOIUrl":"10.1016/j.ref.2025.100718","url":null,"abstract":"<div><div>The increasing adoption of electric vehicles (EVs) emphasizes the critical need to assess their integration into the electricity grid for a sustainable energy transition. Existing literature lacks comprehensive vehicle-to-grid (V2G) impact analyses and methodologies for long-term integration, particularly in developing countries. Moreover, the absence of optimized short-term operational models for EV integration poses challenges in grid management. To bridge these gaps, this research proposes a socio-economic model to estimate EV sales based on the Bass diffusion model and macroeconomic regressions. Additionally, it integrates electricity system expansion planning using the OSeMOSYS tool with a short-term operational model based on unit commitment. In this context, this work endeavors to develop a methodology for estimating the impact of V2G technology, considering both the deployment and utilization of EVs in a Brazilian case study. Applying traditional methodologies that do not consider operational system models can lead to potential future load shedding. It may accentuate disparities between long-term and short-term outcomes, especially with EV and V2G integration. The proposed methodology corrected the overestimation of the energy injection potential of EVs by the traditional model, indicating the need to consider both the expansion and the operation of the electricity system when planning the integration of EVs.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"54 ","pages":"Article 100718"},"PeriodicalIF":4.2,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Decarbonizing Indonesia’s power system: exploring the potential of energy storage systems for a sustainable energy transition 脱碳印尼的电力系统：探索能源存储系统的潜力，实现可持续的能源转型

IF 4.2

Renewable Energy Focus Pub Date : 2025-05-21 DOI: 10.1016/j.ref.2025.100722

Gany Gunawan

{"title":"Decarbonizing Indonesia’s power system: exploring the potential of energy storage systems for a sustainable energy transition","authors":"Gany Gunawan","doi":"10.1016/j.ref.2025.100722","DOIUrl":"10.1016/j.ref.2025.100722","url":null,"abstract":"<div><div>Indonesia’s power sector is the country’s largest source of energy-related carbon emissions, with coal-based generation rising to 66% by 2020 despite national and international decarbonization targets. The Just Energy Transition Partnership (JETP) outlines an ambitious vision to reduce emissions and scale renewables, but achieving these goals requires flexible and coordinated grid planning, especially in systems with high variable renewable energy (VRE) penetration.</div><div>This study evaluates the role of energy storage systems (ESS) in supporting decarbonization in the Java-Bali power grid using a mixed-integer quadratic programming (MIQP) unit commitment model. The framework simulates hourly dispatch and regulation reserve across Moderate and Deep Decarbonization pathways from 2025 to 2050, incorporating carbon taxes, curtailment penalties, ESS operational constraints, and seasonal VRE variability.</div><div>Results show that ESS reduces curtailment by up to 20.1 TWh (Moderate) and 26.5 TWh (Deep) in 2050, with corresponding system cost savings of USD 2.14–2.22 billion under base VRE conditions. Emission reductions reach 1.9–3.2 MtCO<sub>2</sub>, however rebound due to fossil-based charging under aggressive ESS deployment scenarios can raise emissions by up to 1.25 MtCO<sub>2</sub>, highlighting the importance of strategic dispatch.</div><div>These findings confirm ESS as a critical enabler of renewable integration and cost reduction but also emphasize the need for emissions-informed dispatch and integrated planning. The analysis provides a quantitative foundation to support the JETP’s implementation and highlights policy levers needed to align ESS deployment with national decarbonization goals.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"54 ","pages":"Article 100722"},"PeriodicalIF":4.2,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal deployment of reactive power in a renewable energy sources integrated system with EVs demand using local randomized neural networks 基于局部随机神经网络的电动汽车可再生能源集成系统无功优化配置

IF 4.2

Renewable Energy Focus Pub Date : 2025-05-15 DOI: 10.1016/j.ref.2025.100719

Abhishek Kumar Singh, Ashwani Kumar

{"title":"Optimal deployment of reactive power in a renewable energy sources integrated system with EVs demand using local randomized neural networks","authors":"Abhishek Kumar Singh, Ashwani Kumar","doi":"10.1016/j.ref.2025.100719","DOIUrl":"10.1016/j.ref.2025.100719","url":null,"abstract":"<div><div>The rising popularity of Electric vehicles (EV) has resulted in a substantial increase in the amount of charging stations, which extensively affects the electrical grid, causing problems like power quality degradation, voltage fluctuations and higher losses. This paper proposes the novel application of Local Randomized Neural Networks (LRNN) for optimal deployment of reactive power in a renewable energy sources integrated system with EVs demand. The main aim of the proposed work is to reduce both active and reactive power loss and maximize reliability. The LRNN method predicts the optimal location for the fast charging station. The proposed methods performance is excluded in the MATLAB working platform and compared with several existing techniques, with Genetic Algorithm (GA), Sea Horse Optimization (SHO) and Particle Swarm Optimization (PSO).The proposed technique demonstrates superior performance by significantly reducing power losses across all buses in the system. Compared to conventional optimization techniques, the LRNN achieves the lowest computational complexity at 1.82%, and the fastest convergence speed in just 25 iterations. In terms of execution time, it completes in 0.34 s, faster than the Genetic Algorithm at 0.44 s, Sea Horse Optimization at 0.59 s, and Particle Swarm Optimization at 0.65 s. While its efficiency is 98% it offers an excellent balance between computational speed, accuracy, and loss minimization. These results highlight its potential as a highly effective solution for modern power systems integrating renewable sources and electric vehicles.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"54 ","pages":"Article 100719"},"PeriodicalIF":4.2,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Microgrids protection: A review of technologies, challenges, and future trends 微电网保护：技术、挑战和未来趋势综述

IF 4.2

Renewable Energy Focus Pub Date : 2025-05-15 DOI: 10.1016/j.ref.2025.100720

Goutam Kumar Yadav, Mukesh Kumar Kirar, S.C. Gupta

{"title":"Microgrids protection: A review of technologies, challenges, and future trends","authors":"Goutam Kumar Yadav, Mukesh Kumar Kirar, S.C. Gupta","doi":"10.1016/j.ref.2025.100720","DOIUrl":"10.1016/j.ref.2025.100720","url":null,"abstract":"<div><div>The proliferation of distributed generation, particularly renewable energy sources, has catalyzed the emergence of microgrids as a pivotal element in contemporary power system architectures. However, the integration of these sources introduces significant complexities in protection system design due to the inherent dynamic characteristics of microgrids, bidirectional power flow, and operational mode transitions between grid-connected or islanded states. Traditional protection paradigms, predicated on static fault current magnitudes prevalent in passive radial distribution networks, exhibit limitations in microgrid environments characterized by substantial fault current variability. Notably, constrained fault current contribution of inverter-interfaced DG units operating in current-limiting mode impedes the efficacy of traditional overcurrent protection. This necessitates the development of adaptive and intelligent protection methodologies. A hybrid microgrid simulation is employed to analyze fault current variations across diverse operational scenarios, underscoring the imperative for advanced protection strategies. This study evaluates the current state of microgrid protection, identifies existing research lacunae, and proposes potential future research directions to improve resilience, reliability, and security. This review examines various microgrid types, including AC and DC systems, with a focus on their operational conditions, configurations, and the diverse fault types they encounter in relation to different protection device frameworks. The study emphasizes the critical need for advanced protection technologies that are continuously evolving to address the increasing complexity of microgrid systems effectively. By presenting a comprehensive analysis of past advancements and future directions in microgrid protection, this paper aims to guide researchers and scientists, emphasizing the significance of their contributions in shaping the development and innovation of protection strategies in this essential domain.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"55 ","pages":"Article 100720"},"PeriodicalIF":4.2,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Techno-economic feasibility of repurposing retired electric vehicle batteries in residential off-grid photovoltaic systems 住宅离网光伏系统中退役电动汽车电池再利用的技术经济可行性

IF 4.2

Renewable Energy Focus Pub Date : 2025-05-08 DOI: 10.1016/j.ref.2025.100717

Saed Altawabeyeh, Heba Abutayeh, Kholoud Hijazi, Hussein Daoud

引用次数: 0

Active and Reactive Power Control in Three-Phase Grid-Connected Electric Vehicles using Zebra Optimization Algorithm and Multimodal Adaptive Spatio-Temporal Graph Neural Network 基于斑马优化算法和多模态自适应时空图神经网络的三相并网电动汽车有功与无功控制

IF 4.2

Renewable Energy Focus Pub Date : 2025-05-02 DOI: 10.1016/j.ref.2025.100715

E. Shiva Prasad , S.V. Evangelin Sonia , Kokkirapati Naga Suresh , T.G. Shivapanchakshari

{"title":"Active and Reactive Power Control in Three-Phase Grid-Connected Electric Vehicles using Zebra Optimization Algorithm and Multimodal Adaptive Spatio-Temporal Graph Neural Network","authors":"E. Shiva Prasad , S.V. Evangelin Sonia , Kokkirapati Naga Suresh , T.G. Shivapanchakshari","doi":"10.1016/j.ref.2025.100715","DOIUrl":"10.1016/j.ref.2025.100715","url":null,"abstract":"<div><div>Three-phase grid-connected Electric Vehicles (EVs) are critical for optimizing energy flow, managing Active Power (AP) for charging and discharging, and controlling Reactive Power (RP) to ensure voltage regulation. These features enhance grid reliability and support the seamless integration of large-scale EVs into power grids. However, the unpredictable frequency of charging sessions creates challenges such as voltage fluctuations and grid imbalances, adversely affecting power quality (PQ) and stability. To address these issues, this study proposes a hybrid approach for AP and RP control in three-phase grid-connected EVs. The novel ZOA-MASTGNN technique integrates the Zebra Optimization Algorithm (ZOA) with the Multimodal Adaptive Spatio-Temporal Graph Neural Network (MASTGNN). The ZOA dynamically optimizes system parameters, improving power management, reducing Total Harmonic Distortion (THD), and enhancing grid stability. Meanwhile, MASTGNN predicts optimal control actions, mitigating harmonics, regulating voltage dynamically, and adapting to changing operational conditions in grid-interactive EV systems. The suggested method was implemented on the MATLAB platform and evaluated with existing approaches, including Resiliency-Guided Physics-Informed Neural Networks (RPINN), Elman Neural Networks (ENN), Multilayer Feed Forward Neural Networks (ML-FFNN), Deep Neural Networks (DNN), and Particle Swarm Optimization-Artificial Neural Networks (PSO-ANN). Results showed significant improvements, achieving 19.36% load current THD and 3.52% source current THD, while outperforming other approaches in efficiency and effectiveness. This framework addresses key challenges in large-scale EV integration, offering scalable and practical solutions for sustainable power grid operations.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"54 ","pages":"Article 100715"},"PeriodicalIF":4.2,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved hybrid algorithm-based optimization for Integrated Energy Distribution Network System: minimizing voltage deviation, line losses, and costs 基于改进混合算法的综合配电网系统优化：最小化电压偏差、线路损耗和成本

IF 4.2

Renewable Energy Focus Pub Date : 2025-05-01 DOI: 10.1016/j.ref.2025.100716

Yixi Zhang, Heng Chen, Yue Gao, Jingjia Li, Peiyuan Pan

{"title":"Improved hybrid algorithm-based optimization for Integrated Energy Distribution Network System: minimizing voltage deviation, line losses, and costs","authors":"Yixi Zhang, Heng Chen, Yue Gao, Jingjia Li, Peiyuan Pan","doi":"10.1016/j.ref.2025.100716","DOIUrl":"10.1016/j.ref.2025.100716","url":null,"abstract":"<div><div>To address the siting and sizing of an integrated energy distribution network system incorporating PV, WT, EV, SVC, and BES, as well as the operational planning of SVC and BES, this paper proposes an improved hybrid algorithm. In the first stage, a multi-objective genetic algorithm is adopted to plan the siting and sizing of each device in the integrated energy distribution network. In the second stage, based on the siting and sizing results, an adaptive particle swarm optimization algorithm is utilized to schedule the daily energy storage dispatch and reactive power output. Through this two-stage optimization, the issues of unbalanced load distribution and voltage quality in the distribution network are resolved, while minimizing investment costs. The IEEE 69-node simulation results demonstrate that under the optimal scenario, the average voltage deviation of the distribution system remains stable at 1.0 p.u., the line loss rate decreases to 2.90 %, and the initial construction cost and operational cost reach 120,220,000 CNY and 16,923.88 CNY, respectively. Compared with similar algorithms, the proposed hybrid algorithm achieves a 34.5% improvement in loss reduction, significantly enhances voltage stability, and reduces daily operational costs by 9.91 %, demonstrating its effectiveness and superiority.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"54 ","pages":"Article 100716"},"PeriodicalIF":4.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0