International Journal of Electrical Power & Energy Systems最新文献_第8页

Low voltage ride-through capability enhancement using optimal super twisting sliding mode control for grid-tied pv systems 利用最优超扭滑模控制提高并网光伏系统的低压穿越能力

IF 5 2区工程技术

International Journal of Electrical Power & Energy Systems Pub Date : 2025-09-16 DOI: 10.1016/j.ijepes.2025.111124

Ghazi A. Ghazi , Essam A. Al-Ammar , Hany M. Hasanien , Mansoor Khan , Wonsuk Ko , Hyeong-Jin Choi , Sisam Park

{"title":"Low voltage ride-through capability enhancement using optimal super twisting sliding mode control for grid-tied pv systems","authors":"Ghazi A. Ghazi , Essam A. Al-Ammar , Hany M. Hasanien , Mansoor Khan , Wonsuk Ko , Hyeong-Jin Choi , Sisam Park","doi":"10.1016/j.ijepes.2025.111124","DOIUrl":"10.1016/j.ijepes.2025.111124","url":null,"abstract":"<div><div>This paper addresses the critical issue of enhancing the low-voltage ride-through (LVRT) capability of grid-tied photovoltaic power (GTPVP) systems, particularly in compliance with modern grid codes (GCs) during grid faults. It proposes a novel control strategy using super-twisting sliding mode control (STSMC) for a 100-MW PV system, with the gains of the STSMC optimized through a Newton-Raphson-based optimizer (NRBO). The NRBO is also employed as a maximum power point tracker to regulate PV voltage based on a STSMC. Additionally, the STSMCs are integrated into the voltage source inverter for optimal control of various parameters, including the DC-link voltage, active, and reactive currents. Furthermore, the effectiveness of the NRBO-STSMC is validated through comparative analysis against other methods, such as particle swarm optimization (PSO)-tuned STSMC, NRBO-based conventional SMC, and NRBO-tuned proportional-integral (PI) controllers. A MATLAB/Simulink model was utilized for optimization and simulation, demonstrating that the NRBO-STSMC achieved superior performance, with the lowest integral of time-weighted absolute error values and higher efficiency, for both DC-DC and DC-AC converters. It minimizes voltage overshoot at the point of common coupling and ensures stable power injection during severe grid faults. In contrast, the NRBO-SMC method shows significant overshoots, while the NRBO-PI method faces oscillations and poor power balance. The study concludes that the proposed NRBO-STSMC successfully complies with the IEEE 1547 LVRT grid code during the grid fault. It provides a robust, highly efficient, and stable control solution for the LVRT issue of GTPVP systems, proving essential for meeting the demands of modern GCs.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111124"},"PeriodicalIF":5.0,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Short-term load forecasting by a hybrid attention scheme: Multi-feature attention and context awareness 基于混合注意方案的短期负荷预测：多特征注意和上下文感知

IF 5 2区工程技术

International Journal of Electrical Power & Energy Systems Pub Date : 2025-09-16 DOI: 10.1016/j.ijepes.2025.111065

Fang Su , Hao Tang , Shengchun Yang , Tao Zhang , Qi Tan

{"title":"Short-term load forecasting by a hybrid attention scheme: Multi-feature attention and context awareness","authors":"Fang Su , Hao Tang , Shengchun Yang , Tao Zhang , Qi Tan","doi":"10.1016/j.ijepes.2025.111065","DOIUrl":"10.1016/j.ijepes.2025.111065","url":null,"abstract":"<div><div>Accurate short-term load forecasting (STLF) is critical to optimize power generation scheduling and ensure grid stability with increasing penetration of renewable energy. Short-term power load variations typically exhibit strong locality and regularity. Despite significant progress, existing forecasting models still face two major challenges in handling short-term forecasting tasks: (1) Conventional static feature weighting strategies (feature selection, uniform attention, etc.) fail to adaptively capture dynamic interdependencies among heterogeneous features; (2) Transformer-based models suffer from high computational costs and inadequate local pattern extraction, limiting their effectiveness in modeling short-term dependencies. To address these challenges, we propose a hybrid attention scheme with multi-feature attention and context awareness (MFACA). First, the multi-feature attention (MFA) layer dynamically adjusts feature weights in each time step through output dependencies, enabling feature-sensitive prioritization of critical features. Second, the context awareness (CA) layer dynamically weights contextual information based on its correlation with encoder output, thus enhancing the model’s ability to simultaneously decode local fluctuations and global periodic trends. Finally, MFACA jointly optimizes multiscale feature interactions and temporal dependencies within an encoder–decoder architecture. Extensive evaluations using three real-world power load datasets confirm the effectiveness of the proposed model, demonstrating superior performance on multiple metrics, with the MFA and CA components contributing significantly to the improvement.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111065"},"PeriodicalIF":5.0,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed robust optimal control strategy for integrated energy systems based on energy trading 基于能源交易的综合能源系统分布式鲁棒最优控制策略

IF 5 2区工程技术

International Journal of Electrical Power & Energy Systems Pub Date : 2025-09-15 DOI: 10.1016/j.ijepes.2025.111012

Jin Gao , Mohammadreza Lak , Zhenguo Shao , Feixiong Chen

{"title":"Distributed robust optimal control strategy for integrated energy systems based on energy trading","authors":"Jin Gao , Mohammadreza Lak , Zhenguo Shao , Feixiong Chen","doi":"10.1016/j.ijepes.2025.111012","DOIUrl":"10.1016/j.ijepes.2025.111012","url":null,"abstract":"<div><div>Under the background of energy interconnection and low-carbon electricity, integrated energy systems (IES) play an important role in energy conservation and emission reduction. To further promote the low-carbon transition of energy, this paper proposes a distributed robust optimal control strategy for IESs based on energy trading. Firstly, an IES model that includes an electric hydrogen module and gas hydrogen doping combined heat and power is established, and ladder-type carbon trading is introduced to reduce carbon emissions. Secondly, for the energy trading issues between photovoltaic (PV) prosumers and IES, a bi-level model is constructed using Stackelberg game method, where the IES acts as the leader and the PV prosumers as the followers. Noteworthy, a distributed robust optimization method is used to address the uncertainty of renewable energy and load. Additionally, the Nash bargaining method ensures an equitable balance of benefits among the various IESs and encourages them to participate in market transactions. On this basis, an intermediary transaction mode is proposed to address cheating behaviors in trading. Finally, the simulation results demonstrate that the proposed strategy not only effectively promotes cooperative operation among multiple IESs but also significantly reduces the system’s operating costs and carbon emissions.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111012"},"PeriodicalIF":5.0,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transient voltage stability of power systems with virtual synchronous generators or grid-following converters: analysis and enhanced control 带虚拟同步发电机或随网变流器的电力系统暂态电压稳定性：分析与强化控制

IF 5 2区工程技术

International Journal of Electrical Power & Energy Systems Pub Date : 2025-09-15 DOI: 10.1016/j.ijepes.2025.111122

Zhiying Chen, Lin Guan

{"title":"Transient voltage stability of power systems with virtual synchronous generators or grid-following converters: analysis and enhanced control","authors":"Zhiying Chen, Lin Guan","doi":"10.1016/j.ijepes.2025.111122","DOIUrl":"10.1016/j.ijepes.2025.111122","url":null,"abstract":"<div><div>With the global energy transition, power systems are replacing synchronous generators (SGs) with voltage source converters (VSCs), which has significant implications for transient voltage stability. Analyzing VSC’s effect and leveraging its flexibility to improve stability are key challenges. Therefore, this study selects virtual synchronous generator (VSG) control and constant-power grid-following (GFL) control as representative strategies for the two dominant VSC control (grid-forming (GFM) and GFL). Then, this study theoretically derives the active and reactive power demand characteristics of induction motors. Building on these characteristics, along with the power characteristics of different power sources, the influence mechanisms of VSG and GFL on the transient voltage stability are analyzed. Distinct from existing methods, this work provides a comprehensive explanation of how induction motor power dynamics shape transient voltage behavior. The results showed that the integration of VSG and GFL degrades the transient voltage stability, with GFL exhibiting a more pronounced adverse effect. To mitigate these issues, the interaction between the active and reactive power of VSG is considered and an improved VSG control strategy is proposed to enhance the transient voltage stability. In addition, an adaptive coordinated control strategy for active and reactive power of GFL is introduced. Finally, the validity of the mechanism analysis and the effectiveness of the proposed control strategies are verified on a simplified system of a real large-scale power grid using the PSCAD platform.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111122"},"PeriodicalIF":5.0,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resilient energy transition in wildfire-prone regions: A multi-stage investment optimization 野火易发地区弹性能源转型：多阶段投资优化

IF 5 2区工程技术

International Journal of Electrical Power & Energy Systems Pub Date : 2025-09-14 DOI: 10.1016/j.ijepes.2025.111060

Mohannad Alhazmi , Bohan Zhang , Thomas Tongxin Li , Huizu Lin , Chenlu Yang , Xi Cheng

{"title":"Resilient energy transition in wildfire-prone regions: A multi-stage investment optimization","authors":"Mohannad Alhazmi , Bohan Zhang , Thomas Tongxin Li , Huizu Lin , Chenlu Yang , Xi Cheng","doi":"10.1016/j.ijepes.2025.111060","DOIUrl":"10.1016/j.ijepes.2025.111060","url":null,"abstract":"<div><div>The increasing frequency and severity of wildfires and heatwaves in California pose significant threats to energy infrastructure, necessitating a resilient and sustainable energy transition. This paper proposes a multi-stage investment optimization framework that integrates wildfire-induced disruptions, heatwave-driven demand surges, and carbon-neutral transition pathways into long-term power system planning. The framework employs a Distributionally Robust Optimization (DRO) approach to account for uncertainties in wildfire spread, transmission failures, and climate variability, ensuring robustness against worst-case scenarios. A GIS-driven wildfire risk assessment is incorporated to prioritize infrastructure reinforcements, microgrid deployment, and renewable integration in high-risk regions. The model balances three key objectives: (i) minimizing infrastructure investment costs, (ii) maximizing grid resilience through fire-adaptive expansion planning, and (iii) ensuring a carbon-neutral transition in alignment with climate policies. A case study of Los Angeles County during the 2025 Eaton Fire demonstrates the model’s effectiveness in enhancing power grid resilience and optimizing energy investment strategies under extreme climate conditions. The results highlight significant reductions in carbon emissions, improved microgrid reliability, and accelerated post-wildfire grid recovery, showcasing the model’s applicability for future wildfire-prone energy systems.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The influence of load and PV generation on operating P and Q envelopes of distribution network buses 负荷和光伏发电对配电网母线运行P、Q包线的影响

IF 5 2区工程技术

International Journal of Electrical Power & Energy Systems Pub Date : 2025-09-13 DOI: 10.1016/j.ijepes.2025.111100

Ester Thomas Marcel, Jovica V. Milanović

{"title":"The influence of load and PV generation on operating P and Q envelopes of distribution network buses","authors":"Ester Thomas Marcel, Jovica V. Milanović","doi":"10.1016/j.ijepes.2025.111100","DOIUrl":"10.1016/j.ijepes.2025.111100","url":null,"abstract":"<div><div>The increase in consumer-owned small-scale Low Carbon Technologies (LCT), solar photovoltaic (PV) generation in particular, has led to the calculation of feasible operating envelopes, which allows distribution network operators (DNOs) to benefit from those PVs without compromising network operation. This paper calculates and analyses day-ahead feasible operating regions (envelopes) of buses in a distribution network with solar PVs, considering the impacts of controllable loads, load models, and PV sizes and locations. The envelopes were calculated considering network voltage and line loading constraints using Latin Hypercube Sampling (LHS)-based probabilistic load flow simulation and convex hull estimation in DIgSILENT and MATLAB environment. Morris screening sensitivity analysis was used to determine which buses’ envelopes were the most or least sensitive to changes in PV sizes at different locations. The findings revealed that a share of controllable loads could expand or shrink the envelopes depending on the time of the day, providing insight into load curtailment and payback periods. Different load models produced different envelopes, emphasising the significance of accurate load modelling. Moreover, the study demonstrated how changes in envelope sizes in one area affect those in another, highlighting the need for careful consideration prior to connecting/ increasing PV size in an area of the network in response to an increase in network load or LCT connections. The paper also highlighted the methodology’s flexibility in adapting to networks of various sizes, including unbalanced ones. Additionally, the paper introduced operating, risk based, zones within the envelopes to improve the approach’s effectiveness.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111100"},"PeriodicalIF":5.0,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Continuous-Time Markov Chain based sequential analytical reliability assessment approach for power distribution networks 基于连续时间马尔可夫链的配电网序贯分析可靠性评估方法

IF 5 2区工程技术

International Journal of Electrical Power & Energy Systems Pub Date : 2025-09-13 DOI: 10.1016/j.ijepes.2025.111069

Lukun Ge , Kai Hou , Hongjie Jia , Zeyu Liu , Lewei Zhu

{"title":"Continuous-Time Markov Chain based sequential analytical reliability assessment approach for power distribution networks","authors":"Lukun Ge , Kai Hou , Hongjie Jia , Zeyu Liu , Lewei Zhu","doi":"10.1016/j.ijepes.2025.111069","DOIUrl":"10.1016/j.ijepes.2025.111069","url":null,"abstract":"<div><div>Reliability assessment plays a crucial role in the planning and operation of power distribution systems. In this paper, a Continuous-Time Markov Chain with Impact-Increment State Enumeration (CTMC-IISE) method is proposed to enable sequential reliability analysis of distribution networks<strong>.</strong> With the proposed method, the expectation, probabilistic, duration and frequency reliability indices can be obtained considering power flow constrains. To address the computational inefficiency typically associated with CTMC models in large-scale systems, the number of contingency states is significantly reduced through component grouping and state merging. As a result, the proposed approach enhances both the accuracy and efficiency of the reliability assessment process. The effectiveness of the CTMC-IISE method is demonstrated through three case studies: the RBTS Bus 6 system, the IEEE 123-node test feeder, and a practical distribution network in a town. The results highlight the superior performance of the proposed approach compared to Sequential Monte Carlo Simulation (SMCS), traditional CTMC, and Failure Mode and Effects Analysis (FMEA) in terms of computational efficiency and assessment accuracy in distribution system reliability evaluation.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111069"},"PeriodicalIF":5.0,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchical model based on optimized feature decomposition and deep learning for short-term wind-power forecasting 基于优化特征分解和深度学习的风电短期预测层次模型

IF 5 2区工程技术

International Journal of Electrical Power & Energy Systems Pub Date : 2025-09-13 DOI: 10.1016/j.ijepes.2025.111097

Ming Liu , Liming Wang , Xinfu Pang , Zedong Zheng , Haibo Li

{"title":"Hierarchical model based on optimized feature decomposition and deep learning for short-term wind-power forecasting","authors":"Ming Liu , Liming Wang , Xinfu Pang , Zedong Zheng , Haibo Li","doi":"10.1016/j.ijepes.2025.111097","DOIUrl":"10.1016/j.ijepes.2025.111097","url":null,"abstract":"<div><div>Renewable energy development relies heavily on accurate wind-power forecasting. However, predicting wind power presents significant challenges, given the unique operational complexity inherent in wind farms. To overcome these challenges, this study proposes a novel hierarchical model based on optimized feature decomposition and deep learning. First, variational mode decomposition (VMD) is performed to decompose wind energy to mitigate variability and instability. Then, the Rime optimization algorithm (RIME) is implemented to optimize the parameters of VMD, thereby enhancing the effective decomposition of wind power into multiple, smoothly varying modal components. These components and the selected meteorological features are then used to generate sequential data, which are input into a temporal convolutional network (TCN) to extract time-series information from the wind-power data. A bidirectional long short-term memory network (BiLSTM) with self-attention mechanism (Attention) is incorporated to capture both long-term and more complex temporal patterns. During the model-training phase, predictions from the validation set are used to optimize the TCN hyperparameters via the RIME algorithm. Finally, the optimized model is tested on a dataset of forecast wind power. The results show that, compared to the TCN–BiLSTM–Attention model, the root mean square error and mean absolute error of the proposed method are lower by 54.54% and 50.6%, respectively, which verifies the superior prediction accuracy of the proposed model.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111097"},"PeriodicalIF":5.0,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint generation-transmission expansion planning in renewables-dominated power systems based on hybrid quantum-classical computing 基于混合量子经典计算的可再生能源电力系统联发输电扩展规划

IF 5 2区工程技术

International Journal of Electrical Power & Energy Systems Pub Date : 2025-09-13 DOI: 10.1016/j.ijepes.2025.111115

Yue Xu, Zhiyi Li, Xutao Han, Renjie Luo

{"title":"Joint generation-transmission expansion planning in renewables-dominated power systems based on hybrid quantum-classical computing","authors":"Yue Xu, Zhiyi Li, Xutao Han, Renjie Luo","doi":"10.1016/j.ijepes.2025.111115","DOIUrl":"10.1016/j.ijepes.2025.111115","url":null,"abstract":"<div><div>The joint generation-transmission expansion planning (JGTEP) problem offers a broader range of feasible strategies than unilateral planning and enables more favorable cost-benefit outcomes in renewables-dominated power systems. However, it also imposes heavier computational burdens due to the proliferation of binary variables from generation units and transmission networks, as well as slower convergence caused by nonlinear, spatiotemporal coupling constraints. Leveraging the quantum tunneling effect, quantum annealing provides significant efficiency advantages in handling large-scale binary problems, while classical computing remains superior for continuous optimization and binary variable initialization. This complementarity motivates a hybrid quantum–classical framework to accelerate JGTEP. Specifically, the problem is first decomposed into a master problem and a subdual problem using Benders decomposition. A classical computer then generates multiple feasible solutions for the master problem, from which an initial warm-start solution is constructed, and the dimensionality of variables is reduced by exploiting the shared characteristics of these solutions. The master problem is subsequently reformulated as a quadratic unconstrained binary optimization model, which is efficiently solved on a quantum annealer using a warm-start quantum annealing algorithm. Finally, the master problem and subproblem are solved iteratively until convergence. Applied to the IEEE RTS 24-bus system, the proposed quantum-assisted method achieves more than a 70% reduction in computation time and a 50% decrease in iterations compared with classical methods, while also demonstrating strong potential for tackling larger-scale planning problems.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111115"},"PeriodicalIF":5.0,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Frequency regulation market participation of distributed energy storage systems 分布式储能系统的频率调节市场参与

IF 5 2区工程技术

International Journal of Electrical Power & Energy Systems Pub Date : 2025-09-13 DOI: 10.1016/j.ijepes.2025.111099

Tao Xu , Jianhang Sun , He Meng , Rujing Wang , Yu Ji , Ying Zhang , Ping Song , Jiani Xiang

引用次数: 0