IET Energy Systems Integration最新文献

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148093467","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}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148091115","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}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148064881","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}

{"title":"Distributionally Robust Expansion Planning Considering Flexibility for Distribution Network With Soft Open Points","authors":"Qianyu Zhao,&nbsp;Luyang Wang,&nbsp;Xiang Li,&nbsp;Shouxiang Wang,&nbsp;Lanqian Yang,&nbsp;Yanming Tian","doi":"10.1049/esi2.70031","DOIUrl":"https://doi.org/10.1049/esi2.70031","url":null,"abstract":"<p>The rapid integration of renewable energy generators (REGs) alongside flexibility resources into the grid introduces significant data complexities, alongside increased unpredictability and variability within the distribution network. Reasonable allocation of flexibility resources can improve the utilisation of REGs and the flexibility of the distribution network. Addressing these challenges, an innovative two-stage model for distribution network expansion planning is proposed in this paper. The model uniquely combines the construction of new substations, the extension of existing lines, the integration of soft open points (SOPs) and strategic siting and sizing of energy storage systems (ESSs). Specifically, considering that SOPs can quickly regulate node voltages, thereby proposing flexibility zones as novel metrics to gauge the adaptability of the distribution network. Furthermore, a multivariate copula function is employed to delineate the correlation between REGs outputs and loads. This approach, devoid of reliance on extensive historical datasets, leverages discrete scenario ambiguity sets alongside norm theory to enhance scenario generation. To navigate the intricacies of the proposed model, a structured solution methodology encapsulated in a three-level process is developed. Validation through numerical simulations on the modified Portugal 54-bus system underscores the robustness and practicality of the methodology and solution framework in facilitating informed decision-making.</p>","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.70031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146193397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement Learning-Based Fast Frequency Response Using Energy Storage for Remote Microgrids","authors":"Pooja Aslami,&nbsp;Tara Aryal,&nbsp;Niranjan Bhujel,&nbsp;Hossein Moradi Rekabdarkolaee,&nbsp;Zongjie Wang,&nbsp;Timothy M. Hansen","doi":"10.1049/esi2.70030","DOIUrl":"https://doi.org/10.1049/esi2.70030","url":null,"abstract":"<p>The power system is undergoing a significant shift from fossil fuel-based electricity generation to inverter-based renewable energy resources (IBRs), accelerating the transition towards cleaner energy. This transition, however, introduces new challenges for system stability and control. One of the most critical issues is the decline in frequency stability due to reduced system inertia and damping, particularly in isolated or weakly interconnected power systems such as microgrids. Therefore, novel ancillary services capable of delivering fast and effective frequency support that accounts for the dynamic nature of the modern power system are crucial. In this study, we develop a reinforcement learning (RL)-based control framework to provide fast frequency response (FFR) in a microgrid. The RL-based controller is trained through continuous interaction with a simulated microgrid environment using the soft actor-critic (SAC) algorithm, an advanced off-policy RL technique. To enable efficient RL training, a scalable co-simulation framework with a real-time digital environment is employed, allowing a parallel execution of online RL training and microgrid model simulation. The RL training configuration is deployed on the Cordova, Alaska, benchmark microgrid. A detailed evaluation of the trained RL-based controller demonstrates its ability to deliver efficient and timely frequency support to the microgrid, reducing frequency nadirs by 55.03% and 61.78% in cases with and without under-frequency load shedding (UFLS). Load impact assessments confirm the controller's robustness under varying loading scenarios, and the computational times during training and testing validate its real-time applicability for use in microgrids. Additionally, a practical evaluation of energy storage system (ESS) sizing under a 2-day load profile provides valuable insights into resource considerations for real-world FFR implementation.</p>","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.70030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146099332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148090957","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}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148073445","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}

{"title":"Optimal Capacity Configuration of Park Integrated Energy Systems With Inter-Seasonal Flexible Load Participation Characteristics","authors":"Zuoxia Xing,&nbsp;Zhi Zhu,&nbsp;Shoulian Yang,&nbsp;Hao Sun,&nbsp;Jiayao Wang","doi":"10.1049/esi2.70029","DOIUrl":"https://doi.org/10.1049/esi2.70029","url":null,"abstract":"<p>This study introduces an optimised capacity configuration for park integrated energy systems (PIES) to boost energy efficiency, ensure power supply reliability and economy, and advance low-carbon operations. The approach integrates seasonal aspects and flexible load participation's impact on renewable energy absorption, using an enhanced K-means clustering algorithm with mixed-integer linear programming. It includes: (1) creating a probability density model from wind and solar data to categorise power generation scenarios across seasons; (2) integrating flexible loads into PIES optimisation, analysing technology combinations and output distributions; (3) developing a model for electricity, heat, and multi-energy coupling to assess cross-seasonal supply-demand matching; (4) establishing an optimisation model for inter-seasonal energy storage considering operational costs. Case studies confirm the benefits of seasonal factors, flexible load participation, energy coupling, storage, and seasonal dispatch on PIES efficiency and economy.</p>","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.70029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146002167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148070382","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}

{"title":"The Evaluation Indexes and Defence Methods of Critical Information Nodes in Power Information Physical System Considering False Data Injection Attack Propagation","authors":"Xinrui Liu,&nbsp;Zhuofan Shi,&nbsp;Rui Wang,&nbsp;Shufeng Gai,&nbsp;Min Hou,&nbsp;Qiuye Sun","doi":"10.1049/esi2.70027","DOIUrl":"https://doi.org/10.1049/esi2.70027","url":null,"abstract":"<p>False data injection (FDI) attacks pose a great threat to the safe operation of power grid. By attacking nodes with weak defences, high transmission risks and high returns, attackers can cause more damage to the power grid with limited resources. Therefore, it is of great significance to evaluate these critical nodes for active defence of power grid. This paper presents an evaluation index and defence method of critical information node. Firstly, by establishing an FDI attack model, information system model and attack propagation model, quantitative analysis is carried out on basic indicators, such as attack return, attack success probability, transmission risk, transmission intensity and correlation, between attack return and transmission risk of information nodes under FDI attack scene. According to the attack detected and no attack detected scenes, the basic evaluation indexes were selected, respectively, to establish a comprehensive evaluation index. Finally, based on the comprehensive evaluation index, two defence methods are proposed to improve the power grid's ability to resist FDI attacks, respectively, applicable to detected attacks and undetected attacks. The effectiveness of the proposed evaluation index and defence method is verified by the simulation results of IEEE57 nodes system.</p>","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.70027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145964098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}