IET Energy Systems Integration最新文献

{"title":"Voltage Control Strategies in Distribution Systems With High Distributed Generation Penetration: Models, Methods and Future Research","authors":"Htutzaw Hein,&nbsp;Jiebei Zhu,&nbsp;Lujie Yu,&nbsp;Zequn Liu,&nbsp;Xiaolong Liu,&nbsp;Siyang Xu,&nbsp;Yi Hao","doi":"10.1049/esi2.70036","DOIUrl":"10.1049/esi2.70036","url":null,"abstract":"<p>This paper presents a comprehensive review of recent global research on voltage control in distribution systems with high penetration of distributed generation. First, it examines voltage regulation challenges in distribution systems that integrate distributed generation. Second, it reviews the implementation of voltage control devices and strategies and discusses key factors influencing their deployment, including system modelling and optimisation methods. Third, the paper covers a wide range of control strategies, including local, centralized, decentralized, distributed, hierarchical and hybrid approaches and systematically compares them in terms of distribution system structures, control variables, optimisation techniques and solution methodologies, providing insights into their suitability for modern distribution system operation. Fourth, voltage control strategies and solution methodologies are analysed in discussion based on comparative results reported in the literature. Finally, future research directions are outlined, including artificial intelligence (AI)–driven control with hierarchical and hybrid strategies and electric vehicle based active and reactive power support for voltage regulation, as well as system security and privacy considerations.</p>","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.70036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147569768","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":"Distributionally Robust Coordinated Scheduling of Power-Transportation Systems Considering Bounded Rationality and Flexible Swapping of Electric Vehicles","authors":"Mengkai Hu,&nbsp;Sheng Chen,&nbsp;Sixian Li,&nbsp;Zhinong Wei","doi":"10.1049/esi2.70033","DOIUrl":"10.1049/esi2.70033","url":null,"abstract":"<p>Large-scale electric vehicles (EVs) integration demand coordinated power-transportation systems to optimise charging loads and grid flexibility, whereas existing strategies inadequately address range anxiety, peak demands and photovoltaic (PV) operational uncertainties. To address these issues, this work proposes a power-transportation coordination model integrating flexible swapping. The model strategically guides EV users towards swapping stations by embedding bounded rationality parameters within a stochastic user equilibrium framework, mitigating anxiety while balancing charging-swapping loads. This work also develops a distributionally robust optimisation framework to address PV uncertainty, enhancing resilience against generation fluctuations. Numerical simulations demonstrate that battery swapping integration significantly reduces distribution network peak loads and shortens user waiting times compared to charging-only approaches. The proposed method outperforms deterministic models by reducing conservativeness and aligning more closely with real-world operational dynamics, validating its efficacy in harmonising user behaviour and grid constraints under uncertainty.</p>","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":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.70033","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147568666","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":"Stochastic Microgrid Scheduling With Chance-Constrained Resilience Consideration","authors":"Guodong Liu,&nbsp;Michael R. Starke,&nbsp;Tao Jiang","doi":"10.1049/esi2.70034","DOIUrl":"10.1049/esi2.70034","url":null,"abstract":"<p>Traditionally, it is assumed that microgrids transition seamlessly from grid-connected operation to islanded mode in the event of sudden main grid outages. In reality, the islanding process, especially unintentional islanding, is rarely seamless. Instead, it is subject to voltage and frequency fluctuations caused by the instantaneous disconnection of the point of common coupling (PCC) switch, variations in loads and renewable generation output and even the protection tripping of distributed energy resources (DERs). To mitigate these fluctuations and facilitate a smooth islanding process, we propose a stochastic microgrid scheduling model that incorporates chance-constrained resilience measures. Specifically, the resilience measure is defined as the probability of successful islanding (PSI), that is, the probability that a microgrid can mitigate the generation-demand imbalance caused by the disconnection of the PCC switch, variations in load and renewable generation and DER tripping. This measure is modelled using chance constraints. Unlike existing reliability and resilience indices, which typically neglect the possibility of microgrid/DER failure under extreme events and assume their survival while primarily focussing on reducing impact duration or magnitude, the proposed PSI-based framework explicitly addresses microgrid and DER survival during the islanding transition. The formulated nonlinear chance constraints are approximated using a multiinterval approach and equivalently represented as a mixed-integer linear programming (MILP) formulation. Case study results validate the proposed method, showing that the PSI estimation error is reduced to less than 8%, compared to approximately 28% with existing methods. Various sensitivity analyses on the DER tripping rate and PSI settings were performed to validate the robustness of the proposed method. In particular, the necessity of accounting for DER tripping in the PSI calculation was demonstrated.</p>","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":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.70034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147568708","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":"2026-02-25","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}