IET Energy Systems Integration最新文献_第2页

IF 1.7

IET Energy Systems Integration Pub Date : 2026-05-24

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IF 1.7

IET Energy Systems Integration Pub Date : 2026-05-08

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A Data-Driven Framework for Grid Frequency Regulation Leveraging Aggregated Flexibility of Heterogeneous Resources Considering Network Delays 考虑网络延迟、利用异构资源聚合灵活性的电网频率调节数据驱动框架

IF 1.7

IET Energy Systems Integration Pub Date : 2026-03-30 DOI: 10.1049/esi2.70039

Kingshuk Roy, Sanjoy Debbarma

{"title":"A Data-Driven Framework for Grid Frequency Regulation Leveraging Aggregated Flexibility of Heterogeneous Resources Considering Network Delays","authors":"Kingshuk Roy, Sanjoy Debbarma","doi":"10.1049/esi2.70039","DOIUrl":"https://doi.org/10.1049/esi2.70039","url":null,"abstract":"The rise of large-scale renewables has exacerbated frequency instability, revealing the limits of conventional frequency regulation frameworks in controlling deviations and incentivising fast-acting units (FAUs). Although the mileage-based payment framework promotes FAUs’ participation in automatic generation control (AGC) services, efficient instruction dispatch (ID) across an increasing number of heterogeneous AGC units remains challenging. The existing mileage-based payment framework lacks validation under intermittent generation or generation outages scenarios. This work proposes a data-driven ID framework with a modified payment scheme, adding a penalty term for refining mileage calculation to handle intermittent generation or generation outages during AGC operation. The framework uses a multihead attention-based encoder–decoder model, where the encoder extracts latent features and the decoder predicts unit-specific instructions. Attention mechanism improves accuracy by prioritising critical features, whereas L2 normalisation, dropout and k-fold cross-validation enhance models' robustness under unforeseen scenarios. The model aggregates the flexibility of multiple FAUs into a single entity, termed the FAU aggregator. Trained on a synthetic dataset generated from the evolutionary optimisation-based ID framework and validation on an interconnected system accounting for disturbance due to intermittent renewable energy sources' output, FAU variability and stochastic communication effects. The results demonstrate a reduction in both frequency deviation and area control error in comparison with other ID frameworks.","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.70039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147585121","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}

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IF 1.7

IET Energy Systems Integration Pub Date : 2026-03-30

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IF 1.7

IET Energy Systems Integration Pub Date : 2026-03-24

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Parametric Sensitivity Analysis of a Multi-Gigawatt Offshore Multi-Energy System Using Reduced-Order Models 基于降阶模型的多兆瓦海上多能系统参数敏感性分析

IF 1.7

IET Energy Systems Integration Pub Date : 2026-03-24 DOI: 10.1049/esi2.70041

Nakul Narayanan K, Chunjun Huang, Maria Camila Castrillon Franco, Jose Luis Rueda Torres

{"title":"Parametric Sensitivity Analysis of a Multi-Gigawatt Offshore Multi-Energy System Using Reduced-Order Models","authors":"Nakul Narayanan K, Chunjun Huang, Maria Camila Castrillon Franco, Jose Luis Rueda Torres","doi":"10.1049/esi2.70041","DOIUrl":"https://doi.org/10.1049/esi2.70041","url":null,"abstract":"When multiple grid-forming and grid-following converters operate within an offshore energy system (OES), dynamic interactions among them can lead to poorly damped oscillations and potential instability. This paper proposes a computationally efficient reduced-order state-space modelling framework for small-signal stability and parametric sensitivity analysis of large-scale OESs. The approach replaces complex full-order analytical modelling with a practical MATLAB/Simulink-based linearisation procedure, enabling tractable stability assessment of systems comprising multiple wind plants, electrolysers, HVDC links and network components. The reduced-order model preserves the dominant dynamics while significantly decreasing the number of states, thereby improving computational efficiency for eigenvalue and sensitivity analyses. Using the linearised model, the influence of key control parameters is systematically quantified to provide explicit guidance for controller tuning and damping improvement. The accuracy of the proposed model is validated through comparison with detailed electromagnetic transient (EMT) simulations in PSCAD/EMTDC, demonstrating close agreement in dynamic responses and stability characteristics.","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.70041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148012221","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}

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Emergency Power Sharing by Using a Self-Traction System in a Mass Rapid Transit Railway 基于自牵引系统的快速轨道交通应急电力共享

IF 1.7

IET Energy Systems Integration Pub Date : 2026-03-20 DOI: 10.1049/esi2.70040

Banri Khemkladmuk, Chanchai Techawatcharapaikul, Thanatchai Kulworawanichpong

{"title":"Emergency Power Sharing by Using a Self-Traction System in a Mass Rapid Transit Railway","authors":"Banri Khemkladmuk, Chanchai Techawatcharapaikul, Thanatchai Kulworawanichpong","doi":"10.1049/esi2.70040","DOIUrl":"https://doi.org/10.1049/esi2.70040","url":null,"abstract":"Systemwide traction power outages in mass rapid transit systems (MRTSs) can cause trains to stop between stations, leading to service disruption and urgent passenger evacuation requirements. Onboard emergency self-traction systems (ESTSs) can mitigate this risk by enabling a stranded train to reach the nearest station at low speed. However, installing and maintaining ESTS on every train can increase implementation and lifecycle costs, motivating approaches that can ensure evacuation with partial ESTS deployment. This article introduces a power-sharing framework for ESTS in an MRTS. The primary aim is to reduce the number of ESTS units needed by facilitating power sharing between trains equipped with ESTS and those without it, while guaranteeing that all trains can safely and quickly evacuate passengers to the nearest station during a systemwide power outage. This study examines the Bangkok Mass Transit System (BTS) Skytrain Silom Line (dark green line) in Thailand, through computer simulation parameterised by real-world line and operational data, to evaluate the viability of this concept. It analyses variables such as the quantity of trains outfitted with ESTS, the distance from the train to the station where it halted, particular train specifications and additional pertinent aspects. The results reveal that ESTS can efficiently provide backup power to equipped trains and share surplus energy with other trains within the sharing radius with a success rate of 72.41%. This underscores the viability of ESTS operations and aids in the advancement of a more sustainable and efficient MRTS, despite the inherent constraints of ESTS.","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.70040","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147567387","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}

引用次数: 0

IF 1.7

IET Energy Systems Integration Pub Date : 2026-03-20

引用次数: 0

Revolutionising Battery Energy Storage Systems Energy Management: Dynamic-Aware Solutions With Integrated Quantum Particle Swarm Optimisation and Deep Reinforcement Learning 革命性的电池储能系统能量管理：集成量子粒子群优化和深度强化学习的动态感知解决方案

IF 1.7

IET Energy Systems Integration Pub Date : 2026-03-18 DOI: 10.1049/esi2.70038

Yousef Asadi, Mohsen Eskandari, Milad Mansouri

{"title":"Revolutionising Battery Energy Storage Systems Energy Management: Dynamic-Aware Solutions With Integrated Quantum Particle Swarm Optimisation and Deep Reinforcement Learning","authors":"Yousef Asadi, Mohsen Eskandari, Milad Mansouri","doi":"10.1049/esi2.70038","DOIUrl":"https://doi.org/10.1049/esi2.70038","url":null,"abstract":"The increasing integration of inverter-based resources (IBRs) into microgrids (MGs) poses considerable challenges for dynamic stability and energy management, primarily due to their variable and uncertain inertia and damping characteristics. Unlike conventional synchronous generators (SGs), IBRs exhibit complex nonlinear behaviour, complicating both mathematical modelling and real-time control. Battery energy storage systems (BESS) are essential for ensuring grid stability, operational efficiency and flexibility. Nevertheless, dynamic-aware energy management of BESS remains insufficiently explored, with current approaches often lacking adaptability to uncertainty and real-time requirements. This paper proposes a two-step hybrid framework combining quantum particle swarm optimisation (Q-PSO) with deep reinforcement learning (DRL). In the first step, Q-PSO efficiently generates an initial solution, significantly reducing computational demands. Subsequently, DRL dynamically refines this solution, effectively managing real-time uncertainties linked to inertia and damping variations. The proposed method addresses the non-Markovian nature of MG dynamics by constraining the DRL action space using the Q-PSO-derived solution, thereby alleviating the curse of dimensionality and enhancing training stability. Furthermore, dynamic constraints on frequency deviations and the rate of change of frequency (RoCoF) are incorporated to maintain robust grid stability during transients. Extensive simulations demonstrate that the proposed dynamic-aware energy management system (EMS) achieves economic efficiency improvements of 52.2% compared to Q-PSO alone and 22.7% compared to DQN alone. Additionally, BESS charging efficiency improves by 39.6% and 22.7%, whereas discharging efficiency increases by 38.3% and 28.25%, respectively, against the same benchmarks.","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.70038","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566670","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}

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IF 1.7

IET Energy Systems Integration Pub Date : 2026-03-18

引用次数: 0