IET Energy Systems Integration最新文献_第3页

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}

引用次数: 0

IF 1.7

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

引用次数: 0

Attentive Dual-Domain Modelling for Error-Resilient Net Load Forecasting in Intermittent Renewable Power Systems 间断性可再生能源系统误差弹性净负荷预测的细心双域建模

IF 1.7

IET Energy Systems Integration Pub Date : 2026-03-10 DOI: 10.1049/esi2.70037

Bizhi Wu, Zhiyi Li

{"title":"Attentive Dual-Domain Modelling for Error-Resilient Net Load Forecasting in Intermittent Renewable Power Systems","authors":"Bizhi Wu, Zhiyi Li","doi":"10.1049/esi2.70037","DOIUrl":"https://doi.org/10.1049/esi2.70037","url":null,"abstract":"Accurate long-sequence net load forecasting is essential for reliable grid operation and renewable integration, yet it remains challenging under quasi-periodicity, sharp weather-driven variability and long-range error accumulation. We propose HarmoNet, an end-to-end dual-domain architecture for long-horizon deterministic and interval forecasting. HarmoNet (i) encodes coupled low/high-frequency representations with multi-scale temporal signals, (ii) integrates local pattern modelling and global dependency learning via a hybrid convolutional-transformer block and (iii) aggregates horizon-wide representations to mitigate drift in long-range prediction. Uncertainty is estimated with quantile regression (10%–50%–90%). We evaluate HarmoNet on four-year hourly net-load datasets from Belgium, Bulgaria and Italy (2016–2019) derived from the Open Power System Data platform, using eight exogenous meteorological covariates, over horizons of 96/192/336/720 h. Relative to the strongest baseline per dataset-horizon setting, HarmoNet reduces MAE by 14.2% on average (up to 22.5% on Italy at 720 h) and achieves average reductions of 28.4% in the Winkler score and 13.0% in pinball loss. Under deployment-oriented stress tests spanning high-volatility, peak-spike and steep-ramp windows, HarmoNet attains the best deterministic accuracy in 27/36 windows and the best probabilistic performance in 32/36 windows, indicating robust and deployment-friendly long-horizon forecasting.","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.70037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564744","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 : 2026-03-10

引用次数: 0

An Intelligent Robust Control Framework for Stability Enhancement in Renewable Energy Powered Hybrid AC/DC Microgrids With Integrated Hydrogen Energy Systems 集成氢能系统的可再生能源混合交直流微电网稳定性增强智能鲁棒控制框架

IF 1.7

IET Energy Systems Integration Pub Date : 2026-03-07 DOI: 10.1049/esi2.70035

Md Saiful Islam, Israt Jahan Bushra, Tushar Kanti Roy, Amanullah Maung Than Oo

{"title":"An Intelligent Robust Control Framework for Stability Enhancement in Renewable Energy Powered Hybrid AC/DC Microgrids With Integrated Hydrogen Energy Systems","authors":"Md Saiful Islam, Israt Jahan Bushra, Tushar Kanti Roy, Amanullah Maung Than Oo","doi":"10.1049/esi2.70035","DOIUrl":"https://doi.org/10.1049/esi2.70035","url":null,"abstract":"Hybrid AC/DC microgrids that integrate photovoltaic, wind, battery and hydrogen energy systems are prone to DC-bus voltage fluctuations because of their low inertia and converter-based operation. This paper proposes a robust backstepping nonsingular fast terminal integral sliding mode controller that incorporates a virtual capacitor and a fractional-power reaching law to emulate synthetic inertia and improve transient damping. The controller coordinates energy exchange among distributed generation units and ensures precise DC-bus voltage regulation while managing bidirectional power transfer between AC and DC subgrids. An adaptive neuro-fuzzy inference system automatically tunes the controller gains in real time, and system stability is rigorously established through control Lyapunov functions. A detailed MATLAB/Simulink model, comprising PV, PMSG-based wind turbine, battery storage, electrolyser and PEM fuel cell, implements ANN-based MPPT to maximise renewable energy harvesting. The BNFTISMC is evaluated in three case studies against two benchmark controllers: the enhanced integral terminal SMC and the enhanced nonsingular terminal SMC. Under severe disturbances and varying load conditions, the proposed controller cuts overshoot by 75%–100%, reduces rise time by 58%–80% and completely eliminates mean absolute and mean squared errors. Processor-in-the-loop testing confirms zero steady-state error, whereas converter efficiency reaches 95.78% compared with 69.21% for the reference designs, demonstrating improved DC-bus voltage regulation, enhanced microgrid reliability and efficient real-time operation.","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.70035","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147563820","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 : 2026-03-07

引用次数: 0

IF 1.7

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

引用次数: 0

Improved Additional Damping Control for Synchronisation Stability Enhancement of Grid-Following Converters Under Multi-Loop Couplings Effects 多环耦合下电网跟随变换器同步稳定性增强的改进附加阻尼控制

IF 1.7

IET Energy Systems Integration Pub Date : 2026-03-03 DOI: 10.1049/esi2.70032

Guojie Song, Hong Li, Kaishun Xiahou, Bin Liu, Yanming Chen

{"title":"Improved Additional Damping Control for Synchronisation Stability Enhancement of Grid-Following Converters Under Multi-Loop Couplings Effects","authors":"Guojie Song, Hong Li, Kaishun Xiahou, Bin Liu, Yanming Chen","doi":"10.1049/esi2.70032","DOIUrl":"https://doi.org/10.1049/esi2.70032","url":null,"abstract":"In the small-signal synchronisation stability analysis of grid-following converters (GFL), the detailed interactions among multi-loop control mechanisms are often oversimplified or neglected, leading to significant modelling inaccuracies and potential stability assessment failures. To address this issue, this paper presents a motion-equation-based model for GFL converter that comprehensively incorporates multi-loop control dynamics, including the current control loop, AC voltage control loop, DC-link voltage control loop and PLL. Based on the proposed model, the coupling characteristics of multi-loop control are explicitly analysed and visualised in terms of system damping. The analysis reveals that low-frequency oscillations in GFL converters can be attributed to unintended negative damping induced by multi-loop control couplings. To mitigate this issue, an improved additional damping control (IADC) strategy is proposed, which enhances the synchronisation stability via effectively compensating for the negative damping caused by multi-loop interactions. Finally, the simulation and experiment studies are presented to validate the proposed analysis results and the effectiveness of the IADC strategy.","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.70032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147563054","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 : 2026-02-27

引用次数: 0