Electric Power Systems Research最新文献

英文中文

Optimal resource scheduling for tourist islands in winter and summer tourist high seasons 旅游岛屿冬、夏旅游旺季资源优化调度

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-01-27 DOI: 10.1016/j.epsr.2026.112776

Shipeng Ji , Weiqing Wang , Xiaozhu Li , Yuqi Wang , Xin Jing , Zhiqiang Xu

Tourism-oriented island microgrids face significant operational challenges due to pronounced seasonal fluctuations in electricity, heating, and cooling demands. To address this issue, this paper proposes a unified seasonal dispatch framework that integrates wind power, wave energy, combined cooling, heating, and power units, electric boilers, absorption and electric chillers, and a tri-layer storage system (battery, thermal, and ice). A mixed-integer linear programming model is developed to optimize 24-hour multi-energy flows by coordinating thermal-electric coupling and storage operations. In winter, the strategy prioritizes cascaded waste heat recovery and thermal energy storage to stabilize the heating supply. In summer, it leverages time-of-use pricing and an ice storage system for cooling peak shaving. Validated on a representative Chinese tourist island, the proposed strategy achieves primary energy efficiencies of 81.7% in winter and 88.0% in summer. Compared to conventional baseline modes, it reduces daily operating costs by 38.9% (to 1,591.99 CNY) in winter and by 44.0% (to 2,338.57 CNY) in summer, while simultaneously lowering carbon emissions. These results demonstrate that the coordinated seasonal multi-energy dispatch effectively enhances economic performance, energy utilization, and environmental sustainability for island microgrids.

由于电力、供暖和制冷需求的明显季节性波动，以旅游为导向的岛屿微电网面临着重大的运营挑战。为了解决这一问题，本文提出了一个统一的季节性调度框架，该框架将风能、波浪能、冷热电联产机组、电锅炉、吸收式和电冷水机组以及三层储能系统（电池、热能和冰）集成在一起。通过协调热电耦合和储能运行，建立了24小时多能流优化的混合整数线性规划模型。在冬季，该战略优先考虑级联余热回收和热能储存，以稳定供暖供应。在夏季，它利用分时定价和冰储存系统来冷却调峰。在一个具有代表性的中国旅游岛屿上进行了验证，该策略在冬季和夏季实现了81.7%和88.0%的初级能源效率。与常规基准模式相比，冬季每日运营成本降低38.9%（至1,591.99元），夏季降低44.0%（至2,338.57元），同时降低碳排放。结果表明，季节性多能协调调度有效提高了岛屿微电网的经济效益、能源利用率和环境可持续性。

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引用次数: 0

Offshore wind power forecasting via trend-aware just-in-time learning with nearest neighbors 通过与最近邻居的趋势感知实时学习进行海上风电预测

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-01-24 DOI: 10.1016/j.epsr.2026.112767

Yu Pan , Tao Chen

Accurate offshore wind power forecasting is vital for secure grid operation and cost-effective system dispatch but remains challenging due to the high volatility and non-stationarity of offshore environments. Existing forecasting models often rely on offline training and external meteorological data, limiting their adaptability to rapid variations in wind power. This study proposes a trend-aware just-in-time learning (tJITL) framework that integrates trend similarity into an online autor-egressive exogenous (ARX) model. The method dynamically constructs local models online by selecting trend-consistent samples from historical data, thereby capturing transient dynamics without the need for model retraining or external variables. Experimental results demonstrate that the proposed tJITL framework provides a reliable and data-efficient solution for online offshore wind power forecasting, with strong potential for application in intelligent power system operations.

准确的海上风电预测对于电网安全运行和经济高效的系统调度至关重要，但由于海上环境的高波动性和非平稳性，预测仍然具有挑战性。现有的预报模型往往依赖于离线训练和外部气象数据，限制了它们对风力快速变化的适应性。本研究提出了一个趋势感知的即时学习（tJITL）框架，该框架将趋势相似性集成到在线自袭外生（ARX）模型中。该方法通过从历史数据中选择趋势一致的样本，在线动态构建局部模型，从而在不需要模型再训练和外部变量的情况下捕获瞬态动态。实验结果表明，所提出的tJITL框架为海上风电在线预测提供了可靠且数据高效的解决方案，在电力系统智能运行中具有很强的应用潜力。

引用次数: 0

Structural optimization of multi-chamber arrester and research on arc extinguishing effect under multiple lightning strikes 多腔避雷器结构优化及多次雷击灭弧效果研究

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-01-24 DOI: 10.1016/j.epsr.2026.112773

Ying Chen , Xinqiang Wan , Yuhang Chen

Impulse flashover caused by lightning strikes on overhead transmission lines may lead to power-frequency continuous arcs, resulting in insulator ablation and line tripping. To suppress power-frequency arcs, this paper investigates the arc-extinguishing performance of a multi-chamber arrester. An arc model is established, and the effects of electrode spacing and electrode radius on arc extinction are analyzed using COMSOL simulations. The results indicate that electrode spacing and nozzle size significantly influence the arc blowing process. To evaluate the operational performance under repeated lightning strikes, multi-pulse lightning experiments are conducted. Experimental results show that the multi-chamber arrester can effectively suppress power-frequency arcs and maintain adequate overvoltage discharge performance under repeated impulses. Field application results further demonstrate that the arrester reduces the tripping rate of transmission lines at 35 kV and below by approximately 90%, confirming its effectiveness and practical applicability.

在架空输电线路上，雷击引起的脉冲闪络会引起工频连续电弧，导致绝缘子烧蚀和线路跳闸。为了抑制工频电弧，研究了一种多腔避雷器的灭弧性能。建立了电弧模型，利用COMSOL仿真分析了电极间距和电极半径对消弧的影响。结果表明，电极间距和喷嘴尺寸对吹弧过程有显著影响。为了评估多脉冲雷击条件下的作战性能，进行了多脉冲雷击实验。实验结果表明，多腔避雷器能有效抑制工频电弧，并在重复脉冲下保持良好的过压放电性能。现场应用结果进一步表明，该避雷器可使35 kV及以下输电线路的跳闸率降低约90%，证实了该避雷器的有效性和实用性。

引用次数: 0

CCS-CoLearn: A cooperative learning framework for shared carbon capture and storage infrastructure — Application to Liverpool bay and the North-West UK cluster ccs - collearn：共享碳捕集与封存基础设施的合作学习框架——在利物浦湾和英国西北部集群的应用

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-01-24 DOI: 10.1016/j.epsr.2026.112778

Sami Astan , Kavan Fatehi , Amin Hajizadeh

The paper introduces CCS-CoLearn, an integrated cooperative learning framework for the predictive and equitable operation of shared carbon capture and storage (CCS) infrastructures. The methodology unifies probabilistic deep-learning forecasting with game-theoretic optimisation to coordinate multiple industrial emitters linked to a common transport-and-storage system. A Transformer–BiLSTM ensemble generates multi-quantile forecasts of CO₂ inflows, parasitic electrical loads, and market prices, which feed a Nash-bargaining coordination layer ensuring fairness and Pareto efficiency among participants. The system is implemented within a digital-twin simulation of the Liverpool Bay CCS network, coupled to a reduced Great Britain power-grid model. Scenario analyses for 2030 and 2050 show that CCS-CoLearn achieves up to 18 % total-cost reduction, fairness index > 0.9, and 10 % higher CO₂ abatement compared with non-cooperative or rule-based baselines. The framework demonstrates that intelligent coordination can substitute for physical over-capacity, providing a scalable pathway for data-driven and cooperative management of CCS clusters under the UK Net-Zero 2050 strategy.

本文介绍了CCS- colearn，这是一个用于共享碳捕集与封存（CCS）基础设施预测和公平运行的集成合作学习框架。该方法将概率深度学习预测与博弈论优化相结合，以协调与公共运输和存储系统相关的多个工业排放者。变压器- bilstm集成可以生成CO₂流入、寄生电力负荷和市场价格的多分位数预测，从而提供纳什议价协调层，确保参与者之间的公平性和帕累托效率。该系统在利物浦湾CCS网络的数字孪生模拟中实现，并与简化的英国电网模型相结合。2030年和2050年的情景分析表明，与非合作或基于规则的基线相比，CCS-CoLearn可实现高达18%的总成本降低，公平指数为0.9，二氧化碳排放量增加10%。该框架表明，智能协调可以替代物理容量过剩，为英国2050年净零战略下的CCS集群的数据驱动和协作管理提供了可扩展的途径。

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引用次数: 0

A novel CDPFC for harmonic resonance suppression in offshore wind farms 一种用于海上风电场谐波抑制的新型CDPFC

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-01-24 DOI: 10.1016/j.epsr.2026.112763

Qingchuan Fan , Yu Deng , Yanhong Jiang , Yongjie Zhang , Yong Xu , Qiuqin Sun

Offshore wind farms (OWFs) are prone to harmonic resonance problems as it is delivered via submarine cables. Existing methods for harmonic resonance are mostly limited to specific conditions, and may fail when the operating condition of system changes. In this paper, a novel topology based on cascaded distributed power flow controller (CDPFC) is proposed. Its harmonic suppression effect is analyzed by connecting it in series to the OWFs. Optimization of DPFC units using cascaded H-bridge topology to improve harmonic tracking fitting capability. Using the harmonic voltages collected by Hanning-windowed discrete Fourier transform (DFT) as a reference, a dual-vector model predictive control (MPC) strategy with the objective of minimizing the voltage envelopment area is proposed. The method not only suppresses the resonance amplification problem caused by background harmonics, but also optimizes the power quality of the grid in multiple frequency bands. The harmonic content of the grid-connected voltage is greatly reduced through the precise control of the harmonic components.

海上风电场（owf）由于通过海底电缆输送，容易出现谐波共振问题。现有的谐波谐振方法大多局限于特定条件，当系统运行条件发生变化时可能失效。提出了一种基于级联分布式潮流控制器（CDPFC）的新型拓扑结构。通过将其串联到owf上，分析了其谐波抑制效果。采用级联h桥拓扑优化DPFC单元以提高谐波跟踪拟合能力。以汉宁窗离散傅立叶变换（DFT）采集的谐波电压为参考，提出了一种以电压包络面积最小为目标的双矢量模型预测控制策略。该方法不仅抑制了背景谐波引起的谐振放大问题，而且在多个频段优化了电网的电能质量。通过对谐波分量的精确控制，大大降低了并网电压的谐波含量。

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引用次数: 0

A multi-objective evolutionary algorithm with Pre-selection and self-adjusting mechanism for fault reconstruction in multi-load independent power distribution system 多负荷独立配电系统故障重构的多目标预选择自适应进化算法

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-01-24 DOI: 10.1016/j.epsr.2026.112770

Tianhao Gong, Dazhi Wang, Yupeng Zhang, Tianyi Li

Fault reconstruction is vital for the stability of expanding multi-load independent distribution systems. This paper presents an enhanced multi-objective optimization algorithm for fault reconstruction in multi-load independent distribution systems. The method improves upon existing algorithms by integrating a pre-selection mechanism for a superior initial population and self-adjusting strategies for efficient convergence. Combined with a novel network encoding technique, it accurately and efficiently identifies optimal reconstruction schemes. Validation results demonstrate that the approach efficiently determines optimal reconstruction schemes with higher accuracy.

故障重构对于扩容型多负荷独立配电系统的稳定运行至关重要。针对多负荷独立配电系统的故障重构问题，提出了一种改进的多目标优化算法。该方法在现有算法的基础上进行了改进，集成了优选初始种群的预选择机制和有效收敛的自调整策略。结合一种新颖的网络编码技术，准确、高效地识别出最优重构方案。验证结果表明，该方法能有效地确定最优重建方案，具有较高的精度。

引用次数: 0

Vibration-induced micro-discharge at suspension clamp-conductor interfaces under short circuit conditions 短路条件下悬架钳-导体界面振动诱发微放电

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-01-24 DOI: 10.1016/j.epsr.2026.112772

Chuanyi Zheng , Gengbiao Deng , Beisi Huang , Rui Wang , Deming Guo , Gang Liu

The conductor at the suspension clamp is always the weak point of the overhead line due to its unique contact structure. When a short circuit occurs, the discharge generated inside the suspension clamp may cause damage to the conductor and lead to serious safety accidents. This article conducted multi contact vibration discharge experiments at the clamp-conductor interfaces. For the first time, microsecond-level observation of vibration discharge inside the clamp has been achieved. Experiments have shown that spark discharge occurs at the clamp-conductor interfaces during dynamic contact separation. Besides, the resistance between contact points is a key factor affecting discharge energy. The energy reduction exceeded 98% for contact discharge and 99% for separation discharge when the resistance between contact points rose from 1.61 mΩ to 8.07 mΩ. The research results of this paper supplement the damage mechanism of conductor at suspension clamps under short circuit current. It provides experimental support for vibration discharge suppression and online monitoring technology inside the clamp.

悬挂钳处的导线由于其独特的接触结构，一直是架空线的薄弱环节。当发生短路时，悬挂夹内部产生的放电可能会损坏导体，导致严重的安全事故。本文在夹芯-导体界面处进行了多接触振动放电实验。首次实现了钳内振动放电的微秒级观测。实验表明，在动态接触分离过程中，夹芯-导体界面处产生火花放电。此外，接触点之间的电阻是影响放电能量的关键因素。当接触点之间的电阻从1.61 mΩ增加到8.07 mΩ时，接触放电的能量减少超过98%，分离放电的能量减少超过99%。本文的研究结果补充了短路电流作用下悬吊钳处导体的损伤机理。为夹具内部振动放电抑制和在线监测技术提供了实验支持。

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引用次数: 0

Two-stage optimization of appliance scheduling and BESS capacity with comfort level 带舒适度的家电调度和BESS容量两阶段优化

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-01-24 DOI: 10.1016/j.epsr.2026.112771

Zhiling Ren , Xi Chen , Yun Dong

Residential photovoltaic (PV)–battery energy storage system (BESS) planning often neglects impact of comfort level. We propose a two-stage framework that jointly selects BESS capacity and appliance start-up schedules while accounting for appliance-use comfort, thermal comfort, and BESS degradation. Annual days are clustered by k-means to select a set of typical days. Stage 1 performs a discrete capacity scan (coarse grid plus local refinement). For each candidate, Stage 2 solves a typical day mixed-integer linear programming problem with a linear fractional cost–comfort objective via Dinkelbach’s method, maps the resulting binary schedules to all natural days by cluster membership, and then solves an annual rolling linear programming problem for dispatch and degradation-inclusive evaluation. In the baseline case, the selected capacity is 10.75 kWh with an annual total cost of 2623.2 CNY and comfort indices of 0.793 and 0.788; degradation cost is 404.3 CNY. Without the comfort term, the preferred capacity increases to 16.5 kWh and the annual total cost decreases to 2265.9 CNY, while degradation cost rises to 555.1 CNY. Sensitivity analyses show that outcomes vary with comfort settings and time-of-use prices. Overall, the framework quantifies annual cost-comfort-degradation trade-offs and recommends a feasible BESS size and appliance schedules.

住宅光伏-电池储能系统规划往往忽视了舒适性的影响。我们提出了一个两阶段的框架，共同选择BESS容量和设备启动时间表，同时考虑设备使用舒适性，热舒适性和BESS退化。通过k-means对全年天数进行聚类，以选择一组典型天数。阶段1执行离散容量扫描（粗网格加上局部细化）。对于每个候选项目，第二阶段通过Dinkelbach方法解决了一个典型的日混合整数线性规划问题，该问题具有线性分数成本舒适目标，通过聚类隶属度将得到的二进制调度映射到所有自然天数，然后解决了一个年度滚动线性规划问题，用于调度和包含退化的评估。基线情况下，选择容量为10.75 kWh，年总成本2623.2元，舒适度指数分别为0.793和0.788；降解成本为404.3元。去掉舒适项后，优选容量增加到16.5 kWh，年总成本下降到2265.9元，降解成本上升到555.1元。敏感性分析表明，结果因舒适度设置和使用时间价格而异。总体而言，该框架量化了年度成本-舒适-退化权衡，并建议了可行的BESS大小和设备时间表。

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引用次数: 0

Multi-time-scale optimal scheduling of DCP-IES: Low-carbon-economic synergy with high renewable penetration DCP-IES多时间尺度优化调度：高可再生能源渗透率的低碳经济协同效应

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-01-23 DOI: 10.1016/j.epsr.2026.112751

Xu Wu , Lan Yu , Jingtao Hu , Ke Xing , Guo Wang

Against the backdrop of high-proportion renewable energy integration into the main power grid, the low-carbon and high-reliability operation of the Data Center Park (DCP) Integrated Energy System (IES) is crucial for energy transition. However, DCPs-characterized by intensive computing loads and multi-energy coupling-confront dual scheduling challenges: carbon emission reduction and risk management. To address these challenges, this paper proposes a "carbon-risk" dual-constrained dynamic scheduling model featuring day-ahead and intra-day stages. Specifically, the day-ahead stage aims to minimize total operational and carbon costs; it adopts scenario-based methods to construct multi-source uncertainty scenarios, integrates Conditional Value at Risk (CVaR) to quantify risks arising from renewable energy uncertainty, and optimizes the operational strategies of combined cooling, heating, and power (CCHP) systems, energy storage, and renewable energy units. The intra-day stage, by leveraging rolling wind-solar forecasts, mitigates tie-line power deviations and dynamically adjusts carbon emissions via real-time adjustments to equipment output, ensuring grid security and the timeliness of carbon emission constraints. A case study on a DCP in northwest China validates the model’s effectiveness in synergistically reducing carbon footprints, mitigating operational risks, and enhancing economic performance, thus providing an effective solution for the optimal operation of DCP-IES under high-proportion renewable energy integration.

在可再生能源高比例并入主电网的背景下，数据中心园区（DCP）综合能源系统（IES）的低碳、高可靠运行对能源转型至关重要。然而，dcp调度面临着碳减排和风险管理的双重挑战。为了解决这些挑战，本文提出了一种“碳风险”双约束动态调度模型，该模型具有日前和日内两个阶段。具体来说，提前一天阶段旨在将总运营成本和碳排放成本降至最低；采用基于场景的方法构建多源不确定性情景，整合CVaR （Conditional Value at Risk）量化可再生能源不确定性带来的风险，优化冷热电联产系统、储能系统和可再生能源机组的运行策略。在日间阶段，利用滚动的风能-太阳能预测，减轻了配线功率偏差，并通过实时调整设备输出动态调整碳排放，确保电网安全和碳排放约束的及时性。以西北地区某DCP为例，验证了该模型在协同减少碳足迹、降低运行风险、提高经济效益方面的有效性，从而为DCP- ies在高比例可再生能源整合下的优化运行提供了有效的解决方案。

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引用次数: 0

Electric vehicle charging forecasting: Performance comparison and mathematical analysis of LSTM and neural ODE models 电动汽车充电预测：LSTM与神经ODE模型的性能比较与数学分析

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-01-23 DOI: 10.1016/j.epsr.2026.112761

Shakir Uzzaman, Sancoy Barua, Ahmad Afif Alam, Yuki Osone, Daisuke Kodaira

Electric vehicles (EVs) are crucial for a sustainable future, but their unpredictable charging patterns threaten power grid stability. As EV adoption grows, accurate EV charging forecasts are essential to prevent system failures and minimize financial losses. Traditional approaches are often limited, which has led to advanced data-driven solutions. Deep learning methods, particularly Long Short-Term Memory (LSTM) networks, have shown promise for sequential data but perform poorly when time intervals are irregular. Neural Ordinary Differential Equation (ODE) models overcome this limitation by treating temporal data as a continuous flow, improving the handling of irregular intervals. This is the first study to use Neural ODE for EV charging demand prediction, addressing the limitations of traditional recurrent models, including LSTM and its variants, and demonstrating the advantages of continuous-time modeling. Both architectures were compared using several open-access EV charging datasets. Neural ODE consistently achieved lower mean absolute error (MAE) values than LSTM across all three datasets (0.045, 0.024, and 0.121 vs. 0.072, 0.054, and 0.18), corresponding to improvements of 32 %–55 %. This performance difference arises from fundamental architectural distinctions. By modeling temporal data as a continuous flow, Neural ODE better captures irregular patterns in real-world charging behavior. Mathematical analysis and additional testing across hyperparameter settings confirmed the robustness of Neural ODE. These findings highlight the potential of continuous-time neural models for accurate and reliable EV charging demand forecasting in smart grid applications.

电动汽车（ev）对于可持续发展的未来至关重要，但其不可预测的充电模式威胁着电网的稳定性。随着电动汽车的普及，准确的电动汽车充电预测对于防止系统故障和最大限度地减少经济损失至关重要。传统的方法往往是有限的，这导致了先进的数据驱动的解决方案。深度学习方法，特别是长短期记忆（LSTM）网络，已经显示出对序列数据的希望，但当时间间隔不规则时表现不佳。神经常微分方程（ODE）模型克服了这一限制，将时间数据视为连续流，改进了对不规则区间的处理。这是首次将Neural ODE用于电动汽车充电需求预测的研究，解决了传统循环模型（包括LSTM及其变体）的局限性，并展示了连续时间建模的优势。使用几个开放访问的电动汽车充电数据集对两种架构进行了比较。在所有三个数据集上，Neural ODE始终比LSTM获得更低的平均绝对误差（MAE）值（0.045、0.024和0.121 vs. 0.072、0.054和0.18），相应的改进幅度为32% - 55%。这种性能差异源于基本的体系结构差异。通过将时间数据建模为连续流，Neural ODE可以更好地捕捉真实充电行为中的不规则模式。数学分析和跨超参数设置的附加测试证实了Neural ODE的鲁棒性。这些发现突出了连续时间神经模型在智能电网应用中准确可靠地预测电动汽车充电需求的潜力。

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