Electric Power Systems Research最新文献

NARX neural network-based black-box equivalence model of external microgrids in a multi-microgrid including DFIG and BESS 基于NARX神经网络的DFIG和BESS多微网外部微网黑盒等效模型

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

Electric Power Systems Research

Pub Date : 2026-06-01 Epub Date: 2026-01-09 DOI: 10.1016/j.epsr.2026.112720

M. Shafiee Souderjani, M.E. Hamedani Golshan

To capture the entire dynamic response of a multi-microgrid (MMG) system, detailed modeling of the MMG is necessary; however, the computational burden of such models limits their suitability for efficient dynamic studies. When the analysis focuses on a single microgrid (MG) within a MMG, external MGs can be represented using simplified equivalents that preserve accuracy while significantly reducing computational demands. To balance model detail with computational efficiency, this paper proposes a model order reduction (MOR) technique based on a nonlinear autoregressive exogenous (NARX) neural network to replace external MGs with an artificial intelligence (AI)-based black-box equivalent. To consider all dynamic modes in different disturbances, a detailed MMG model is introduced where each MG comprises doubly-fed induction generators (DFIGs), battery energy storage systems (BESSs), loads, and distribution feeders capable of operating in both grid-connected and islanded modes. To demonstrate the method’s scalability, a MMG composed of six MGs with total dynamic order of 360 has been studied. The designed training and validation scenarios capture the dynamic responses of external MGs to a wide range of representative events occurring on the target MG. The performance of the proposed reduced-order model is evaluated in comparison with a long short-term memory (LSTM) based alternative and the detailed model, which serves as the ground truth. The NARX-based equivalent achieves high accuracy while reducing simulation time by over 90%, providing a practical solution for computationally efficient MMG dynamic studies.

为了捕捉多微电网系统的整个动态响应，有必要对多微电网系统进行详细的建模；然而，这些模型的计算负担限制了它们对有效动力学研究的适用性。当分析的重点是MMG中的单个微电网（MG）时，外部MG可以使用简化的等效物来表示，从而在保持准确性的同时显着降低计算需求。为了平衡模型细节和计算效率，本文提出了一种基于非线性自回归外生（NARX）神经网络的模型降阶（MOR）技术，用基于人工智能（AI）的黑盒等效物取代外部mg。为了考虑不同扰动下的所有动态模式，引入了一个详细的MMG模型，其中每个MG包括双馈感应发电机（DFIGs），电池储能系统（BESSs），负载和能够在并网和孤岛模式下运行的配电馈线。为了证明该方法的可扩展性，研究了一个由6个动态阶数为360的MMG组成的MMG。设计的训练和验证场景捕获外部MG对目标MG上发生的广泛代表性事件的动态响应。将该降阶模型的性能与基于长短期记忆（LSTM）的备选方案和作为基础真值的详细模型进行比较。基于narx的等效体在实现高精度的同时，将仿真时间缩短了90%以上，为计算效率高的MMG动态研究提供了实用的解决方案。

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引用次数: 0

Adaptive dynamic virtual resistor method for suppressing synchronous frequency resonance 抑制同步频率共振的自适应动态虚电阻方法

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

Electric Power Systems Research

Pub Date : 2026-06-01 Epub Date: 2026-01-31 DOI: 10.1016/j.epsr.2026.112791

Biao Feng, Li Zhang, Qi Han

Virtual synchronous generator (VSG) control has become one of the core control strategies for grid-connected converters by providing virtual inertia and damping that can effectively improve system stability. However, in low resistance-to-reactance ratio (R/X) grids, their power loops are prone to synchronous frequency resonance (SFR). This paper establishes a small-signal frequency-domain model of the VSG power loops to reveal the mechanism of SFR, and uses dynamic relative gain array (DRGA) to quantify the exacerbating effect of resonance on power coupling in low R/X systems, elucidating the influence of R/X on resonance peak values and stability margins. Furthermore, an adaptive dynamic virtual resistor (ADVR) method based on online impedance identification (OII) is proposed: this method suppresses resonance through dynamic virtual resistors combining OII, and adaptively adjusts the virtual resistors to accelerate resonance decay while avoiding exacerbating power coupling, it effectively addresses the issue of resonance suppression failure caused by changes in line impedance parameters. This article presents an electromagnetic transient simulation model developed in Matlab/Simulink, validating theoretical analysis and evaluating the effectiveness of the proposed method for enhanced accuracy.

虚拟同步发电机（VSG）控制通过提供虚拟惯性和阻尼来有效地提高系统的稳定性，已成为并网变流器的核心控制策略之一。然而，在低阻抗比（R/X）电网中，其电源回路容易发生同步频率谐振（SFR）。本文建立了VSG功率回路的小信号频域模型，揭示了SFR的机理，并利用动态相对增益阵列（DRGA）量化了低R/X系统中谐振对功率耦合的加剧作用，阐明了R/X对谐振峰值和稳定裕度的影响。提出了一种基于在线阻抗识别（OII）的自适应动态虚拟电阻（ADVR）方法：该方法通过动态虚拟电阻结合OII来抑制谐振，并自适应调整虚拟电阻加速谐振衰减，同时避免加剧功率耦合，有效解决了线路阻抗参数变化导致的谐振抑制失效问题。本文介绍了在Matlab/Simulink中开发的电磁瞬变仿真模型，验证了理论分析并评估了所提出方法的有效性，以提高精度。

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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 : 2026-06-01 Epub 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

Dependence-aware day-ahead unit commitment and economic dispatch for a CHP-centered microgrid 以热电联产为中心的微电网依赖感知日前机组承诺和经济调度

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

Electric Power Systems Research

Pub Date : 2026-06-01 Epub Date: 2026-01-31 DOI: 10.1016/j.epsr.2026.112786

Syed Mahboob Ul Hassan

Integrating high levels of solar photovoltaic (PV) generation into combined heat and power (CHP) microgrids presents scheduling challenges due to forecast uncertainty and thermal coupling between electric and heating demands. Traditional point-forecast scheduling is unreliable under forecast errors and inconsistent with joint electric-heating behavior, while scenario-based stochastic methods are computationally expensive for day-ahead operations. This study proposes a dependence-aware deterministic unit commitment and economic dispatch (UC/ED) framework that addresses uncertainty in PV output and coupled electric-heating demands using quantile regression forecasting. The method produces day-ahead quantile forecasts, then uses a rolling historical window to estimate empirical joint quantile-occurrence distributions for electric and heating loads and marginal distributions for PV. These distributions construct hourly probability-weighted day-ahead profiles that serve as deterministic inputs to a single mixed-integer UC/ED optimization. Five scheduling strategies are compared across different rolling window lengths (7-, 12-, 17-, and 30-day) versus median-only dispatch. The 30-day window achieves optimal performance with operating costs of $251.12, representing a 16.07% reduction from median-only scheduling ($299.22). Savings derive primarily from reduced CHP fuel consumption and improved battery energy storage system efficiency.

由于预测的不确定性和电力和供暖需求之间的热耦合，将高水平的太阳能光伏发电（PV）集成到热电联产（CHP）微电网中提出了调度挑战。传统的点预测调度在预测误差下不可靠，且与联合供热行为不一致，而基于场景的随机调度方法在日前运行时计算成本高。本研究提出了一个依赖感知的确定性单元承诺和经济调度（UC/ED）框架，该框架使用分位数回归预测来解决光伏输出和耦合电加热需求的不确定性。该方法产生一天前的分位数预测，然后使用滚动历史窗口来估计电和热负荷的经验联合分位数分布以及光伏的边际分布。这些分布构建了每小时概率加权的日前概况，作为单个混合整数UC/ED优化的确定性输入。在不同的滚动窗口长度（7天、12天、17天和30天）和仅中位数调度之间比较了五种调度策略。30天的窗口期实现了最佳性能，运营成本为251.12美元，比中位调度（299.22美元）减少了16.07%。节省主要来自减少热电联产燃料消耗和提高电池储能系统效率。

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引用次数: 0

A PV prediction model based on mechanistic data-driven feature generation with temporal cross-scale alignment mechanism 基于时间跨尺度对齐机制的机械数据驱动特征生成PV预测模型

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

Electric Power Systems Research

Pub Date : 2026-06-01 Epub Date: 2026-01-20 DOI: 10.1016/j.epsr.2026.112755

Keqi Wang , Junye Zhu , Yangshu Lin , Chao Yang , Zhongwei Zhang , Zhongyang Zhao , Can Zhou , Lijie Wang , Chenghang Zheng

During photovoltaic (PV) power generation, the stochastic fluctuation of solar energy poses significant challenges for grid-connected systems, making accurate PV power forecasting essential for maintaining grid reliability and stability. This study proposes a PV power forecasting model that integrates mechanistic data-driven feature generation with a temporal cross-scale alignment mechanism (TCSAM). Two key features—effective irradiance and module temperature—highly correlated with power output, are derived through irradiance calculations on the tilted PV surface and heat transfer mechanisms. Various network modules extract features at different scales, capturing both slow time-varying and time-series characteristics. The model utilizes changes in features across both long-term and short-term time scales to assess their relationship with future meteorological features, identifying critical factors that significantly influence upcoming power generation. This approach enables the model to effectively detect underlying patterns and connections between past information and future outcomes. On four seasonal test sets, the model reduces RMSE by 20 %-30 % and increases R² by 2 %-3 % compared to the best baseline, highlighting its superior performance. This study offers innovative insights to enhance the accuracy and robustness of PV power forecasting, contributing to the stable operation of power grids.

在光伏发电过程中，太阳能的随机波动给并网系统带来了巨大的挑战，准确的光伏发电功率预测对于维持电网的可靠性和稳定性至关重要。本文提出了一种集成了机械数据驱动特征生成和时间跨尺度对齐机制（TCSAM）的光伏发电功率预测模型。两个关键特征-有效辐照度和组件温度-与功率输出高度相关，是通过对倾斜PV表面的辐照度计算和传热机制得出的。不同的网络模块提取不同尺度的特征，捕获慢时变特征和时间序列特征。该模型利用长期和短期时间尺度上的特征变化来评估它们与未来气象特征的关系，确定对即将到来的发电产生重大影响的关键因素。这种方法使模型能够有效地检测过去信息和未来结果之间的潜在模式和联系。在四个季节测试集上，与最佳基线相比，该模型的RMSE降低了20% - 30%，R²增加了2% - 3%，突出了其优越的性能。本研究为提高光伏发电功率预测的准确性和鲁棒性提供了创新的见解，有助于电网的稳定运行。

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引用次数: 0

A coordinated hierarchical frequency control strategy for islanded microgrid using multiple flexibility resources 基于多柔性资源的孤岛微电网协调分层频率控制策略

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

Electric Power Systems Research

Pub Date : 2026-06-01 Epub Date: 2026-02-05 DOI: 10.1016/j.epsr.2026.112820

Xijin Yang, Qinfen Lu

To address the stringent dual requirements for frequency stability and operational economy in island microgrids under source-load uncertainties, this paper proposes a hierarchical coordinated frequency control strategy with incorporating economic dispatch. In the primary frequency control stage, a Sigmoid function-based adaptive Virtual Synchronous Generator (VSG) control is proposed. This method adjusts the virtual damping in real-time to enhance the dynamic response capability of renewable energy sources (RES). In the secondary frequency control stage, a Model Predictive Control (MPC) framework integrating an economic cost function is constructed to achieve the deep integration of frequency restoration and optimal power allocation. Simulation results in Matlab/Simulink demonstrate that the load response rate of RES in the primary control stage increases by over 280 %. In the secondary control stage, the RES response rate reaches approximately 34 %, and the comprehensive cumulative generation cost is reduced by about 73.9 %. Furthermore, the change rate of frequency deviation remains at the order of magnitude of

1 0^{- 6}

under system parameter perturbations, which verifying the robustness of the proposed strategy.

针对源负荷不确定条件下孤岛微电网对频率稳定性和运行经济性的双重要求，提出了一种包含经济调度的分层协调频率控制策略。在主频率控制阶段，提出了一种基于Sigmoid函数的自适应虚拟同步发电机（VSG）控制方法。该方法实时调节虚拟阻尼，提高可再生能源的动态响应能力。在二次频率控制阶段，构建了集成经济成本函数的模型预测控制（MPC）框架，实现频率恢复与最优功率分配的深度融合。在Matlab/Simulink中的仿真结果表明，在初始控制阶段，RES的负载响应率提高了280%以上。在二次控制阶段，可再生能源响应率达到约34%，综合累计发电成本降低约73.9%。此外，在系统参数扰动下，频率偏差的变化率保持在10−6数量级，验证了所提策略的鲁棒性。

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引用次数: 0

Iterative-learning enhanced error-based active disturbance rejection control for power regulation of offshore wind turbines 海上风力发电机组功率调节的迭代学习增强误差自抗扰控制

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

Electric Power Systems Research

Pub Date : 2026-06-01 Epub Date: 2026-02-11 DOI: 10.1016/j.epsr.2026.112833

Guolian Hou , Qingwei Li , Qi Yu , Ting Huang

Offshore wind power has become a key driver of the global clean energy transition. However, the inherent stochasticity of the wind field leads to pronounced power fluctuations that conventional controllers cannot effectively mitigate, particularly under periodic disturbances. To address these issues, this paper proposes an error-based active disturbance rejection control strategy enhanced with iterative learning (EADRC-IL) for offshore wind turbines (OWTs) to suppress power fluctuations. Integrating iterative learning enables feedforward compensation of repeatable components, reducing estimation burden of the extended state observer and strengthening disturbance rejection while preserving EADRC’s robustness to stochastic disturbances. In addition, a stability analysis of EADRC-IL is conducted using singular perturbation theory to ensure theoretical soundness. Moreover, the chaotic sine-cosine-assisted mountain gazelle optimizer is devised to efficiently tune controller parameters, ensuring both convergence speed and regulation performance. High-fidelity OpenFAST simulations on a 5-MW OWT show that EADRC-IL consistently outperforms baselines, reducing overshoot by up to 20.1% under step winds and MAE by 36.63% under turbulent winds.

海上风电已成为全球清洁能源转型的关键驱动力。然而，风场固有的随机性导致了明显的功率波动，传统的控制器无法有效地缓解，特别是在周期性干扰下。为了解决这些问题，本文提出了一种基于误差的基于迭代学习的自抗扰控制策略（EADRC-IL），用于海上风力发电机（OWTs）抑制功率波动。集成迭代学习实现了可重复分量的前馈补偿，减少了扩展状态观测器的估计负担，增强了抗扰性，同时保持了EADRC对随机干扰的鲁棒性。此外，利用奇异摄动理论对EADRC-IL进行了稳定性分析，以保证理论的合理性。此外，设计了混沌正弦余弦辅助山羚优化器，有效地调整控制器参数，保证了收敛速度和调节性能。在5兆瓦的OWT上进行的高保真OpenFAST模拟表明，EADRC-IL始终优于基线，在阶梯风下减少了20.1%的超调，在湍流风下减少了36.63%的MAE。

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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 : 2026-06-01 Epub 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

Adaptive distributed cyber-resilient secondary control for islanded AC microgrid against switch DoS attacks and unbounded FDI attacks 孤岛交流微电网抗交换机DoS攻击和无界FDI攻击的自适应分布式网络弹性二次控制

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

Electric Power Systems Research

Pub Date : 2026-06-01 Epub Date: 2026-02-06 DOI: 10.1016/j.epsr.2026.112785

Haitao Zhang , Dong Ding , Zhigang Zhang , Ze Tang

This paper mainly addresses the problem of voltage and frequency regulation as well as active power sharing in islanded AC microgrid (MG) under hybrid cyber-attacks by adopting the adaptive distributed resilient secondary control strategy. A novel adaptive distributed resilient secondary controller incorporating a lightweight adaptive compensation term is designed to counteract the impacts of switch denial-of-service (DoS) attacks and unbounded false data injection (FDI) attacks. The capability of the distributed resilient secondary controller to achieve asymptotically uniformly bounded (AUB) control under hybrid attacks can be significantly enhanced by adjusting the parameters of the resilient controller. The achievement of confining the recovery errors of voltage and frequency as well as the active power sharing errors within a bounded range by the proposed distributed resilient secondary controller is rigorously proven, respectively, where sufficient conditions for the closed-loop system state matrix to be Hurwitz under hybrid cyber-attacks are explicitly derived by leveraging the diagonalization of block matrix together with the stability criterion for second-order complex coefficient polynomials. Finally, simulations of a test islanded AC MG validate the effectiveness of the proposed theories.

本文采用自适应分布式弹性二次控制策略，研究了混合网络攻击下孤岛交流微电网的电压、频率调节和有功共享问题。针对交换机拒绝服务（DoS）攻击和无界虚假数据注入（FDI）攻击的影响，设计了一种新型的自适应分布式弹性二级控制器，该控制器包含轻量级自适应补偿项。通过调整弹性控制器的参数，可以显著提高分布式弹性副控制器在混合攻击下实现渐近均匀有界控制的能力。严格证明了所提出的分布式弹性二次控制器能够将电压和频率的恢复误差以及有功共享误差控制在一定范围内。利用分块矩阵的对角化，结合二阶复系数多项式的稳定性判据，明确推导了混合网络攻击下闭环系统状态矩阵为Hurwitz的充分条件。最后，对一个孤岛AC MG进行了仿真，验证了所提理论的有效性。

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引用次数: 0

Power distribution network reconfiguration for distributed generation maximization 分布式发电最大化的配电网重构

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

Electric Power Systems Research

Pub Date : 2026-06-01 Epub Date: 2026-01-30 DOI: 10.1016/j.epsr.2026.112779

Kin Cheong Sou , Gabriel Malmer , Lovisa Thorin , Olof Samuelsson

Network reconfiguration can significantly increase the hosting capacity (HC) for distributed generation (DG) in radially operated systems, thereby reducing the need for costly infrastructure upgrades. However, when the objective is DG maximization, jointly optimizing topology and power dispatch remains computationally challenging. Existing approaches often rely on relaxations or approximations, yet we provide counterexamples showing that interior point methods, linearized DistFlow and second-order cone relaxations all yield erroneous results. To overcome this, we propose a solution framework based on the exact DistFlow equations, formulated as a bilinear program and solved using spatial branch-and-bound (SBB). Numerical studies on standard benchmarks and a 533-bus real-world system demonstrate that our proposed method reliably performs reconfiguration and dispatch within time frames compatible with real-time operation.

网络重构可以显著增加径向操作系统中分布式发电（DG）的托管容量（HC），从而减少对昂贵的基础设施升级的需求。然而，当目标是DG最大化时，联合优化拓扑和电力调度仍然具有计算挑战性。现有的方法通常依赖于松弛或近似，但我们提供了反例，表明内点方法，线性化DistFlow和二阶锥体松弛都会产生错误的结果。为了克服这一点，我们提出了一个基于精确DistFlow方程的解决框架，该框架被表述为双线性规划，并使用空间分支定界（SBB）进行求解。在标准基准测试和533总线实际系统上的数值研究表明，我们提出的方法在与实时运行兼容的时间框架内可靠地执行重构和调度。

引用次数: 0