International Journal of Electrical Power & Energy Systems最新文献_第5页

Stability enhancement of multi-parallel inverter systems in distributed power grids with unconventional control delay 具有非常规控制延迟的分布式电网多并联逆变系统的稳定性增强

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-02-01 DOI: 10.1016/j.ijepes.2026.111662

Jie Ye, Ziao Ren, Songtao Huang, Yukai Huang, Ming Chi, Jinbang Xu, Anwen Shen

As the scale of distributed generation systems continues to expand, the capacity requirements for multi-parallel inverter systems (MPIS) are also increasing. The greater the number of grid-connected inverters (GCIs) operating in parallel, the higher the requirements for the stability and the dynamic performance of the entire system. Adjusting the control delay can enhance the dynamic response capability of GCIs, but it may impose additional impacts on their stability. In most existing studies, control delay is assumed to be one-sample delay, while few studies have explored the impact of unconventional control delays on MPIS. To address this issue, this paper simplifies the analysis of MPIS using the passivity analysis method. By modeling the output admittance of the GCIs, it is pointed out that changes in control delay will alter the negative region of the real part of the output admittance, thereby affecting system stability. Therefore, this paper derives in detail the constraint conditions on the gain and phase of the controller imposed by control delay, aiming to eliminate the negative real part region of the output admittance. Based on these constraint conditions, a controller design method using a second-order Finite Impulse Response (FIR) filter is proposed. The proposed design method ensures the passivity of GCI with unconventional control delays, thereby guaranteeing the stability of MPIS. Finally, a parallel experiment with two inverters is conducted, and the proposed method ensures the stable operation of the system and achieves plug-and-play characteristics.

随着分布式发电系统规模的不断扩大，对多并联逆变系统（MPIS）的容量要求也越来越高。并网逆变器并联运行的数量越多，对整个系统的稳定性和动态性能的要求就越高。调整控制延迟可以增强gci的动态响应能力，但也会对gci的稳定性产生额外的影响。在现有的研究中，大多数将控制延迟假设为单样本延迟，而很少有研究探讨非常规控制延迟对MPIS的影响。针对这一问题，本文采用被动分析方法对MPIS的分析进行了简化。通过对gci输出导纳的建模，指出控制延迟的变化会改变输出导纳实部的负区域，从而影响系统的稳定性。因此，本文详细推导了控制延迟对控制器增益和相位的约束条件，旨在消除输出导纳的负实部区域。基于这些约束条件，提出了一种采用二阶有限脉冲响应（FIR）滤波器的控制器设计方法。该设计方法保证了GCI在非常规控制时滞下的无源性，从而保证了MPIS的稳定性。最后，对两台逆变器进行了并联实验，实验结果表明，该方法保证了系统的稳定运行，实现了即插即用的特性。

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引用次数: 0

Ultra-short-term photovoltaic power prediction based on reprogrammed large language models 基于可编程大语言模型的超短期光伏功率预测

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-02-01 DOI: 10.1016/j.ijepes.2026.111642

Renfeng Liu , Zhao Huang , Yaqin Li , Cao Yuan , Peihua Xu , Yifei Wang

Ultra-short-term photovoltaic (PV) power forecasting is critical for power grid stability, yet existing deep learning models suffer from two major bottlenecks: insufficient robustness under complex weather conditions and poor cross-station generalization. To address this, we propose a reprogrammed large language model framework, SolarTime-LLM. This framework innovatively designs a Multi-feature Gating Fusion Reprogramming (MFGFR) module to capture multi-scale dynamics during high-volatility weather and integrates a domain-specific Simplified Prompt-as-Prefix (SPaP) to achieve Pareto optimality (Pareto, 1964) between prediction performance and inference costs within the LLM reprogramming paradigm. Comprehensive experiments on three real-world PV sites across different climate zones demonstrate that SolarTime-LLM significantly outperforms baseline models in all intra-site tests, maintaining top accuracy even under limited-data conditions; notably, on the data-limited site (approx. 11 months), its winter RMSE was reduced by 9.17% compared to the next-best model. Critically, in rigorous cross-station zero-shot transfer tests, where most baselines failed due to generalization collapse, SolarTime-LLM still achieved exceptional accuracy (up to 92.95%), nearly matching locally-trained performance. Furthermore, this study reveals the efficiency mechanism of SPaP from an attention mechanism perspective. SolarTime-LLM provides a high-accuracy, robust, and rapid cold-start solution for new PV stations, holding significant value for enhancing renewable energy integration.

超短期光伏发电预测对电网稳定至关重要，但现有的深度学习模型存在两大瓶颈：复杂天气条件下的鲁棒性不足和跨站泛化能力差。为了解决这个问题，我们提出了一个重新编程的大型语言模型框架，soltime - llm。该框架创新地设计了一个多特征门控融合重编程（MFGFR）模块，用于在高波动天气下捕获多尺度动态，并集成了一个特定领域的简化提示前缀（SPaP），以实现LLM重编程范式中预测性能和推理成本之间的帕累托最优性（Pareto, 1964）。在三个不同气候带的真实光伏站点进行的综合实验表明，soltime - llm在所有站点内测试中都明显优于基线模型，即使在有限的数据条件下也能保持最高的准确性。值得注意的是，在数据有限的站点上(大约。11个月)，其冬季RMSE较次优模型降低了9.17%。关键的是，在严格的跨站零射击转移测试中，大多数基线由于泛化崩溃而失败，soltime - llm仍然取得了出色的准确性（高达92.95%），几乎与本地训练的性能相匹配。此外，本研究还从注意机制的角度揭示了SPaP的效率机制。soltime - llm为新建光伏电站提供了高精度、稳健、快速的冷启动解决方案，对加强可再生能源整合具有重要价值。

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引用次数: 0

Multi-objective capacity planning and coordinated control for high-altitude integrated energy system considering uncertainty and seasonal Variability 考虑不确定性和季节变化的高空综合能源系统多目标容量规划与协调控制

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-02-01 DOI: 10.1016/j.ijepes.2026.111618

Yuanyuan Chen , Shaobing Yang , Yibo Wang , Yong Zhao , Mingli Wu

High-altitude regions play a crucial role in global energy transition and carbon neutrality goals, but they face severe challenges including harsh cold, oxygen deficiency, uncertainties in renewable energy output and load demand, and seasonal energy imbalances. Existing integrated energy systems (IES) exhibit limitations in high-altitude environments, such as poor environmental adaptability, inadequate oxygen supply coordination, and insufficient seasonal responsiveness. To address these gaps, this paper proposes a high-altitude integrated energy system (HAIES) integrating electricity, heat, hydrogen, and oxygen flows. A battery storage model modified for high-altitude conditions is established, along with a four-dimensional multi-objective optimization framework encompassing economy, reliability, environmental performance, and user comfort. A season-adaptive dual-mode coordinated control strategy is designed for seasonal load variations. To handle uncertainties without relying on probability distributions, an improved information gap decision theory (IGDT) integrated with the entropy weight method (EWM) is adopted. A bi-level algorithm combining non-dominated sorting genetic algorithm II (NSGA-II) and mixed-integer linear programming (MILP) synergistically optimizes system capacity configuration and operational strategies. Case studies conducted in a community at 3500 m above sea level in Qinghai Province demonstrate that the proposed HAIES achieves a 14.36% comprehensive performance index. Compared with traditional control strategies and uncertainty methods, the HAIES outperforms its counterparts by 1.77–9.12% in comprehensive performance, effectively balancing multi-objective benefits and mitigating risks from high-altitude complexities and seasonal imbalances. This research provides a robust theoretical and engineering basis for the design and operation of efficient, reliable, and sustainable energy systems in high-altitude regions.

高海拔地区在全球能源转型和碳中和目标中发挥着至关重要的作用，但它们面临着严峻的挑战，包括严寒、缺氧、可再生能源产出和负荷需求的不确定性以及季节性能源失衡。现有的综合能源系统（IES）在高海拔环境中表现出局限性，如环境适应性差、氧气供应协调不足和季节性响应能力不足。为了解决这些差距，本文提出了一种集成电、热、氢和氧流的高空综合能源系统（HAIES）。建立了适用于高海拔条件的电池储能模型，并建立了包含经济性、可靠性、环保性和用户舒适性的四维多目标优化框架。针对季节负荷变化，设计了一种季节自适应双模协调控制策略。为了在不依赖概率分布的情况下处理不确定性，采用了改进的信息差距决策理论（IGDT）和熵权法（EWM）相结合的方法。一种结合非支配排序遗传算法II （NSGA-II）和混合整数线性规划（MILP）的双级算法协同优化系统容量配置和运行策略。在青海省海拔3500 m的一个社区进行的实例研究表明，所提出的HAIES综合性能指标达到14.36%。与传统控制策略和不确定性方法相比，HAIES综合性能优于传统控制策略1.77-9.12%，有效地平衡了多目标效益，降低了高原复杂性和季节不平衡带来的风险。该研究为高海拔地区高效、可靠和可持续的能源系统的设计和运行提供了坚实的理论和工程基础。©2017 Elsevier Inc.版权所有。

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引用次数: 0

Two-stage stability enhancement framework based on S-domain modal analysis for renewable energy power systems 基于s域模态分析的可再生能源电力系统两阶段稳定性增强框架

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-02-01 DOI: 10.1016/j.ijepes.2026.111663

Yujing Li , Pengfei Hu , Wensheng Liu , Zaixin Yang

Power systems with a high proportion of renewable energy face the risk of broadband oscillations, and existing studies lack multi-scenario stability enhancement methods covering both planning and operating stages. To this end, this paper proposes a two-stage stability enhancement framework: optimizing the parameters of weak nodes during the planning stage to improve stability under the rated condition; and suppressing oscillations under multiple operating conditions with data-driven methods. Firstly, by establishing the s-domain admittance matrix model, system modes and participation factors are derived to give insight into the system small-signal stability. Then, to improve the robustness of critical modes in the planning stage, the parameters of voltage source converters connected to weak nodes are optimized to balance weak nodes and reduce the coupling of weak nodes. In the operating stage, the controllability-based updating data-enabled predictive control is proposed with an updating strategy according to the participation factor analysis. Finally, the effectiveness of the proposed two-stage stability enhancement framework is verified in both the IEEE 9-bus system and the 6-generator 25-bus system. The stability margin of the optimized system is significantly improved in the planning stage, and oscillations caused by changes in system topology or operating points are effectively suppressed in the operating stage. These results indicate that the proposed two-stage stability enhancement framework offers a viable pathway toward integrating the planning and operating stages for small-signal stability enhancement.

可再生能源比例高的电力系统面临宽带振荡风险，现有研究缺乏涵盖规划和运行阶段的多场景稳定性增强方法。为此，本文提出了两阶段的增稳框架：在规划阶段对弱节点参数进行优化，提高额定工况下的稳定性；用数据驱动的方法抑制多种工况下的振荡。首先，通过建立s域导纳矩阵模型，推导出系统模态和参与因子，深入了解系统的小信号稳定性；然后，为了提高规划阶段关键模式的鲁棒性，对连接弱节点的电压源变换器参数进行优化，实现弱节点的平衡，减少弱节点的耦合；在运行阶段，提出了基于可控性的更新数据支持预测控制，并提出了基于参与因子分析的更新策略。最后，在IEEE 9总线系统和6-发电机25总线系统中验证了所提出的两阶段稳定性增强框架的有效性。优化后的系统在规划阶段稳定裕度显著提高，在运行阶段因系统拓扑或工作点变化引起的振荡得到有效抑制。这些结果表明，所提出的两阶段稳定性增强框架为整合小信号稳定性增强的规划和操作阶段提供了可行的途径。

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引用次数: 0

Analysis and mitigation of circulating currents for grid-forming DERs during parallel black-start in islanded systems 孤岛系统并联黑启动时并网der循环电流分析与抑制

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-02-01 DOI: 10.1016/j.ijepes.2026.111678

Li Sun, Hong Wang, Guangzhong Dong

Following a large-scale power outage, grid-forming (GFM) distributed energy resources (DERs) can facilitate rapid system recovery using parallel black start technology. However, the DER parallel-operation systems (DERPS) may generate circulating currents that destabilize the system and damage equipment. This article first derives an explicit expression for circulating currents and examines the electrical parameters that influence them, focusing on three scenarios: imbalances in voltage amplitude, voltage phase, and output impedance. Then a variable step-size black start scheme is proposed for mitigating circulating currents, which relies solely on local information and without the need for communication. This scheme features three key control components:

P

–

f

control with a negative frequency feedback, variable step-size soft-start for voltage buildup, and

Q

–

\dot{V}

droop control for removing sensitivity to impedance. This control scheme effectively mitigates circulating currents in DERPS during black start and load pickup, even amid imbalances in voltage amplitude, voltage phase, and output impedance. Numerical experiments conducted on two-DER and five-DER islanded systems in real-time simulators, accounting for various topologies and operational disturbances, validate the effectiveness of the proposed mitigation control.

在大规模停电后，并网分布式能源可以利用并行黑启动技术实现系统的快速恢复。然而，DER并联运行系统（DERPS）可能会产生破坏系统稳定和损坏设备的循环电流。本文首先推导了循环电流的显式表达式，并检查了影响循环电流的电气参数，重点关注三种情况：电压幅值、电压相位和输出阻抗的不平衡。在此基础上，提出了一种仅依赖局部信息而不需要通信的变步长黑启动方案。该方案具有三个关键控制组件：带负频率反馈的P-f控制，用于电压积累的可变步长软启动，以及用于消除阻抗敏感性的Q-V下降控制。该控制方案有效地减轻了黑启动和负载拾取期间DERPS中的循环电流，即使在电压幅值、电压相位和输出阻抗不平衡的情况下也是如此。在考虑各种拓扑结构和操作干扰的实时仿真器中对二der和五der孤岛系统进行了数值实验，验证了所提出的缓解控制的有效性。

{"title":"Analysis and mitigation of circulating currents for grid-forming DERs during parallel black-start in islanded systems","authors":"Li Sun, Hong Wang, Guangzhong Dong","doi":"10.1016/j.ijepes.2026.111678","DOIUrl":"10.1016/j.ijepes.2026.111678","url":null,"abstract":"<div><div>Following a large-scale power outage, grid-forming (GFM) distributed energy resources (DERs) can facilitate rapid system recovery using parallel black start technology. However, the DER parallel-operation systems (DERPS) may generate circulating currents that destabilize the system and damage equipment. This article first derives an explicit expression for circulating currents and examines the electrical parameters that influence them, focusing on three scenarios: imbalances in voltage amplitude, voltage phase, and output impedance. Then a variable step-size black start scheme is proposed for mitigating circulating currents, which relies solely on local information and without the need for communication. This scheme features three key control components: <span><math><mi>P</mi></math></span>–<span><math><mi>f</mi></math></span> control with a negative frequency feedback, variable step-size soft-start for voltage buildup, and <span><math><mi>Q</mi></math></span>–<span><math><mover><mrow><mi>V</mi></mrow><mrow><mo>̇</mo></mrow></mover></math></span> droop control for removing sensitivity to impedance. This control scheme effectively mitigates circulating currents in DERPS during black start and load pickup, even amid imbalances in voltage amplitude, voltage phase, and output impedance. Numerical experiments conducted on two-DER and five-DER islanded systems in real-time simulators, accounting for various topologies and operational disturbances, validate the effectiveness of the proposed mitigation control.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111678"},"PeriodicalIF":5.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-driven estimation for electric machine emulator operation in AI-driven sensorless control of electric vehicle energy networks 人工智能驱动的电动汽车能源网络无传感器控制中电机模拟器运行的数据驱动估计

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-02-01 DOI: 10.1016/j.ijepes.2026.111581

C.-W. Huang , S.-H. Hsu , T.-C. Chou , P.-H. Lee

Global warming and accelerated climate change highlight the urgent need for low-carbon transportation, with electric vehicles (EVs) emerging as a cornerstone of net-zero emission strategies. The electric machine emulator (EME) has demonstrated effectiveness in mitigating range anxiety and supports bidirectional energy interaction. However, challenges persist in connecting the capability of vehicle-to-grid (V2G) with EME energy-conversion efficiency, often resulting in system voltage fluctuations under unbalanced grid connection conditions. Conventional control strategies fall short in explicitly revealing the decarbonized pathway.

This study proposes a data-driven framework leveraging the proposed AI-driven sensorless controller to mitigate the carbon footprint of EV energy networks. Specifically, a hybrid GRU-LSTM structure with hyperparameters is fine-tuned for identifying the most critical electric machine emulator (EME) operational patterns for failure diagnosis within AI-driven sensorless control applications. The proposed mechanism is benchmarked against state-of-the-art deep learning models, including a convolutional neural network (CNN), recurrent neural network (RNN), gate-recurrent unit (GRU), and long short-term memory (LSTM), to evaluate accuracy, robustness, and efficiency. Additionally, the trade-off between output power and carbon intensity (CI) provides operational insights for sustainable operation. Simulated and hardware-in-the-loop validation confirm superior emission-path tracking (MAPE < 3%), enhanced stability under variable grid conditions, and significant improvements in energy utilization and carbon reduction.

全球变暖和加速的气候变化凸显了对低碳交通的迫切需求，电动汽车（ev）正成为净零排放战略的基石。电机仿真器（EME）在缓解距离焦虑和支持双向能量交互方面已被证明是有效的。然而，将车辆到电网（V2G）的能力与EME能量转换效率相结合仍然存在挑战，在不平衡并网条件下，系统电压往往会出现波动。传统的控制策略在明确揭示脱碳途径方面存在不足。本研究提出了一个数据驱动的框架，利用所提出的人工智能驱动的无传感器控制器来减少电动汽车能源网络的碳足迹。具体来说，具有超参数的混合GRU-LSTM结构经过微调，用于识别人工智能驱动的无传感器控制应用中最关键的电机模拟器（EME）运行模式，以进行故障诊断。所提出的机制以最先进的深度学习模型为基准，包括卷积神经网络（CNN）、循环神经网络（RNN）、门循环单元（GRU）和长短期记忆（LSTM），以评估准确性、鲁棒性和效率。此外，输出功率和碳强度（CI）之间的权衡为可持续运营提供了操作见解。模拟和硬件在环验证证实了优越的排放路径跟踪（MAPE < 3%），增强了可变电网条件下的稳定性，并显著改善了能源利用和碳减排。

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引用次数: 0

Second order exponential asymptotic expansion for probabilistic load flow analysis 概率潮流分析的二阶指数渐近展开

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-02-01 DOI: 10.1016/j.ijepes.2026.111582

Hooman Basirat , Mohammad Mohammadi , Dariush Keihan Asl

Due to the inherent randomness in certain resources and load demands, load flow analysis must be performed using efficient and robust probabilistic methods to accurately capture power system uncertainties. This paper proposes a novel non-iterative and non-parametric framework, called the second-order exponential asymptotic expansion (SOEAE) method, to solve the probabilistic load flow problem. Unlike classical methods such as first-order second moment, saddlepoint approximation method, or point estimation methods, the proposed technique maintains a consistent computational cost regardless of the number of random variables. Hence, only a single iteration is sufficient to obtain the Taylor series expansion of the output variables as functions of the input random variables. Also, this method can approximate the density functions of unknown variables, regardless of the input variables’ distribution type. In addition to lower computational cost and higher accuracy, the proposed method preserves key advantages of traditional methods and derives cumulative distribution functions without integration. The suggested method is examined on IEEE 14-bus and IEEE 118-bus test systems, and results with reasonable accuracy are achieved. The results are compared with those obtained using Monte Carlo simulation and saddlepoint approximation methods.

由于某些资源和负荷需求具有固有的随机性，负荷潮流分析必须采用高效、鲁棒的概率方法来准确捕捉电力系统的不确定性。本文提出了一种新的非迭代非参数框架，即二阶指数渐近展开（SOEAE）方法，用于求解概率负荷流问题。与经典方法如一阶二阶矩、鞍点近似方法或点估计方法不同，无论随机变量的数量如何，所提出的技术都保持一致的计算成本。因此，只需一次迭代就足以得到输出变量作为输入随机变量函数的泰勒级数展开式。此外，无论输入变量的分布类型如何，该方法都可以近似未知变量的密度函数。该方法不仅计算成本低、精度高，而且保留了传统方法的主要优点，无需积分即可推导出累积分布函数。在ieee14总线和ieee118总线测试系统上对该方法进行了验证，得到了精度合理的结果。并与蒙特卡罗模拟和鞍点近似法的结果进行了比较。

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引用次数: 0

Distribution expansion planning considering direct effect of flexibility and load priorities 考虑柔性和负荷优先级直接影响的配电网扩容规划

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-02-01 DOI: 10.1016/j.ijepes.2026.111656

Alireza Azad, Seyed Pouria Miri, Hossein Gharibvand, G.B. Gharehpetian

Conventional distribution expansion planning (DEP) involves determining the location, installation time, and sizing of feeders, transformers, and substations in order to have a balance between generation and consumption in planning horizon. In future distribution networks, the penetration of renewable resources will increase. Since these resources are intermittent in nature, it is important to consider methods required to create a balance between generation and consumption in the DEP. In this paper, flexibility, as a key concept in responding to consumption and generation imbalances, is analyzed in the context of DEP. To enable the simultaneous minimization of DEP costs and enhancement of energy flexibility, it is considered as a term in the objective function of DEP. To determine the optimal location and capacity of flexibility resources, load priorities and node self-sufficiency are incorporated into the problem, and a techno-economic analysis is carried out. DEP is studied and evaluated in an 18-bus distribution system using different scenarios for cases with and without considering the flexibility. Two common flexibility resources, energy storage systems (ESS) and distributed generation (DG), are considered as candidates, and sensitivity analysis is conducted. Results of the study showed that DGs are more effective than ESSs for enhancing flexibility. The higher cost of ESS compared to DG was the limiting factor for utilizing ESS, leading to a lower flexibility-to-cost ratio compared to DG.

传统的配电扩展规划（DEP）包括确定馈线、变压器和变电站的位置、安装时间和规模，以便在规划范围内实现发电量和用电量的平衡。在未来的配电网中，可再生资源的渗透率将会增加。由于这些资源本质上是间歇性的，因此考虑在DEP中实现发电和用电平衡所需的方法是很重要的。本文在DEP的背景下分析了灵活性，灵活性是应对消费和发电不平衡的一个关键概念。将其作为DEP目标函数中的一项，将负荷优先级和节点自给率纳入问题中，确定柔性资源的最优位置和容量，并进行技术经济分析。在考虑和不考虑灵活性的情况下，对一个18总线配电系统的DEP进行了研究和评估。将储能系统（ESS）和分布式发电（DG）两种常见的柔性资源作为备选资源，进行敏感性分析。研究结果表明，DGs在增强柔韧性方面比ess更有效。与DG相比，ESS的高成本是利用ESS的限制因素，导致与DG相比，ESS的灵活性成本比更低。

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引用次数: 0

A data-driven high impedance fault detection framework for distribution network based on improved temporal convolutional network 基于改进时间卷积网络的数据驱动配电网高阻抗故障检测框架

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-02-01 DOI: 10.1016/j.ijepes.2026.111658

Changyu Liu, Xiaojun Wang, Zhao Liu, Dahai Zhang, Guomin Luo, Jinghan He

High-impedance faults (HIFs) pose a great challenge for the protection of new-type distribution networks because of their weak characteristics and strong similarity to disturbance conditions such as capacitor switching and load switching. Therefore, an accurate and reliable HIF detection method is crucial for the safe power supply of distribution networks. A data-driven HIF fault detection framework for distribution networks is proposed, built upon an improved temporal convolutional network with confidence enhancement and interpretability. Firstly, the efficient channel attention temporal convolutional network (ECA-TCN) based HIF detection model is designed to exploit the temporal features in the transient zero-sequence current. Secondly, this paper proposes a HIF detection framework based on time-series sliding window and confidence level check, which can effectively achieve the balance between accuracy and rapidity and enhance the credibility of the HIF detection model in practical applications. Then, the score class activation map (Score-CAM) analysis method provides an explanation for the model’s decision-making process and enhances the model interpretability. Finally, the proposed framework is validated on both the MATLAB/Simulink platform and field tests, demonstrating superior performance compared with existing methods.

由于高阻抗故障具有较弱的特性，且与电容切换、负载切换等扰动条件具有很强的相似性，因此对新型配电网的保护提出了很大的挑战。因此，准确可靠的HIF检测方法对配电网的安全供电至关重要。提出了一种数据驱动的配电网络HIF故障检测框架，该框架基于改进的时间卷积网络，具有置信度增强和可解释性。首先，利用瞬态零序电流的时间特征，设计了基于高效通道关注时间卷积网络（ECA-TCN）的HIF检测模型；其次，本文提出了一种基于时间序列滑动窗口和置信水平检查的HIF检测框架，可以有效地实现精度与快速性的平衡，增强HIF检测模型在实际应用中的可信度。然后，分数类激活图（score - cam）分析方法为模型的决策过程提供了解释，增强了模型的可解释性。最后，在MATLAB/Simulink平台和现场测试中对该框架进行了验证，与现有方法相比，该框架具有更好的性能。

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引用次数: 0

Centralized multi-microgrid control: peer-to-peer power trading for battery life extension and enhanced outage/fault resilience under generation uncertainty and demand variations 集中式多微电网控制：在发电不确定性和需求变化下，为延长电池寿命和增强停电/故障恢复能力而进行的点对点电力交易

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-02-01 DOI: 10.1016/j.ijepes.2026.111650

Hossien Faraji, Amir Khorsandi, Seyed Hossein Hosseinian

This article presents several strategies for managing multi-level peer-to-peer power trading between the microgrids (MGs) within a multi-microgrid (MMG) system and the utility grid. These strategies apply to cases when the MGs are in on-grid mode, operating off-grid, and engaging in mutual power trading among interconnected MGs. The MMG under consideration includes two MGs, each equipped with either a wind turbine or a photovoltaic system, along with a battery unit to support variable three-phase loads. In each MG, there is a microgrid central controller (MGCC), which is responsible for communicating, exchanging information, and issuing necessary commands to the local controllers (LCs) under its command. A multi-microgrid central controller (MMGCC) is also responsible for monitoring and supervising the MGCCs. In on-grid mode, each MG uses control schemes to manage power exchange with the main grid. These strategies aim to minimize battery charging and discharging cycles when selling excess power to the grid, while also considering factors such as the uncertainty of distributed generation (DG) outputs and fluctuations in load demand. In off-grid mode, MGs are designed to operate independently under normal conditions, with specific control strategies implemented for islanding scenarios. Furthermore, MGs can provide mutual support during critical outages and fault situations and work in an interconnected mode. Nonlinear simulations conducted using MATLAB/Simulink demonstrate that the proposed control methods effectively achieve all objectives. These purposes include adequately supplying MG loads, accounting for uncertainty in power generation and demand variations, managing power exchanges between MGs and the utility grid while reducing the frequency of battery charging and discharging cycles in on-grid mode, ensuring optimal performance of each MG in off-grid conditions, and enabling robust performance of off-grid MGs through peer-to-peer communication between MGs in interconnected conditions.

本文提出了在多微电网系统和公用电网中管理微电网之间多级点对点电力交易的几种策略。这些策略适用于并网模式、离网运行和相互连接的mg之间进行相互电力交易的情况。正在考虑的MMG包括两个MMG，每个配备风力涡轮机或光伏系统，以及支持可变三相负载的电池单元。在每个微电网中，都有一个微电网中央控制器（MGCC），负责向其指挥下的本地控制器（lc）进行通信、交换信息并发出必要的命令。多微电网中央控制器（MMGCC）也负责监测和监督多微电网。并网模式下，各主电网通过控制方案管理与主电网的电力交换。这些策略的目的是在向电网出售多余电力时最大限度地减少电池充放电周期，同时还考虑分布式发电（DG）输出的不确定性和负载需求波动等因素。在离网模式下，mgg被设计为在正常条件下独立运行，并针对孤岛场景实施特定的控制策略。此外，mg可以在严重中断和故障情况下提供相互支持，并以互联模式工作。利用MATLAB/Simulink进行的非线性仿真表明，所提出的控制方法有效地实现了所有目标。这些目的包括充分提供MG负载，考虑发电和需求变化的不确定性，管理MG和公用电网之间的电力交换，同时减少并网模式下电池充电和放电周期的频率，确保每个MG在离网条件下的最佳性能，并通过MG之间的点对点通信实现离网MG的强大性能。

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