Sustainable Energy Grids & Networks最新文献_第8页

Real-time management of electric and hydrogen vehicle infrastructure using mobile and integrated charging stations 使用移动和集成充电站实时管理电动和氢燃料汽车基础设施

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-17 DOI: 10.1016/j.segan.2025.102008

Muhammed Ali Beyazıt , Mohammad Reza Salehizadeh , Emre Demirel , Akın Taşcıkaraoǧlu , Jay Liu

The rapid growth of Battery Electric Vehicles (BEVs) and Fuel Cell Electric Vehicles (FCEVs) is accelerating the demand for reliable charging and refueling infrastructure, yet current systems face persistent challenges. Two critical issues are the overstaying phenomenon—where EV drivers continue to occupy chargers after completing charging, causing congestion and reducing station efficiency—and the limited availability of hydrogen at refueling stations. These shortcomings threaten the scalability and user acceptance of electromobility. To address these gaps, this study proposes a novel, holistic framework of three Integrated Charging Stations (ICSs) that combine Fixed Charging Stations (FCSs) and Hydrogen Refueling Stations (HRSs), enhanced by photovoltaic (PV) generation, electrolyzers, hydrogen storage, and Mobile Charging Stations (MCSs). Beyond introducing this system-level architecture, we also develop an operational strategy whereby the MCS mitigates overstaying and, when idle, supports hydrogen production through powering electrolyzers. As an extension of our earlier research, we formulate a new mathematical optimization model to coordinate the real-time operation of these coupled facilities, incorporating dynamic MCS routing between ICSs to alleviate congestion and strengthen hydrogen supply. The effectiveness of the proposed approach is demonstrated through three case studies, with results confirming its potential to improve infrastructure utilization, enhance user satisfaction, and support the sustainable expansion of BEVs and FCEVs.

电池电动汽车（bev）和燃料电池电动汽车（fcev）的快速发展加速了对可靠充电和加油基础设施的需求，但目前的系统面临着持续的挑战。两个关键的问题是过度停留现象——电动汽车司机在充电完成后继续占用充电器，造成拥堵并降低加油站的效率——以及加油站的氢气供应有限。这些缺点威胁到电动汽车的可扩展性和用户接受度。为了解决这些差距，本研究提出了一个由三个集成充电站（ics）组成的全新整体框架，该框架结合了固定充电站（FCSs）和加氢站（HRSs），并通过光伏（PV）发电、电解槽、储氢和移动充电站（MCSs）进行增强。除了引入这种系统级架构外，我们还制定了一项运营策略，使MCS能够减少超时停留，并在闲置时通过为电解槽供电来支持制氢。在此基础上，我们建立了一个新的数学优化模型来协调这些耦合设施的实时运行，并在ICSs之间引入动态MCS路由，以缓解拥堵并加强氢供应。通过三个案例研究证明了所提出方法的有效性，结果证实了其在提高基础设施利用率、提高用户满意度以及支持纯电动汽车和氢燃料电池汽车可持续发展方面的潜力。

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引用次数: 0

Enhanced grid integration through machine-learning optimized bidirectional EV chargers 通过机器学习优化双向电动汽车充电器增强电网整合

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-17 DOI: 10.1016/j.segan.2025.102007

Pulkit Kumar , Harpreet Kaur Channi , Sita Rani , Aman Kataria , Punam Rattan

Large-scale electric vehicles (EVs) implementation depends on reliable and stable bidirectional charging that can be effectively implemented, efficient, and practical. This paper proposes a Bidirectional Learning-based Electric Vehicle Charger (BLEVC) with Battery Energy Storage (BES) that boosts grid stability during the Grid-to-Vehicle (G2V) and the Vehicle-to-Grid (V2G) operation modes. The innovation is the methodical comparison of 3 machine learning (ML) controllers: Dynamic Time Reversal (DTR), Recurrent Neural Network (RNN), and Support Vector Machine (SVM) with the traditional Proportional-Integral (PI) controller under the same testing parameters. Findings indicate the definite merits of ML strategies. RNN lowered G2V charging time (PI 35 mins to 8 mins) and voltage ripple at 48 G2V, and DTR showed a stable steady state response, although its computational requirement was high. On the other hand, SVM had infinite settling time and large ripple time; poor evidence of using it on dynamic duty-cycle regulation. In V2G mode, RNN and DTR have quicker and more constant energy dispatch than PI. Integration of the BES enhanced peak shaving 22 % and could smooth the state of charge to within 5 %, confirming its usefulness in grid support and demand reshaping. This contributed work offers a validated architecture of BLEVC and a comparative framework, which is a gap in the literature. Future work directions will be typhoon hardware in-loop (HIL)-hybrid ML-PI controllers, hyperparameter optimization, and pilot-scale tests with utilities to enable secure, scalable EV-grid integration.

大规模电动汽车的实施有赖于可靠、稳定的双向充电，才能有效实施、高效、实用。本文提出了一种具有电池储能（BES）的双向学习式电动汽车充电器（BLEVC），该充电器可提高电网对车（G2V）和车对网（V2G）运行模式下电网的稳定性。创新之处在于，在相同的测试参数下，将3种机器学习（ML）控制器：动态时间反转（DTR）、循环神经网络（RNN）和支持向量机（SVM）与传统的比例积分（PI）控制器进行了系统的比较。研究结果表明了机器学习策略的明确优点。RNN降低了G2V充电时间（PI 35 min至8 min）和48g2v时的电压纹波，DTR表现出稳定的稳态响应，但计算量较高。另一方面，支持向量机具有无限的沉降时间和较大的纹波时间；在动态占空比调节中使用它的证据不足。在V2G模式下，RNN和DTR比PI具有更快、更稳定的能量调度。集成的BES可提高调峰22% %，并可使充电状态平滑到5% %以内，证实了其在电网支持和需求重塑方面的有效性。这项贡献的工作提供了一个有效的BLEVC架构和一个比较框架，这是文献中的一个空白。未来的工作方向将是台风硬件在环(HIL)-混合ML-PI控制器，超参数优化，以及与公用事业公司进行中试规模测试，以实现安全，可扩展的电动汽车电网集成。

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引用次数: 0

Classification of load waveform distortion signature based on novelty detection for electric railway systems 基于新颖性检测的电气化铁路系统负载波形失真特征分类

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-17 DOI: 10.1016/j.segan.2025.102010

Rafael S. Salles , Sarah K. Rönnberg , Andrea Mariscotti

Electric railway systems (ERS) are characterized by several particularities regarding the return current circuits, moving loads, multiple sources of waveform distortion, and extensive deployment of static converters from various manufacturers, topologies, and solutions. This work presents a methodology for application of load monitoring to classify rolling stock (RS) waveform distortion signatures. The proposed methodology combines the benefits and advantages of unsupervised deep learning and reconstruction error performance classification for performing non-intrusive load monitoring (NILM) in ERS. It consists of adapting autoencoder-based novelty detection for load classification problems. The method is applied to pantograph measurements from four rolling stock items using two types of data input (harmonic spectra up to kHz and VI diagram images), which are compared in binary classifications of the same kind of railway electrification. The methodology shows suitable classification performance with high accuracy, scoring an average of 98.81 % for spectrum input and 97.77 % for VI diagram input. It has also been validated with a NILM dataset (LIT) for multi-class applications showing 99.13 % for spectrum input and 94.28 % for VI diagram input. The proposed method has suitable computational times and scalability, allowing application to a wide range of NILM and classification problems using distortion signatures.

电气铁路系统（ERS）具有以下几个特点：回流电路、移动负载、多种波形失真源，以及各种制造商、拓扑结构和解决方案广泛部署的静态转换器。本文提出了一种应用负荷监测对机车车辆（RS）波形失真特征进行分类的方法。该方法结合了无监督深度学习和重构误差性能分类在ERS中执行非侵入式负载监测（NILM）的优点和优点。它包括自适应的基于自编码器的新颖性检测来解决负载分类问题。将该方法应用于四种机车车辆的受电弓测量，使用两种类型的数据输入（最高kHz谐波谱和VI图图像），并将其在同一类型铁路电气化的二分类中进行比较。该方法具有良好的分类性能和较高的分类准确率，光谱输入的平均准确率为98.81%，VI图输入的平均准确率为97.77%。它还通过一个多类别应用的NILM数据集（LIT）进行了验证，显示光谱输入为99.13%，VI图输入为94.28%。该方法具有合适的计算时间和可扩展性，允许应用于广泛的NILM和使用失真签名的分类问题。

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引用次数: 0

Impact of limited information on estimating aggregated electric vehicle loading: A distribution system operator perspective 有限信息对估计电动汽车总负荷的影响：一个配电系统运营商的观点

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-17 DOI: 10.1016/j.segan.2025.102012

Damianos Cheilas, Henrik W. Bindner, Tilman Weckesser

The major deployment of electric vehicles (EVs) brings challenges to the planning and operation of distribution grids, necessitating effective models and estimation approaches for EV charging in distribution grid management. However, distribution system operators (DSOs) may have limited or no access to relevant charging data. This paper investigates how diverse levels of charging information affect the estimation accuracy of aggregated price-sensitive EV power profiles, focusing on distribution system management. Three approaches utilizing varying levels of information are implemented to model the EV charging profiles: a full-information formulation and two reduced-information formulations that rely on aggregate energy measurements combined with either charger occupancy or plug-in statistics. The approaches are evaluated against the individually price-optimized EV schedules over a year, and the resulting cost-based optimal power profiles and associated errors are analyzed. The results indicate that while more detailed information increases the general accuracy of the estimated profiles, reduced-information models can still provide robust estimates for peak load assessment to support congestion management decisions, considering the data constraints and the risk-averse attitude of DSOs.

电动汽车的大规模部署给配电网的规划和运行带来了挑战，配电网管理中需要有效的电动汽车充电模型和估算方法。然而，配电系统运营商（dso）对相关收费数据的访问可能有限或无法访问。本文以配电系统管理为重点，研究了不同水平的充电信息对价格敏感型电动汽车总功率分布估计精度的影响。三种方法利用不同级别的信息来模拟电动汽车充电曲线：一种全信息公式和两种减少信息公式，这些公式依赖于总能量测量，结合充电器占用率或插电统计数据。在一年内，对这些方法进行了单独的价格优化电动汽车计划评估，并分析了基于成本的最优功率分布图和相关误差。结果表明，虽然更详细的信息增加了估计概况的总体准确性，但考虑到数据约束和dso的风险规避态度，减少信息模型仍然可以为峰值负载评估提供稳健的估计，以支持拥塞管理决策。

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引用次数: 0

A distributionally robust optimization strategy for electric-thermal complementary systems considering joint peaking of thermal power units and pumped-storage hydroelectric units 考虑火电机组和抽水蓄能水电机组联合调峰的电-热互补系统分布鲁棒优化策略

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-17 DOI: 10.1016/j.segan.2025.102015

Zhifan Zhang , Zhe Yin , Jiacuo Yixi , Yifan Zhang , Ruijin Zhu

The variability and uncertainty of wind and solar generation create major challenges for power system dispatch. To reduce the regulation burden on coal-fired thermal power units (TPUs) and enhance renewable integration, this paper proposes a distributionally robust optimization (DRO) strategy for electricity–heat complementary systems that jointly coordinates TPUs and pumped-storage hydro units (PSUs). Detailed operational models for TPUs and PSUs are developed, together with a hybrid energy storage framework that integrates electrochemical batteries and concentrated solar power (CSP) with thermal tanks to support both electricity and heating demands. A two-stage min–max–min DRO model is formulated, with uncertainty captured by a dual-norm ambiguity set and solved using the column-and-constraint generation algorithm. Simulation results show that the proposed method improves flexibility and renewable utilization, reducing operating costs by 22.7 % and carbon emissions by 6.2 % compared with benchmark cases. Sensitivity analyses further confirm robustness under variations in key parameters, demonstrating the model’s engineering applicability.

风能和太阳能发电的可变性和不确定性给电力系统调度带来了重大挑战。为了减轻燃煤火电机组（tpu）的监管负担，提高可再生能源并网能力，本文提出了一种联合协调燃煤火电机组和抽水蓄能水电机组（psu）的分布式鲁棒优化策略。开发了tpu和psu的详细操作模型，以及将电化学电池和聚光太阳能（CSP）与热罐集成在一起的混合能源存储框架，以支持电力和加热需求。建立了两阶段最小-最大-最小DRO模型，其中不确定性由双范数模糊集捕获，并使用列约束生成算法求解。仿真结果表明，与基准案例相比，该方法提高了灵活性和可再生能源利用率，运行成本降低22.7% %，碳排放降低6.2% %。灵敏度分析进一步证实了模型在关键参数变化下的鲁棒性，证明了模型的工程适用性。

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引用次数: 0

Fast power market cross-zonal capacity co-optimization using inner approximations: Method, validation, and application 使用内部近似的快速电力市场跨区域容量协同优化：方法、验证和应用

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-17 DOI: 10.1016/j.segan.2025.102013

Dawn Virginillo , Asja Derviškadić , Marc Hohmann

Recent trends in market liberalization and increasing congestion in the European interconnected grid have motivated studies of parameters provided to power markets, especially in the intraday and balancing timeframes. In this paper, we present a method using single-box inner polytope approximation to co-optimize the Available Transfer Capacities (ATCs) between multiple bidding zones. The feasible region is bounded by linear flow constraints consisting of AC PTDFs, computed for N

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1 contingency cases. Thanks to its formulation as a linear programming problem, the method provides efficient capacity computation, well-suited for applications close to real-time. The method is validated using the IEEE 39-Bus model and the behaviour of the algorithm is demonstrated using real case studies on the Swiss transmission system. Results demonstrate the formulation’s computational efficiency and enable analysis of the linearization accuracy. The proposed method is implemented in a near-real time decision support tool used to compute N

-

1 secure ATCs, which is in operation in the control room of Swissgrid, the Swiss Transmission System Operator.

市场自由化和欧洲互联电网日益拥挤的最新趋势促使人们研究向电力市场提供的参数，特别是在日内和平衡时间框架内。本文提出了一种利用单盒内多面体近似的方法来共同优化多个投标区之间的可用传输容量。可行区域由由AC ptdf组成的线性流动约束限定，为N−1个偶然性情况计算。由于将其表述为线性规划问题，该方法提供了高效的容量计算，非常适合于接近实时的应用。使用IEEE 39-Bus模型验证了该方法，并使用瑞士传输系统的实际案例研究证明了该算法的行为。结果证明了该公式的计算效率和线性化精度分析。所提出的方法在近实时决策支持工具中实现，该工具用于计算N−1安全atc，该工具在瑞士输电系统运营商瑞士电网的控制室中运行。

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引用次数: 0

Bilevel optimization model for optimal time-and-level-of-use electricity pricing in smart grids 智能电网最优分时级电价的双层优化模型

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-17 DOI: 10.1016/j.segan.2025.102005

Huihui Huang , Chaoyang Zheng , Bowen Xu , Qiang Wei , Ruilong Deng , Yangyang Geng

This study introduces a novel Bilevel Optimization (BLO) model for optimizing Time-and-Level-of-Use (TLOU) electricity pricing in smart grids. The model overcomes limitations of prior studies by enabling dynamic tier adjustments and incorporating Levelized Cost of Electricity (LCOE), market competition, and consumer behavioral responses under spatio-temporal coupling constraints. Two methods simplify the BLO into a single-layer problem: the dual theory approach stands out for its streamlined linearization and reduced computational complexity, with mathematical proofs confirming transformation equivalence. Simulations across four real-world scenarios validate the model’s effectiveness in enhancing Load Shifting Ratio (LSR) and reducing Supply Flattening Ratio (SFR), while balancing supplier profits and consumer costs. Key findings reveal

L_{3}

-tiered (low/medium/high) pricing yields $900 annualized benefits per 50-consumer cohort compared to

L_{2}

-tiered (low/high) pricing. This work advances precision demand management for high-renewable grids, with future extensions to 500+ dynamic profiles and stochastic intermittency modeling.

本文提出了一种新的双能级优化（BLO）模型，用于优化智能电网的分时分时电价。该模型在时空耦合约束下考虑了平准化电力成本（LCOE）、市场竞争和消费者行为反应等因素，克服了以往研究的局限性。有两种方法将BLO简化为单层问题：对偶理论方法以其简化的线性化和降低的计算复杂度而突出，并用数学证明证实了变换等价。通过四个实际场景的仿真验证了该模型在提高负载转移比（LSR）和降低供应平坦比（SFR），同时平衡供应商利润和消费者成本方面的有效性。主要研究结果显示，l3级（低/中/高）定价与l2级（低/高）定价相比，每50名消费者群体的年化收益为900美元。这项工作推进了高可再生电网的精确需求管理，未来将扩展到500多个动态剖面和随机间歇性建模。

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引用次数: 0

Multi-uncertainty risk analysis framework for hydropower-wind-photovoltaic hybrid systems 水电-风-光伏混合系统多不确定性风险分析框架

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-16 DOI: 10.1016/j.segan.2025.102016

Zhiyuan Wu , Guohua Fang , Jian Ye , Xianfeng Huang , Min Yan

The stability and economic efficiency of hydropower-wind-photovoltaic hybrid systems are significantly influenced by various uncertainties and risks. However, existing research lacks a systematic framework to evaluate the synergistic effects of these uncertainties, identify adverse conditions that trigger risk events, and assess the role of forecasting accuracy in risk evaluation. To address these gaps, this study proposes a multi-uncertainty risk analysis framework designed to systematically quantify the impact of uncertainties on system risks. The framework evaluates system risk distribution and employs clustering and correlation analysis to identify adverse conditions, which provide critical inputs for refining scheduling strategies. Additionally, the framework conducts an ablation study to quantify the synergistic effects of multiple uncertainties and clarify their influence on system risks. It further examines the relationship between forecasting accuracy and system risk levels. The effectiveness of the framework was validated through an annual operation case study of a hydropower-wind-photovoltaic hybrid system in the Yalong River Basin. The framework systematically evaluated power shortage and over-generation risks across subsystems under multiple uncertainties using an ablation study, risk event extraction, and correlation analysis. Based on these analyses, an improvement strategy was formulated to mitigate system risks.

水电-风-光伏混合发电系统的稳定性和经济性受到各种不确定性和风险的显著影响。然而，现有的研究缺乏一个系统的框架来评估这些不确定性的协同效应，识别触发风险事件的不利条件，以及评估预测准确性在风险评估中的作用。为了解决这些差距，本研究提出了一个多不确定性风险分析框架，旨在系统地量化不确定性对系统风险的影响。该框架评估系统风险分布，并采用聚类和相关分析来识别不利条件，为优化调度策略提供关键输入。此外，该框架还进行了消融研究，以量化多个不确定性的协同效应，并阐明它们对系统风险的影响。进一步探讨了预测精度与系统风险水平之间的关系。通过雅砻江流域水电-风电-光伏混合发电系统的年度运行案例研究，验证了该框架的有效性。该框架通过消融研究、风险事件提取和相关性分析，系统地评估了多个不确定因素下子系统的电力短缺和发电过剩风险。基于这些分析，制定了改进策略以降低系统风险。

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引用次数: 0

A two-stage operation reliability evaluation method of distribution system considering dynamic scheduling of flexible resources 考虑柔性资源动态调度的配电系统两阶段运行可靠性评估方法

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-16 DOI: 10.1016/j.segan.2025.102011

Hejun Yang , Yangxu Yue , Yuan Gao , Yue Liu , Dabo Zhang , Yuming Shen

The penetration rate of distributed generation (DG) has been increasing year by year, exacerbating the volatility and uncertainty of the distribution system. In order to improve the ability of anti-interference of distribution system and evaluate its operation risk, this paper proposes a two-stage operation reliability evaluation method for distribution system considering dynamic scheduling of flexible resources. The scientific aim of the work is to improve the ability of the distribution system to anti interference and evaluate the operation risks of the system. This study extends the existing research on scheduling of flexible resources and operation reliability evaluation method. Two stages of causing power loss load are presented to describe flexible resource’s power supply and power-ramping balance process (i.e., load shedding before the fault because of the insufficient power-ramping and unrecovered power loss load after the fault). Through dynamic scheduling of flexible resources in two stages, the operation reliability of distribution system is optimized. Firstly, For responding the output fluctuation and power prediction error of distributed sources and considering the ramping ability and output constraints of flexible resources, an optimization scheduling model for flexible resources is established to dynamically adjust the output of flexible resources in the first stage; Secondly, based on the output data of flexible resources, dynamic scheduling of flexible resources in the second stage after the system fault is carried out, and the fault recovery strategy is formulated for collaborative distributed sources and tie lines. An operation reliability evaluation method for distribution system is proposed based on the two-stage scheduling strategy to quantify the operation risk of the system; Finally, the second-order cone method was used to transform the model into a mixed integer second-order cone programming problem, which can be solved directly by using the solver. The effectiveness of the model was verified using the improved IEEE 33 distribution system, and under the condition of designed cases, the system average interruption duration index is increased by 31.89 % and the average energy not supplied is increased by 24.34 % compared to traditional evaluation methods.

分布式发电的普及率逐年上升，加剧了配电系统的波动性和不确定性。为了提高配电系统抗干扰能力，评估配电系统运行风险，提出了一种考虑柔性资源动态调度的配电系统两阶段运行可靠性评估方法。这项工作的科学目的是提高配电系统的抗干扰能力和评估系统的运行风险。本文对已有的柔性资源调度和运行可靠性评估方法进行了扩展。为了描述柔性资源的供电和爬坡平衡过程（即故障前由于爬坡不足导致的负荷下降和故障后无法恢复的失电负荷），提出了造成失电负荷的两个阶段。通过两阶段柔性资源的动态调度，优化配电系统的运行可靠性。首先，针对分布式电源的输出波动和功率预测误差，考虑柔性资源的爬坡能力和输出约束，建立了柔性资源优化调度模型，对第一阶段的柔性资源输出进行动态调整；其次，基于柔性资源的输出数据，对系统故障后的第二阶段柔性资源进行动态调度，并制定协同分布式源和联络线的故障恢复策略；提出了一种基于两阶段调度策略的配电系统运行可靠性评估方法，以量化配电系统的运行风险；最后，利用二阶锥法将模型转化为混合整数二阶锥规划问题，该问题可直接用求解器求解。以改进后的IEEE 33配电系统为例，验证了模型的有效性，在设计工况条件下，与传统评价方法相比，系统平均中断时间指标提高了31.89 %，平均不供能提高了24.34 %。

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引用次数: 0

Optimal expansion planning of microgrids clusters: A robust collaborative approach 微电网集群的优化扩展规划：一种强大的协作方法

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-16 DOI: 10.1016/j.segan.2025.102004

Marcos Tostado-Véliz , Pablo Horrillo-Quintero , Pablo García-Triviño , Luis M. Fernández-Ramírez , Francisco Jurado

Integrating electrical demands and distributed generators into microgrids facilitates their coordination and enables safe and reliable power supply to remote areas. When multiple microgrids share the same geographical area and transmission network, they can be organized into clusters to exchange energy in a peer-to-peer fashion, improving the overall efficiency and economy of the system. This paper proposes a novel methodology for optimal expansion planning of microgrid clusters, explicitly considering resource sharing. The model preserves the privacy of each microgrid by exchanging only boundary information. A three-level formulation is presented, incorporating uncertainties in renewable generation and demand through polyhedral uncertainty sets, whose bounds are determined using a novel clustering strategy. The resulting model is solved with a tailored algorithm based on robust optimization and a column-and-constraint generation scheme. The methodology is tested on a three-microgrid cluster, demonstrating its ability to manage uncertainty robustly and adapt to different levels of risk and budget constraints. In the case study, increasing robustness leads to higher costs (+31 %), lower renewable generation (-13 %), and increased unserved energy (+60 %). Finally, sensitivity analyses on fuel costs and the number of microgrids show that the proposed approach scales well with system size.

将电力需求和分布式发电机整合到微电网中，有助于它们之间的协调，并为偏远地区提供安全可靠的电力供应。当多个微电网共享同一地理区域和输电网络时，它们可以组织成集群，以点对点的方式交换能量，从而提高系统的整体效率和经济性。本文提出了一种明确考虑资源共享的微电网集群优化扩展规划方法。该模型通过仅交换边界信息来保护每个微电网的隐私。通过多面体不确定性集，结合可再生能源发电和需求的不确定性，提出了一种三层次的不确定性公式，并利用一种新的聚类策略确定了多面体不确定性集的边界。采用基于鲁棒优化和列约束生成方案的定制算法求解得到的模型。该方法在三个微电网集群上进行了测试，证明了其稳健管理不确定性和适应不同风险水平和预算约束的能力。在案例研究中，鲁棒性的增加会导致更高的成本（+ 31% %），更低的可再生能源发电量（- 13% %），以及更多的未服务能源（+ 60% %）。最后，对燃料成本和微电网数量的敏感性分析表明，所提出的方法可以很好地适应系统规模。

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引用次数: 0