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首页> 外文期刊>International journal of electrical power and energy systems >Joint planning of active distribution networks considering renewable power uncertainty
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Joint planning of active distribution networks considering renewable power uncertainty

机译:考虑可再生能源不确定性的有源配电网联合规划

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摘要

This paper proposes a multi-objective joint planning model for Active Distribution Networks (ADNs). The model determines the location and size of Electric Vehicle Charging Stations (EVCSs), Renewable Energy Sources (RESs), Battery Energy Storage System (BESSs), and distribution network expansion schemes, with the objectives of minimizing the total investment and reliability cost of the distribution network and maximizing the EVCSs' charging service capability. A scenario-based stochastic modelling approach based on Wasserstein distance metric and K-medoids scenario analysis is developed to model the stochastic nature of renewable generation. A multi-objective optimization algorithm, Multi-Objective Natural Aggregation Algorithm (MONAA), is applied to solve the proposed model. Case studies are conducted on a coupled 54-node distribution system and 25-node traffic system to validate the effectiveness of the proposed method.
机译:本文提出了一种主动分配网络(ADN)的多目标联合计划模型。该模型确定了电动汽车充电站(EVCS),可再生能源(RES),电池储能系统(BESS)和配电网络扩展方案的位置和大小,其目标是最大程度地减少电动汽车充电站的总投资和可靠性成本。分配网络,最大限度地提高EVCS的充电服务能力。开发了一种基于Wasserstein距离度量和K-medoids情景分析的基于情景的随机建模方法,以对可再生能源发电的随机性质进行建模。应用多目标优化算法,即多目标自然聚集算法(MONAA)来求解所提出的模型。对耦合的54节点分配系统和25节点流量系统进行了案例研究,以验证所提出方法的有效性。

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