在有季节性负荷的配电系统中协同部署多种加固方法以降低网络损耗

Yizhe Xie, Kai Xing, Lizi Luo, Shuai Lu, Cheng Chen, Xiaoming Wang, Wenguang Zhao, Mert Korkali
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摘要

季节性负荷(如谷物烘烤和水产品加工负荷)的整合往往会导致配电系统在特定时期出现明显的电压偏差和严重的峰值负荷,从而增加网络损耗。由于季节性负荷在时空上的分布不均,传统的减少网络损耗的方法在有效性和成本效益方面都越来越低。为解决这一问题,本研究提出了一种优化模型,该模型将移动储能、开关电容器和连接线协同整合在一起,以在受季节性负荷影响的特殊规划场景中最大限度地降低年度网络损耗。该模型深入分析了多种增援方法的部署策略,大大提高了协同部署模型的可解释性和可行性。然后,利用内切正十二边形近似方法将所提出的模型重新表述为混合整数线性规划模型,从而使其可以被最先进的求解器跟踪。为了说明该模型的有效性,对位于华东地区的一个独特的 55 总线配电系统进行了案例研究,该系统包含具有大量季节性变化的水产养殖负荷和一般负荷的给料机。通过详细的数值结果,全面分析了多种加固方法的有效性。此外,还对投资预算进行了敏感性分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Collaborative deployment of multiple reinforcement methods for network-loss reduction in distribution system with seasonal loads

The integration of seasonal loads, such as cereal baking and aquatic-product processing loads, often leads to significant voltage deviations and severe peak loads of the distribution system during specific periods, resulting in increased network losses. Traditional approaches for reducing network losses are becoming less effective and cost-efficient due to the spatiotemporally uneven distribution characteristics of seasonal loads. To address this issue, this study proposes an optimisation model that collaboratively integrates mobile energy storage, switching capacitors, and tie lines to minimise annual network losses in special planning scenarios affected by seasonal loads. The deployment strategies of multiple reinforcement methods are thoroughly analysed, greatly enhancing the explainability and feasibility of the collaborative deployment model. Then, the proposed model is reformulated to a mixed-integer linear programming model using the inscribed regular dodecagon approximation approach, thereby making it trackable for state-of-the-art solvers. To illustrate the effectiveness of the model, case studies are conducted on a unique 55-bus distribution system located in East China, which contains feeders with substantial seasonal variation aquaculture loads and with general loads. The effectiveness of multiple reinforcement methods is thoroughly analysed through detailed numerical results. Furthermore, a sensitivity analysis of the investment budget is conducted.

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