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考虑低碳和柔性负荷的有源配网扩展规划模型
引用本文:刘艾旺,朱萧轶,姚宝明,谢益峰,舒能文,邹 健,施云辉. 考虑低碳和柔性负荷的有源配网扩展规划模型[J]. 电力需求侧管理, 2023, 25(4): 15-20
作者姓名:刘艾旺  朱萧轶  姚宝明  谢益峰  舒能文  邹 健  施云辉
作者单位:国网浙江海盐县供电有限公司,浙江 嘉兴 314003;浙江大学,杭州 310027
基金项目:国家自然科学基金项目(51877190)
摘    要:在配电网低碳化的背景下,提出了考虑低碳和柔性负荷的有源配电网扩展规划模型,其目标是在满足网络运行约束和 CO2排放上限的前提下,给出总成本最小的投资策略。决策变量包括更换过载线路、投建新能源和储能装置,以及投建稳压器和电容器组等电压控制设备。模型提出了多项式形式的电压相关型的柔性负荷特性,针对柔性负荷模型以及网络重构约束中的非线性约束,建立分段McCormick 包络法和虚拟需荷和能源价格的不确定性,采用基于场景聚类的两阶段随机优化方法进行求解。通过69节点系统对该模型进行测试,结果表明,所提模型不仅总规划成本较低,而且有助于减少碳排放。

关 键 词:有源配网;低碳规划;柔性负荷;混合整数线性规划
收稿时间:2023-03-09
修稿时间:2023-05-19

Active distribution network expansion planning model considering low carbon and flexible load
LIU Aiwang,ZHU Xiaozhi,YAO Baoming,XIE Yifeng,SHU Nengwen,ZOU Jian,SHI Yunhui. Active distribution network expansion planning model considering low carbon and flexible load[J]. Power Demand Side Management, 2023, 25(4): 15-20
Authors:LIU Aiwang  ZHU Xiaozhi  YAO Baoming  XIE Yifeng  SHU Nengwen  ZOU Jian  SHI Yunhui
Affiliation:State Grid Zhejiang Haiyang Power Supply Co., Ltd., Jiaxing 314003, China; Zhejiang University,Hangzhou 310027, China
Abstract:Under the background of low-carbonization of distribution networks, an active distribution network expansion planning model that considers low-carbon and flexible loads is proposed. The objective is to provide the investment strategy with the minimum total cost under the premise of satisfying network operation constraints and CO2 emission limits. Decision variables include replacing overloaded lines, constructing new energy and energy storage devices,and constructing voltage control devices such as stabilizers and capacitor banks. The model proposes a polynomial form of voltage-related flexible load characteristics. To address the non-linear constraints in the flexible load model and network reconstruction constraints, the paper proposes a segmented McCormick envelope method and a virtual demand method to transform them into mixed-integer second-order cone optimization. Considering the uncertainty of new energy output,load, and energy prices, a two-stage stochastic optimization method based on scenario clustering is used for solving. The proposed model is tested on a 69-node system, and theions.
Keywords:
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