Active distribution network expansion planning model considering low carbon and flexible load

刘艾旺; 朱萧轶; 姚宝明; 谢益峰; 舒能文; 邹 健; 施云辉

doi:10.3969/j.issn.1009-1831.2023.04.003

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Active distribution network expansion planning model considering low carbon and flexible load

更新时间：2026-03-05

- Active distribution network expansion planning model considering low carbon and flexible load
- Vol. 25, Issue 4, (2023)
- 作者机构：
  
  1. 国网浙江海盐县供电有限公司,浙江,嘉兴,314003
  2. 浙江大学,杭州,310027
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2023.04.003
  CLC：
- Published：2023
- 稿件说明：
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刘艾旺, 朱萧轶, 姚宝明, et al. Active distribution network expansion planning model considering low carbon and flexible load[J]. 2023, 25(4).
DOI：

刘艾旺, 朱萧轶, 姚宝明, et al. Active distribution network expansion planning model considering low carbon and flexible load[J]. 2023, 25(4). DOI： 10.3969/j.issn.1009-1831.2023.04.003.

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。

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.

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