Optimal planning for siting and sizing of distributed generator including electric vehicles considering correlations of source and load

LONG Chuan; ZHANG Wentao; YE Shengyong; YONG You; HAN Yuqi; LIU Xuna; ZHAO Dawei; LIU Jieying; WANG Siqi

doi:10.19666/j.rlfd.201911263

English

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Optimal planning for siting and sizing of distributed generator including electric vehicles considering correlations of source and load

Thermal Power Generation Vol. 49, Issue 6, Pages: 97-104(2020)

更新时间：2025-09-28

- Optimal planning for siting and sizing of distributed generator including electric vehicles considering correlations of source and load
- 作者机构：
  
  1. 国网四川省电力公司经济技术研究院
  2. 西南交通大学电气工程学院
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.201911263
  CLC： TM715
- Published Online：24 May 2020，
  
  Published：2020
- 稿件说明：
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LONG Chuan, ZHANG Wentao, YE Shengyong, et al. Optimal planning for siting and sizing of distributed generator including electric vehicles considering correlations of source and load[J]. Thermal Power Generation, 2020, 49(6): 97-104.
DOI：

LONG Chuan, ZHANG Wentao, YE Shengyong, et al. Optimal planning for siting and sizing of distributed generator including electric vehicles considering correlations of source and load[J]. Thermal Power Generation, 2020, 49(6): 97-104. DOI： 10.19666/j.rlfd.201911263.

摘要

分布式电源大规模并入电网与电动汽车的广泛应用在优化能源结构的同时

也给配电网的选址定容规划带来新的挑战。本文通过拉丁超立方抽样并结合Cholesky分解和Nataf变换

得到具有相关性的风速、光照强度与需求负荷样本;利用蒙特卡洛模拟得到具有随机性的电动汽车充电负荷

与考虑不确定因素的风光荷样本相结合

由K-means聚类方法进行场景聚类得到典型的运行场景;利用机会约束规划方法建立规划模型

并以收缩因子粒子群优化算法求解;通过典型IEEE 33节点算例分析了风光荷相关性与电动汽车不同充电方式对规划方案的影响。结果表明:电动汽车用户选择常规充电方式能够降低电网负荷;考虑风力、光伏与负荷之间的相关性

得到的规划方案将更经济且更具有指导意义。

Abstract

The large-scale integration of distributed generator and the extensive application of electric vehicles(EVs) not only optimize the energy structure

but also bring new challenges to siting and sizing of distribution network.In this paper

the wind speed

light intensity and demand load samples with correlations are obtained through combining the Latin hypercube sampling

Cholesky decomposition and Nataf transformation.The Monte Carlo simulation is used to obtain the charging load of the electric vehicles with randomness.Combined with the wind-charge sample considering the uncertain factors

the typical operation scenarios are obtained by K-means clustering method.Based on the chance-constrained programming

the programming model which is solved by the constriction factor particle swarm optimization algorithm is established.The IEEE 33-bus distribution network is applied to analyze the influence of correlation and different charging modes of EVs on the planning scheme.The results show that

the grid load can be reduced if the EVs chose the conventional charging mode

and considering the correlation between wind

photovoltaic and load can enhance the economic efficiency of the planning scheme and make it more instructive.

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