多类型电源与储能协同的配电网规划优化

doi:10.3969/j.issn.2097-0706.2025.08.006

摘要/Abstract

摘要：

随着小火电、储能、风电、光伏及电动汽车等分布式电源的大规模接入，为优化配电网的系统运行成本和输电损耗，根据配电网内大量分布式电源的出力以及不同时段的负荷需求，建立多目标优化模型并对不同配电网的线路规划方案进行分析。采用改进的多目标灰狼算法结合前推回代潮流算法进行优化计算，实现每个节点的电压和分布式电源的出力优化。构建具备不同典型特征的规划路线场景，分析电动汽车接入不同的节点对线路损耗和系统运行成本的影响，得到最佳的线路规划方案。采用IEEE 33节点进行仿真计算，结果表明：与传统单一的供电方式相比，分布式电源接入后输电损耗降低了52.39%；利用新能源+电动汽车充电桩，可以有效降低系统运行成本，为未来大规模充电桩接入配电网提供参考。

关键词: 配电网, 分布式电源, 优化规划, 电动汽车充电桩, 多目标灰狼算法, 前推回代算法, 输电损耗

Abstract:

With the large-scale integration of distributed energy resources such as small thermal power units， energy storage systems， wind turbines， photovoltaic systems， and electric vehicle charging stations， it is essential to optimize the system operating costs and transmission loss in distribution networks. Based on the output of multiple distributed energy resources and the load demand at different time periods within distribution networks， a multi-objective optimization model was established to analyze different line planning schemes for distribution networks. An improved multi-objective grey wolf optimizer integrated with the forward/backward sweep algorithm was used for optimization to optimize voltage and output of distributed energy resources at each node. Planning route scenarios with different typical characteristics were developed to analyze the effect of electric vehicle access at different nodes on line loss and system operating costs， thereby obtaining the optimal line planning scheme. Simulation calculations were conducted using the IEEE 33-bus system. The results showed that compared to traditional single-source power supply methods， the integration of distributed energy resources reduced transmission loss by 52.39%. Additionally， the hybrid system combining renewable energy with electric vehicle charging stations could effectively reduce system operating costs， providing a reference for large-scale integration of charging stations into distribution networks in the future.

Key words: distribution network, distributed energy resources, optimization planning, electric vehicle charging station, multi-objective grey wolf optimizer, forward/backward sweep algorithm, transmission loss

中图分类号:

TK01

王砥凡, 魏飞, 蒋德玉, 韩树山, 李中凯, 张盛林, 王政伟, 陈衡. 多类型电源与储能协同的配电网规划优化[J]. 综合智慧能源, 2025, 47(8): 49-57.

WANG Difan, WEI Fei, JIANG Deyu, HAN Shushan, LI Zhongkai, ZHANG Shenglin, WANG Zhengwei, CHEN Heng. Optimization planning of distribution networks with multiple energy resources and energy storage coordination[J]. Integrated Intelligent Energy, 2025, 47(8): 49-57.

图/表 13

图1

图2

图3

表1

系统模型参数

参数	数值
$p P V$ /(元·kW^-1)	0.336
$p P V Y W$ /(元·kW^-1)	0.008
$p B A$ /(元·kW^-1)	0.235
$p B A Y W$ /(元·kW^-1)	0.030
$p W T$ /(元·kW^-1)	0.509
$p W T Y W$ /(元·kW^-1)	0.009
E/(kW·h)	500
$P i m a x$ /kW	300
$P i m i n$ /kW	100
$S O C, m a x$	1.0
$S O C, m i n$	0.3
b/(元·kW^-1)	0.000 6
c/(元·kW^-1)	0.308 8
d/(元·kW^-1)	40.000 0
$U i, m a x$ （标幺值）	1.03
$U i, m i n$ （标幺值）	0.97

表1

图4

表2

图5

图6

图7

图8

表3

图9

图10

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方案	输电损耗/（kW·h）	日运行成本/元
1	5 116.859	79 154.15
2	4 862.223	71 936.21
3	4 723.218	70 520.72

算法	电压偏差（标幺值）	损耗成本/元
PSO	0.005 08	1 525.69
MOGWO	0.004 68	1 504.13
IMOGWO	0.004 25	1 480.01