Optimization planning of distribution networks with multiple energy resources and energy storage coordination

doi:10.3969/j.issn.2097-0706.2025.08.006

Abstract

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

CLC Number:

TK01

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.

Figures/Tables 13

Fig.1

Fig.2

Fig.3

Table 1

System model parameters

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

Table 1

Fig.4

Table 2

Fig.5

Fig.6

Fig.7

Fig.8

Table 3

Fig.9

Fig.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