Multiobjective optimization configuration of energy storage in distribution networks based on improved marine predator algorithm

doi:10.19799/j.cnki.2095-4239.2023.0267

Abstract

Abstract:

The extensive integration of distributed generation (DG) is one of the vital development trends in distribution networks. The reasonable configuration of energy storage systems is an essential means to enhance the ability of distribution networks to accept DG. This article considers the problem of power quality degradation caused by the high proportion of DG connected to the distribution network and establishes a multiobjective optimization configuration model for energy storage that minimizes voltage deviation, line loss rate, and optimal energy storage planning cost in the distribution network. Because of the shortcomings of the traditional optimization algorithms focusing on improving the performance of solving the multiobjective optimization of energy storage configurations, a method based on fast nondominated sorting and boundary crossing construction weight to set reference points is adopted to improve the marine predator algorithm and then solve multiobjective optimization model of energy storage in the distribution network to obtain the optimal grid connection position, rated capacity, and charging and discharging power of energy storage batteries in the distribution network during the scheduling period. By conducting numerical analysis on an IEEE-33 node system, the improved algorithm can effectively solve the energy storage configuration scheme that ensures stable operation of the distribution network at the optimal planning cost. Moreover, by comparing various intelligent optimization algorithms, the proposed improved algorithm has good convergence and distribution performance in solving multiobjective optimization configuration problems for energy storage.

Key words: improved marine predators algorithm, multi-objective algorithm optimization, distribution networks, distributed generation, energy storage configuration

CLC Number:

TM 732

Xiaolong XIAO, Mingming SHI, Qi ZHOU, Yukai WEI, Bo ZHAO. Multiobjective optimization configuration of energy storage in distribution networks based on improved marine predator algorithm[J]. Energy Storage Science and Technology, 2023, 12(8): 2565-2574.

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情景	综合评价指标值	储能规划方案			配电网运行结果
情景	综合评价指标值	安装节点	容量/kWh	C_pro/万元	电压偏移指标	线损率
1	—	—	—	—	12.15	6.28%
2	5.846	18	2000	0.360	11.16	6.00%
3	5.711	18	2311	0.413	10.80	5.90%
4	5.700	18	2774	0.494	10.71	5.87%

对比算法	IMOMPA	NSGA-Ⅱ	MOPSO
f 1结果范围	0.355～0.584	0.340～0.442	0.360～0.658
f 2结果范围	10.7～12.1	10.8～17.3	14.1～17.9
f 3结果范围	5.87～6.01	5.92～6.27	5.88～6.07
IGD值	0.15	0.28	3.55
迭代优化时间	42.9 s	35.9 s	23.2 s

参数	数值
充电效率	90%
放电效率	90%
能量倍率	0.25 C
功率成本系数	0.16
容量成本系数	0.25
辅助设备成本系数	0.28
电池折旧成本系数	0.1
电池等效寿命/年	15
折现率	6.5%
荷电状态范围	[0.1, 0.9]
初始SOC	45%

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