基于多目标粒子群算法的配电网储能优化配置研究

doi:10.3969/j.issn.2097-0706.2023.06.002

综合智慧能源 ›› 2023, Vol. 45 ›› Issue (6): 9-16.doi: 10.3969/j.issn.2097-0706.2023.06.002

基于多目标粒子群算法的配电网储能优化配置研究

刘子祺¹(), 苏婷婷¹(), 何佳阳¹(), 王裕¹^,²^,^*()

1.广东工业大学自动化学院，广州 510006
2.中国科学院广州能源研究所广东省新能源和可再生能源研究开发与应用重点实验室，广州 510640

收稿日期:2022-10-29 修回日期:2023-01-09 接受日期:2023-05-08 出版日期:2023-06-25 发布日期:2023-06-14
通讯作者: 王裕
作者简介:刘子祺（1999），男，在读硕士研究生，从事储能系统优化配置与运行方面的研究，ziqi.liu.647@hotmail.com；
苏婷婷（1999），女，在读硕士研究生，从事新型电力系统稳定性分析与控制方面的研究，tingting.su2022@hotmail.com；
何佳阳（1999），男，在读硕士研究生，从事双有源全桥直流固态变流器方面的研究，jiayang_he@hotmail.com
基金资助:
国家自然科学基金项目(62073084);广东省自然科学基金项目(2021A1515012398);广东省新能源和可再生能源研究开发与应用重点实验室开放基金项目(E139kf0401)

Research on the optimal allocation of energy storage in distribution network based on multi-objective particle swarm optimization algorithm

LIU Ziqi¹(), SU Tingting¹(), HE Jiayang¹(), WANG Yu¹^,²^,^*()

1. School of Automation， Guangdong University of Technology，Guangzhou 510006， China
2. Guangdong Provincial Key Laboratory of New and Renewable Energy Research and Development， Guangzhou Institute of Energy Conversion， Chinese Academy of Sciences， Guangzhou 510640， China

Received:2022-10-29 Revised:2023-01-09 Accepted:2023-05-08 Online:2023-06-25 Published:2023-06-14
Contact: WANG Yu
Supported by:
National Natural Science Foundation of China(62073084);Natural Science Foundation of Guangdong Province(2021A1515012398);Guangdong Provincial Key Laboratory of New and Renewable Energy Research and Development(E139kf0401)

摘要/Abstract

摘要：

储能技术具有对功率和能量的时间迁移能力，能有效改善可再生能源的输出特性和可调度性，是构建新型电力系统解决可再生能源大规模并网问题及促进资源利用的关键技术。研究不同应用场景下储能优化配置对其在配电网中的有效应用具有重要意义。针对综合考量技术性和经济性指标的配电网储能配置多目标优化问题，提出一种基于多目标粒子群优化（MOPSO）算法的配电网储能优化配置方法。采用MOPSO算法对多目标储能配置模型求解，并在种群更新过程中引入自适应变异策略以扩大粒子对空间的探索能力，有效改善种群多样性的同时保证后期收敛性，在储能配置问题上得到全局最优解，实现技术与经济指标多目标综合优化。通过Matlab仿真验证所提方法的可行性与优越性。该研究成果对探索配电网储能优化配置方案具有重要的理论与工程价值。

关键词: 配电网, 储能优化配置, 多目标粒子群优化算法, 新型电力系统, 可再生能源, 大规模并网, 碳中和

Abstract:

The energy storage technology has the ability to adjust power and the time of energy，so as to effectively improve the output characteristics and shedulability of renewable energy. Thus， it is important to study the energy storage optimized configurations under different scenarios.Taking the technical and economic indicators into consideration comprehensively， an energy storage allocation method based on multi-objective particle swarm optimization（MOPSO）algorithm is proposed. The multi-objective energy storage configuration model can be solved by MOPSO， and the adaptive mutation strategy is introduced in the population updating process to improve the exploration capability of particles and ensure the population diversity and the late convergence. The global optimal solution for energy storage comprehensively optimizes the technical and economic indicators. The feasibility and superiority of the proposed method are verified by Matlab simulation， and the research results have theoretical and engineering value for the optimal configurations of energy storage systems in distribution network.

Key words: distribution network, optimal configuration of energy storage, multi-objective particle swarm optimization, new power system, renewable energy, large-scale grid connection, carbon neutrality

中图分类号:

TK01：TM715

刘子祺, 苏婷婷, 何佳阳, 王裕. 基于多目标粒子群算法的配电网储能优化配置研究[J]. 综合智慧能源, 2023, 45(6): 9-16.

LIU Ziqi, SU Tingting, HE Jiayang, WANG Yu. Research on the optimal allocation of energy storage in distribution network based on multi-objective particle swarm optimization algorithm[J]. Integrated Intelligent Energy, 2023, 45(6): 9-16.

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参数及变量	取值
最大迭代次数/次	50
种群规模/个	50
档案库规模/个	200
惯性权重范围	0.5~0.9
储能电量（SOC）范围/%	20~90
充放电效率η/%	90
学习因子c₁, c₂	1.7,1.8
储能单位功率成本α/(万元·MW^-1)	0.12
储能单位容量成本β/(万元·MW^-1)	0.18
储能年运维成本C_y/(万元·MW^-1)	5

场景	储能接入位置及容量/(MW·h)	f₁	f₂/万元	f₃/MW
1	—	1.083 6	—	1.706 9
2	10(0.987 3)，12(1.104 5)	1.165 6	0.903 1	1.580 2
	10(0.965 5)，14(0.940 7)	1.158 6	0.879 3	1.601 0
	12(1.011 5)，14(1.114 8)	1.150 5	0.840 9	1.617 0
	平均值	1.158 2	0.874 4	1.599 4
3	10(0.941 9)，12(1.051 5)	1.235 0	0.844 5	1.499 7
	10(0.911 0)，14(0.897 2)	1.220 6	0.821 2	1.501 0
	12(0.971 3)，14(1.069 6)	1.219 0	0.799 6	1.528 6
	平均值	1.224 9	0.821 8	1.509 8

基于多目标粒子群算法的配电网储能优化配置研究

Research on the optimal allocation of energy storage in distribution network based on multi-objective particle swarm optimization algorithm

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