基于改进粒子群优化算法的源网荷储系统容量配置研究

doi:10.3969/j.issn.2097-0706.2024.09.004

摘要/Abstract

摘要：

随着可再生能源技术的发展，源网荷储系统成为保证电力系统可靠性和稳定性的重要解决方案。合理的容量配置可优化投资成本、保证系统的供电可靠性并减少可再生能源的浪费，对提升系统性能和经济效益至关重要。提出了一种改进的粒子群优化（PSO）算法，结合可变惯性权重，增强算法的搜索能力和收敛速度，采用多目标优化，在保证供电可靠性、尽可能减少弃风弃光量的前提下，寻找投资成本最低的源荷储容量及功率配置方案。将所提算法应用于小岛源网荷储测试系统，并与典型优化算法进行比较。仿真结果表明，基于改进PSO算法的多目标优化方法不仅能够有效实现源网荷储系统的容量配置，而且收敛速度和解的质量都有显著提升，为电力系统规划和运行提供了一种新的优化工具。

关键词: 可再生能源, 源网荷储系统, 容量设计, 粒子群优化算法, 可变惯性权重, 优化配置

Abstract:

With the advancement of renewable energy technologies， source-network-load-storage systems have become an important solution for reliable and stable operation of power systems. Since a rational capacity configuration can reduce the investment， ensure the power supply capacity and improve the renewable energy utilization rate of a power system， it is a vital parameter for system economic benefits and performance improvement. Thus， an improved Particle Swarm Optimization （PSO） is proposed to obtain the capacity and power configuration scheme with the minimal investment， the lowest renewable energy abandon rate and stable power supply for a source-load-storage system through multi-objective optimization. And variable inertia weights can enhance the search capability and convergence speed of the algorithm. The proposed algorithm is applied to an islanded source-network-load-storage system and compared with other typical optimization algorithms. Simulation results demonstrate that the multi-objective optimization based on the improved PSO can choose a proper capacity configuration for the system with decent convergence. The multi-objective optimization based on the improved PSO not only effectively realizes the capacity configuration for source-grid-load-storage systems but also significantly improves the convergence speed and solution quality. It offers a novel optimization tool for power system planning and operation.

Key words: renewable energy, source-load-grid-storage system, capacity configuration, particle swarm optimization, variable inertia weight, optimization configuration

中图分类号:

TK01

王晓燕, 吴书泉. 基于改进粒子群优化算法的源网荷储系统容量配置研究[J]. 综合智慧能源, 2024, 46(9): 28-36.

WANG Xiaoyan, WU Shuquan. Research on capacity allocation for source-grid-load-storage systems based on improved PSO[J]. Integrated Intelligent Energy, 2024, 46(9): 28-36.

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项目	数值
总成本/万元	158.65
缺电率/%	0.22
弃电率/%	16.68

项目	数值
总成本/万元	201.26
缺电率/%	0.01
弃电率/%	21.00

项目	数值
光伏容量/MW	71
储能容量/（MW·h）	32
储能功率上限/MW	16
光伏发电量/（MW·h）	124 368
风力发电量/（MW·h）	120 034
弃电量/（MW·h）	11 802
弃电率/%	4.83
年用电量/（MW·h）	232 601
总成本/亿元	4.368
储能吸收电量/（MW·h）	5 945
储能供电量/（MW·h）	4 816
输电线路利用率/%	50.04
新能源供电占比/%	100