基于改进的多目标粒子群算法的分布式光伏电源优化配置
Optimal configuration of distributed photovoltaic generation based on improved multi-objective swarm optimization algorithm
涂福荣 1刘克天2
作者信息
- 1. 厦门海洋职业技术学院 海洋机电学院,福建 厦门 361100
- 2. 南京工程学院 电力工程学院,江苏 南京 211167
- 折叠
摘要
针对不合理的分布式光伏电源选址定容给配网造成电压质量降低、功率倒送等问题,建立计及光照条件的配电网有功网损最小、电压偏差最小、投资成本最低的多目标选址定容模型,并提出一种改进的多目标粒子群算法求解该模型.该算法采用非线性惯性权重和学习因子提高算法的收敛精度,引入早熟扰动因子增强算法跳出局部最优的能力,提出拥挤距离更新策略,以保证Pareto解集分布的均匀性和广泛性;通过标准测试函数验证所提算法的优越性;最后,基于IEEE-69节点系统进行分析.仿真结果表明,所建模型在兼顾配电网经济性和安全性的同时,可有效将分布式光伏电源配置在光照资源丰富的地区.
Abstract
Aiming at the problems such as voltage quality reduction and power inversion,etc,caused by unreasonable distributed photovoltaic generation location and capacity,a multi-objective location and capacity model for distribu-ted network is proposed,which takes into consideration least network loss,least voltage deviation,lowest investment cost and solar radiation.An improved multi-objective particle swarm optimization algorithm(IMOPSO)is proposed to solve the model.The algorithm uses nonlinear inertia weight and learning factor to improve the convergence accu-racy of the algorithm,and the premature disturbance factor is introduced to enhance the ability of the algorithm to es-cape the local optimization,then a crowding distance update strategy is adopted to ensure the uniformity and diversity of Pareto front distribution.Benchmark functions are used to verify the superiority of the IMOSPSO.Finally,the simu-lation based on the IEEE-69 distribution network is implemented.The results prove that the model can effectively deploy distributed PV in areas with rich solar irradiance resources while taking into account the economic and securi-ty requirements of the distribution network.
关键词
分布式光伏电源/选址定容/多目标粒子群算法/配电网Key words
distributed photovoltaic generation/location and capacity/multi-objective particle swarm optimization algorithm/distributed network引用本文复制引用
基金项目
厦门海洋职业技术学院科研项目(KYZ202209)
出版年
2024
NETL
NSTL
维普
万方数据