基于PSO-DE算法的分布式光伏优化配置研究

为解决分布式光伏电源接入配电网的优化配置问题,提出一种基于粒子群和差分进化的PSO-DE算法,同时构建了包含网损最小、投资成本最低、电压质量最优的无偏好多目标分布式光伏选址定容综合优化模型。首先对差分进化算法的变异过程进行改进,然后利用粒子群算法对差分进化算法中的缩放因子和杂交因子进行优化,采用标准测试函数对PSO-DE算法进行测试和参数敏感度分析,验证了算法的客观性和稳定性;并利用无偏好可变权重对多目标模型进行处理;最后以分布式光伏选址定容优化的实际应用为例,并与其他算法对比,验证了模型和算法的有效性和实用性。

Research on Optimal Configuration of Distributed Photovoltaic Generation with PSO-DE Algorithm

To optimize the configuration of distributed photovoltaic generation(DPV)connected to distribution network,a hybrid optimization algorithm based on improved particle swarm optimization(PSO)and differential evolution(DE)is proposed,which is called PSO-DE algorithm.Meanwhile,a comprehensive optimization model of non-preference multi-objective DPV location and capacity configuration with minimum network losses,minimum investment cost and optimal voltage quality is built,which is processed by non-preference weights.In the PSO-DE algorithm,DE algorithm is used to improve the mutation process.PSO algorithm is to optimize the variation and crossover factors in DE algorithm.This paper took three standard test functions to test the proposed algorithm and performed parameter sensitivity analysis to verify the objectivity and stability of the algorithm.This paper also processed the multi-objective model by non-preference weights.Finally,a case study of location and capacity optimization of DPV and comparison with other algorithms demonstrates the effectiveness and practicability of the proposed model and algorithm.