基于改进FPA算法的含分布式光伏配电网选址定容多目标优化方法

Multi-objective optimization method for location and capacity of a distribution network with distributed photovoltaic energy based on an improved FPA algorithm

近年来配电网分布式光伏数量不断增加,不合理的分布式光伏接入位置和容量给配电网带来了极大的冲击。针对分布式光伏接入位置和容量不合理给配电网带来的影响,提出了一种以投资成本最低、网损最小、电压质量最优为优化目标的选址定容模型。结合遗传算法、混沌序列和花授粉算法求解优化模型。通过混沌序列对花粉位置进行初始化,保证种群的多样性。在花授粉算法局部最优时,最优解被用作遗传算法的初始参数进行选择、交叉、变异来更新种群,保持种群的多样性,提高算法的寻优能力。通过仿真对所提方法的可行性进行验证。结果表明,改进算法的收敛性明显提高,从改进前300次提升到改进后40次迭代后开始收敛。优化配置后,电压效应较差的节点和损耗都得到了明显改善。该研究为含分布式电源的配电网选址定容提供一定的参考和借鉴。

In recent years, with the increasing amount of distributed PV in distribution network, unreasonable distributed photovoltaic access location and capacity has a large negative impact on the distribution network. Given this impact, a location and capacity model with the optimization objectives of minimum investment cost, minimum network loss and optimal voltage quality is proposed. The optimization model is solved by combining a genetic algorithm, a chaotic sequence and flower pollination algorithm. The pollen position is initialized by chaotic sequence to ensure population diversity. When the flower pollination algorithm is locally optimal, the optimal solution is used as the initial parameter of the genetic algorithm to select, cross and mutate to update the population, maintain the diversity of the population and improve the optimization ability of the algorithm. The feasibility of this method is verified by simulation. The results show that the convergence of the improved algorithm is significantly improved, from 300 iterations before the improvement to 40 iterations after the improvement. After the optimal configuration, the nodes and losses with poor voltage effect are significantly improved. This study provides a reference for the location and capacity of a distribution network with distributed generation.