NSGA-II与改进粒子群算法相结合的储能优化配置

储能系统的选址定容在接入新能源的电力系统中具有重要意义,合理选择储能容量可以很好地平抑风电接入后的系统有功功率波动,同时减少能源浪费。本文提出了一种混合智能优化算法(Hybrid Intelligent Optimization Algorithm,HIOA),以电压偏差、负荷波动最小及最少储能配置容量为目标进行优化配置。该算法将多目标粒子群算法改进后(IMOPSO)与精英非支配排序算法(NSGA-II)结合,与常规多目标粒子群算法相比,大幅提高了算法的寻优性能,保证了Pareto 解的多样性和分布性。通过非支配排序求解获得Pareto 最优解集,采用逼近理想解排序(technique for order preference by similarity to an ideal solution,TOPSIS)选出储能的最优接入方案。通过IEEE-33 节点测试系统的仿真实验验证了本文所提算法的准确性及有效性,在求解配电网储能选址定容问题中有很好的收敛性和全局寻优能力。

Optimal Configuration of Energy Storage based on NSGA-II with Improved Multi-Objective Particle Swarm Optimization

Energy storage locating and sizing play an important role in the power system with new energy connected. The reasonable selection of storage capacity can smooth active power fluctuations well after wind power integration, and reduce energy loss meanwhile. An optimization model is built to solve multi-objective optimization problem, with voltage fluctuation, load fluctuation and storage capacity as objectives. This paper presents a hybrid intelligent optimization algorithm (HIOA), which combine improved multi-objective particle swarm optimization (IMOPSO) with elitist non-dominated sorting genetic algorithm II (NSGA-II). By introducing fast non-dominated sorting, elitism strategy and dynamic crowding-distance, the searching performance of the algorithm is improved greatly. Besides, the diversity and distribution of the Pareto solutions are guaranteed. The optimized Pareto solutions are acquired through non-dominated sorting. The optimal decision for energy storage is chosen from the optimized Pareto solutions, with the application of technique for order preference by similarity to an ideal solution (TOPSIS). The validity and the accuracy of the proposed algorithm are verified based on the simulation of IEEE-33 nodes distribution system. Simulation results show that the proposed algorithm has rapid convergence speed and superb global search ability.