基于改进自适应权重多目标粒子群算法的分布式电源优化配置

根据配电网和分布式电源相关特性,建立考虑多种约束条件下配电网网损最小、分布式电源成本最低、电网电压稳定性最好的多目标优化数学模型。该文提出一种改进自适应权重多目标粒子群算法对配电网进行分布式电源优化配置,相较于传统的多目标粒子群算法容易陷入局部最优,以及单目标算法只能给出单一配置方案,该方法在保证得到更接近全局最优解的同时提供一系列可供选择的方案(一组Pareto解集)。采用不同算法对IEEE 69节点算例进行求解计算,仿真结果充分证明了算法的优越性,为配电网中分布式电源配置提供了更为灵活的可行性方案。

Optimal allocation of distributed generation based on improved adaptive weight multi-objective particle swarm optimization

According to the characteristics of distribution network and distributed generation,this paper establishes a multi-objective optimal model by minimizing the distribution network loss,distributed power costs and maximizing voltage stability.A multi-objective particle swarm optimization algorithm based on improved adaptive weight strategy for optimizing DG configuration is proposed.Compared with traditional multi-objective algorithms that are readily trapped at the local minimum,it guarantees to provide an solution closer to the global minimum.Meanwhile the algorithm offers a series of alternative solutions in better fitness to the Pareto optimal boundary in comparison with single-objective algorithms.Finally,a numerical example of an IEEE 69-bus system is employed to verify the reliability,adaptability and practicability of the proposed algorithm.