基于改进自适应量子行为粒子群算法的交直流混合配电网协调优化方法

Coordination Optimization Method for AC/DC Hybrid Distribution Network Based on Improved AQPSO

随着系统日益庞大,交直流混合配电网的拓扑结构与系统潮流计算将更为复杂,各单元的协调控制已成为配电网经济、稳定运行的关键,恰当的控制方案可助力各单元协调控制进一步优化。针对此问题,提出一种多时间尺度下交直流混合配电网的能量管理系统优化运行的控制策略,通过与分布式电源、蓄电池、主从换流站等本地控制相结合,使得配电网能最大限度地降低总体电能损耗和各节点电压偏差。本文首先描述交直流混合配电网系统模型,然后建立交直流混合配电网能量管理系统的优化模型,在此基础上,采用改进的自适应量子行为粒子群算法,求解能量管理系统多目标函数的优化问题,以提高算法的求解效率和全局搜索的能力。最后,为验证算法的可行性和有效性,选择33节点的算例进行仿真验证。

With the increasing scale of AC/DC hybrid distribution network,its topology and system power flow calculation will become more complex.The coordinated control of each unit has become the key to the economic and stable operation of the distribution network,and appropriate control schemes can help further optimize the coordinated control of each unit.A control strategy for the optimal operation of the energy management system of the AC/DC hybrid distribution network under multiple time scales was proposed.By combining with local control such as distributed generation,storage battery and master slave converter station,the distribution network can minimize the overall power loss and voltage deviation at each node.Firstly,the AC/DC hybrid distribution network system model was established.Then,the optimization model of the energy management system of the AC/DC hybrid distribution network was established.On this basis,the improved adaptive quantum behavior particle swarm optimization(AQPSO)algorithm was used to solve the multi-objective function optimization problem of the energy management system,so as to improve the algorithm’s solving efficiency and global search ability.Finally,the feasibility and effectiveness of the algorithm was verified by an example analysis of a 33-node.