基于粒子群算法的主动配电网多目标优化调度

Multi-Objective Optimization of Active Distribution Network Based on Particle Swarm Optimization

随着分布式可再生能源的大规模并网,传统配电网也由单一潮流逐渐发展为复杂的双向潮流。针对主动配电网技术中传统调度方式无法直接应用的问题,文中从智能算法和优化模型两个方面探索改进措施。在考虑“源网荷储”关联性的基础上,以提升削峰填谷效果、提高配电网经济性以及减少配电网网损为目标,对风光出力情况进行预测,提升了数据有效性,建立了两阶段双层联合优化调度模型。文中分析了传统粒子群算法的优劣势,提出采用改进的HE-MOPSO算法对模型进行求解。求解ZDT1~4测试函数并采用扩展的IEEE33节点进行仿真验算,结果分析证明了改进算法及模型的优越性。

With the large-scale grid integration of distributed renewable energy,the traditional distribution network has gradually developed from a single flow to a complex two-way flow.In view of the problem that the traditional dispatching method in active distribution network technology cannot be directly applied,this study explores improvement measures from two aspects of intelligent algorithm and optimization model.On the basis of considering the relevance of“source network load and storage”,aiming at improving the effect of peak shaving and valley filling,improving the economy of distribution network,and reducing the loss of distribution network,the forecast of wind and solar output has been carried out to improve the validity of the data,and a two-stage two-layer joint optimal dispatch model has been established.The study analyzes the advantages and disadvantages of the traditional particle swarm algorithm,and proposes to use the improved HE-MOPSO algorithm to solve the model.By solving the ZDT1~4 test function and using the extended IEEE33 node to perform the simulation calculation,the experimental results proved the superiority of the improved algorithm and model.