基于遗传与免疫算法的含分布式电源的配电网无功优化

Reactive power optimization of distribution network with distributed generation based on genetic and immune algorithm

随着新能源发电技术的快速发展,分布式发电日益引起关注。分布式发电的优势是投资少、清洁环保、灵活可靠,可以持续地输出或吸收无功、参与无功优化、保持系统无功平衡、优化无功分配。文章首先研究了典型配电网的潮流模型,将分布式电源作为可持续调节的无功设备,与传统无功补偿设备结合参与系统无功优化。其次,以系统损失最小为目标函数,使用遗传算法和免疫算法联合求解,提高了算法的求解效率。最后,IEEE33节点系统仿真计算验证了分布式电源能够有效减少系统损失、促进电压稳定,联合使用遗传算法和免疫算法进行求解,能够避免算法陷入局部最优解,且全局优化能力得到了极大提升。

With the rapid development of new energy generation technology,distributed generation has attracted more and more attention.The advantages of distributed generation are small investment,clean and environmental protection,flexibility and reliability.It can continuously output or absorb reactive power,participate in reactive power optimization,maintain reactive power balance of system and optimize distribution of reactive power.Firstly,the power flow model of typical distribution network is studied.The distributed generation is regarded as a reactive power device for sustainable regulation,which is combined with the traditional reactive power compensation equipment to participate in reactive power optimization of the system.Then,taking the minimum system loss as the objective function,genetic algorithm and immune algorithm are used to solve the problem jointly,which improve the efficiency of the algorithm.Finally,IEEE33 node system simulation results show that the distributed generation can effectively reduce the system loss and promote the voltage stability.Meanwhile,the combination of genetic algorithm and immune algorithm can avoid the algorithm falling into the local optimal solution,and the global optimization ability is greatly improved.