基于免疫遗传算法的智能配电网分布式电源选址定容

合理地对分布式电源进行选址和定容对于智能配电网规划非常重要。文章在研究分布式电源规划的基础上，建立了以有功网损最小为目标函数的优化模型，用罚函数法将分布式电源规划问题转化为无约束问题，针对该模型特点，首次将免疫遗传算法应用到分布式电源选址和定容问题的求解中。该算法综合了免疫系统和遗传算法的优点，具有良好的全局收敛能力。对33节点配电测试系统进行仿真计算，将仿真结果与标准遗传算法进行比较。验证了免疫遗传算法具有一定的收敛性和适应性。

Setting and Sizing of Distributed Generation Based on Immune Genetic Algorithm in Smart Grid

Suitable location and optimal sizing is very important subjects in power system, especially for smart distribution grids. An optimal model based on distributed generation planning is proposed to minimize the power loss combined with penalty function which is used to transfer the distributed generation（DG） planning problem into a problem without constraints. Immune genetic algorithm（IGA）, which integrates advantages of the immune system ＆ genetic algorithm（GA） and has better global convergence, is firstly utilized to solve the setting and sizing of the distributed generation. The case studies have been carried out on 33-bus distribution test system, and simulation results show that the IGA is of high convergence rate and favorable self-adaptive characteristics comparing with GA.