分布式发电(Distributed Generation,DG)的电源位置和注入容量对配电网有重要影响。以配电网网损最小及电压稳定指标为目标,将DG位置和容量优化问题转化为一个多目标的非线性规划问题。构造模糊性的多目标优化算法,将2个优化子目标转化为单一目标,同时将节点电压越限和DG有功出力越限以罚函数的方式进行处理。采用惯性因子自适应的粒子群算法进行求解,为了加快求解速度,先计算节点有功网损微增率并进行排序,选出绝对值较大的节点作为DG安装候选节点。仿真结果表明,此算法收敛效率高,具有较强的搜索能力和自适应能力。

The power location and injected capacity of the Distributed Generation (DG) have a major impact on the distribution network. Based on the smallest power loss and voltage stability index, the optimization problem involving DG location and capacity is turned into a multi-objective nonlinear programming problem. A fuzzy multi-objective optimization algorithm is constructed in order to turn two optimal sub-objectives into a single object. The penalty function is introduced to deal with the beyond-boundaries of the node voltage and DG active power output. Inertia factor adaptive particle swarm optimization algorithm is adopted to solve the problem. In order to speed up the solving speed, firstly the active power loss incremental rates of the nodes are calculated and sequenced; then the nodes with larger absolute values are selected as the candidate nodes for DG installation. The simulation results show that the algorithm proposed has high converged efficiency, strong search ability, and adaptive ability.