基于生物地理优化算法的BIPV多目标规划

Multi-objective BIPV Planning Based on Biogeography-based Optimization Algorithm

本文针对城市光伏建筑一体化(BIPV)接入城市配电网的优化规划问题,建立了以光伏发电(PV)投资的动态回收年限最小和光伏发电接入后配电系统的静态电压稳定性最好为目标的多目标优化规划模型。将NSGA-Ⅱ中的快速非支配排序策略与精英保留策略引入生物地理算法,形成多目标生物地理算法(MOBBO),并用此算法求解PV接入城市配电网的位置及容量的Pareto最优解集。最后以IEEE33节点配电系统为例进行PV的多目标优化规划,并将优化结果与NSGA-Ⅱ算法进行比较,结果表明多目标生物地理算法具有更好的收敛性能和寻优能力,最后的优化结果大大增加了PV优化配置的灵活性和科学性。

In allusion to the optimal planning problem of building integrated photovoltaic(BIPV)in the distribution network,a multi-objective,in which the minimization of dynamic payback period as well as optimal stability of steady state voltage are token as objectives,is built.The optimal Pareto solution set of network-connecting positions and configured capacity of PV are solved by multiobjective biogeography-based optimization algorithm(MOBBO),which is formed by putting rapid non-dominated sorting strategy and elitism strategy of NSGA-Ⅱalgorithm into biogeography-based optimization algorithm.Finally,taking testing system of IEEE33 node distribution network as an example to proceed multi-objective optimal planning of PV.The proposed algorithm has better global convergence and searching capability compared to the results obtained with the NSGA-Ⅱalgorithm.The final optimal results increased the flexibility and scientificity of the optimized configuration of PV.