光伏并网优化模型的启发式退火进化求解算法

Heuristic Annealing Evolutionary Algorithm for Optimization Model of Grid-connected Photovoltaic

为提升分布式光伏并网优化模型的求解效率和精度,提出一种用于求解分布式光伏并网优化模型的启发式算法。将启发式算子引入退火进化算法之中,构建启发式退火进化算法HAEA(heuristic annealing evolutionary algorithm),采用熵权法EWM(entropy weighting method)对多个优化目标进行权重分配。结合某地区48节点网架对所构建的多目标优化模型求解算法进行验证,与传统优化算法相比,HAEA算法解决了传统算法的早熟问题,优化效率和精度更高,算法给出的规划方案在电压质量和网损等方面表现更优。此外,重点分析了阶段性接入策略与传统接入策略的差异。

To improve the solving efficiency and precision for an optimization model of grid-connected distributed photo⁃voltaic,a heuristic algorithm is proposed in this paper.A heuristic annealing evolutionary algorithm(HAEA)is con⁃structed by introducing a heuristic operator to the annealing evolutionary algorithm,and the entropy weighting method(EWM)is used to obtain the weights of different optimization objectives.The proposed solving algorithm for multi-objec⁃tive optimization model is verified by combining a 48-node network topology of one region.Compared with those of the traditional algorithms,the optimization efficiency and accuracy of HAEA are better and the premature problem is solved.The scheduled scheme given by the proposed algorithm is improved in terms of voltage quality and network loss.In addition,the differences between the staged and traditional access strategies are analyzed in detail.