含分布式电源的配电站选址机会约束规划

Chance Constrained Planning for Location of Substations Including Distributed Generations

越来越多的分布式电源DG(distributed generation)自发接入配电网,因此研究考虑含DG波动性的配电站选址规划具有很强的实际意义。建立了含DG的配电站机会约束规划模型,并采用蒙特卡洛仿真的方法来进行区间估计。面对负荷点分配问题,对基于沃罗诺伊图(Voronoi diagram)的贪心算法与基于蚁群的启发式算法两种算法的优劣进行了比较。规划主流程采用遗传算法,并将配电站的选择进行编码。根据上海某地区实例进行了计算,计算结果证明了算法的有效性,并讨论了配电站选择与置信度对目标函数的影响、蚁群算法优化结果等问题。

With more and more distributed generations （DGs） being connected to distribution network spontaneously, it is of practical significance to study the location planning of substations with the consideration of the volatility of DGs. In this paper, a chance constrained planning model is established for the substations including DGs, and Monte Carlo simulation is used in interval estimation. For the distribution problem of load points, the greedy algorithm based on Voronoi diagram and the heuristic algorithm based on ant colony algorithm （ACA） are compared in terms of advantages and disadvantages. Genetic algorithm （GA） is adopted in the main process of planning, and the substation selection is coded. Based on the actual data from a certain area in Shanghai, a numerical example is calculated, which verifies the proposed algorithm. Moreover, the impacts of substation selection and confidence level on the object function and the optimization output of ACA are also discussed.