考虑DG时序特性及EV时空特性的配电网规划

分布式电源(DG)出力以及电动汽车(EV)充电的不确定性给配电网规划带来巨大挑战。首先，利用季节场景与时段划分法构建DG和常规负荷时序特性模型；其次，利用蒙特卡洛模拟法和交通起讫点分析法构建EV充电负荷时空分布模型；最后，基于2个模型得到的配电网各节点各时刻的DG出力、不同类型常规负荷及EV充电负荷，以配电网运营商年收益最大为目标函数，充分考虑网架新建、网架替换和DG选址定容等因素，构建考虑时序特性含DG和EV的配电网机会约束规划模型，并采用蒙特卡洛模拟嵌入双种群协同进化遗传算法的方法对模型进行求解。以某配电网为算例，验证了所提模型和算法的合理性和有效性。

Distribution network planning considering DG timing characteristics and EV spatiotemporal characteristics

The uncertainty of DG(Distributed Generator) output and EV(Electric Vehicle) charging brings huge challenges to distribution network planning. Firstly, the season-scene and time-division methods are used to build timing characteristic model of DG and conventional load. Secondly, the Monte Carlo method and traffic origin-destination analysis method are adopted to build a spatial-temporal distribution model of EV charging load. Finally, based on the DG output, different types of the conventional load and EV charging load of distribution network at each point and each time obtained by the two models, a chance-constrained planning model of distribution network with DG and EV is built considering timing characteristics, which takes the maximum annual profit of distribution network operator as its objective function, and fully considers the factors of network construction and replacement and locating and sizing of DG and so on. The DCGA-MCS(Monte Carlo Simulation-embedded Double-population Co-evolution Genetic Algorithm) is adopted to solve the model. A distribution network is taken as an example, and the rationality and validity of the proposed model and algorithm are verified.