农网电动汽车充电站及分布式光伏协同规划研究

Research on Collaborative Planning of Electric Vehicle Charging Stations and Distributed Photovoltaics in Rural Power Grid

为推动电动汽车下乡,亟需对县域农网内电动汽车充电设施进行合理规划,并对农网内电动汽车和分布式光伏引起的稳定性问题进行无功优化。首先,结合出行链理论和乡村用户出行习惯,运用蒙特卡洛模拟对需求点电动汽车充电负荷进行预测,并进行光伏不确定性建模。其次,建立了多目标双层规划模型,上层模型考虑经济性,以全社会年总成本最低为目标函数;下层模型考虑安全性,以农网有功网损最低和电压偏移量最小为目标函数。然后,采用了Voronoi图和改进粒子群算法联合求解,充分利用粒子群算法的全局随机寻优能力和Voronoi图的分区域局部寻优特点,达到对模型的精准求解。最后,以某县域规划区进行仿真,仿真结果表明,所提模型能够有效指导农网电动汽车充电站的规划,并且通过无功优化能够显著降低网络损耗、提高电压质量,达到经济性和安全性的双重需求。

In order to promote the development of electric vehicles in rural areas,it is urgent to plan the charging facilities of electric vehicles in the county level rural power grid reasonably,and optimize the reactive power of stability issues caused by electric vehicles and distributed photovoltaics in the rural power grid.Firstly,combined with travel chain theory and rural user travel habits,it uses Monte Carlo simulation to predict the charging load of electric vehicles at demand points,and conducts photovoltaic uncertainty modeling.Then,a multi-objective bilevel programming model is established,with the upper level model considering economy and the objective function being the lowest annual total cost of the entire society.The lower level model considers safety and the objective functions is the lowest active power loss and voltage offset of the rural power grid.Secondly,the Voronoi diagram and improved particle swarm optimization algorithm are used to jointly solve the problem,fully utilizing the global random optimization ability of particle swarm algorithm and the local optimization characteristics of Voronoi diagram,thereby achieving accurate solution of the model.Finally,a simulation is conducted in a certain county planning area,and the simulation results show that the proposed model can guide the planning of electric vehicle charging stations in rural power grids.Through reactive power optimization,it can reduce the power grid losses,improve the voltage quality,and achieve the dual requirements of economy and safety.