计及引导型充电控制策略的电动汽车充电设施与配电系统协同规划

A joint planning model for the charging facilities of electric vehicles and the distribution network concerned with a guided interactive charging strategy

对电动汽车(electricvehicle,EV)充电设施与配电网络进行协同规划可减少EV充电负荷对配电系统的负面影响,并降低各参与方的总社会成本。在开展二者的协同规划时,需要适当考虑引导型充电控制策略对充电负荷时空分布的影响。在此背景下,研究考虑引导型充电控制策略的EV充电设施与含有分布式光伏、储能的配电系统的协同规划问题,建立双层优化模型。上层优化模型以充电设施与配电网络协同规划的总投资成本最小为优化目标,考虑电力系统运行安全约束与EV充电站建设和运行约束,系混合整数非线性规划问题;下层优化模型则以配电系统运行成本与EV充电设施周围交通道路阻塞的时间等效成本之和最小为优化目标,并考虑配电网络运行约束与EV用户充电需求约束,也是混合整数非线性规划问题。求解上层优化模型得到充电设施与配电网络协同规划结果并作为下层优化模型的输入,而由下层优化模型求得的充电需求时空分布结果作为上层优化模型的输入。采用商业求解器CPLEX确定下层引导型充电控制策略,利用遗传算法对所构建的双层优化模型进行求解。最后,以宁波市海曙区东部区域的EV充电设施与配电网络协同规划为例,对所提出的方法进行说明。

The negative impacts of the charging loads of electric vehicles (EVs) on the distribution system concerned can be mitigated by joint planning of the EV charging facilities and the distribution network. In this way, the social cost of all participants is reduced. To carry out the joint planning, it is necessary to properly consider impacts of the guided EV charging control strategy on the spatial-temporal distributions of EV charging loads. Under this background, a joint planning model that considers a guided EV charging strategy for the EV charging facilities and the distribution network concerned with distributed photovoltaics and energy storage devices is proposed. Firstly, a bi-level optimization model is presented to address the joint planning problem. The objective of the upper-level optimization model is to minimize the total investment cost associated with the joint planning, taking the secure operation constraints of the power system as well the construction and operation constraints of the EV charging facilities all into consideration. A mixed integer nonlinear programming model is then attained. The objective of the lower-level one is to minimize the sum of the operating costs of the distribution system and the equivalent costs of the traffic jam around the EV charging station under the operation constraints of the distribution network and the charging demands of EV users. Then the other mixed integer nonlinear programming problem is attained. The joint planning scheme attained in the upper-level optimization model is used as the input to the lower-level optimization model, while the spatial-temporal distributions of EV charging demands obtained in the lower-level optimization model are used as the inputs to the upper-level optimization model. The commercial solver CPELX is applied to attain a guided interactive charging strategy in the lower-level optimization model, while the well-established genetic algorithm employed to solve the developed bi-level optimization model. Finally, the proposed joint planning method is demonstrated through the applications in the eastern part of Haishu District in Ningbo City.