考虑集中充电站的电池换电站选址定容规划

电动汽车电池换电站的选址定容是具有多变量和多约束的复杂非线性问题, 难以用一般的数学方法进行求解, 本文对此提出一种Voronoi图与改进蝙蝠算法(Improved Bats Algorithm, IBA)联合求解的选址定容方法。考虑集中充电站的影响, 建立以年建设投资成本、选址满意度和年电池换电成本为目标的多目标决策模型。引入模糊熵权法避免主观赋权与客观赋权的不足, 将多目标决策模型转化成单目标权重模型。通过引入惯性权重并考虑个体极值引导速度更新的IBA, 以多目标决策的加权总值最小为条件, 结合Voronoi图划分各电池换电站的服务区域, 得出电池换电站的选址定容规划方案的最优解。算例仿真结果验证了所提模型和算法的合理性及可行性。

Locating and Sizing Planning of Battery Swapping Stations Considering Centralized Charging Station

The locating and sizing planning of battery swapping stations for electric vehicles is a complex nonlinear problem with multiple variables and constraints, which is hard to be solved by general mathematical methods. A joint method of Voronoi diagram and improved bats algorithm (IBA) is proposed to solve the problem. Considering the influence of centralized charging station, a multi-objective decision model considering annual infrastructure investment costs, location satisfaction and annual battery swapping costs is established. In order to transform the multi-objective decision model into single-objective weight model, the method of fuzzy entropy weight is introduced to avoid the shortcomings of both subjective weighting and objective weighting. An improved bats algorithm (IBA) is proposed, which introduces inertia weight and considers the speed update of individual extremum, taking the minimum weighted gross of the multi-objective decision as the condition, combining with the Voronoi diagram which divides the service area of each battery swapping station, and the optimal solution of locating and sizing planning of battery swapping stations is obtained. The simulation results prove the rationality and feasibility of the model and improved algorithm.