电动汽车充电负荷空间分配优化算法

Electric Vehicle Charging Load Spatial Allocation Optimization Algorithm

大量需要充电的电动汽车作为可以移动的电力负荷,在空间上的无序充电行为可能会导致电网出现局部过负荷、线路拥塞等问题。为降低无序充电行为对电网的影响,完成电动汽车充电负荷在空间上的分配引导,研究了以充电负荷均匀分配和充电时间、路程最少为目标的多目标优化。分别采用粒子群算法和遗传算法求解,粒子群算法通过解空间的转换处理约束条件,遗传算法通过编码处理约束条件,后者有效地降低了问题的维数,提高了计算速度。在仿真算例中对比了两种算法的求解性能,算例结果表明,两种算法均能够解决区域内的电动汽车充电负荷空间分配优化问题,验证了算法的有效性和可行性,但遗传算法在车辆较多时的性能明显优于粒子群算法,具有较高的实用性。

As the movable power load large number of electric vehicles needing to be charged maybe lead to partial overload, line congestion and other issues by their disordered charging on the space. In order to reduce the effects of disordered charging behavior on the grid and complete electric vehicle charging load spatial allocation ,the multi-objective optimization of minimizing system charging time, distance and dispatching charging load evenly is researched. The optimization is solved by PSO（particle swarm optimization） and GA（genetic algorithm） respectively. Constraints are handled by solution space conversion in PSO and by code in GA. The latter can effectively reduce the dimension of the problems and improve the calculation speed. The performances of both algorithms are compared in the same simulation example.The simulation results show that both algorithms can solve the electric vehicle charging load spatial allocation optimization in certain area. The effectiveness and feasibility of algorithm are shown by the results. When the vehicles are more the genetic algorithm＇s performance is much better than the PSO algorithm＇s and GA has higher practicality.