基于改进量子粒子群算法电动汽车充放电优化调度

Optimal Scheduling of Electric Vehicle Charging and Discharging Based on Improved Quantum Particle Swarm Algorithm

随着电动汽车的保有量的逐年上升以及在“双碳”战略的目标引领下,无规划的大批量电动汽车充电行为易导致电网波动过大,降低电网运行可靠性。根据现行分时电价和电动汽车进网情况,通过改进量子粒子群算法求解考虑电网负荷波动和电动汽车用户成本的调度优化模型。分析分时电价与固定电价仿真结果,结果表明:改进量子粒子群算法在优化电动汽车充放电计划上运用的有效性,能够有效达到对电网负荷的“削峰填谷”作用。

As the number of electric vehicles is increasing year by year and under the goal of"double carbon"strategy,the unplanned charging behavior of large quantities of electric vehicles may lead to excessive fluctuations in the power grid and reduce the reliability of power grid operationTherefore,it is significant to set the charging be havior of electric vehicles reasonably according to the load situationBased on the current timesharing tariff and EVs entering the grid,a scheduling optimization model considering grid load fluctuation and EV user cost is solved by improved quantum particle swarm algorithmThe simulation results of timesharing tariff and fixed tariff are ana lyzed,and the results show that the improved quantum particle swarm algorithm is effective in optimizing the char ging and discharging schedule of EVs,and can effectively achieve the effect of"peak shaving and valley filling"on the grid load.