实时电价下电动汽车充电调度优化方法研究  

Study on Electric Vehicle Charging Dispatch Optimization Method Based on Real-Time Electricity Price

作　　者：吴铁洲[1] 蒋小维[1] 张琪[1] 罗蒙[1] 尹其林[1] 王平[2] 李倩颖[3] 

机构地区：[1]湖北工业大学太阳能高效利用湖北省协同创新中心,武汉430068 [2]上海交通大学电子信息与电气工程学院,上海200030 [3]湖北工业大学电气与电子工程学院,武汉430068

出　　处：《武汉理工大学学报》2016年第4期81-85,共5页Journal of Wuhan University of Technology

基　　金：国家自然科学基金(51247004)

摘　　要：探讨了在实时电价的环境下,以用户充电费用最小为目的,利用电价调节充电负荷分配,实现有序充电调度的方法。在保证用户充电需求的同时,满足在充电费用最优以及日负荷波动和电网负荷峰谷差尽可能小的前提下,建立了新型的电动汽车充电模型。采用遗传算法求解此充电模型,从而得到一个最优方案,根据此方案合理安排各个时段充电内的车辆数。根据蒙特卡洛模拟的电车运行的相关信息数值,运用得到的最优方法以此来对电车充电模拟仿真。仿真结果表明:考虑了峰谷差和日负荷波动约束的有序充电方案可明显降低电动汽车充电费用,同时也能保证电力系统安全稳定运行。In the environment of the real-time electricity price, for the purpose of minimizing the user charge cost, we used electricity price to adjust EV charging load distribmion, and achieved orderly charging scheduling. Under the premise of ensuring users charging requirements, in order to meet the optimal charging cost while the daily load fluctuations and the power grid load peak-valley difference as small as possible, we established new-model EV charging model. GA was used to solve the EV charging model, then the minimum cost charging scheme was gotten, so that the price interval number of EV charging was rationalized. With the EV charging data simulated by Monte Carlo stochastic analysis, the best scheme was used to simulate. The simulation results show that： the coordinated charging consider the peak-valley difference and the daily load fluctuations can significantly reduce the cost of electric charging, but also to ensure the safe and stable operation of power systems.

关 键 词：实时电价 遗传算法 电动汽车充电模型 有序充电 

分 类 号：TM73[电气工程—电力系统及自动化]