随着私家电动汽车(private electric vehicles,PREV)的普及,大规模PREV的无序充电将引起用电负荷高峰,影响配电网安全。针对商业停车场环境下的PREV充电问题,首先提出一种车辆准入机制,尽可能提高车辆准入数量,并确保准入车辆能够在预...随着私家电动汽车(private electric vehicles,PREV)的普及,大规模PREV的无序充电将引起用电负荷高峰,影响配电网安全。针对商业停车场环境下的PREV充电问题,首先提出一种车辆准入机制,尽可能提高车辆准入数量,并确保准入车辆能够在预定时间内完成充电需求;其次,采用基于熵权法确定适应度函数权重的遗传模拟退火算法(GASA),提出一种面向多目标优化的PREV充电调度策略,综合优化停车场运营商利润和车主充电满意度。实验结果表明,基于GASA的PREV充电调度策略性能良好,与极端情况(车辆数为600的无序充电)相比,该策略的运营商利润和车主充电满意度分别提高了12.3%和109.7%,综合适应度函数值增加了35.2%;另外,其能够有效平缓配电网负荷分布,在保障配电网安全前提下实现停车场运营商和PREV车主的双赢。展开更多
EVs （electric vehicles） have been widely accepted as a promising solution for reducing oil consumption, air pollution and greenhouse gas emission. The number of EVs is growing very fast over the years. However, the ...EVs （electric vehicles） have been widely accepted as a promising solution for reducing oil consumption, air pollution and greenhouse gas emission. The number of EVs is growing very fast over the years. However, the high adoption of EVs will impose a burden on the power system, especially for neighborhood level network. In this paper, we propose a mixed control framework for EV charging scheduling to mitigate its impact on the power network. A metric for modeling customer＇s satisfaction is also proposed to compare the user satisfaction for different algorithms. The impacts of the proposed algorithms on EV charging cost, EV penetration and peak power reduction are evaluated with real data for a neighborhood level network. The simulation results demonstrate the effectiveness of the proposed algorithms.展开更多
文摘随着私家电动汽车(private electric vehicles,PREV)的普及,大规模PREV的无序充电将引起用电负荷高峰,影响配电网安全。针对商业停车场环境下的PREV充电问题,首先提出一种车辆准入机制,尽可能提高车辆准入数量,并确保准入车辆能够在预定时间内完成充电需求;其次,采用基于熵权法确定适应度函数权重的遗传模拟退火算法(GASA),提出一种面向多目标优化的PREV充电调度策略,综合优化停车场运营商利润和车主充电满意度。实验结果表明,基于GASA的PREV充电调度策略性能良好,与极端情况(车辆数为600的无序充电)相比,该策略的运营商利润和车主充电满意度分别提高了12.3%和109.7%,综合适应度函数值增加了35.2%;另外,其能够有效平缓配电网负荷分布,在保障配电网安全前提下实现停车场运营商和PREV车主的双赢。
文摘EVs （electric vehicles） have been widely accepted as a promising solution for reducing oil consumption, air pollution and greenhouse gas emission. The number of EVs is growing very fast over the years. However, the high adoption of EVs will impose a burden on the power system, especially for neighborhood level network. In this paper, we propose a mixed control framework for EV charging scheduling to mitigate its impact on the power network. A metric for modeling customer＇s satisfaction is also proposed to compare the user satisfaction for different algorithms. The impacts of the proposed algorithms on EV charging cost, EV penetration and peak power reduction are evaluated with real data for a neighborhood level network. The simulation results demonstrate the effectiveness of the proposed algorithms.
