Electric vehicle charging and discharging scheduling strategy based on dynamic electricity price
Electric vehicle charging and discharging scheduling strategy based on dynamic electricity price
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DOI：
10.1016/j.engappai.2023.106320
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Lina Ren;Mingming Yuan;X. Jiao
中科院分区：
文献类型：
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作者：
Lina Ren;Mingming Yuan;X. Jiao
电动汽车（EV）数量的快速增长大大增加了居民的电力需求。此外，由于电动汽车充电时间与用户用电高峰期高度重合，电动汽车乱充电会导致电网Transformer过载。传统的控制方法缺乏一定的鲁棒性，没有充分考虑电动汽车的不确定性。因此，电动汽车的V2 G参与率无法确定，控制可靠性低。针对上述问题，本文设计了一种长短期记忆网络强化学习框架和改进线性规划算法（LSTM-ILP）对电动汽车V2 G进行控制，综合考虑电动汽车充电需求、放电潜力、大电网电价、聚合器、用户利益需求等因素。首先，以电动汽车充放电费用和电网负荷峰谷差最小为目标，建立了基于长短期记忆神经网络（LSTM）的动态电价模型。然后，采用改进的线性规划算法（ILP）求解电动汽车充放电优化问题，并将结果反馈给LSTM下一次迭代更新的输入，最终得到最优电价和电动汽车充放电调度。仿真结果表明，LSTM-ILP框架不仅可以降低电动汽车的充电费用，而且可以实现电网负荷的峰谷微调。电动汽车用户的充电成本比无序充电降低了42.1%，比有序充电降低了22%。
The rapid growth in the number of electric vehicles (EVs) has significantly increased the demand for electricity for residents. In addition, because the charging time of EVs highly coincides with the peak period of user electricity consumption, the disorderly charging of EVs will lead to the overload of the power grid transformer. Traditional control methods lack certain robustness and do not fully consider the uncertainty of EVs. As a result, the V2G participation rate of electric vehicles cannot be determined, and the control reliability is low. To solve the above problems, this paper designs a reinforcement learning framework of Long Short-Term Memory network and Improved Linear programming algorithm (LSTM-ILP) to control the V2G of EVs.This paper comprehensively considers the overall electric vehicle charging demand, discharge potential, large grid electricity price, aggregator, and users’ interests demands. Firstly, aiming to minimize the charging and discharging fee of EVs and the load peak-to-valley difference of the power grid, a dynamic electricity price based on Long Short-Term Memory neural network (LSTM) is established. Then, the improved linear programming algorithm (ILP) is used to solve the charging and discharging optimization problem of EV, and the results are fed back to the input of the next iterative update of the LSTM, and finally, the optimal electricity price and EV charging and discharging schedule are achieved. The simulation results show that the LSTM-ILP framework can not only reduce the charging fee of electric vehicles, but also achieve the Peak and valley trimming of the grid load. Charging costs for EV users were reduced by 42.1% compared with unordered charging, and by 22% percent compared with orderly charging.