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基于深度强化学习的电动汽车实时调度策略
作者:
作者单位:

电力传输与功率变换控制教育部重点实验室(上海交通大学),上海市 200240

摘要:

电动汽车(EV)作为一种分布式储能装置,对抑制功率波动有着巨大的潜力。考虑EV接入的随机性及可再生能源出力和负荷的不确定性,利用不基于模型的深度强化学习方法,建立了以最小功率波动及最小充放电费用为目标的实时调度模型。为满足用户的用电需求,采用充放电能量边界模型表征电动汽车的充放电行为。在对所提模型进行日前训练及参数保存后,针对日内每一时刻系统运行的实时状态量,生成该时刻充放电调度策略。最后以某微电网为例,验证了所提基于深度强化学习的调度方法在满足用户充电需求的前提下,可以有效减小微电网内的功率波动,降低EV充放电费用;日内不需要迭代计算,可以满足实时调度的要求。

关键词:

基金项目:

国家自然科学基金资助项目(51877133)。

通信作者:

作者简介:

李航(1995—),男,博士研究生,主要研究方向:人工智能在电力系统中的应用。E-mail:lihang9596@sjtu.edu.cn
李国杰(1965—),男,通信作者,教授,博士生导师,主要研究方向:新能源控制与接入、微电网分析与控制。E-mail:liguojie@sjtu.edu.cn
汪可友(1979—),男,教授,博士生导师,主要研究方向:电力电子化电力系统稳定与控制等。E-mail:wangkeyou@sjtu.edu.cn


Real-time Dispatch Strategy for Electric Vehicles Based on Deep Reinforcement Learning
Author:
Affiliation:

Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education (Shanghai Jiao Tong University), Shanghai 200240, China

Abstract:

As distributed energy storage devices, electric vehicles (EVs) have huge potential to curb power fluctuations. Considering the randomness of EV integration, the uncertainty of renewable energy generation and load, a real-time dispatch model with objectives of the minimum power fluctuations and minimum charging and discharging cost is established by using a model-free deep reinforcement learning method. In order to satisfy the user's demand, a charging and discharging energy boundary model is used to characterize the charging and discharging behaviors of EVs. After day-ahead training and parameter preservation of the proposed model, the corresponding charging and discharging dispatch strategy is generated in real time for the intraday real-time states of the system operation at each moment. Finally, taking a microgrid as an example, it is verified that the proposed deep reinforcement learning based dispatch method can effectively reduce power fluctuations in the microgrid and reduce EV charging and discharging cost on the premise of satisfying users' charging demands, while satisfying the demands of real-time dispatch without the need of iterative calculation for intraday dispatch.

Keywords:

Foundation:
This work is supported by National Natural Science Foundation of China (No. 51877133).
引用本文
[1]李航,李国杰,汪可友.基于深度强化学习的电动汽车实时调度策略[J].电力系统自动化,2020,44(22):161-167. DOI:10.7500/AEPS20200331009.
LI Hang, LI Guojie, WANG Keyou. Real-time Dispatch Strategy for Electric Vehicles Based on Deep Reinforcement Learning[J]. Automation of Electric Power Systems, 2020, 44(22):161-167. DOI:10.7500/AEPS20200331009.
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  • 收稿日期:2020-03-31
  • 最后修改日期:2020-06-28
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  • 在线发布日期: 2020-11-18
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