The mobile energy storage system (MESS) plays an increasingly important role in energy systems because of its spatial and temporal flexibilities, while the high upfront investment cost requires developing corresponding operation and arbitrage strategies. In the existing literature, the MESS arbitrage problems are usually cast as mixed-integer programming models. However, the performance of this model-based method is deteriorated by the uncertainties of power and transportation networks and the complicated operational characteristics of batteries. To overcome the deficiencies of existing methods, this article proposes a data-driven uncertainty-adaptive MESS arbitrage method considering MESS mobility rules, battery degradation, and operational efficiencies. A two-layer deep reinforcement learning (DRL) method is developed to obtain the discrete mobility and continuous charging or discharging power, and a sequential training strategy is designed to accelerate the convergence of model training. The proposed method is tested using the real-world electricity prices and traffic information of charging stations. Compared with traditional model-based methods that rely on entire and accurate future information, the proposed DRL method obtains high arbitrage profits by learning arbitrage strategies from historical data and making effective decisions with limited real-time information.

中文翻译：

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基于两层深度强化学习的电力套利移动储能系统路由与调度


移动储能系统（MESS）因其空间和时间的灵活性而在能源系统中发挥着越来越重要的作用，而前期投资成本较高，需要制定相应的运营和套利策略。在现有文献中，MESS 套利问题通常被描述为混合整数规划模型。然而，这种基于模型的方法的性能因电力和交通网络的不确定性以及电池的复杂运行特性而恶化。为了克服现有方法的缺陷，本文提出了一种考虑 MESS 移动性规则、电池退化和运行效率的数据驱动的不确定性自适应 MESS 套利方法。开发了两层深度强化学习（DRL）方法来获取离散移动性和连续充电或放电功率，并设计了顺序训练策略以加速模型训练的收敛。使用现实世界的电价和充电站的交通信息对所提出的方法进行了测试。与依赖完整且准确的未来信息的传统基于模型的方法相比，所提出的DRL方法通过从历史数据中学习套利策略并利用有限的实时信息做出有效决策来获得高套利利润。