In this paper, the stochastic energy management of electric bus charging stations (EBCSs) is investigated, where the photovoltaic (PV) with integrated battery energy storage systems (BESS) and bus-to-grid (B2G) capabilities of electric buses (EBs) are included for cost-effective charging of EBs. Also, the day-ahead dynamic prices are derived to mitigate charging impacts on power distribution systems. This problem is formulated as a distributionally robust Markov decision process (DRMDP) with uncertain transition probabilities and costs to address the impacts of random bus loads with inaccurate probability density function estimation. An event-based ambiguity set with combined statistical distance and moment information is developed to achieve minimax-regret criteria for less-conservative and robust solutions. To facilitate practical applications with reduced computational complexity, a heuristic regret function is proposed, based on which the dynamic prices are derived. Case studies based on EB data from St. Albert Transit and IEEE test feeders indicate that the proposed method can minimize EB charging cost with mitigated impacts on power distribution systems.

中文翻译：

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具有可再生能源整合和B2G功能的电动公交充电站的随机能源管理

本文研究了电动公交充电站（EBCS）的随机能量管理，其中光伏（PV）具有集成的电池储能系统（BESS）和电动公交（EBs）的公交至电网（B2G）功能包括用于经济高效地收取EB费用。此外，还可以导出日前动态价格，以减轻充电对配电系统的影响。该问题被公式化为具有不确定过渡概率和成本的分布鲁棒马尔可夫决策过程（DRMDP），以不正确的概率密度函数估计来解决随机总线负载的影响。开发了基于事件的歧义集，结合了统计距离和力矩信息，以实现极小后悔保守程度较低且健壮的解决方案的标准。为了简化计算复杂性的实际应用，提出了一种启发式后悔函数，并以此为基础推导出了动态价格。基于来自圣艾伯特公交公司和IEEE测试馈线的EB数据的案例研究表明，所提出的方法可以最大程度地降低EB充电成本，同时减轻对配电系统的影响。