Stochastic Energy Management of Electric Bus Charging Stations With Renewable Energy Integration and B2G Capabilities

Abstract

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.