Exploring planning and operations design space for EV charging stations

EVs suffer from long charging times and short-drive ranges, limiting EV usage to daily short-range commuting rather than general purpose use. Among the candidates for EV charging infrastructures, the public EV charging station architecture has benefits in that it allows an efficient investment of costly equipments, and a long-range travel with multiple charging cycles. This paper focuses on an EC charging station architecture comprising PV panels, an energy storage system (ESS) and multiple fast-DC charging posts. Systematically deriving the optimal planning, i.e., determining the optimal sizes of these components, is a complicated problem as the EV charging station operations and planning are intertwined. In this paper, we derive EV charging station operation policies by formulating an average reward Markov decision process (MDP) maximization problem to synthesize controllers that maximize the operating income. Then, these controllers are used to evaluate the operating income, for the purpose of EV charging station planning. For efficient exploration of the design space, we perform a mixed search-based technique combining sequential quadratic programming (SQP) with a greedy algorithm. There will be significant gain in terms of long-term operating cost when the costs of ESS and PV panels continue to reduce in the future. Our solution framework is a helpful tool for such reasoning, and for identifying optimal planning and operation policies for public EV charging stations.