iEMS for large scale charging of electric vehicles: Architecture and optimal online scheduling

The problem of large scale charging of electric vehicles (EVs) is considered. An architecture for the energy management system (EMS) is proposed based on the concept of network switched charging where chargers are controlled by a scheduler that optimizes the overall operating profit of the service provider. It is assumed that the EMS has access to collocated renewable sources (e.g. solar power) and can supplement the renewable with purchased electricity from the grid. The renewable source may vary arbitrarily, and requests of all EVs accepted for service must be completed by their respective deadlines. Under a deterministic model for arbitrary arrivals, charging requests, and service deadlines, online scheduling of EV charging is formulated as a multi-processor deadline scheduling problem for which the optimal scheduler maximizes the competitive ratio against the best offline scheduler. An online scheduling algorithm, referred to as TAGS, is proposed based on the principle of threshold admission and greedy scheduling. TAGS has the complexity of O(n log n) where n is the number of EVs in the facility. It is shown that, when the price offered to the EV customers is higher than the purchasing price of electricity from the grid, TAGS achieves the competitive ratio of 1. Otherwise, TAGS achieves the maximum competitive ratio given by the inverse of a real root of a certain polynomial. Simulations are used to evaluate the performance of TAGS against standard benchmarks and for the setting of optimal charging price.