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According to some forecast models, electric vehicles (EVs) are expected to have a high level of penetration in the coming decades. To support the daily operation of EVs, charging is necessary. Generally, there are three main charging scenes in the future, i.e., charging at workplace, charging at home, and charging at commercial station. For a data center operator, the synchronous EV charging of the employees at the workplace during the working hours would incur an additional large demand charge. To avoid/reduce such demand charge, we investigate a joint energy management problem for geo-distributed data centers and EVs of the employees in this paper. Specifically, we intend to minimize the cost of a data center operator by jointly scheduling data center workload and EV charging under the given power limits, where the cost consists of electricity bill, revenue loss associated with workloads, and battery depreciation cost. We first formulate a total cost minimization problem with the consideration of heterogeneous demands of EVs and the given power limits. Since the formulated problem is a large-scale convex optimization problem with temporally-coupled and spatially-coupled constraints, we propose a distributed algorithm to solve it. Based on the proposed algorithm, we design a joint energy management strategy for geo-distributed data centers and EVs. Simulation results based on real-world traces show that the proposed strategy could reduce the cost for the data center operator by up to 5.324%.
Yu et al. (Tue,) studied this question.