Optimal Decentralized Protocols for Electric Vehicle Charging

We propose decentralized algorithms for optimally scheduling electric vehicle (EV) charging. The algorithms exploit the elasticity and controllability of electric vehicle loads in order to fill the valleys in electric demand profiles. We first formulate a global optimization problem, whose objective is to impose a generalized notion of valley-filling, and study the properties of optimal charging profiles. We then give two decentralized algorithms, one synchronous (i.e., information update takes place in each iteration) and one asynchronous (i.e., EVs may use outdated information with bounded delay in some of the iterations) to solve the problem. In each iteration of the proposed algorithms, EVs choose their own charging profiles according to the price profile broadcast by the utility, and the utility updates the price profile to guide their behavior. Both algorithms are guaranteed to converge to optimal charging profiles (that are as “flat” as they can possibly be) irrespective of the specifications (e.g., maximum charging rate and deadline) of electric vehicles. Furthermore, both algorithms only require each EV solving its local problem, hence their implementation requires low computation capability. We also extend the algorithms to accommodate the objective of tracking a given demand profile and to real-time implementations.