Online learning of load elasticity for electric vehicle charging

While electric vehicles (EVs) are expected to provide environmental and economical benefits, judicious coordination of EV charging may be necessary to prevent overloading of the distribution grid. Leveraging the smart grid infrastructure, the utility company can adjust the electricity price intelligently for individual customers to elicit desirable load curves. In this context, the present paper addresses the problem of predicting the EV charging behavior of the consumers at different prices, which is a prerequisite for the price adjustment. The dependencies on price responsiveness among neighbouring consumers are captured by adopting a conditional random field (CRF) model. To account for temporal dynamics even in an adversarial setting, the framework of online convex optimization is adopted to develop an efficient online algorithm for estimating the CRF parameters. Numerical tests verify the proposed approach.