Agent-Based Decentralized Optimal Charging Strategy for Plug-in Electric Vehicles

This paper presents a game-theoretic decentralized electric vehicle charging schedule for minimizing the customers' payments, maximizing the grid efficiency, and providing the maximum potential capacity for ancillary services. Most of the available methods for electric vehicle charging assume that the customers are rational, there is a low-latency perfect two-way communication infrastructure without communication/computation limitation between the distribution company and all the customers, and they have perfect knowledge about the system parameters. To avoid these strong assumptions and preserve the customers' privacy, we take advantage of the regret matching and the Nash Folk theorems. In the considered game, the players (customers) interact and communicate locally with only their neighbors. We propose a mechanism for this game, which results in a full Nash Folk theorem. We demonstrate and prove that the on-off charging strategy provides the maximum regulation capacity. However, our mechanism is quite general, takes into account the battery characteristics and degradation costs of the vehicles, provides a real-time dynamic pricing model, and supports the vehicle-to-grid and modulated charging protocols. Moreover, the developed mechanism is robust to the data disruptions and takes into account the long/short-term uncertainties.