Optimal Dynamic Pricing of Fast Charging Stations Considering Bounded Rationality of Users and Market Regulation
Abstract
Electric vehicles (EVs), as popular transportation carriers and flexible electric loads, couple both the power distribution networks and transportation networks. The pricing schemes of fast charging stations significantly affects the EVs’ on-tirp charging behaviors and the operations of coupled power and transportation networks. This interdisciplinary paper proposes an optimal dynamic pricing method for fast charging stations to boost charging network operator’ (CNO’s) profits and avoid excessive charging costs of EVs. Time-varying traffic flow and charging demands are generated by a dynamic traffic assignment simulation, where a boundedly rational dynamic user equilibrium model is presented to capture the cost sensitivity of EVs and gasoline vehicle users. Additionally, a market regulator supervises the CNO’s pricing, balancing the interests of the CNO and users via a regulation constraint. To address the optimal dynamic pricing issue, we propose a three-level Stackelberg game involving the distribution network operator, CNO, and users. The existence of a game equilibrium is assured by the fixed-point theory. A Gauss-Seidel iteration algorithm with inertia weight is designed to solve the pricing problem. The numerical results have corroborated the effectiveness of the proposed method, illuminating the effects of bounded rationality and market regulation on the CNO’s profits and users’ travel costs.
Yuanyi Chen
PhD Student
Simon Hu
Assistant Professor
