Fast-Charging Station Deployment Considering Elastic Demand
Xiaoying Gan, Haoxiang Zhang, Gai Hang, Zhida Qin, Haiming Jin
Abstract
Electric vehicles (EVs), as part of sustainable transport, are believed to be helpful to reduce global warming. In this article, we focus on the fast-charging station (FCS) deployment problem, which is one of the key issues of the EV ecosystem. Specifically, elastic demand is considered, i.e., charging demand will be suppressed because of either long driving distance to get charging or long waiting time at the station. A fixed-point equation is proposed to capture the nature of the EV users' charging behavior. It considers both spatial and temporal penalties by establishing a connection between the resulting arrival rate and a combination of driving distance and waiting time. We formulate the FCS deployment problem as a nonlinear integer problem, which seeks to figure out the optimal locations to build the FCSs and the optimal number of charging piles of each selected FCS. A genetic-algorithm-based heuristic algorithm is adopted to tackle the problem. Simulation results prove the effectiveness of our proposed algorithm. The importance of a match between the power grid capacity and the amount of charging demand is revealed, both in terms of increasing profit and reducing outage probability.