A Novel Methodology for Charging Station Deployment

Abstract

Abstract. Lack of charging stations has been a main obstacle to the promotion of electric vehicles. This paper studies deploying charging stations in traffic networks considering grid constraints to balance the charging demand and grid stability. First, we propose a statistical model for charging demand. Then we combine the charging demand model with power grid constraints and give the formulation of the charging station deployment problem. Finally, we propose a theoretical solution for the problem by transforming it to a Markov Decision Process. IntroductionOne critical issue holding back the widespread of electric vehicles (EVs) is the scarcity of charging facilities. For example, EVs in ShenZhen had increased by 6958 during 2009-2014, but only 3091 new chargers were constructed during this period. Even so, the utilization of chargers is less than 33%. On the other hand, numerous EVs charging synchronously will shake the power networks seriously. K. Clement-Nyns [1] shows that, when the EV penetration reaches 30%, the uncoordinated EV charging will cause 5% total power loss and 10% voltage deviation. Thus, when deploying charging stations, both the charging demand in traffic networks and the stability in power grid should be considered.There are some papers focusing on the charging station deployment. L. Feng [2] models the charging demand by a weighted voronoi diagram to minimize the detours But it only considers the size and location in traffic networks. C. Upchurch [3] and M. Kuby [4] model the charging demand as a flow refueling location model to maximize the captured flows. A. Lam [5] provides a structure for charging station placement including the formulation, complexity and solutions. In spite of the various models and solutions proposed, the impacts on power grid are untouched.There are also some papers focusing on deploying charging stations in power networks. M. Aghaebrahimi [6], P. Sadeghi-Barzani [7], Z. Liu [8] locate charging stations in distribution systems to minimize costs. But mapping the power network constraints to costs is not proper, because violating the constraints may cause disasters. N. Ariyapim [9] proposes an ant colony optimization method to optimize the charging station locations to minimize the feeder loss in distribution systems. Although these papers study the deployment of charging stations in power networks, none of them consider the spatio-temporal features of charging demand.