Joint Routing and Scheduling of Mobile Charging Infrastructure for V2V Energy Transfer

Abstract

An adequate charging infrastructure advocates to ameliorate the range anxiety to propel the disparaged electric vehicle (EV) market. But, the high initial installation cost, requirement of suitable places and the anticipated immense load on the grid during peak times hinder to elongate the charging station network, especially in urban areas. Fortunately, the bidirectional energy transferring capability between vehicles (i.e., V2V) may act as an auxiliary solution to charge an EV at any place and at any time without leaning on a stationary charging infrastructure. In this work, we assume a market where charging providers each has a number of charging trucks equipped with a larger battery and a fast charger to charge a number of EVs at some particular parking lots. A provider intends to maximize the served number of EVs using its limited number of charging trucks, when an EV should be considered as served only if it would be fully charged during its declared charging window. All charging requests are assumed to be received by an agent which provisions a route and schedule for each charging truck and all trucks should return to the depot after serving EVs. We formulate this combinatorially hard problem as an integer linear program (ILP) to maximize the number of served EVs by determining the optimal trajectory of each truck. Owing to its complexity, we present a solution methodology by decomposing the problem using Dantzig-Wolfe decomposition approach; we divide the problem into one master problem and a set of pricing problems (one for each EV) and achieve the solution iteratively. Though the solution achieved from the decomposition might not be optimal, it is faster to be applicable in practice. We also compare the performance with two heuristic algorithms and report on the collected results.