An approximate dynamic programming algorithm for short-term electric vehicle fleet operation under uncertainty

作     者：Lee, Sangmin Boomsma, Trine Krogh 

作者机构：Univ Copenhagen Dept Math Sci DK-2100 Copenhagen Denmark Maersk R&D DK-1263 Copenhagen Denmark 

出 版 物：《APPLIED ENERGY》 (实用能源)

年 卷 期：2022年第325卷

核心收录：

学科分类：0820[工学-石油与天然气工程] 0817[工学-化学工程与技术] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：S. Lee and T. K. Boomsma gratefully acknowledge support from the project Analyses of Hourly Electricity Demand (AHEAD) funded by ForskEl 2017. All authors approved the version of the manuscript to be published 

主　　题：Plug-in hybrid vehicles Approximate dynamic programming Least squares Monte Carlo 

摘      要：This paper considers the dynamic problem of optimally operating a fleet of plug-in hybrid electric vehicles in a market environment. With uncertainty in future electricity prices and driving demands, we formulate a Markov decision process and determine a cost-minimizing policy for using the engine and charging and discharging the battery. As such, the policy is based on the trade-off between the costs of gasoline and electricity and between current and future power prices. To accommodate an inhomogeneous fleet composition and overcome the computational challenges of stochastic and dynamic optimization, including large-scale state and action spaces, we adopt the methodology of approximate dynamic programming. More specifically, using simulation and value function approximation by linear regression, we apply a least squares Monte Carlo method. This methodology allows for scaling with respect to fleet size and we are able to establish convergence of our algorithm for 100 vehicles by using 5000 samples in the simulation. Our results show that the vehicles should generally discharge the battery rather than using the engine unless battery capacity is insufficient to fully cover driving demand, but the timing of battery charging should be according to power prices. When comparing our policy to the simple policy of immediate charging, we demonstrate superiority for small and medium-sized fleets, with 2%-4% cost differences.