Reinforcement learning improves pricing for hybrid vehicle stations
Reinforcement Learning-Based Pricing and Operation for Hybrid Electric and Hydrogen Vehicle Stations
Computational Engineering, Finance, and Science
Summary
Electric and hydrogen vehicles are better for the environment, but getting people to use them is hard partly because of limited refueling stations and pricing challenges. The researchers created a computer simulation of a station that handles both electric and hydrogen vehicles and used reinforcement learning to help decide prices in real time. Their system made more profit than traditional static pricing methods. This tool could help station owners make smarter pricing and purchasing decisions in complex situations.
What this means in practice
- •For ev and hydrogen station operators: Optimize pricing and operations at combined electric and hydrogen vehicle stations to boost profits and manage grid effects.
- •For energy grid managers: Use dynamic pricing informed by reinforcement learning agents to reduce stress on electrical grids from vehicle refueling stations.
Authors
Lowell Welburn, Yang Chen, Xudong Wang, Abhilasha Saroj, Shaked Regev
Abstract
Electric vehicles (EVs) and hydrogen fuel cell vehi- cles (HFCVs) are promising technologies in the efforts to reduce carbon emissions. Challenges such as limited infrastructure, con- sumer dissatisfaction, and electrical grid strain inhibit progress in the adoption of these technologies. A zero emission vehicle (ZEV) station, capable of handling EVs and HFCVs, addresses these challenges by focusing on maximizing station profit and consumer utility, while minimizing grid impacts. The purpose of this research is to develop a realistic simulation of a ZEV station that is capable of handling real-time pricing decisions enabled by reinforcement learning (RL) and to compare the performance of the RL agent to static pricing methods. After training, the RL agent gains 10.83% greater station profit than the baseline manual parameter search. This work serves as a useful tool for ZEV station owners when handling complex pricing and purchasing decisions.