Deep Reinforcement Learning-Based Charging Price Determination Considering the Coordinated Operation of Hydrogen Fuel Cell Electric Vehicle, Power Network and Transportation Network

Currently, hydrogen fuel cell electric vehicles (HFCEVs) are becoming more financially accessible as an alternative to petroleum-powered vehicles, while also decreasing carbon dioxide emissions. However, the coordinated scheduling of HFCEVs refuelling with the operations of hydrogen refuelling stations, electrical power network (PN), and transportation network (TN) represents an integral challenge. This complex problem encompasses a combinatorial mixed-integer nonlinear optimization problem with a sizeable number of decision variables. Existing methods struggle to adequately solve this problem. In this article, deep reinforcement learning (DRL) is deployed to determine the refuelling price to guide the HFCEV refuelling in the TN. First, HFCEV traffic flow model based on the refuelling price in real-world TN is presented. Then, the HFCEVs hydrogen demands in the H2 refuelling station (H2RS) microgrid are presented. After that, an IEEE 30 nodes PN exporting electricity to H2RS microgrids is presented. At last, DRL (DDPG, TD3, SAC, PPO) is deployed to determine the price based on the traffic condition of the TN and the voltage condition of the PN. The simulation results demonstrate that through the DRL price agent, the total travel time of the TN and the total operation costs of the PN are all reduced, and multi agent DDPG and TD3 algorithm have the best performance.

Automated summary

In this article, deep reinforcement learning is used to determine the refuelling price for HFCEVs in a real-world transportation network. The simulation results show that this method can reduce the total travel time of the network and the total operation costs of the power network.