Abstract
To improve the operating efficiency and economic benefits, this article proposes a modified rainbow-based deep reinforcement learning (DRL) strategy to realize the charging station (CS) optimal scheduling. As the charging process is a real-time matching between electric vehicles ‘(EVs) charging demand and CS equipment resources, the CS charging scheduling problem is duly formulated as a finite Markov decision process (FMDP). Considering the multi-stakeholder interaction among EVs, CSs, and distribution networks (DNs), a comprehensive information perception model was constructed to extract the environmental state required by the agent. According to the random behavior characteristics of the EV charging arrival and departure times, the startup of the charging pile control module was regarded as the agent’s action space. To tackle this issue, the modified rainbow approach was utilized to develop a time-scale-based CS scheme to compensate for the resource requirements mismatch on the energy scale. Case studies were conducted within a CS integrated with the photovoltaic and energy storage system. The results reveal that the proposed method effectively reduces the CS operating cost and improves the new energy consumption.
Highlights
As an environment-friendly means of transportation, electric vehicles (EVs) have received much attention in recent years
After the evaluation network completes 300 updates, the trained parameters are copied to the target network for one update
The optimal scheduling model was established as an finite Markov decision process (FMDP), and improved mechanisms were introduced to the classical deep Q network (DQN)
Summary
As an environment-friendly means of transportation, electric vehicles (EVs) have received much attention in recent years. A batch reinforcement learning-based optimal charging strategy is proposed in [14] to reduce the charging cost of EV users It realizes the proper scheduling of CSs in different dimensions. To the best of our knowledge, this is the first time to propose a CS scheduling strategy that combines the EV random charging behavior characteristics with DRL It improves the agent’s perception and learning ability based on the comprehensive perception of the “EV-CS-DN” environment information. The proposed method can reasonably solve the overstay issue and improve the operation efficiency of CSs. As the basic version of the DQN-based rainbow algorithm has shortcomings of overlearning and poor stability in the late training stage, we improved it by introducing the learning rate attenuation strategy.
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