Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163392
Title: A modified rainbow-based deep reinforcement learning method for optimal scheduling of charging station
Authors: Wang, Ruisheng
Chen, Zhong
Xing, Qiang
Zhang, Ziqi
Zhang, Tian
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Wang, R., Chen, Z., Xing, Q., Zhang, Z. & Zhang, T. (2022). A modified rainbow-based deep reinforcement learning method for optimal scheduling of charging station. Sustainability (Switzerland), 14(3), 1884-. https://dx.doi.org/10.3390/su14031884
Journal: Sustainability (Switzerland)
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.
URI: https://hdl.handle.net/10356/163392
ISSN: 2071-1050
DOI: 10.3390/su14031884
Schools: School of Electrical and Electronic Engineering 
Rights: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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