A modified rainbow-based deep reinforcement learning method for optimal scheduling of charging station
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 dema...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/163392 |
| _version_ | 1811693602419507200 |
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| author | Wang, Ruisheng Chen, Zhong Xing, Qiang Zhang, Ziqi Zhang, Tian |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Wang, Ruisheng Chen, Zhong Xing, Qiang Zhang, Ziqi Zhang, Tian |
| author_sort | Wang, Ruisheng |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T06:54:17Z |
| format | Journal Article |
| id | ntu-10356/163392 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:54:17Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1633922022-12-05T07:00:40Z A modified rainbow-based deep reinforcement learning method for optimal scheduling of charging station Wang, Ruisheng Chen, Zhong Xing, Qiang Zhang, Ziqi Zhang, Tian School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Charging Station Electric Vehicle 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. Published version 2022-12-05T07:00:40Z 2022-12-05T07:00:40Z 2022 Journal Article 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 2071-1050 https://hdl.handle.net/10356/163392 10.3390/su14031884 2-s2.0-85124368330 3 14 1884 en Sustainability (Switzerland) © 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/). application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Charging Station Electric Vehicle Wang, Ruisheng Chen, Zhong Xing, Qiang Zhang, Ziqi Zhang, Tian A modified rainbow-based deep reinforcement learning method for optimal scheduling of charging station |
| title | A modified rainbow-based deep reinforcement learning method for optimal scheduling of charging station |
| title_full | A modified rainbow-based deep reinforcement learning method for optimal scheduling of charging station |
| title_fullStr | A modified rainbow-based deep reinforcement learning method for optimal scheduling of charging station |
| title_full_unstemmed | A modified rainbow-based deep reinforcement learning method for optimal scheduling of charging station |
| title_short | A modified rainbow-based deep reinforcement learning method for optimal scheduling of charging station |
| title_sort | modified rainbow based deep reinforcement learning method for optimal scheduling of charging station |
| topic | Engineering::Electrical and electronic engineering Charging Station Electric Vehicle |
| url | https://hdl.handle.net/10356/163392 |
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