Novel Energy Trading System Based on Deep-Reinforcement Learning in Microgrids
Inefficiencies in energy trading systems of microgrids are mainly caused by uncertainty in non-stationary operating environments. The problem of uncertainty can be mitigated by analyzing patterns of primary operation parameters and their corresponding actions. In this paper, a novel energy trading s...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/17/5515 |
| _version_ | 1829454150226673664 |
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| author | Seongwoo Lee Joonho Seon Chanuk Kyeong Soohyun Kim Youngghyu Sun Jinyoung Kim |
| author_facet | Seongwoo Lee Joonho Seon Chanuk Kyeong Soohyun Kim Youngghyu Sun Jinyoung Kim |
| author_sort | Seongwoo Lee |
| collection | DOAJ |
| description | Inefficiencies in energy trading systems of microgrids are mainly caused by uncertainty in non-stationary operating environments. The problem of uncertainty can be mitigated by analyzing patterns of primary operation parameters and their corresponding actions. In this paper, a novel energy trading system based on a double deep Q-networks (DDQN) algorithm and a double Kelly strategy is proposed for improving profits while reducing dependence on the main grid in the microgrid systems. The DDQN algorithm is proposed in order to select optimized action for improving energy transactions. Additionally, the double Kelly strategy is employed to control the microgrid’s energy trading quantity for producing long-term profits. From the simulation results, it is confirmed that the proposed strategies can achieve a significant improvement in the total profits and independence from the main grid via optimized energy transactions. |
| first_indexed | 2024-03-10T08:12:07Z |
| format | Article |
| id | doaj.art-d07330705569487fba3a0b1ad2064f2e |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T08:12:07Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-d07330705569487fba3a0b1ad2064f2e2023-11-22T10:35:50ZengMDPI AGEnergies1996-10732021-09-011417551510.3390/en14175515Novel Energy Trading System Based on Deep-Reinforcement Learning in MicrogridsSeongwoo Lee0Joonho Seon1Chanuk Kyeong2Soohyun Kim3Youngghyu Sun4Jinyoung Kim5Department of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, KoreaDepartment of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, KoreaDepartment of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, KoreaDepartment of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, KoreaDepartment of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, KoreaDepartment of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, KoreaInefficiencies in energy trading systems of microgrids are mainly caused by uncertainty in non-stationary operating environments. The problem of uncertainty can be mitigated by analyzing patterns of primary operation parameters and their corresponding actions. In this paper, a novel energy trading system based on a double deep Q-networks (DDQN) algorithm and a double Kelly strategy is proposed for improving profits while reducing dependence on the main grid in the microgrid systems. The DDQN algorithm is proposed in order to select optimized action for improving energy transactions. Additionally, the double Kelly strategy is employed to control the microgrid’s energy trading quantity for producing long-term profits. From the simulation results, it is confirmed that the proposed strategies can achieve a significant improvement in the total profits and independence from the main grid via optimized energy transactions.https://www.mdpi.com/1996-1073/14/17/5515microgridenergy transactionenergy self-sufficient systemsdouble deep Q-networks (DDQN)double Kelly strategy |
| spellingShingle | Seongwoo Lee Joonho Seon Chanuk Kyeong Soohyun Kim Youngghyu Sun Jinyoung Kim Novel Energy Trading System Based on Deep-Reinforcement Learning in Microgrids Energies microgrid energy transaction energy self-sufficient systems double deep Q-networks (DDQN) double Kelly strategy |
| title | Novel Energy Trading System Based on Deep-Reinforcement Learning in Microgrids |
| title_full | Novel Energy Trading System Based on Deep-Reinforcement Learning in Microgrids |
| title_fullStr | Novel Energy Trading System Based on Deep-Reinforcement Learning in Microgrids |
| title_full_unstemmed | Novel Energy Trading System Based on Deep-Reinforcement Learning in Microgrids |
| title_short | Novel Energy Trading System Based on Deep-Reinforcement Learning in Microgrids |
| title_sort | novel energy trading system based on deep reinforcement learning in microgrids |
| topic | microgrid energy transaction energy self-sufficient systems double deep Q-networks (DDQN) double Kelly strategy |
| url | https://www.mdpi.com/1996-1073/14/17/5515 |
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