Novel Energy Trading System Based on Deep-Reinforcement Learning in Microgrids

Seongwoo Lee, Joonho Seon, Chanuk Kyeong, Soohyun Kim, Youngghyu Sun and Jinyoung Kim
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Seongwoo Lee: Department of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, Korea
Joonho Seon: Department of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, Korea
Chanuk Kyeong: Department of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, Korea
Soohyun Kim: Department of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, Korea
Youngghyu Sun: Department of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, Korea
Jinyoung Kim: Department of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, Korea

Energies, 2021, vol. 14, issue 17, 1-14

Abstract: 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.

Keywords: microgrid; energy transaction; energy self-sufficient systems; double deep Q-networks (DDQN); double Kelly strategy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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