Energy Management Strategy of Multi-agent Microgrid Based on Q-learning Algorithm

Authors

Miaomiao Ma, School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China; State Key Laboratory of Alternate Electric Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, ChinaFollow
Lipeng Dong, School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Xiangjie Liu, School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China; State Key Laboratory of Alternate Electric Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China

Abstract

Abstract: This paper proposes a multi-agent microgrid energy management method for the energy trading and benefit distribution in the microgrid power market based on the Q-learning algorithm. Based on the electricity market, microgrid system and transaction process are constructed to clarify the responsibilities of each unit. The mathematical models of distributed power generations are established by considering the changes in wind speed, light intensity and ambient temperature, as well as the upper and lower limit constraints of the output power of each power generation unit. On this basis, the distributed power generations and user loads are regarded as agents, and the Markov decision-making process is designed based on the Q-learning algorithm. Aiming at maximizing the benefits of distributed power generations and minimizing the costs of user loads, a microgrid energy management scheme based on Q-learning algorithm is proposed. The results show that the proposed method can not only increase the benefits of distributed power generations but also reduce the costs of user loads in different scenarios.

Recommended Citation

Ma, Miaomiao; Dong, Lipeng; and Liu, Xiangjie (2023) "Energy Management Strategy of Multi-agent Microgrid Based on Q-learning Algorithm," Journal of System Simulation: Vol. 35: Iss. 7, Article 6.
DOI: 10.16182/j.issn1004731x.joss.22-0378
Available at: https://dc-china-simulation.researchcommons.org/journal/vol35/iss7/6

First Page

1487

Last Page

1496

CLC

TP391.9; TM732

Recommended Citation

Ma Miaomiao, Dong Lipeng, Liu Xiangjie. Energy Management Strategy of Multi-agent Microgrid Based on Q-learning Algorithm[J]. Journal of System Simulation, 2023, 35(7): 1487-1496.

DOI

10.16182/j.issn1004731x.joss.22-0378