Model-Based Reinforcement Learning Method for Microgrid Optimization Scheduling

Jinke Yao, Jiachen Xu, Ning Zhang () and Yajuan Guan
Additional contact information
Jinke Yao: Department of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Jiachen Xu: Department of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Ning Zhang: School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
Yajuan Guan: Department of Energy, Aalborg University, 266100 Elburg, Denmark

Sustainability, 2023, vol. 15, issue 12, 1-18

Abstract: Due to the uncertainty and randomness of clean energy, microgrid operation is often prone to instability, which requires the implementation of a robust and adaptive optimization scheduling method. In this paper, a model-based reinforcement learning algorithm is applied to the optimal scheduling problem of microgrids. During the training process, the current learned networks are used to assist Monte Carlo Tree Search (MCTS) in completing game history accumulation, and updating the learning network parameters to obtain optimal microgrid scheduling strategies and a simulated environmental dynamics model. We establish a microgrid environment simulator that includes Heating Ventilation Air Conditioning (HVAC) systems, Photovoltaic (PV) systems, and Energy Storage (ES) systems for simulation. The simulation results show that the operation of microgrids in both islanded and connected modes does not affect the training effectiveness of the algorithm. After 200 training steps, the algorithm can avoid the punishment of exceeding the red line of the bus voltage, and after 800 training steps, the training result converges and the loss values of the value and reward network converge to 0, showing good effectiveness. This proves that the algorithm proposed in this paper can be applied to the optimization scheduling problem of microgrids.

Keywords: microgrid scheduling method; model-based reinforcement learning; Monte Carlo Tree Search; environment simulator (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/12/9235/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/12/9235/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:12:p:9235-:d:1165959

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().