A Q-Cube Framework of Reinforcement Learning Algorithm for Continuous Double Auction among Microgrids

Decision-making of microgrids in the condition of a dynamic uncertain bidding environment has always been a significant subject of interest in the context of energy markets. The emerging application of reinforcement learning algorithms in energy markets provides solutions to this problem. In this pa...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Energies (Basel) Jg. 12; H. 15; S. 2891
Hauptverfasser: Wang, Ning, Xu, Weisheng, Shao, Weihui, Xu, Zhiyu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 26.07.2019
Schlagworte:
Algorithms
Alternative energy sources
battery energy storage system, Q-cube framework
Behavior
bidding strategy
Blockchain
continuous double auction
Decision making
Electric power
Electricity distribution
Electricity generation
Energy industry
Energy management
International conferences
microgrids
Peer to peer computing
Q-learning algorithm
United States > US
China
Germany
ISSN:1996-1073, 1996-1073
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Decision-making of microgrids in the condition of a dynamic uncertain bidding environment has always been a significant subject of interest in the context of energy markets. The emerging application of reinforcement learning algorithms in energy markets provides solutions to this problem. In this paper, we investigate the potential of applying a Q-learning algorithm into a continuous double auction mechanism. By choosing a global supply and demand relationship as states and considering both bidding price and quantity as actions, a new Q-learning architecture is proposed to better reflect personalized bidding preferences and response to real-time market conditions. The application of battery energy storage system performs an alternative form of demand response by exerting potential capacity. A Q-cube framework is designed to describe the Q-value distribution iteration. Results from a case study on 14 microgrids in Guizhou Province, China indicate that the proposed Q-cube framework is capable of making rational bidding decisions and raising the microgrids’ profits.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1996-1073
1996-1073
DOI:10.3390/en12152891