LAPSE:2023.33290
Published Article
LAPSE:2023.33290
Analyzing Trade in Continuous Intra-Day Electricity Market: An Agent-Based Modeling Approach
April 21, 2023
Abstract
In recent years, the vast penetration of renewable energy sources has introduced a large degree of uncertainty into the power system, thus leading to increased trading activity in the continuous intra-day electricity market. In this paper, we propose an agent-based modeling framework to analyze the behavior and the interactions between renewable energy sources, consumers and thermal power plants in the European Continuous Intra-day (CID) market. Additionally, we propose a novel adaptive trading strategy that can be used by the agents that participate in CID market. The agents learn how to adapt their behavior according to the arrival of new information and how to react to changing market conditions by updating their willingness to trade. A comparative analysis was performed to study the behavior of agents when they adopt the proposed strategy as opposed to other benchmark strategies. The effects of unexpected outages and information asymmetry on the market evolution and the market liquidity were also investigated.
Keywords
adaptive learning, agent-based modeling, intra-day electricity markets, renewable energy sources
Suggested Citation
Shinde P, Boukas I, Radu D, Manuel de Villena M, Amelin M. Analyzing Trade in Continuous Intra-Day Electricity Market: An Agent-Based Modeling Approach. (2023). LAPSE:2023.33290
Author Affiliations
Shinde P: Division of Electric Power and Energy Systems, KTH Royal Institute of Technology, 11428 Stockholm, Sweden [ORCID]
Boukas I: Department of Electrical Engineering and Computer Science, University of Liège, 4000 Liège, Belgium [ORCID]
Radu D: Department of Electrical Engineering and Computer Science, University of Liège, 4000 Liège, Belgium
Manuel de Villena M: Department of Electrical Engineering and Computer Science, University of Liège, 4000 Liège, Belgium [ORCID]
Amelin M: Division of Electric Power and Energy Systems, KTH Royal Institute of Technology, 11428 Stockholm, Sweden [ORCID]
Journal Name
Energies
Volume
14
Issue
13
First Page
3860
Year
2021
Publication Date
2021-06-27
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en14133860, Publication Type: Journal Article
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LAPSE:2023.33290
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https://doi.org/10.3390/en14133860
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