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Analyzing Trade in Continuous Intra-Day Electricity Market: An Agent-Based Modeling Approach

Author

Listed:
  • Priyanka Shinde

    (Division of Electric Power and Energy Systems, KTH Royal Institute of Technology, 11428 Stockholm, Sweden
    These authors contributed equally to this work.)

  • Ioannis Boukas

    (Department of Electrical Engineering and Computer Science, University of Liège, 4000 Liège, Belgium
    These authors contributed equally to this work.)

  • David Radu

    (Department of Electrical Engineering and Computer Science, University of Liège, 4000 Liège, Belgium)

  • Miguel Manuel de Villena

    (Department of Electrical Engineering and Computer Science, University of Liège, 4000 Liège, Belgium)

  • Mikael Amelin

    (Division of Electric Power and Energy Systems, KTH Royal Institute of Technology, 11428 Stockholm, Sweden)

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.

Suggested Citation

  • Priyanka Shinde & Ioannis Boukas & David Radu & Miguel Manuel de Villena & Mikael Amelin, 2021. "Analyzing Trade in Continuous Intra-Day Electricity Market: An Agent-Based Modeling Approach," Energies, MDPI, vol. 14(13), pages 1-31, June.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3860-:d:583191
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    References listed on IDEAS

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    1. Ioannis Boukas & Damien Ernst & Thibaut Th'eate & Adrien Bolland & Alexandre Huynen & Martin Buchwald & Christelle Wynants & Bertrand Corn'elusse, 2020. "A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding," Papers 2004.05940, arXiv.org.
    2. Vermeulen, Ben & Pyka, Andreas, 2016. "Agent-based modeling for decision making in economics under uncertainty," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 10, pages 1-33.
    3. Karsten Neuhoff & Nolan Ritter & Aymen SalahAbou-El-Enien & Philippe Vassilopoulos, 2016. "Intraday Markets for Power: Discretizing the Continuous Trading?," Working Papers EPRG 1609, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    4. Aliabadi, Danial Esmaeili & Kaya, Murat & Şahin, Güvenç, 2017. "An agent-based simulation of power generation company behavior in electricity markets under different market-clearing mechanisms," Energy Policy, Elsevier, vol. 100(C), pages 191-205.
    5. Dave Cliff, 2018. "BSE: A Minimal Simulation of a Limit-Order-Book Stock Exchange," Papers 1809.06027, arXiv.org.
    6. Davide Ciferri & Maria Chiara D’Errico & Paolo Polinori, 2020. "Integration and convergence in European electricity markets," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 37(2), pages 463-492, July.
    7. Le, Hong Lam & Ilea, Valentin & Bovo, Cristian, 2019. "Integrated European intra-day electricity market: Rules, modeling and analysis," Applied Energy, Elsevier, vol. 238(C), pages 258-273.
    8. Sun, Junjie & Tesfatsion, Leigh S., 2007. "An Agent-Based Computational Laboratory for Wholesale Power Market Design," Staff General Research Papers Archive 12776, Iowa State University, Department of Economics.
    9. Poplavskaya, Ksenia & Lago, Jesus & de Vries, Laurens, 2020. "Effect of market design on strategic bidding behavior: Model-based analysis of European electricity balancing markets," Applied Energy, Elsevier, vol. 270(C).
    10. Esmaeili Aliabadi, Danial & Kaya, Murat & Sahin, Guvenc, 2017. "Competition, risk and learning in electricity markets: An agent-based simulation study," Applied Energy, Elsevier, vol. 195(C), pages 1000-1011.
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