IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i12p2045-d121412.html
   My bibliography  Save this article

Two-Sided Tacit Collusion: Another Step towards the Role of Demand-Side

Author

Listed:
  • Mehdi Jabbari Zideh

    (Faculty of Engineering, University of Guilan, Rasht 43514, Iran)

  • Seyed Saeid Mohtavipour

    (Faculty of Engineering, University of Guilan, Rasht 43514, Iran)

Abstract

In the context of agent-based simulation framework of collusion, this paper seeks for two-sided tacit collusion among supply-side and demand-side participants in a constrained network and impacts of this collusion on the market outcomes. Tacit collusion frequently occurs in electricity markets due to strategic behavior of market participants arose from daily repetition of energy auctions. To attain detailed analysis of tacit collusion, state-action-reward-state-action (SARSA) learning algorithm and the standard Boltzmann exploration strategy based on the Q-value are used to model market participants’ behavior. A model is presented that integrates exploration and exploitation into a single framework, with the purpose of tuning exploration in the algorithm. In order to appraise the feasibility of collusion, a theoretical study on a three-node power system with three scenarios is depicted considering three Gencos and two Discos which proves the formation of two-sided tacit collusion between Genco and Disco. Simulation results show different collusive strategies of participants and how parameters of the algorithm impact on simulation outcomes. It is also shown that congestion on transmission line has a significant influence on behavior of market participants.

Suggested Citation

  • Mehdi Jabbari Zideh & Seyed Saeid Mohtavipour, 2017. "Two-Sided Tacit Collusion: Another Step towards the Role of Demand-Side," Energies, MDPI, vol. 10(12), pages 1-19, December.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:2045-:d:121412
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/12/2045/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/10/12/2045/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Emmanuel Dechenaux & Dan Kovenock, 2007. "Tacit collusion and capacity withholding in repeated uniform price auctions," RAND Journal of Economics, RAND Corporation, vol. 38(4), pages 1044-1069, December.
    2. Weidlich, Anke & Veit, Daniel, 2008. "A critical survey of agent-based wholesale electricity market models," Energy Economics, Elsevier, vol. 30(4), pages 1728-1759, July.
    3. Veit, Daniel J. & Weidlich, Anke & Krafft, Jacob A., 2009. "An agent-based analysis of the German electricity market with transmission capacity constraints," Energy Policy, Elsevier, vol. 37(10), pages 4132-4144, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cristian Zambrano & Yris Olaya, 2017. "An agent-based simulation approach to congestion management for the Colombian electricity market," Annals of Operations Research, Springer, vol. 258(2), pages 217-236, November.
    2. Li, Hongyan & Tesfatsion, Leigh, 2012. "Co-learning patterns as emergent market phenomena: An electricity market illustration," Journal of Economic Behavior & Organization, Elsevier, vol. 82(2), pages 395-419.
    3. Gaivoronskaia, E. & Tsyplakov, A., 2018. "Using a Modified Erev-Roth Algorithm in an Agent-Based Electricity Market Model," Journal of the New Economic Association, New Economic Association, vol. 39(3), pages 55-83.
    4. Anatolitis, Vasilios & Welisch, Marijke, 2017. "Putting renewable energy auctions into action – An agent-based model of onshore wind power auctions in Germany," Energy Policy, Elsevier, vol. 110(C), pages 394-402.
    5. Li, Gong & Shi, Jing & Qu, Xiuli, 2011. "Modeling methods for GenCo bidding strategy optimization in the liberalized electricity spot market–A state-of-the-art review," Energy, Elsevier, vol. 36(8), pages 4686-4700.
    6. 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.
    7. Cristina Ballester & Dolores Furió, 2017. "Impact of Wind Electricity Forecasts on Bidding Strategies," Sustainability, MDPI, vol. 9(8), pages 1-17, August.
    8. Block, C. & Collins, J. & Ketter, W. & Weinhardt, C., 2009. "A Multi-Agent Energy Trading Competition," ERIM Report Series Research in Management ERS-2009-054-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    9. Li, Gong & Shi, Jing, 2012. "Agent-based modeling for trading wind power with uncertainty in the day-ahead wholesale electricity markets of single-sided auctions," Applied Energy, Elsevier, vol. 99(C), pages 13-22.
    10. Young, David & Poletti, Stephen & Browne, Oliver, 2014. "Can agent-based models forecast spot prices in electricity markets? Evidence from the New Zealand electricity market," Energy Economics, Elsevier, vol. 45(C), pages 419-434.
    11. Emiliano Brancaccio & Mauro Gallegati & Raffaele Giammetti, 2022. "Neoclassical influences in agent‐based literature: A systematic review," Journal of Economic Surveys, Wiley Blackwell, vol. 36(2), pages 350-385, April.
    12. Liu, Zhen & Zhang, Xiliang & Lieu, Jenny, 2010. "Design of the incentive mechanism in electricity auction market based on the signaling game theory," Energy, Elsevier, vol. 35(4), pages 1813-1819.
    13. Newbery, David M. & Greve, Thomas, 2017. "The strategic robustness of oligopoly electricity market models," Energy Economics, Elsevier, vol. 68(C), pages 124-132.
    14. Liu, Beibei & He, Pan & Zhang, Bing & Bi, Jun, 2012. "Impacts of alternative allowance allocation methods under a cap-and-trade program in power sector," Energy Policy, Elsevier, vol. 47(C), pages 405-415.
    15. Balint, T. & Lamperti, F. & Mandel, A. & Napoletano, M. & Roventini, A. & Sapio, A., 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Ecological Economics, Elsevier, vol. 138(C), pages 252-265.
    16. Browne, Oliver & Poletti, Stephen & Young, David, 2015. "How does market power affect the impact of large scale wind investment in 'energy only' wholesale electricity markets?," Energy Policy, Elsevier, vol. 87(C), pages 17-27.
    17. Peng Hao & Jun-Peng Guo & Eoghan O’Neill & Yong-Heng Shi, 2023. "When Will First-Price Work Well? The Impact of Anti-Corruption Rules on Photovoltaic Power Generation Procurement Auctions," Sustainability, MDPI, vol. 15(4), pages 1-24, February.
    18. Rahimiyan, Morteza, 2014. "A statistical cognitive model to assess impact of spatially correlated wind production on market behaviors," Applied Energy, Elsevier, vol. 122(C), pages 62-72.
    19. Vijayanarasimha Hindupur Pakka & Richard Mark Rylatt, 2016. "Design and Analysis of Electrical Distribution Networks and Balancing Markets in the UK: A New Framework with Applications," Energies, MDPI, vol. 9(2), pages 1-20, February.
    20. Will, Christian & Zimmermann, Florian & Ensslen, Axel & Fraunholz, Christoph & Jochem, Patrick & Keles, Dogan, 2024. "Can electric vehicle charging be carbon neutral? Uniting smart charging and renewables," Applied Energy, Elsevier, vol. 371(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:2045-:d:121412. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.