IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v36y2011i11p6367-6374.html
   My bibliography  Save this article

Optimal bidding strategy in a competitive electricity market based on agent-based approach and numerical sensitivity analysis

Author

Listed:
  • Mahvi, M.
  • Ardehali, M.M.

Abstract

The objective of this study is to present a new method for determination of the optimal bidding strategies among generating companies (GenCo) in the electricity markets using agent-based approach and numerical sensitivity analysis (NSA). While agent-based approach provides for decision making, NSA can help with identifying the critical control points that lead to proper decisions to be taken by GenCos. To achieve the objective, the pricing mechanism used for settling the electricity market and determining the GenCos rewards is locational marginal pricing (LMP) and the sensitivity of each GenCo reward with respect to its bid is analyzed, then, the optimal strategy is determined. An example and a case study are used to illustrate the efficiency of the proposed method. The LMPs and allocated generations of GenCos show that the proposed method leads GenCos to learn a strategic manner and, as a result, increase prices and maximize their rewards. To validate the proposed method, the results from this study are compared with those available in the literature. The comparison of results shows an improved simulation time by 8.16 percent and total reward of market by 2.46 percent.

Suggested Citation

  • Mahvi, M. & Ardehali, M.M., 2011. "Optimal bidding strategy in a competitive electricity market based on agent-based approach and numerical sensitivity analysis," Energy, Elsevier, vol. 36(11), pages 6367-6374.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:11:p:6367-6374
    DOI: 10.1016/j.energy.2011.09.037
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544211006475
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2011.09.037?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Junjie Sun & Leigh Tesfatsion, 2007. "Dynamic Testing of Wholesale Power Market Designs: An Open-Source Agent-Based Framework," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 291-327, October.
    2. Jamalzadeh, R. & Ardehali, M.M. & Rashidinejad, M., 2008. "Development of modified rational buyer auction for procurement of ancillary services utilizing participation matrix," Energy Policy, Elsevier, vol. 36(2), pages 900-909, February.
    3. 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.
    4. Fesanghary, M. & Ardehali, M.M., 2009. "A novel meta-heuristic optimization methodology for solving various types of economic dispatch problem," Energy, Elsevier, vol. 34(6), pages 757-766.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wu, Shengyang & Ding, Zhaohao & Wang, Jingyu & Shi, Dongyuan, 2023. "Unveiling bidding uncertainties in electricity markets: A Bayesian deep learning framework based on accurate variational inference," Energy, Elsevier, vol. 276(C).
    2. Gao, Xiang & Chan, Ka Wing & Xia, Shiwei & Zhou, Bin & Lu, Xi & Xu, Da, 2019. "Risk-constrained offering strategy for a hybrid power plant consisting of wind power producer and electric vehicle aggregator," Energy, Elsevier, vol. 177(C), pages 183-191.
    3. Huiru Zhao & Yuwei Wang & Sen Guo & Mingrui Zhao & Chao Zhang, 2016. "Application of a Gradient Descent Continuous Actor-Critic Algorithm for Double-Side Day-Ahead Electricity Market Modeling," Energies, MDPI, vol. 9(9), pages 1-20, September.
    4. Min, C.G. & Kim, M.K. & Park, J.K. & Yoon, Y.T., 2013. "Game-theory-based generation maintenance scheduling in electricity markets," Energy, Elsevier, vol. 55(C), pages 310-318.
    5. Shinji Kuno & Kenji Tanaka & Yuji Yamada, 2022. "Effectiveness and Feasibility of Market Makers for P2P Electricity Trading," Energies, MDPI, vol. 15(12), pages 1-24, June.
    6. Ostadi, Bakhtiar & Motamedi Sedeh, Omid & Husseinzadeh Kashan, Ali, 2020. "Risk-based optimal bidding patterns in the deregulated power market using extended Markowitz model," Energy, Elsevier, vol. 191(C).
    7. Kuznetsova, Elizaveta & Li, Yan-Fu & Ruiz, Carlos & Zio, Enrico & Ault, Graham & Bell, Keith, 2013. "Reinforcement learning for microgrid energy management," Energy, Elsevier, vol. 59(C), pages 133-146.

    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. 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.
    2. 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.
    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. repec:spo:wpmain:info:hdl:2441/1nlv566svi86iqtetenms15tc4 is not listed on IDEAS
    5. Mauro Napoletano, 2018. "A Short Walk on the Wild Side: Agent-Based Models and their Implications for Macroeconomic Analysis," Revue de l'OFCE, Presses de Sciences-Po, vol. 0(3), pages 257-281.
    6. repec:hal:spmain:info:hdl:2441/53r60a8s3kup1vc9l564igg8g is not listed on IDEAS
    7. Albert Banal-Estañol & Augusto Rupérez-Micola, 2010. "Are agent-based simulations robust? The wholesale electricity trading case," Economics Working Papers 1214, Department of Economics and Business, Universitat Pompeu Fabra.
    8. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Rafal Weron, 2013. "Going green: Agent-based modeling of the diffusion of dynamic electricity tariffs," HSC Research Reports HSC/13/05, Hugo Steinhaus Center, Wroclaw University of Technology.
    9. Salehizadeh, Mohammad Reza & Soltaniyan, Salman, 2016. "Application of fuzzy Q-learning for electricity market modeling by considering renewable power penetration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1172-1181.
    10. repec:spo:wpmain:info:hdl:2441/2qdhj5485p93jrnf08s1meeap9 is not listed on IDEAS
    11. Alexander J. M. Kell & Matthew Forshaw & A. Stephen McGough, 2019. "ElecSim: Monte-Carlo Open-Source Agent-Based Model to Inform Policy for Long-Term Electricity Planning," Papers 1911.01203, arXiv.org.
    12. Auke Hoekstra & Maarten Steinbuch & Geert Verbong, 2017. "Creating Agent-Based Energy Transition Management Models That Can Uncover Profitable Pathways to Climate Change Mitigation," Complexity, Hindawi, vol. 2017, pages 1-23, December.
    13. Ringler, Philipp & Keles, Dogan & Fichtner, Wolf, 2016. "Agent-based modelling and simulation of smart electricity grids and markets – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 205-215.
    14. 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).
    15. Azadeh, A. & Skandari, M.R. & Maleki-Shoja, B., 2010. "An integrated ant colony optimization approach to compare strategies of clearing market in electricity markets: Agent-based simulation," Energy Policy, Elsevier, vol. 38(10), pages 6307-6319, October.
    16. repec:spo:wpecon:info:hdl:2441/53r60a8s3kup1vc9l564igg8g is not listed on IDEAS
    17. Jean-Luc Gaffard & Mauro Napoletano, 2012. "Agent-based models and economic policy," Post-Print hal-03461120, HAL.
    18. repec:spo:wpmain:info:hdl:2441/5qr7f0k4sk8rbq4do5u6v70rm0 is not listed on IDEAS
    19. Dina A. Zaki & Mohamed Hamdy, 2022. "A Review of Electricity Tariffs and Enabling Solutions for Optimal Energy Management," Energies, MDPI, vol. 15(22), pages 1-17, November.
    20. repec:hal:spmain:info:hdl:2441/2qdhj5485p93jrnf08s1meeap9 is not listed on IDEAS
    21. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    22. Battula, Swathi & Tesfatsion, Leigh & McDermott, Thomas E., 2020. "An ERCOT test system for market design studies," Applied Energy, Elsevier, vol. 275(C).
    23. J. Farmer & Cameron Hepburn & Penny Mealy & Alexander Teytelboym, 2015. "A Third Wave in the Economics of Climate Change," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(2), pages 329-357, October.
    24. Nazari, M.E. & Ardehali, M.M. & Jafari, S., 2010. "Pumped-storage unit commitment with considerations for energy demand, economics, and environmental constraints," Energy, Elsevier, vol. 35(10), pages 4092-4101.
    25. repec:hal:spmain:info:hdl:2441/1nlv566svi86iqtetenms15tc4 is not listed on IDEAS
    26. Banal-Estañol, Albert & Rupérez Micola, Augusto, 2011. "Behavioural simulations in spot electricity markets," European Journal of Operational Research, Elsevier, vol. 214(1), pages 147-159, October.
    27. repec:hal:spmain:info:hdl:2441/5qr7f0k4sk8rbq4do5u6v70rm0 is not listed on IDEAS
    28. 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.

    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:eee:energy:v:36:y:2011:i:11:p:6367-6374. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.