IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v321y2022ics0306261922007139.html
   My bibliography  Save this article

Optimization-based modelling and game-theoretic framework for techno-economic analysis of demand-side flexibility: A real case study

Author

Listed:
  • Sayfutdinov, Timur
  • Patsios, Charalampos
  • Greenwood, David
  • Peker, Meltem
  • Sarantakos, Ilias

Abstract

This paper proposes a two-step framework for techno-economic analysis of a demand-side flexibility service in distribution networks. Step one applies optimization-based modelling to propose a generic problem formulation which determines the offer curve, in terms of available flexible capacity and its marginal cost, for flexible distribution-connected assets. These offer curves form an input to the second step, which uses a multi-agent iterative game framework to determine the benefits of demand-side flexibility for the Distribution System Operator (DSO) and the service providers. The combined two-step framework simultaneously accounts for the objectives of each flexibility provider, technical constraints of flexible assets, customer preferences, market clearing mechanisms, and strategic bidding by service providers, omission of any of which can lead to erroneous results. The proposed two-step framework has been applied to a real case study in the North East of England to examine four market mechanisms and three bidding strategies. The results showed that among all considered market mechanisms, flexibility markets that operate under discriminatory pricing, such as pay-as-bid and Dutch reverse auctions, are prone to manipulations, especially in the lack of competition. In contrast, uniform pricing pay-as-cleared auction provides limited opportunities for manipulation even when competition is low.

Suggested Citation

  • Sayfutdinov, Timur & Patsios, Charalampos & Greenwood, David & Peker, Meltem & Sarantakos, Ilias, 2022. "Optimization-based modelling and game-theoretic framework for techno-economic analysis of demand-side flexibility: A real case study," Applied Energy, Elsevier, vol. 321(C).
    • Handle: RePEc:eee:appene:v:321:y:2022:i:c:s0306261922007139
    DOI: 10.1016/j.apenergy.2022.119370
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922007139
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.119370?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. Haji Hosseinloo, Ashkan & Ryzhov, Alexander & Bischi, Aldo & Ouerdane, Henni & Turitsyn, Konstantin & Dahleh, Munther A., 2020. "Data-driven control of micro-climate in buildings: An event-triggered reinforcement learning approach," Applied Energy, Elsevier, vol. 277(C).
    2. Michael H. Rothkopf, 2007. "Thirteen Reasons Why the Vickrey-Clarke-Groves Process Is Not Practical," Operations Research, INFORMS, vol. 55(2), pages 191-197, April.
    3. Zhanle Wang & Raman Paranjape & Zhikun Chen & Kai Zeng, 2019. "Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing," Energies, MDPI, vol. 12(15), pages 1-15, July.
    4. Philippe Gillen & Alexander Rasch & Achim Wambach & Peter Werner, 2016. "Bid pooling in reverse multi-unit Dutch auctions: an experimental investigation," Theory and Decision, Springer, vol. 81(4), pages 511-534, November.
    5. Madzharov, D. & Delarue, E. & D'haeseleer, W., 2014. "Integrating electric vehicles as flexible load in unit commitment modeling," Energy, Elsevier, vol. 65(C), pages 285-294.
    6. 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).
    7. Reis, Inês F.G. & Gonçalves, Ivo & Lopes, Marta A.R. & Antunes, Carlos Henggeler, 2020. "A multi-agent system approach to exploit demand-side flexibility in an energy community," Utilities Policy, Elsevier, vol. 67(C).
    8. Sayfutdinov, Timur & Vorobev, Petr, 2022. "Optimal utilization strategy of the LiFePO4 battery storage," Applied Energy, Elsevier, vol. 316(C).
    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. Jeremy Bulow & Jonathan Levin & Paul Milgrom, 2009. "Winning Play in Spectrum Auctions," NBER Working Papers 14765, National Bureau of Economic Research, Inc.
    2. Mehran Garmehi & Morteza Analoui & Mukaddim Pathan & Rajkumar Buyya, 2015. "An economic mechanism for request routing and resource allocation in hybrid CDN–P2P networks," International Journal of Network Management, John Wiley & Sons, vol. 25(6), pages 375-393, November.
    3. Nicolas C. Bedard & Jacob K. Goeree & Philippos Louis & Jingjing Zhang, 2020. "The Favored but Flawed Simultaneous Multiple-Round Auction," Working Paper Series 2020/03, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    4. Zou, Peng & Chen, Qixin & Xia, Qing & He, Chang & Kang, Chongqing, 2015. "Incentive compatible pool-based electricity market design and implementation: A Bayesian mechanism design approach," Applied Energy, Elsevier, vol. 158(C), pages 508-518.
    5. Chen, Ting & Vandendriessche, Frederik, 2023. "Enabling independent flexibility service providers to participate in electricity markets: A legal analysis of the Belgium case," Utilities Policy, Elsevier, vol. 81(C).
    6. Luis V. M. Freitas & Wilfredo L. Maldonado, 2021. "Quadratic Funding with Incomplete Information," Working Papers, Department of Economics 2021_24, University of São Paulo (FEA-USP).
    7. Bai, Yang & Zhong, Haiwang & Xia, Qing & Kang, Chongqing & Xie, Le, 2015. "A decomposition method for network-constrained unit commitment with AC power flow constraints," Energy, Elsevier, vol. 88(C), pages 595-603.
    8. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    9. Sandro Brusco & Giuseppe Lopomo & Leslie M. Marx, 2011. "The Economics of Contingent Re-auctions," American Economic Journal: Microeconomics, American Economic Association, vol. 3(2), pages 165-193, May.
    10. Liao, Qi & Tu, Renfu & Zhang, Wan & Wang, Bohong & Liang, Yongtu & Zhang, Haoran, 2023. "Auction design for capacity allocation in the petroleum pipeline under fair opening," Energy, Elsevier, vol. 264(C).
    11. Colmenar-Santos, Antonio & Linares-Mena, Ana-Rosa & Borge-Diez, David & Quinto-Alemany, Carlos-Domingo, 2017. "Impact assessment of electric vehicles on islands grids: A case study for Tenerife (Spain)," Energy, Elsevier, vol. 120(C), pages 385-396.
    12. Mezzetti, Claudio & Tsetlin, Ilia, 2009. "Auctions in which losers set the price," Games and Economic Behavior, Elsevier, vol. 66(2), pages 855-864, July.
    13. Cao, K.H. & Qi, H.S. & Tsai, C.H. & Woo, C.K. & Zarnikau, J., 2021. "Energy trading efficiency in the US Midcontinent electricity markets," Applied Energy, Elsevier, vol. 302(C).
    14. Szinai, Julia K. & Sheppard, Colin J.R. & Abhyankar, Nikit & Gopal, Anand R., 2020. "Reduced grid operating costs and renewable energy curtailment with electric vehicle charge management," Energy Policy, Elsevier, vol. 136(C).
    15. Satoru Fujishige & Zaifu Yang, 2020. "A Universal Dynamic Auction for Unimodular Demand Types: An Efficient Auction Design for Various Kinds of Indivisible Commodities," Discussion Papers 20/08, Department of Economics, University of York.
    16. Astriani, Yuli & Shafiullah, GM & Shahnia, Farhad, 2021. "Incentive determination of a demand response program for microgrids," Applied Energy, Elsevier, vol. 292(C).
    17. Zhiling Guo & Gary J. Koehler & Andrew B. Whinston, 2012. "A Computational Analysis of Bundle Trading Markets Design for Distributed Resource Allocation," Information Systems Research, INFORMS, vol. 23(3-part-1), pages 823-843, September.
    18. Cai, Qingsen & Luo, XingQi & Wang, Peng & Gao, Chunyang & Zhao, Peiyu, 2022. "Hybrid model-driven and data-driven control method based on machine learning algorithm in energy hub and application," Applied Energy, Elsevier, vol. 305(C).
    19. Goeree, Jacob K. & Lien, Yuanchuan, 2014. "An equilibrium analysis of the simultaneous ascending auction," Journal of Economic Theory, Elsevier, vol. 153(C), pages 506-533.
    20. Viet-Cuong Trieu & Fu-Ren Lin, 2022. "The Development of a Service System for Facilitating Food Resource Allocation and Service Exchange," Sustainability, MDPI, vol. 14(19), pages 1-29, September.

    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:appene:v:321:y:2022:i:c:s0306261922007139. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.