IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1709.02667.html
   My bibliography  Save this paper

Implementing Flexible Demand: Real-time Price vs. Market Integration

Author

Listed:
  • Florian Kuhnlenz
  • Pedro H. J. Nardelli
  • Santtu Karhinen
  • Rauli Svento

Abstract

This paper proposes an agent-based model that combines both spot and balancing electricity markets. From this model, we develop a multi-agent simulation to study the integration of the consumers' flexibility into the system. Our study identifies the conditions that real-time prices may lead to higher electricity costs, which in turn contradicts the usual claim that such a pricing scheme reduces cost. We show that such undesirable behavior is in fact systemic. Due to the existing structure of the wholesale market, the predicted demand that is used in the formation of the price is never realized since the flexible users will change their demand according to such established price. As the demand is never correctly predicted, the volume traded through the balancing markets increases, leading to higher overall costs. In this case, the system can sustain, and even benefit from, a small number of flexible users, but this solution can never upscale without increasing the total costs. To avoid this problem, we implement the so-called "exclusive groups." Our results illustrate the importance of rethinking the current practices so that flexibility can be successfully integrated considering scenarios with and without intermittent renewable sources.

Suggested Citation

  • Florian Kuhnlenz & Pedro H. J. Nardelli & Santtu Karhinen & Rauli Svento, 2017. "Implementing Flexible Demand: Real-time Price vs. Market Integration," Papers 1709.02667, arXiv.org, revised Feb 2018.
  • Handle: RePEc:arx:papers:1709.02667
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1709.02667
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dirk Helbing & Stefano Balietti, "undated". "Fundamental and Real-World Challenges in Economics," Working Papers CCSS-10-013, ETH Zurich, Chair of Systems Design.
    2. James Nicolaisen & Valentin Petrov & Leigh Tesfatsion, 2000. "Market Power and Efficiency in a Computational Electricity Market with Discriminatory Double-Auction Pricing," Computational Economics 0004005, University Library of Munich, Germany.
    3. Peter O. Steiner, 1957. "Peak Loads and Efficient Pricing," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 71(4), pages 585-610.
    4. Vandezande, Leen & Meeus, Leonardo & Belmans, Ronnie & Saguan, Marcelo & Glachant, Jean-Michel, 2010. "Well-functioning balancing markets: A prerequisite for wind power integration," Energy Policy, Elsevier, vol. 38(7), pages 3146-3154, July.
    5. Lion Hirth & Inka Ziegenhagen, 2013. "Control Power and Variable Renewables A Glimpse at German Data," Working Papers 2013.46, Fondazione Eni Enrico Mattei.
    6. Botterud, Audun & Kristiansen, Tarjei & Ilic, Marija D., 2010. "The relationship between spot and futures prices in the Nord Pool electricity market," Energy Economics, Elsevier, vol. 32(5), pages 967-978, September.
    7. Nicolosi, S., 2010. "Wind power integration, negative prices and power system flexibility - An empirical analysis of extreme events in Germany," MPRA Paper 31834, University Library of Munich, Germany.
    8. Elma, Onur & Taşcıkaraoğlu, Akın & Tahir İnce, A. & Selamoğulları, Uğur S., 2017. "Implementation of a dynamic energy management system using real time pricing and local renewable energy generation forecasts," Energy, Elsevier, vol. 134(C), pages 206-220.
    9. Hirth, Lion, 2013. "The market value of variable renewables," Energy Economics, Elsevier, vol. 38(C), pages 218-236.
    10. Kopsakangas Savolainen, Maria & Svento, Rauli, 2012. "Real-Time Pricing in the Nordic Power markets," Energy Economics, Elsevier, vol. 34(4), pages 1131-1142.
    11. Dallinger, David & Wietschel, Martin, 2012. "Grid integration of intermittent renewable energy sources using price-responsive plug-in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3370-3382.
    12. Gottwalt, Sebastian & Ketter, Wolfgang & Block, Carsten & Collins, John & Weinhardt, Christof, 2011. "Demand side management—A simulation of household behavior under variable prices," Energy Policy, Elsevier, vol. 39(12), pages 8163-8174.
    13. J. Doyne Farmer & Duncan Foley, 2009. "The economy needs agent-based modelling," Nature, Nature, vol. 460(7256), pages 685-686, August.
    14. Yousefi, Shaghayegh & Moghaddam, Mohsen Parsa & Majd, Vahid Johari, 2011. "Optimal real time pricing in an agent-based retail market using a comprehensive demand response model," Energy, Elsevier, vol. 36(9), pages 5716-5727.
    15. Severin Borenstein, 2005. "The Long-Run Efficiency of Real-Time Electricity Pricing," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 93-116.
    16. Kim, Jin-Ho & Shcherbakova, Anastasia, 2011. "Common failures of demand response," Energy, Elsevier, vol. 36(2), pages 873-880.
    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. Wanapinit, Natapon & Thomsen, Jessica & Kost, Christoph & Weidlich, Anke, 2021. "An MILP model for evaluating the optimal operation and flexibility potential of end-users," Applied Energy, Elsevier, vol. 282(PB).
    2. Alipour, Manijeh & Zare, Kazem & Seyedi, Heresh & Jalali, Mehdi, 2019. "Real-time price-based demand response model for combined heat and power systems," Energy, Elsevier, vol. 168(C), pages 1119-1127.
    3. Correia-da-Silva, João & Soares, Isabel & Fernández, Raquel, 2020. "Impact of dynamic pricing on investment in renewables," Energy, Elsevier, vol. 202(C).
    4. Milis, Kevin & Peremans, Herbert & Van Passel, Steven, 2018. "Steering the adoption of battery storage through electricity tariff design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 125-139.
    5. Francesco Mancini & Sabrina Romano & Gianluigi Lo Basso & Jacopo Cimaglia & Livio de Santoli, 2020. "How the Italian Residential Sector Could Contribute to Load Flexibility in Demand Response Activities: A Methodology for Residential Clustering and Developing a Flexibility Strategy," Energies, MDPI, vol. 13(13), pages 1-25, July.
    6. Mauricio de Castro Tomé & Pedro H. J. Nardelli & Hafiz Majid Hussain & Sohail Wahid & Arun Narayanan, 2020. "A Cyber-Physical Residential Energy Management System via Virtualized Packets," Energies, MDPI, vol. 13(3), pages 1-18, February.
    7. Michaelis, Anne & Hanny, Lisa & Körner, Marc-Fabian & Strüker, Jens & Weibelzahl, Martin, 2024. "Consumer-centric electricity markets: Six design principles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    8. Yin, Linfei & Qiu, Yao, 2022. "Long-term price guidance mechanism of flexible energy service providers based on stochastic differential methods," Energy, Elsevier, vol. 238(PB).
    9. Finck, Christian & Li, Rongling & Zeiler, Wim, 2020. "Optimal control of demand flexibility under real-time pricing for heating systems in buildings: A real-life demonstration," Applied Energy, Elsevier, vol. 263(C).
    10. Heydarian-Forushani, Ehsan & Golshan, Mohamad Esmail Hamedani & Shafie-khah, Miadreza & Catalão, João P.S., 2020. "A comprehensive linear model for demand response optimization problem," Energy, Elsevier, vol. 209(C).
    11. Sachin Kahawala & Daswin De Silva & Seppo Sierla & Damminda Alahakoon & Rashmika Nawaratne & Evgeny Osipov & Andrew Jennings & Valeriy Vyatkin, 2021. "Robust Multi-Step Predictor for Electricity Markets with Real-Time Pricing," Energies, MDPI, vol. 14(14), pages 1-20, July.
    12. Sousa, Joana & Soares, Isabel, 2020. "Demand response, market design and risk: A literature review," Utilities Policy, Elsevier, vol. 66(C).
    13. O'Connell, Sarah & Reynders, Glenn & Keane, Marcus M., 2021. "Impact of source variability on flexibility for demand response," Energy, Elsevier, vol. 237(C).
    14. Arega Getaneh Abate & Rosana Riccardi & Carlos Ruiz, 2021. "Dynamic tariffs-based demand response in retail electricity market under uncertainty," Papers 2105.03405, arXiv.org, revised Feb 2024.
    15. Bernardo A. Furtado & Miguel A. Fuentes & Claudio J. Tessone, 2019. "Policy Modeling and Applications: State-of-the-Art and Perspectives," Complexity, Hindawi, vol. 2019, pages 1-11, February.

    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. Kühnlenz, Florian & Nardelli, Pedro H.J. & Karhinen, Santtu & Svento, Rauli, 2018. "Implementing flexible demand: Real-time price vs. market integration," Energy, Elsevier, vol. 149(C), pages 550-565.
    2. Boßmann, Tobias & Eser, Eike Johannes, 2016. "Model-based assessment of demand-response measures—A comprehensive literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1637-1656.
    3. Katz, Jonas & Andersen, Frits Møller & Morthorst, Poul Erik, 2016. "Load-shift incentives for household demand response: Evaluation of hourly dynamic pricing and rebate schemes in a wind-based electricity system," Energy, Elsevier, vol. 115(P3), pages 1602-1616.
    4. Vesterberg, Mattias, 2016. "The hourly income elasticity of electricity," Energy Economics, Elsevier, vol. 59(C), pages 188-197.
    5. Krishnamurthy, Chandra Kiran B. & Vesterberg, Mattias & Böök, Herman & Lindfors, Anders V. & Svento, Rauli, 2018. "Real-time pricing revisited: Demand flexibility in the presence of micro-generation," Energy Policy, Elsevier, vol. 123(C), pages 642-658.
    6. Florian Kuhnlenz & Pedro H. J. Nardelli, 2016. "Agent-based Model for Spot and Balancing Electricity Markets," Papers 1612.04512, arXiv.org.
    7. Jin, Ming & Feng, Wei & Marnay, Chris & Spanos, Costas, 2018. "Microgrid to enable optimal distributed energy retail and end-user demand response," Applied Energy, Elsevier, vol. 210(C), pages 1321-1335.
    8. Joan Batalla-Bejerano & Elisa Trujillo-Baute, 2015. "Analysing the sensitivity of electricity system operational costs to deviations in supply and demand," Working Papers 2015/8, Institut d'Economia de Barcelona (IEB).
    9. 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.
    10. Mattias Vesterberg and Chandra Kiran B. Krishnamurthy, 2016. "Residential End-use Electricity Demand: Implications for Real Time Pricing in Sweden," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    11. Hirth, Lion & Ueckerdt, Falko, 2013. "Redistribution effects of energy and climate policy: The electricity market," Energy Policy, Elsevier, vol. 62(C), pages 934-947.
    12. Batalla-Bejerano, Joan & Costa-Campi, Maria Teresa & Trujillo-Baute, Elisa, 2016. "Collateral effects of liberalisation: Metering, losses, load profiles and cost settlement in Spain’s electricity system," Energy Policy, Elsevier, vol. 94(C), pages 421-431.
    13. Vesterberg, Mattias, 2017. "Power to the people: Electricity demand and household behavior," Umeå Economic Studies 942, Umeå University, Department of Economics.
    14. Pahle, Michael & Schill, Wolf-Peter & Gambardella, Christian & Tietjen, Oliver, 2016. "Renewable Energy Support, Negative Prices, and Real-time Pricing," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 37, pages 147-169.
    15. Auer, Benjamin R., 2016. "How does Germany's green energy policy affect electricity market volatility? An application of conditional autoregressive range models," Energy Policy, Elsevier, vol. 98(C), pages 621-628.
    16. Simshauser, Paul, 2022. "Rooftop solar PV and the peak load problem in the NEM's Queensland region," Energy Economics, Elsevier, vol. 109(C).
    17. Ketterer, Janina C., 2014. "The impact of wind power generation on the electricity price in Germany," Energy Economics, Elsevier, vol. 44(C), pages 270-280.
    18. Lion Hirth, 2015. "The Optimal Share of Variable Renewables: How the Variability of Wind and Solar Power affects their Welfare-optimal Deployment," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    19. Freier, Julia & von Loessl, Victor, 2022. "Dynamic electricity tariffs: Designing reasonable pricing schemes for private households," Energy Economics, Elsevier, vol. 112(C).
    20. Anees, Amir & Chen, Yi-Ping Phoebe, 2016. "True real time pricing and combined power scheduling of electric appliances in residential energy management system," Applied Energy, Elsevier, vol. 165(C), pages 592-600.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:arx:papers:1709.02667. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.