IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v94y2016icp828-843.html
   My bibliography  Save this article

Prosumer bidding and scheduling in electricity markets

Author

Listed:
  • Ottesen, Stig Ødegaard
  • Tomasgard, Asgeir
  • Fleten, Stein-Erik

Abstract

We propose short-term decision-support models for aggregators that sell electricity to prosumers and buy back surplus electricity. The key element is that the aggregator can control flexible energy units at the prosumers. Our objective is total cost minimization by trading in an electricity spot market also taking into consideration costs from grid tariffs, use of fuels and imbalance penalization. We explicitly model the flexibility properties of the underlying energy systems in the prosumers' buildings. In addition, we include the bidding rules and handle the interrelations between hours. Finally, we capture the information structure of uncertain parameters through scenario trees. This results in a two-stage stochastic mixed integer linear program where the bidding decision is made in the first stage and the scheduling in the second. We illustrate the approach in a case study with a diverse portfolio of prosumers. By simulating over a two-month period, we calculate the value of flexibility and the value of stochastic planning.

Suggested Citation

  • Ottesen, Stig Ødegaard & Tomasgard, Asgeir & Fleten, Stein-Erik, 2016. "Prosumer bidding and scheduling in electricity markets," Energy, Elsevier, vol. 94(C), pages 828-843.
    • Handle: RePEc:eee:energy:v:94:y:2016:i:c:p:828-843
    DOI: 10.1016/j.energy.2015.11.047
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544215015972
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2015.11.047?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. Faria, Pedro & Soares, Tiago & Vale, Zita & Morais, Hugo, 2014. "Distributed generation and demand response dispatch for a virtual power player energy and reserve provision," Renewable Energy, Elsevier, vol. 66(C), pages 686-695.
    2. Zare, Kazem & Moghaddam, Mohsen Parsa & Sheikh El Eslami, Mohammad Kazem, 2010. "Demand bidding construction for a large consumer through a hybrid IGDT-probability methodology," Energy, Elsevier, vol. 35(7), pages 2999-3007.
    3. Ottesen, Stig Odegaard & Tomasgard, Asgeir, 2015. "A stochastic model for scheduling energy flexibility in buildings," Energy, Elsevier, vol. 88(C), pages 364-376.
    4. Garnier, Ernesto & Madlener, Reinhard, 2014. "Day-Ahead versus Intraday Valuation of Demand-Side Flexibility for Photovoltaic and Wind Power Systems," FCN Working Papers 17/2014, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    5. Kristiansen, Tarjei, 2012. "Forecasting Nord Pool day-ahead prices with an autoregressive model," Energy Policy, Elsevier, vol. 49(C), pages 328-332.
    6. Seljom, Pernille & Tomasgard, Asgeir, 2015. "Short-term uncertainty in long-term energy system models — A case study of wind power in Denmark," Energy Economics, Elsevier, vol. 49(C), pages 157-167.
    7. Fleten, Stein-Erik & Kristoffersen, Trine Krogh, 2007. "Stochastic programming for optimizing bidding strategies of a Nordic hydropower producer," European Journal of Operational Research, Elsevier, vol. 181(2), pages 916-928, September.
    8. Garnier, Ernesto & Madlener, Reinhard, 2014. "Balancing Forecast Errors in Continuous-Trade Intraday Markets," FCN Working Papers 2/2014, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    9. 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.
    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. Kumbartzky, Nadine & Schacht, Matthias & Schulz, Katrin & Werners, Brigitte, 2017. "Optimal operation of a CHP plant participating in the German electricity balancing and day-ahead spot market," European Journal of Operational Research, Elsevier, vol. 261(1), pages 390-404.
    2. Mestre, Guillermo & Sánchez-Úbeda, Eugenio F. & Muñoz San Roque, Antonio & Alonso, Estrella, 2022. "The arithmetic of stepwise offer curves," Energy, Elsevier, vol. 239(PE).
    3. Kuttner, Leopold, 2022. "Integrated scheduling and bidding of power and reserve of energy resource aggregators with storage plants," Applied Energy, Elsevier, vol. 321(C).
    4. Héctor Marañón-Ledesma & Asgeir Tomasgard, 2019. "Analyzing Demand Response in a Dynamic Capacity Expansion Model for the European Power Market," Energies, MDPI, vol. 12(15), pages 1-24, August.
    5. Ernesto Garnier and Reinhard Madlener, 2016. "The Influence of Policy Regime Risks on Investments in Innovative Energy Technology," The Energy Journal, International Association for Energy Economics, vol. 0(Bollino-M).
    6. Backe, Stian & Ahang, Mohammadreza & Tomasgard, Asgeir, 2021. "Stable stochastic capacity expansion with variable renewables: Comparing moment matching and stratified scenario generation sampling," Applied Energy, Elsevier, vol. 302(C).
    7. Bohlayer, Markus & Fleschutz, Markus & Braun, Marco & Zöttl, Gregor, 2020. "Energy-intense production-inventory planning with participation in sequential energy markets," Applied Energy, Elsevier, vol. 258(C).
    8. Gro Klæboe & Jørgen Braathen & Anders Lund Eriksrud & Stein-Erik Fleten, 2022. "Day-ahead market bidding taking the balancing power market into account," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 683-703, October.
    9. Jin, Xiaolong & Wu, Qiuwei & Jia, Hongjie, 2020. "Local flexibility markets: Literature review on concepts, models and clearing methods," Applied Energy, Elsevier, vol. 261(C).
    10. Mostafa Bakhtvar & Amer Al-Hinai, 2021. "Robust Operation of Hybrid Solar–Wind Power Plant with Battery Energy Storage System," Energies, MDPI, vol. 14(13), pages 1-18, June.
    11. Marañón-Ledesma, Hector & Tomasgard, Asgeir, 2019. "Long-Term Electricity Investments Accounting for Demand and Supply Side Flexibility," MPRA Paper 92957, University Library of Munich, Germany.
    12. Kara, Güray & Tomasgard, Asgeir & Farahmand, Hossein, 2022. "Characterizing flexibility in power markets and systems," Utilities Policy, Elsevier, vol. 75(C).
    13. Prilly Oktoviany & Robert Knobloch & Ralf Korn, 2021. "A machine learning-based price state prediction model for agricultural commodities using external factors," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 1063-1085, December.
    14. Florian Ziel & Rick Steinert & Sven Husmann, 2015. "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets," Papers 1501.00818, arXiv.org, revised Dec 2015.
    15. Marchetti, Isabella & Rego, Erik Eduardo, 2022. "The impact of hourly pricing for renewable generation projects in Brazil," Renewable Energy, Elsevier, vol. 189(C), pages 601-617.
    16. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    17. Sinha, Pankaj & Mathur, Kritika, 2016. "Empirical Analysis of Developments in the Day Ahead Electricity Markets in India," MPRA Paper 72969, University Library of Munich, Germany.
    18. Florian Ziel & Rick Steinert & Sven Husmann, 2014. "Efficient Modeling and Forecasting of the Electricity Spot Price," Papers 1402.7027, arXiv.org, revised Oct 2014.
    19. Koltsaklis, Nikolaos E. & Nazos, Konstantinos, 2017. "A stochastic MILP energy planning model incorporating power market dynamics," Applied Energy, Elsevier, vol. 205(C), pages 1364-1383.
    20. Silva, Ana R. & Pousinho, H.M.I. & Estanqueiro, Ana, 2022. "A multistage stochastic approach for the optimal bidding of variable renewable energy in the day-ahead, intraday and balancing markets," Energy, Elsevier, vol. 258(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:eee:energy:v:94:y:2016:i:c:p:828-843. 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.