IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i4p450-d94790.html
   My bibliography  Save this article

Aggregators’ Optimal Bidding Strategy in Sequential Day-Ahead and Intraday Electricity Spot Markets

Author

Listed:
  • Xiaolin Ayón

    (Department of Electrical Engineering, Universidad Carlos III de Madrid, Avenida de la Universidad 30, 28911 Leganés, Madrid, Spain)

  • María Ángeles Moreno

    (Department of Electrical Engineering, Universidad Carlos III de Madrid, Avenida de la Universidad 30, 28911 Leganés, Madrid, Spain)

  • Julio Usaola

    (Department of Electrical Engineering, Universidad Carlos III de Madrid, Avenida de la Universidad 30, 28911 Leganés, Madrid, Spain)

Abstract

This paper proposes a probabilistic optimization method that produces optimal bidding curves to be submitted by an aggregator to the day-ahead electricity market and the intraday market, considering the flexible demand of his customers (based in time dependent resources such as batteries and shiftable demand) and taking into account the possible imbalance costs as well as the uncertainty of forecasts (market prices, demand, and renewable energy sources (RES) generation). The optimization strategy aims to minimize the total cost of the traded energy over a whole day, taking into account the intertemporal constraints. The proposed formulation leads to the solution of different linear optimization problems, following the natural temporal sequence of electricity spot markets. Intertemporal constraints regarding time dependent resources are fulfilled through a scheduling process performed after the day-ahead market clearing. Each of the different problems is of moderate dimension and requires short computation times. The benefits of the proposed strategy are assessed comparing the payments done by an aggregator over a sample period of one year following different deterministic and probabilistic strategies. Results show that probabilistic strategy reports better benefits for aggregators participating in power markets.

Suggested Citation

  • Xiaolin Ayón & María Ángeles Moreno & Julio Usaola, 2017. "Aggregators’ Optimal Bidding Strategy in Sequential Day-Ahead and Intraday Electricity Spot Markets," Energies, MDPI, vol. 10(4), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:4:p:450-:d:94790
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/4/450/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/4/450/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chaves-Ávila, J.P. & Fernandes, C., 2015. "The Spanish intraday market design: A successful solution to balance renewable generation?," Renewable Energy, Elsevier, vol. 74(C), pages 422-432.
    2. Dehghani, Hamed & Vahidi, Behrooz & Hosseinian, Seyed Hossein, 2017. "Wind farms participation in electricity markets considering uncertainties," Renewable Energy, Elsevier, vol. 101(C), pages 907-918.
    3. Fernandes, Camila & Frías, Pablo & Reneses, Javier, 2016. "Participation of intermittent renewable generators in balancing mechanisms: A closer look into the Spanish market design," Renewable Energy, Elsevier, vol. 89(C), pages 305-316.
    4. Eduardo Faria & Stein-Erik Fleten, 2011. "Day-ahead market bidding for a Nordic hydropower producer: taking the Elbas market into account," Computational Management Science, Springer, vol. 8(1), pages 75-101, April.
    5. Pandžić, Hrvoje & Morales, Juan M. & Conejo, Antonio J. & Kuzle, Igor, 2013. "Offering model for a virtual power plant based on stochastic programming," Applied Energy, Elsevier, vol. 105(C), pages 282-292.
    6. Bornapour, Mosayeb & Hooshmand, Rahmat-Allah & Khodabakhshian, Amin & Parastegari, Moein, 2016. "Optimal coordinated scheduling of combined heat and power fuel cell, wind, and photovoltaic units in micro grids considering uncertainties," Energy, Elsevier, vol. 117(P1), pages 176-189.
    7. Ferruzzi, Gabriella & Cervone, Guido & Delle Monache, Luca & Graditi, Giorgio & Jacobone, Francesca, 2016. "Optimal bidding in a Day-Ahead energy market for Micro Grid under uncertainty in renewable energy production," Energy, Elsevier, vol. 106(C), pages 194-202.
    8. Ottesen, Stig Ødegaard & Tomasgard, Asgeir & Fleten, Stein-Erik, 2016. "Prosumer bidding and scheduling in electricity markets," Energy, Elsevier, vol. 94(C), pages 828-843.
    9. Zhou, Yue & Wang, Chengshan & Wu, Jianzhong & Wang, Jidong & Cheng, Meng & Li, Gen, 2017. "Optimal scheduling of aggregated thermostatically controlled loads with renewable generation in the intraday electricity market," Applied Energy, Elsevier, vol. 188(C), pages 456-465.
    10. Sajjad Abedi & Gholam Riahy & Seyed Hossein Hosseinian & Mehdi Farhadkhani, 2013. "Improved Stochastic Modeling: An Essential Tool for Power System Scheduling in the Presence of Uncertain Renewables," Chapters, in: Hasan Arman & Ibrahim Yuksel (ed.), New Developments in Renewable Energy, IntechOpen.
    11. Laia, R. & Pousinho, H.M.I. & Melíco, R. & Mendes, V.M.F., 2016. "Bidding strategy of wind-thermal energy producers," Renewable Energy, Elsevier, vol. 99(C), pages 673-681.
    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. Dapeng Chen & Zhaoxia Jing & Huijuan Tan, 2019. "Optimal Bidding/Offering Strategy for EV Aggregators under a Novel Business Model," Energies, MDPI, vol. 12(7), pages 1-19, April.
    2. Morteza Vahid-Ghavidel & Mohammad Sadegh Javadi & Matthew Gough & Sérgio F. Santos & Miadreza Shafie-khah & João P.S. Catalão, 2020. "Demand Response Programs in Multi-Energy Systems: A Review," Energies, MDPI, vol. 13(17), pages 1-17, August.
    3. Jon Martinez-Rico & Ekaitz Zulueta & Unai Fernandez-Gamiz & Ismael Ruiz de Argandoña & Mikel Armendia, 2020. "Forecast Error Sensitivity Analysis for Bidding in Electricity Markets with a Hybrid Renewable Plant Using a Battery Energy Storage System," Sustainability, MDPI, vol. 12(9), pages 1-18, April.
    4. Micha{l} Narajewski & Florian Ziel, 2021. "Optimal bidding in hourly and quarter-hourly electricity price auctions: trading large volumes of power with market impact and transaction costs," Papers 2104.14204, arXiv.org, revised Feb 2022.
    5. Dharmesh Dabhi & Kartik Pandya & Joao Soares & Fernando Lezama & Zita Vale, 2022. "Cross Entropy Covariance Matrix Adaptation Evolution Strategy for Solving the Bi-Level Bidding Optimization Problem in Local Energy Markets," Energies, MDPI, vol. 15(13), pages 1-20, July.
    6. Okur, Özge & Heijnen, Petra & Lukszo, Zofia, 2021. "Aggregator’s business models in residential and service sectors: A review of operational and financial aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    7. Narajewski, Michał & Ziel, Florian, 2022. "Optimal bidding in hourly and quarter-hourly electricity price auctions: Trading large volumes of power with market impact and transaction costs," Energy Economics, Elsevier, vol. 110(C).
    8. Ignacio Blanco & Daniela Guericke & Anders N. Andersen & Henrik Madsen, 2018. "Operational Planning and Bidding for District Heating Systems with Uncertain Renewable Energy Production," Energies, MDPI, vol. 11(12), pages 1-26, November.
    9. Biggins, F.A.V. & Travers, D. & Ejeh, J.O. & Lee, R. & Buckley, A. & Brown, S., 2023. "The economic impact of location on a solar farm co-located with energy storage," Energy, Elsevier, vol. 278(C).
    10. Pedro Faria, 2019. "Distributed Energy Resources Management," Energies, MDPI, vol. 12(3), pages 1-3, February.
    11. Ayman Esmat & Julio Usaola & Mª Ángeles Moreno, 2018. "A Decentralized Local Flexibility Market Considering the Uncertainty of Demand," Energies, MDPI, vol. 11(8), pages 1-32, August.
    12. Pedro Faria & Zita Vale, 2019. "A Demand Response Approach to Scheduling Constrained Load Shifting," Energies, MDPI, vol. 12(9), pages 1-16, May.
    13. Finhold, E. & Gärtner, C. & Grindel, R. & Heller, T. & Leithäuser, N. & Röger, E. & Schirra, F., 2023. "Optimizing the marketing of flexibility for a virtual battery in day-ahead and balancing markets: A rolling horizon case study," Applied Energy, Elsevier, vol. 352(C).
    14. Micha{l} Narajewski & Florian Ziel, 2020. "Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories," Papers 2005.01365, arXiv.org, revised Aug 2020.
    15. Terlouw, Tom & AlSkaif, Tarek & Bauer, Christian & van Sark, Wilfried, 2019. "Multi-objective optimization of energy arbitrage in community energy storage systems using different battery technologies," Applied Energy, Elsevier, vol. 239(C), pages 356-372.
    16. Herding, Robert & Ross, Emma & Jones, Wayne R. & Charitopoulos, Vassilis M. & Papageorgiou, Lazaros G., 2023. "Stochastic programming approach for optimal day-ahead market bidding curves of a microgrid," Applied Energy, Elsevier, vol. 336(C).
    17. Mahmood Hosseini Imani & Shaghayegh Zalzar & Amir Mosavi & Shahaboddin Shamshirband, 2018. "Strategic Behavior of Retailers for Risk Reduction and Profit Increment via Distributed Generators and Demand Response Programs," Energies, MDPI, vol. 11(6), pages 1-24, June.
    18. Donghun Lee & Kwanho Kim, 2019. "Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information," Energies, MDPI, vol. 12(2), pages 1-22, January.
    19. Davarzani, Sima & Pisica, Ioana & Taylor, Gareth A. & Munisami, Kevin J., 2021. "Residential Demand Response Strategies and Applications in Active Distribution Network Management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    20. Narajewski, Michał & Ziel, Florian, 2020. "Ensemble forecasting for intraday electricity prices: Simulating trajectories," Applied Energy, Elsevier, vol. 279(C).

    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. Banaei, Mohsen & Oloomi-Buygi, Majid & Zabetian-Hosseini, Seyed-Mahdi, 2018. "Strategic gaming of wind power producers joined with thermal units in electricity markets," Renewable Energy, Elsevier, vol. 115(C), pages 1067-1074.
    2. Angelica, Gianfreda & Lucia, Parisio & Matteo, Pelagatti, 2017. "The RES-induced Switching Effect Across Fossil Fuels: An Analysis of the Italian Day-Ahead and Balancing Prices and Their Connected Costs," Working Papers 360, University of Milano-Bicocca, Department of Economics, revised 03 Feb 2017.
    3. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Ke, Deping & Zhang, Zhen & Wang, Jing, 2018. "A bi-level scheduling model for virtual power plants with aggregated thermostatically controlled loads and renewable energy," Applied Energy, Elsevier, vol. 224(C), pages 659-670.
    4. Brijs, Tom & De Jonghe, Cedric & Hobbs, Benjamin F. & Belmans, Ronnie, 2017. "Interactions between the design of short-term electricity markets in the CWE region and power system flexibility," Applied Energy, Elsevier, vol. 195(C), pages 36-51.
    5. Gianfreda, Angelica & Parisio, Lucia & Pelagatti, Matteo, 2018. "A review of balancing costs in Italy before and after RES introduction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 549-563.
    6. 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).
    7. Bogdan-Constantin Neagu & Ovidiu Ivanov & Gheorghe Grigoras & Mihai Gavrilas & Dumitru-Marcel Istrate, 2020. "New Market Model with Social and Commercial Tiers for Improved Prosumer Trading in Microgrids," Sustainability, MDPI, vol. 12(18), pages 1-43, September.
    8. Bornapour, Mosayeb & Hooshmand, Rahmat-Allah & Parastegari, Moein, 2019. "An efficient scenario-based stochastic programming method for optimal scheduling of CHP-PEMFC, WT, PV and hydrogen storage units in micro grids," Renewable Energy, Elsevier, vol. 130(C), pages 1049-1066.
    9. Spodniak, Petr & Ollikka, Kimmo & Honkapuro, Samuli, 2019. "The relevance of wholesale electricity market places: the Nordic case," Papers WP631, Economic and Social Research Institute (ESRI).
    10. Oluwaseun Oladimeji & Álvaro Ortega & Lukas Sigrist & Luis Rouco & Pedro Sánchez-Martín & Enrique Lobato, 2022. "Optimal Participation of Heterogeneous, RES-Based Virtual Power Plants in Energy Markets," Energies, MDPI, vol. 15(9), pages 1-19, April.
    11. Bornapour, Mosayeb & Hooshmand, Rahmat-Allah & Khodabakhshian, Amin & Parastegari, Moein, 2017. "Optimal stochastic coordinated scheduling of proton exchange membrane fuel cell-combined heat and power, wind and photovoltaic units in micro grids considering hydrogen storage," Applied Energy, Elsevier, vol. 202(C), pages 308-322.
    12. Moreno, Blanca & Díaz, Guzmán, 2019. "The impact of virtual power plant technology composition on wholesale electricity prices: A comparative study of some European Union electricity markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 99(C), pages 100-108.
    13. Lobato, E. & Doenges, K. & Egido, I. & Sigrist, L., 2020. "Limits to wind aggregation: Empirical assessment in the Spanish electricity system," Renewable Energy, Elsevier, vol. 147(P1), pages 1321-1330.
    14. Hu, Jing & Harmsen, Robert & Crijns-Graus, Wina & Worrell, Ernst & van den Broek, Machteld, 2018. "Identifying barriers to large-scale integration of variable renewable electricity into the electricity market: A literature review of market design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2181-2195.
    15. Gomes, I.L.R. & Melicio, R. & Mendes, V.M.F. & Pousinho, H.M.I., 2019. "Decision making for sustainable aggregation of clean energy in day-ahead market: Uncertainty and risk," Renewable Energy, Elsevier, vol. 133(C), pages 692-702.
    16. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    17. Okur, Özge & Heijnen, Petra & Lukszo, Zofia, 2021. "Aggregator’s business models in residential and service sectors: A review of operational and financial aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    18. Karsten Neuhoff & Sophia Rüster & Sebastian Schwenen, 2015. "Power Market Design beyond 2020: Time to Revisit Key Elements?," Discussion Papers of DIW Berlin 1456, DIW Berlin, German Institute for Economic Research.
    19. Hugo Algarvio & Fernando Lopes & António Couto & Ana Estanqueiro, 2019. "Participation of wind power producers in day‐ahead and balancing markets: An overview and a simulation‐based study," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(5), September.
    20. Wang, Meng & Infante Ferreira, Carlos A., 2017. "Absorption heat pump cycles with NH3 – ionic liquid working pairs," Applied Energy, Elsevier, vol. 204(C), pages 819-830.

    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:4:p:450-:d:94790. 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.