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Forecast Error Sensitivity Analysis for Bidding in Electricity Markets with a Hybrid Renewable Plant Using a Battery Energy Storage System

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  • Jon Martinez-Rico

    (Automation and Control Unit, Fundación Tekniker, Basque Research and Technology Alliance (BRTA), 20600 Eibar, Spain
    School of Engineering, University of the Basque Country, Ing. Torres Quevedo, 1, 48013 Bilbao, Spain)

  • Ekaitz Zulueta

    (Automatic Control and System Engineering Department, University of the Basque Country (UPV/EHU), Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain)

  • Unai Fernandez-Gamiz

    (Nuclear Engineering and Fluid Mechanics Department, University of the Basque Country (UPV/EHU), Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain)

  • Ismael Ruiz de Argandoña

    (Automation and Control Unit, Fundación Tekniker, Basque Research and Technology Alliance (BRTA), 20600 Eibar, Spain)

  • Mikel Armendia

    (Automation and Control Unit, Fundación Tekniker, Basque Research and Technology Alliance (BRTA), 20600 Eibar, Spain)

Abstract

Deep integration of renewable energies into the electricity grid is restricted by the problems related to their intermittent and uncertain nature. These problems affect both system operators and renewable power plant owners since, due to the electricity market rules, plants need to report their production some hours in advance and are, hence, exposed to possible penalties associated with unfulfillment of energy production. In this context, energy storage systems appear as a promising solution to reduce the stochastic nature of renewable sources. Furthermore, batteries can also be used for performing energy arbitrage, which consists in shifting energy and selling it at higher price hours. In this paper, a bidding optimization algorithm is used for enhancing profitability and minimizing the battery loss of value. The algorithm considers the participation in both day-ahead and intraday markets, and a sensitivity analysis is conducted to check the profitability variation related to prediction uncertainty. The obtained results highlight the importance of bidding in intraday markets to compensate the prediction errors and show that, for the Iberian Electricity Market, the uncertainty does not significantly affect the final benefits.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:9:p:3577-:d:351321
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    References listed on IDEAS

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    Cited by:

    1. Peng, Feixiang & Hu, Shubo & Fan, Xuanxuan & Sun, Hui & Zhou, Wei & Guo, Furan & Song, Wenzhuo, 2021. "Sequential coalition formation for wind-thermal combined bidding," Energy, Elsevier, vol. 236(C).
    2. Ramin Sakipour & Hamdi Abdi, 2020. "Optimizing Battery Energy Storage System Data in the Presence of Wind Power Plants: A Comparative Study on Evolutionary Algorithms," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
    3. Saray Martínez-Lastras & Laura Frías-Paredes & Diego Prieto-Herráez & Martín Gastón-Romeo & Diego González-Aguilera, 2023. "Analysis of the Suitability of the EOLO Wind-Predictor Model for the Spanish Electricity Markets," Energies, MDPI, vol. 16(3), pages 1-16, January.

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