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Bidding Strategy for VPP and Economic Feasibility Study of the Optimal Sizing of Storage Systems to Face the Uncertainty of Solar Generation Modelled with IGDT

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  • Michelle Maceas Henao

    (Grupo de Automática de la Universidad Nacional de Colombia, GAUNAL, Departamento de Energía Eléctrica y Automática, Universidad Nacional de Colombia Sede Medellín, Medellín 050034, Colombia)

  • Jairo José Espinosa Oviedo

    (Grupo de Automática de la Universidad Nacional de Colombia, GAUNAL, Departamento de Energía Eléctrica y Automática, Universidad Nacional de Colombia Sede Medellín, Medellín 050034, Colombia)

Abstract

Virtual power plants (VPP) emerge as a new participant that, in order to maximise their visibility and income, represents a group of distributed energy resources (DER) in the electricity market. However, this DER aggregation brings challenges, such as fluctuating renewable sources dependent on weather variables and guaranteeing power set points. One way to deal with these intermittencies is to incorporate the energy storage system (ESS) into the VPPs. Therefore, this paper presents a novel bidding strategy of VPP that includes modelling the uncertainty associated with solar generation using information gap decision theory (IGDT) and the optimal sizing of ESS systems so as to deal with solar generation fluctuations. Additionally, a study is carried out to determine the economic viability of this methodology in the short, medium and long terms using the return on investment (ROI).

Suggested Citation

  • Michelle Maceas Henao & Jairo José Espinosa Oviedo, 2022. "Bidding Strategy for VPP and Economic Feasibility Study of the Optimal Sizing of Storage Systems to Face the Uncertainty of Solar Generation Modelled with IGDT," Energies, MDPI, vol. 15(3), pages 1-13, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:953-:d:736471
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    References listed on IDEAS

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    Cited by:

    1. Bianca Goia & Tudor Cioara & Ionut Anghel, 2022. "Virtual Power Plant Optimization in Smart Grids: A Narrative Review," Future Internet, MDPI, vol. 14(5), pages 1-22, April.
    2. Lucas Feksa Ramos & Luciane Neves Canha & Josue Campos do Prado & Leonardo Rodrigues Araujo Xavier de Menezes, 2022. "A Novel Virtual Power Plant Uncertainty Modeling Framework Using Unscented Transform," Energies, MDPI, vol. 15(10), pages 1-13, May.
    3. Fernando García-Muñoz & Miguel Alfaro & Guillermo Fuertes & Manuel Vargas, 2022. "DC Optimal Power Flow Model to Assess the Irradiance Effect on the Sizing and Profitability of the PV-Battery System," Energies, MDPI, vol. 15(12), pages 1-16, June.
    4. Shinji Kuno & Kenji Tanaka & Yuji Yamada, 2022. "Effectiveness and Feasibility of Market Makers for P2P Electricity Trading," Energies, MDPI, vol. 15(12), pages 1-24, June.

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