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A Novel Virtual Power Plant Uncertainty Modeling Framework Using Unscented Transform

Author

Listed:
  • Lucas Feksa Ramos

    (Department of Electrical Engineering, Federal University of Rondônia, Porto Velho 76801-059, Brazil)

  • Luciane Neves Canha

    (Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Brazil)

  • Josue Campos do Prado

    (School of Engineering and Computer Science, Washington State University, Vancouver, WA 98686, USA)

  • Leonardo Rodrigues Araujo Xavier de Menezes

    (Department of Electrical Engineering, University of Brasilia, Brasilia 70910-900, Brazil)

Abstract

This paper proposes a new strategy for modeling predictability uncertainty in a stochastic context for decision making within a Virtual Power Plant (VPP). Modeling variable renewable energy generation is an essential step for effective VPP planning and operation. However, it is also a challenging task due to the uncertain nature of its sources. Therefore, developing tools to effectively predict these uncertainties is essential for the optimal participation of VPPs in the electricity market. The purpose of this paper is to present a novel method to model the uncertainties associated with energy dispatching in a VPP using the Unscented Transform (UT) method. The proposed algorithm minimizes the risks associated with the VPP operation in a computationally efficient and simple manner, and can be used in real-time on a power system. The proposed framework was evaluated based on an Electric Power System (EPS) model with historical data. Case studies have been performed to demonstrate the effectiveness of the proposed framework in minimizing power demand and renewable-energy-forecasting uncertainty for a VPP.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3716-:d:818866
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    References listed on IDEAS

    as
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