A Novel Virtual Power Plant Uncertainty Modeling Framework Using Unscented Transform

Lucas Feksa Ramos, Luciane Neves Canha, Josue Campos do Prado and Leonardo Rodrigues Araujo Xavier de Menezes
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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

Energies, 2022, vol. 15, issue 10, 1-13

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.

Keywords: forecast uncertainty; virtual power plant; unscented transform (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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