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Modeling Uncertainty Energy Price Based on Interval Optimization and Energy Management in the Electrical Grid

Author

Listed:
  • Julio César Machaca Mamani

    (National University José María Arguedas)

  • Freddy Carrasco-Choque

    (Frontera National University of Sullana)

  • Edith Fernanda Paredes-Calatayud

    (National Amazonian University of Madre de Dios)

  • Helfer Cusilayme-Barrantes

    (National Amazonian University of Madre de Dios)

  • Rocío Cahuana-Lipa

    (Ciencias de la Salud, Universidad Tecnológica de los Andes)

Abstract

Energy providers are faced with the challenge of effectively managing electrical energy systems amidst uncertainties. This study focuses on the management and dispatch of energy demand in the electricity microgrid, employing an interval optimization strategy to address electricity price uncertainties. The demand response program (DRP) incentive modeling is utilized to implement demand dispatch. To mitigate the impact of electricity price uncertainties, an incentive modeling approach based on offering reduced electricity demand during peak periods is proposed. The interval optimization approach is employed to minimize operational costs, with the epsilon constraint-based fuzzy method utilized to solve and address the problem. The effectiveness of the proposed modeling approach under conditions of uncertainty is demonstrated through the use of the microgrid in various case studies and numeric simulations. The participation of the DRP leads to minimizing the average and deviation costs by 9.5% and 6.5% in comparison with non-participation.

Suggested Citation

  • Julio César Machaca Mamani & Freddy Carrasco-Choque & Edith Fernanda Paredes-Calatayud & Helfer Cusilayme-Barrantes & Rocío Cahuana-Lipa, 2024. "Modeling Uncertainty Energy Price Based on Interval Optimization and Energy Management in the Electrical Grid," SN Operations Research Forum, Springer, vol. 5(1), pages 1-17, March.
    • Handle: RePEc:spr:snopef:v:5:y:2024:i:1:d:10.1007_s43069-023-00289-2
    DOI: 10.1007/s43069-023-00289-2
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    2. Pavlik, Martin & Michalski, Grzegorz, 2025. "Monte Carlo Simulations for Resolving Verifiability Paradoxes in Forecast Risk Management and Corporate Treasury Applications," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 13(2), pages 1-38.

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