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Optimum Stochastic Allocation for Demand Response for Power Markets in Microgrids

Author

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  • Edwin Garcia

    (Smart Grid Research Group—GIREI (Spanish Acronym—Grupo de Investigación en Redes Eléctricas Inteligentes), Salesian Polytechnic University, Quito EC170702, Ecuador)

  • Alexander Águila

    (Smart Grid Research Group—GIREI (Spanish Acronym—Grupo de Investigación en Redes Eléctricas Inteligentes), Salesian Polytechnic University, Quito EC170702, Ecuador)

  • Leony Ortiz

    (Smart Grid Research Group—GIREI (Spanish Acronym—Grupo de Investigación en Redes Eléctricas Inteligentes), Salesian Polytechnic University, Quito EC170702, Ecuador)

  • Milton Ruiz

    (Smart Grid Research Group—GIREI (Spanish Acronym—Grupo de Investigación en Redes Eléctricas Inteligentes), Salesian Polytechnic University, Quito EC170702, Ecuador)

Abstract

This research incorporates an electricity market model based on a stochastic allocation of distributed resources and the analysis of an optimal demand response for a smart microgrid. This research develops a methodology that allows the application and comparison of various demand-response mechanisms and the analysis of the differences between them and the case of no-demand response, emphasizing economics, environmental care, demand curves, and other factors. By enabling more active participation by residential users of the smart microgrid, these demand-response methods help to flatten the demand curve and support the goals set by the electricity market model. Both conventional and non-conventional generators compete in the electricity market, with renewable energy sources preferred to encourage green generation. Conventional generators are required to supply electricity gradually, starting with the lowest pollution level. In addition, conventional generators are compensated for dispatch, system reliability, and availability. In addition, random variables are used in this study to predict initial load, solar radiation analysis, and biomass input before resources are optimized to meet demand.

Suggested Citation

  • Edwin Garcia & Alexander Águila & Leony Ortiz & Milton Ruiz, 2024. "Optimum Stochastic Allocation for Demand Response for Power Markets in Microgrids," Energies, MDPI, vol. 17(5), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1037-:d:1343958
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    References listed on IDEAS

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    1. Liping Wang & Minghao Liu & Boquan Wang & Jiajie Wu & Chuangang Li, 2017. "Study on Nested-Structured Load Shedding Method of Thermal Power Stations Based on Output Fluctuations," Energies, MDPI, vol. 10(10), pages 1-16, September.
    2. Behrangrad, Mahdi & Sugihara, Hideharu & Funaki, Tsuyoshi, 2011. "Effect of optimal spinning reserve requirement on system pollution emission considering reserve supplying demand response in the electricity market," Applied Energy, Elsevier, vol. 88(7), pages 2548-2558, July.
    3. Ferreira, L.R.A. & Otto, R.B. & Silva, F.P. & De Souza, S.N.M. & De Souza, S.S. & Ando Junior, O.H., 2018. "Review of the energy potential of the residual biomass for the distributed generation in Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 440-455.
    4. Sai Nikhil Vodapally & Mohd Hasan Ali, 2022. "A Comprehensive Review of Solar Photovoltaic (PV) Technologies, Architecture, and Its Applications to Improved Efficiency," Energies, MDPI, vol. 16(1), pages 1-18, December.
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