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Assessment of the Modeling of Demand Response as a Dispatchable Resource in Day-Ahead Hydrothermal Unit Commitment Problems: The Brazilian Case

Author

Listed:
  • Rosane Santos

    (Electrical Engineering Department, UFF—Federal Fluminense University, Niterói 24210-240, Brazil)

  • André Luiz Diniz

    (CEPEL—Brazilian Electric Energy Research Center, Rio de Janeiro 21941-911, Brazil)

  • Bruno Borba

    (Electrical Engineering Department, UFF—Federal Fluminense University, Niterói 24210-240, Brazil)

Abstract

Modern power systems have experienced large increases in intermittent and non-dispatchable sources and a progressive reduction in the size of hydro reservoirs for inflow regularization. One method to mitigate the high uncertainty and intermittency of the net load is by Demand Response (DR) mechanisms, to allow a secure and reliable system dispatch. This work applied a mixed integer linear programming formulation to model DR as a dispatchable resource in the day-ahead hydrothermal scheduling problem, taking into account minimum load curtailment constraints, minimum up/down load deduction times, as well as piecewise linear bid curves for load shedding in eligible loads. The methodology was implemented in the official model used in Brazil and tested in large-scale problems to obtain the optimal daily dispatch and hourly pricing. The results show the positive impact of dispatchable DR loads in cost reduction and in mitigating peak values of energy prices, even for predominantly hydro systems, helping to preserve the reservoir levels and increasing the security of the supply in the future.

Suggested Citation

  • Rosane Santos & André Luiz Diniz & Bruno Borba, 2022. "Assessment of the Modeling of Demand Response as a Dispatchable Resource in Day-Ahead Hydrothermal Unit Commitment Problems: The Brazilian Case," Energies, MDPI, vol. 15(11), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:3928-:d:824582
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    References listed on IDEAS

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    1. Layon Mescolin de Oliveira & Ivo Chaves da Silva Junior & Ramon Abritta, 2022. "Search Space Reduction for the Thermal Unit Commitment Problem through a Relevance Matrix," Energies, MDPI, vol. 15(19), pages 1-16, September.

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