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Schedule Optimization in a Smart Microgrid Considering Demand Response Constraints

Author

Listed:
  • Julian Garcia-Guarin

    (Electrical and Electronics Engineering Department, Engineering Faculty, Universidad Nacional de Colombia, Bogotá 111321, Colombia)

  • David Alvarez

    (Electrical and Electronics Engineering Department, Engineering Faculty, Universidad Nacional de Colombia, Bogotá 111321, Colombia)

  • Arturo Bretas

    (Engineering Faculty, University of Florida, Gainesville, FL 32611, USA)

  • Sergio Rivera

    (Electrical and Electronics Engineering Department, Engineering Faculty, Universidad Nacional de Colombia, Bogotá 111321, Colombia
    Engineering Faculty, University of Florida, Gainesville, FL 32611, USA)

Abstract

Smart microgrids (SMGs) may face energy rationing due to unavailability of energy resources. Demand response (DR) in SMGs is useful not only in emergencies, since load cuts might be planned with a reduction in consumption but also in normal operation. SMG energy resources include storage systems, dispatchable units, and resources with uncertainty, such as residential demand, renewable generation, electric vehicle traffic, and electricity markets. An aggregator can optimize the scheduling of these resources, however, load demand can completely curtail until being neglected to increase the profits. The DR function (DRF) is developed as a constraint of minimum size to supply the demand and contributes solving of the 0-1 knapsack problem (KP), which involves a combinatorial optimization. The 0-1 KP stores limited energy capacity and is successful in disconnecting loads. Both constraints, the 0-1 KP and DRF, are compared in the ranking index, load reduction percentage, and execution time. Both functions turn out to be very similar according to the performance of these indicators, unlike the ranking index, in which the DRF has better performance. The DRF reduces to 25% the minimum demand to avoid non-optimal situations, such as non-supplying the demand and has potential benefits, such as the elimination of finite combinations and easy implementation.

Suggested Citation

  • Julian Garcia-Guarin & David Alvarez & Arturo Bretas & Sergio Rivera, 2020. "Schedule Optimization in a Smart Microgrid Considering Demand Response Constraints," Energies, MDPI, vol. 13(17), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4567-:d:408303
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    References listed on IDEAS

    as
    1. Julian Garcia-Guarin & Diego Rodriguez & David Alvarez & Sergio Rivera & Camilo Cortes & Alejandra Guzman & Arturo Bretas & Julio Romero Aguero & Newton Bretas, 2019. "Smart Microgrids Operation Considering a Variable Neighborhood Search: The Differential Evolutionary Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 12(16), pages 1-13, August.
    2. Di Somma, M. & Graditi, G. & Heydarian-Forushani, E. & Shafie-khah, M. & Siano, P., 2018. "Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects," Renewable Energy, Elsevier, vol. 116(PA), pages 272-287.
    3. Laura M. Cruz & David L. Alvarez & Ameena S. Al-Sumaiti & Sergio Rivera, 2020. "Load Curtailment Optimization Using the PSO Algorithm for Enhancing the Reliability of Distribution Networks," Energies, MDPI, vol. 13(12), pages 1-15, June.
    4. João Soares & Bruno Canizes & Cristina Lobo & Zita Vale & Hugo Morais, 2012. "Electric Vehicle Scenario Simulator Tool for Smart Grid Operators," Energies, MDPI, vol. 5(6), pages 1-19, June.
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    Cited by:

    1. Tostado-Véliz, Marcos & Kamel, Salah & Hasanien, Hany M. & Turky, Rania A. & Jurado, Francisco, 2022. "Uncertainty-aware day-ahead scheduling of microgrids considering response fatigue: An IGDT approach," Applied Energy, Elsevier, vol. 310(C).
    2. Jianying Li & Minsheng Yang & Yuexing Zhang & Jianqi Li & Jianquan Lu, 2023. "Micro-Grid Day-Ahead Stochastic Optimal Dispatch Considering Multiple Demand Response and Electric Vehicles," Energies, MDPI, vol. 16(8), pages 1-15, April.
    3. Joao Soares & Bruno Canizes & Zita Vale, 2021. "Rethinking the Distribution Power Network Planning and Operation for a Sustainable Smart Grid and Smooth Interaction with Electrified Transportation," Energies, MDPI, vol. 14(23), pages 1-4, November.

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