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Microgrid Management Strategies for Economic Dispatch of Electricity Using Model Predictive Control Techniques: A Review

Author

Listed:
  • Juan Moreno-Castro

    (Ciencias Básicas, Instituto Tecnológico de Santo Domingo, Santo Domingo 10602, Dominican Republic)

  • Victor Samuel Ocaña Guevara

    (Ciencias Básicas, Instituto Tecnológico de Santo Domingo, Santo Domingo 10602, Dominican Republic
    Centre for Energy Studies and Environmental Technologies (CEETA), Carretera a Camajuaní Km 5 1/2, Universidad Central “Marta Abreu” de Las Villas, Santa Clara 50100, Cuba)

  • Lesyani Teresa León Viltre

    (Departamento de Ingeniería Eléctrica y Electrónica, Universidad del Bío-Bío, Concepción 4051381, Chile)

  • Yandi Gallego Landera

    (Departamento de Ingeniería Eléctrica y Electrónica, Universidad del Bío-Bío, Concepción 4051381, Chile)

  • Oscar Cuaresma Zevallos

    (Department of Electrical Engineering, State University of Rio de Janeiro UERJ, Rio de Janeiro 20550-900, Brazil)

  • Miguel Aybar-Mejía

    (Engineering Area, Instituto Tecnológico de Santo Domingo, Santo Domingo 10602, Dominican Republic)

Abstract

In recent years, microgrid (MG) deployment has significantly increased, utilizing various technologies. MGs are essential for integrating distributed generation into electric power systems. These systems’ economic dispatch (ED) aims to minimize generation costs within a specific time interval while meeting power generation constraints. By employing ED in electric MGs, the utilization of distributed energy resources becomes more flexible, enhancing energy system efficiency. Additionally, it enables the anticipation and proper utilization of operational limitations and encourages the active involvement of prosumers in the electricity market. However, implementing controllers and algorithms for optimizing ED requires the independent handling of constraints. Numerous algorithms and solutions have been proposed for the ED of MGs. These contributions suggest utilizing techniques such as particle swarm optimization (PSO), mixed-integer linear programming (MILP), CPLEX, and MATLAB. This paper presents an investigation of the use of model predictive control (MPC) as an optimal management tool for MGs. MPC has proven effective in ED by allowing the prediction of environmental or dynamic models within the system. This study aims to review MGs’ management strategies, specifically focusing on MPC techniques. It analyzes how MPC has been applied to optimize ED while considering MGs’ unique characteristics and requirements. This review aims to enhance the understanding of MPC’s role in efficient MG management, guiding future research and applications in this field.

Suggested Citation

  • Juan Moreno-Castro & Victor Samuel Ocaña Guevara & Lesyani Teresa León Viltre & Yandi Gallego Landera & Oscar Cuaresma Zevallos & Miguel Aybar-Mejía, 2023. "Microgrid Management Strategies for Economic Dispatch of Electricity Using Model Predictive Control Techniques: A Review," Energies, MDPI, vol. 16(16), pages 1-24, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:5935-:d:1215103
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    References listed on IDEAS

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    Cited by:

    1. Fatemeh Marzbani & Akmal Abdelfatah, 2024. "Economic Dispatch Optimization Strategies and Problem Formulation: A Comprehensive Review," Energies, MDPI, vol. 17(3), pages 1-31, January.

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