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

Juan Moreno-Castro, Victor Samuel Ocaña Guevara, Lesyani Teresa León Viltre (), Yandi Gallego Landera, Oscar Cuaresma Zevallos and Miguel Aybar-Mejía
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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
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

Energies, 2023, vol. 16, issue 16, 1-24

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.

Keywords: bi-level optimization; economic dispatch; microgrid; model predictive control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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