Artículo
A MPC Strategy for the Optimal Management of Microgrids Based on Evolutionary Optimization
Autor/es | Rodríguez del Nozal, Álvaro
Gutiérrez Reina, Daniel Alvarado-Barrios, Lázaro Tapia Córdoba, Alejandro Escaño González, Juan Manuel |
Departamento | Universidad de Sevilla. Departamento de Ingeniería Eléctrica Universidad de Sevilla. Departamento de Ingeniería Electrónica Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2019-11 |
Fecha de depósito | 2020-02-14 |
Publicado en |
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Resumen | In this paper, a novel model predictive control strategy, with a 24-h prediction horizon, is
proposed to reduce the operational cost of microgrids. To overcome the complexity of the optimization
problems arising from the ... In this paper, a novel model predictive control strategy, with a 24-h prediction horizon, is proposed to reduce the operational cost of microgrids. To overcome the complexity of the optimization problems arising from the operation of the microgrid at each step, an adaptive evolutionary strategy with a satisfactory trade-off between exploration and exploitation capabilities was added to the model predictive control. The proposed strategy was evaluated using a representative microgrid that includes a wind turbine, a photovoltaic plant, a microturbine, a diesel engine, and an energy storage system. The achieved results demonstrate the validity of the proposed approach, outperforming a global scheduling planner-based on a genetic algorithm by 14.2% in terms of operational cost. In addition, the proposed approach also better manages the use of the energy storage system. |
Identificador del proyecto | DPI2016-75294-C2-2-R
(Programa Horizonte 2020) 764090 |
Cita | Rodríguez del Nozal, Á., Gutiérrez Reina, D., Alvarado-Barrios, L., Tapia Córdoba, A. y Escaño González, J.M. (2019). A MPC Strategy for the Optimal Management of Microgrids Based on Evolutionary Optimization. Electronics, 8 (11). Article number 1371. |
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