RODRÍGUEZ DEL NOZAL, ÁLVARO, GUTIÉRREZ REINA, DANIEL, ALVARADO BARRIOS, LÁZARO, TAPIA CÓRDOBA, ALEJANDRO, ESCAÑO GONZÁLEZ, JUAN MANUEL
No
Electronics
Article
Científica
2.412
0.303
01/11/2019
000502269500167
2-s2.0-85075494150
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.
Evolutionary optimization; Genetic algorithm; Microgrid; Model predictive control