Title A MPC Strategy for the Optimal Management of Microgrids Based on Evolutionary Optimization
Authors RODRÍGUEZ DEL NOZAL, ÁLVARO, GUTIÉRREZ REINA, DANIEL, ALVARADO BARRIOS, LÁZARO, TAPIA CÓRDOBA, ALEJANDRO, ESCAÑO GONZÁLEZ, JUAN MANUEL, ALVARADO BARRIOS, LÁZARO, TAPIA CÓRDOBA, ALEJANDRO, RODRÍGUEZ DEL NOZAL, ÁLVARO, ESCAÑO GONZÁLEZ, JUAN MANUEL, GUTIÉRREZ REINA, DANIEL
External publication No
Means Electronics
Scope Article
Nature Científica
JCR Quartile 2
SJR Quartile 1
JCR Impact 2.41200
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075494150&doi=10.3390%2felectronics8111371&partnerID=40&md5=e71d69957a119e7447aad9533897a307
Publication date 01/11/2019
ISI 000502269500167
Scopus Id 2-s2.0-85075494150
DOI 10.3390/electronics8111371
Abstract 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.
Keywords Evolutionary optimization; Genetic algorithm; Microgrid; Model predictive control
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