| 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 |
| External publication | No |
| Means | Electronics (Switzerland) |
| Scope | Article |
| Nature | Científica |
| JCR Quartile | 2 |
| SJR Quartile | 2 |
| JCR Impact | 2.412 |
| SJR Impact | 0.303 |
| 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\n prediction horizon, is proposed to reduce the operational cost of\n microgrids. To overcome the complexity of the optimization problems\n arising from the operation of the microgrid at each step, an adaptive\n evolutionary strategy with a satisfactory trade-off between exploration\n and exploitation capabilities was added to the model predictive control.\n The proposed strategy was evaluated using a representative microgrid\n that includes a wind turbine, a photovoltaic plant, a microturbine, a\n diesel engine, and an energy storage system. The achieved results\n demonstrate the validity of the proposed approach, outperforming a\n global scheduling planner-based on a genetic algorithm by 14.2% in terms\n of operational cost. In addition, the proposed approach also better\n manages the use of the energy storage system. |
| Keywords | Evolutionary optimization; Genetic algorithm; Microgrid; Model predictive control |
| Universidad Loyola members |