Title An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes
Authors ALVARADO BARRIOS, LÁZARO, RODRÍGUEZ DEL NOZAL, ÁLVARO, TAPIA CÓRDOBA, ALEJANDRO, Martinez-Ramos, J. L., GUTIÉRREZ REINA, DANIEL, ALVARADO BARRIOS, LÁZARO, TAPIA CÓRDOBA, ALEJANDRO, RODRÍGUEZ DEL NOZAL, ÁLVARO, GUTIÉRREZ REINA, DANIEL
External publication No
Means Energies
Scope Article
Nature Científica
JCR Quartile 3
SJR Quartile 1
JCR Impact 2.70200
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066953538&doi=10.3390%2fen12112143&partnerID=40&md5=bf9f66348bede1c0b7a2b8f3a30f8c50
Publication date 01/06/2019
ISI 000472635900110
Scopus Id 2-s2.0-85066953538
DOI 10.3390/en12112143
Abstract In the last decades, new types of generation technologies have emerged and have been gradually integrated into the existing power systems, moving their classical architectures to distributed systems. Despite the positive features associated to this paradigm, new problems arise such as coordination and uncertainty. In this framework, microgrids constitute an effective solution to deal with the coordination and operation of these distributed energy resources. This paper proposes a Genetic Algorithm (GA) to address the combined problem of Unit Commitment (UC) and Economic Dispatch (ED). With this end, a model of a microgrid is introduced together with all the control variables and physical constraints. To optimally operate the microgrid, three operation modes are introduced. The first two attend to optimize economical and environmental factors, while the last operation mode considers the errors induced by the uncertainties in the demand forecasting. Therefore, it achieves a robust design that guarantees the power supply for different confidence levels. Finally, the algorithm was applied to an example scenario to illustrate its performance. The achieved simulation results demonstrate the validity of the proposed approach.
Keywords microgrids; Unit Commitment; Economic Dispatch; Genetic Algorithm
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