An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes
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 a...
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MDPI AG
2019-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/11/2143 |
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| author | L. Alvarado-Barrios A. Rodríguez del Nozal A. Tapia J. L. Martínez-Ramos D. G. Reina |
| author_facet | L. Alvarado-Barrios A. Rodríguez del Nozal A. Tapia J. L. Martínez-Ramos D. G. Reina |
| author_sort | L. Alvarado-Barrios |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T05:17:01Z |
| format | Article |
| id | doaj.art-ea0bb9869e33467ebf8321ca219aa647 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T05:17:01Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ea0bb9869e33467ebf8321ca219aa6472022-12-22T02:10:20ZengMDPI AGEnergies1996-10732019-06-011211214310.3390/en12112143en12112143An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation ModesL. Alvarado-Barrios0A. Rodríguez del Nozal1A. Tapia2J. L. Martínez-Ramos3D. G. Reina4Departamento de Ingeniería, Universidad Loyola Andalucía, 41014 Seville, SpainDepartamento de Ingeniería, Universidad Loyola Andalucía, 41014 Seville, SpainDepartamento de Ingeniería, Universidad Loyola Andalucía, 41014 Seville, SpainElectrical Engineering Department, University of Seville, 41092 Seville, SpainElectronic Engineering Department, University of Seville, 41092 Seville, SpainIn 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.https://www.mdpi.com/1996-1073/12/11/2143microgridsUnit CommitmentEconomic DispatchGenetic Algorithm |
| spellingShingle | L. Alvarado-Barrios A. Rodríguez del Nozal A. Tapia J. L. Martínez-Ramos D. G. Reina An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes Energies microgrids Unit Commitment Economic Dispatch Genetic Algorithm |
| title | An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes |
| title_full | An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes |
| title_fullStr | An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes |
| title_full_unstemmed | An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes |
| title_short | An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes |
| title_sort | evolutionary computational approach for the problem of unit commitment and economic dispatch in microgrids under several operation modes |
| topic | microgrids Unit Commitment Economic Dispatch Genetic Algorithm |
| url | https://www.mdpi.com/1996-1073/12/11/2143 |
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