Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques
The optimal location and sizing of distributed generation is a suitable option for improving the operation of electric systems. This paper proposes a parallel implementation of the Population-Based Incremental Learning (PBIL) algorithm to locate distributed generators (DGs), and the use of Particle...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-04-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/11/4/1018 |
| _version_ | 1828273228521406464 |
|---|---|
| author | Luis Fernando Grisales-Noreña Daniel Gonzalez Montoya Carlos Andres Ramos-Paja |
| author_facet | Luis Fernando Grisales-Noreña Daniel Gonzalez Montoya Carlos Andres Ramos-Paja |
| author_sort | Luis Fernando Grisales-Noreña |
| collection | DOAJ |
| description | The optimal location and sizing of distributed generation is a suitable option for improving the operation of electric systems. This paper proposes a parallel implementation of the Population-Based Incremental Learning (PBIL) algorithm to locate distributed generators (DGs), and the use of Particle Swarm Optimization (PSO) to define the size those devices. The resulting method is a master-slave hybrid approach based on both the parallel PBIL (PPBIL) algorithm and the PSO, which reduces the computation time in comparison with other techniques commonly used to address this problem. Moreover, the new hybrid method also reduces the active power losses and improves the nodal voltage profiles. In order to verify the performance of the new method, test systems with 33 and 69 buses are implemented in Matlab, using Matpower, for evaluating multiple cases. Finally, the proposed method is contrasted with the Loss Sensitivity Factor (LSF), a Genetic Algorithm (GA) and a Parallel Monte-Carlo algorithm. The results demonstrate that the proposed PPBIL-PSO method provides the best balance between processing time, voltage profiles and reduction of power losses. |
| first_indexed | 2024-04-13T06:18:44Z |
| format | Article |
| id | doaj.art-1ea29ca1e58044abb07a1f0e12c1391e |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T06:18:44Z |
| publishDate | 2018-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-1ea29ca1e58044abb07a1f0e12c1391e2022-12-22T02:58:44ZengMDPI AGEnergies1996-10732018-04-01114101810.3390/en11041018en11041018Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO TechniquesLuis Fernando Grisales-Noreña0Daniel Gonzalez Montoya1Carlos Andres Ramos-Paja2Departamento de Electromecánica y Mecatrónica, Instituto Tecnológico Metropolitano, Medellín 050013, ColombiaDepartamento de Electrónica y Telecomunicaciones, Instituto Tecnológico Metropolitano, Medellín 050013, ColombiaDepartamento Energía Eléctrica y Automática, Universidad Nacional de Colombia, Medellín 050041, ColombiaThe optimal location and sizing of distributed generation is a suitable option for improving the operation of electric systems. This paper proposes a parallel implementation of the Population-Based Incremental Learning (PBIL) algorithm to locate distributed generators (DGs), and the use of Particle Swarm Optimization (PSO) to define the size those devices. The resulting method is a master-slave hybrid approach based on both the parallel PBIL (PPBIL) algorithm and the PSO, which reduces the computation time in comparison with other techniques commonly used to address this problem. Moreover, the new hybrid method also reduces the active power losses and improves the nodal voltage profiles. In order to verify the performance of the new method, test systems with 33 and 69 buses are implemented in Matlab, using Matpower, for evaluating multiple cases. Finally, the proposed method is contrasted with the Loss Sensitivity Factor (LSF), a Genetic Algorithm (GA) and a Parallel Monte-Carlo algorithm. The results demonstrate that the proposed PPBIL-PSO method provides the best balance between processing time, voltage profiles and reduction of power losses.http://www.mdpi.com/1996-1073/11/4/1018distribution system (DS)optimization techniquesPBIL algorithmPSO algorithmdistributed generationparallel processing |
| spellingShingle | Luis Fernando Grisales-Noreña Daniel Gonzalez Montoya Carlos Andres Ramos-Paja Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques Energies distribution system (DS) optimization techniques PBIL algorithm PSO algorithm distributed generation parallel processing |
| title | Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques |
| title_full | Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques |
| title_fullStr | Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques |
| title_full_unstemmed | Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques |
| title_short | Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques |
| title_sort | optimal sizing and location of distributed generators based on pbil and pso techniques |
| topic | distribution system (DS) optimization techniques PBIL algorithm PSO algorithm distributed generation parallel processing |
| url | http://www.mdpi.com/1996-1073/11/4/1018 |
| work_keys_str_mv | AT luisfernandogrisalesnorena optimalsizingandlocationofdistributedgeneratorsbasedonpbilandpsotechniques AT danielgonzalezmontoya optimalsizingandlocationofdistributedgeneratorsbasedonpbilandpsotechniques AT carlosandresramospaja optimalsizingandlocationofdistributedgeneratorsbasedonpbilandpsotechniques |