Optimization of distribution system operation by network reconfiguration and DG integration using MPSO algorithm. (September 2020)
- Record Type:
- Journal Article
- Title:
- Optimization of distribution system operation by network reconfiguration and DG integration using MPSO algorithm. (September 2020)
- Main Title:
- Optimization of distribution system operation by network reconfiguration and DG integration using MPSO algorithm
- Authors:
- Essallah, Sirine
Khedher, Adel - Abstract:
- Highlights: A Mixed Particle Swarm Optimization model is developed for optimal network reconfiguration and DG integration. Optimal network reconfiguration allow a better penetration of renewable energy in the system. Optimal integration of DG units dramatically increases the voltage level and the optimal exploitation of the DS. DG integration along with optimal NR greatly contribute to power losses minimization and voltage level enhancement. Abstract : This paper introduces a Mixed Particle Swarm Optimization (MPSO) approach for active power loss minimization and voltage profile improvement in the distribution network. The developed technique joins the Binary Particle Swarm Optimization (BPSO) and the conventional PSO algorithms. The first one is devoted to identify the optimal distribution network configuration, while the second one is used to solve Distributed Generation (DG) placement and sizing problems. To evaluate the performance of the developed approach, three different load scenarios were assessed during network reconfiguration (NR) and DG integration. Simulations are conducted on two distribution test systems, namely, the IEEE-33-bus and the IEEE-69-bus. The obtained results clearly demonstrate the performance and the effectiveness of the proposed method to find optimal status of switches, as well as DG locations and sizes. A benchmark comparison is presented to prove the efficiency of the proposed MPSO with regard to other optimization techniques. The results showHighlights: A Mixed Particle Swarm Optimization model is developed for optimal network reconfiguration and DG integration. Optimal network reconfiguration allow a better penetration of renewable energy in the system. Optimal integration of DG units dramatically increases the voltage level and the optimal exploitation of the DS. DG integration along with optimal NR greatly contribute to power losses minimization and voltage level enhancement. Abstract : This paper introduces a Mixed Particle Swarm Optimization (MPSO) approach for active power loss minimization and voltage profile improvement in the distribution network. The developed technique joins the Binary Particle Swarm Optimization (BPSO) and the conventional PSO algorithms. The first one is devoted to identify the optimal distribution network configuration, while the second one is used to solve Distributed Generation (DG) placement and sizing problems. To evaluate the performance of the developed approach, three different load scenarios were assessed during network reconfiguration (NR) and DG integration. Simulations are conducted on two distribution test systems, namely, the IEEE-33-bus and the IEEE-69-bus. The obtained results clearly demonstrate the performance and the effectiveness of the proposed method to find optimal status of switches, as well as DG locations and sizes. A benchmark comparison is presented to prove the efficiency of the proposed MPSO with regard to other optimization techniques. The results show that MPSO outperforms these techniques in terms of quality of solution, power loss reduction and voltage profile enhancement. This study is an extension of the earlier published conference paper. … (more)
- Is Part Of:
- Renewable energy focus. Volume 34(2020)
- Journal:
- Renewable energy focus
- Issue:
- Volume 34(2020)
- Issue Display:
- Volume 34, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 34
- Issue:
- 2020
- Issue Sort Value:
- 2020-0034-2020-0000
- Page Start:
- 37
- Page End:
- 46
- Publication Date:
- 2020-09
- Subjects:
- Renewable energy sources -- Periodicals
Solar energy -- Periodicals
333.79405 - Journal URLs:
- http://www.elsevier.com/journals ↗
- DOI:
- 10.1016/j.ref.2020.04.002 ↗
- Languages:
- English
- ISSNs:
- 1755-0084
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 7364.190500
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 14030.xml