Multi‐objective multi‐period optimal site and size of distributed generation along with network reconfiguration
Abstract While extensive research has focused on enhancing distribution networks through either maximizing Distributed Generation (DG) integration or network reconfiguration at specific times, there is a need for further investigation into concurrently optimal network reconfiguration and DG allocati...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Renewable Power Generation |
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| Online Access: | https://doi.org/10.1049/rpg2.12949 |
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| author | Ghulam Abbas Zhi Wu Aamir Ali |
| author_facet | Ghulam Abbas Zhi Wu Aamir Ali |
| author_sort | Ghulam Abbas |
| collection | DOAJ |
| description | Abstract While extensive research has focused on enhancing distribution networks through either maximizing Distributed Generation (DG) integration or network reconfiguration at specific times, there is a need for further investigation into concurrently optimal network reconfiguration and DG allocation. To reduce the cost of energy delivered, the cost of energy loss, and voltage deviation, this study gives a dynamic multi‐objective network reconfiguration together with siting and sizing of dispatchable and non‐dispatchable DGs. The widely used IEEE 33‐bus and a large‐scale 118‐bus radial test system are employed while considering the time sequence fluctuations in sunlight irradiation and load. To address the pointed‐out challenge of multiperiod optimal DG allocation and reconfiguration while simultaneously decreasing the cost of energy supplied, the cost of energy lost, and the voltage deviation, a novel Multi‐objective Bidirectional co‐evolutionary algorithm (BiCo) is implemented. For better exploration and exploitation, the proposed algorithm integrating the constraint domination principle evolves the population from the feasible and infeasible search space with the help of a novel angle‐based density section. Simulation results demonstrate that the proposed approach outperforms previously published Multi‐objective Evolutionary Algorithms (MOEAs) by discovering a vast collection of uniformly spaced non‐dominated solutions in a single simulation run. Further, a fuzzy set theory is applied to find the best compromise solution among obtained final non‐dominated solutions. The results establish that the Pareto solutions significantly improved the system's voltage profile, with savings of over 22% compared to the baseline case and an exceptional improvement of over 80% in voltage deviation and power loss. |
| format | Article |
| id | doaj-art-7a3e27c410a343b491bbb71d8586c003 |
| institution | Kabale University |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-7a3e27c410a343b491bbb71d8586c0032025-01-30T12:15:53ZengWileyIET Renewable Power Generation1752-14161752-14242024-12-0118163704373010.1049/rpg2.12949Multi‐objective multi‐period optimal site and size of distributed generation along with network reconfigurationGhulam Abbas0Zhi Wu1Aamir Ali2School of Electrical Engineering Southeast University Nanjing ChinaSchool of Electrical Engineering Southeast University Nanjing ChinaDepartment of Electrical Engineering Quaid‐e‐Awam University of Engineering Science and Technology Nawabshah Sindh PakistanAbstract While extensive research has focused on enhancing distribution networks through either maximizing Distributed Generation (DG) integration or network reconfiguration at specific times, there is a need for further investigation into concurrently optimal network reconfiguration and DG allocation. To reduce the cost of energy delivered, the cost of energy loss, and voltage deviation, this study gives a dynamic multi‐objective network reconfiguration together with siting and sizing of dispatchable and non‐dispatchable DGs. The widely used IEEE 33‐bus and a large‐scale 118‐bus radial test system are employed while considering the time sequence fluctuations in sunlight irradiation and load. To address the pointed‐out challenge of multiperiod optimal DG allocation and reconfiguration while simultaneously decreasing the cost of energy supplied, the cost of energy lost, and the voltage deviation, a novel Multi‐objective Bidirectional co‐evolutionary algorithm (BiCo) is implemented. For better exploration and exploitation, the proposed algorithm integrating the constraint domination principle evolves the population from the feasible and infeasible search space with the help of a novel angle‐based density section. Simulation results demonstrate that the proposed approach outperforms previously published Multi‐objective Evolutionary Algorithms (MOEAs) by discovering a vast collection of uniformly spaced non‐dominated solutions in a single simulation run. Further, a fuzzy set theory is applied to find the best compromise solution among obtained final non‐dominated solutions. The results establish that the Pareto solutions significantly improved the system's voltage profile, with savings of over 22% compared to the baseline case and an exceptional improvement of over 80% in voltage deviation and power loss.https://doi.org/10.1049/rpg2.12949distribution networkevolutionary computationoptimisationrenewable energy sources |
| spellingShingle | Ghulam Abbas Zhi Wu Aamir Ali Multi‐objective multi‐period optimal site and size of distributed generation along with network reconfiguration IET Renewable Power Generation distribution network evolutionary computation optimisation renewable energy sources |
| title | Multi‐objective multi‐period optimal site and size of distributed generation along with network reconfiguration |
| title_full | Multi‐objective multi‐period optimal site and size of distributed generation along with network reconfiguration |
| title_fullStr | Multi‐objective multi‐period optimal site and size of distributed generation along with network reconfiguration |
| title_full_unstemmed | Multi‐objective multi‐period optimal site and size of distributed generation along with network reconfiguration |
| title_short | Multi‐objective multi‐period optimal site and size of distributed generation along with network reconfiguration |
| title_sort | multi objective multi period optimal site and size of distributed generation along with network reconfiguration |
| topic | distribution network evolutionary computation optimisation renewable energy sources |
| url | https://doi.org/10.1049/rpg2.12949 |
| work_keys_str_mv | AT ghulamabbas multiobjectivemultiperiodoptimalsiteandsizeofdistributedgenerationalongwithnetworkreconfiguration AT zhiwu multiobjectivemultiperiodoptimalsiteandsizeofdistributedgenerationalongwithnetworkreconfiguration AT aamirali multiobjectivemultiperiodoptimalsiteandsizeofdistributedgenerationalongwithnetworkreconfiguration |