Multiobjective distribution system operation with demand response to optimize solar hosting capacity, voltage deviation index and network loss
Abstract In this research, demand response impact on the hosting capacity of solar photovoltaic for distribution system is investigated. The suggested solution model is formulated and presented as a tri-objective optimization that consider maximization of solar PV hosting capacity (HC), minimization...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-82379-7 |
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| author | Kabulo Loji Sachin Sharma Gulshan Sharma Tanuj Rawat |
| author_facet | Kabulo Loji Sachin Sharma Gulshan Sharma Tanuj Rawat |
| author_sort | Kabulo Loji |
| collection | DOAJ |
| description | Abstract In this research, demand response impact on the hosting capacity of solar photovoltaic for distribution system is investigated. The suggested solution model is formulated and presented as a tri-objective optimization that consider maximization of solar PV hosting capacity (HC), minimization of network losses (Loss) and maintaining node voltage deviation (VDev) within acceptable limits. These crucial objectives are optimized simultaneously as well as individually. To assess the efficacy of the solution, different multi-objective case studies are scrutinised based on the combinations of (i) HC and Loss, (ii) HC and VDev, (iii) Loss and VDev, (iv) HC Loss and VDev simultaneously with the effect of demand response. The multi-objective research problem is formulated as non-linear and non-convex programming approach. To solve this complex problem, the modified crow search optimization (MCSO) is proposed. The MCSO achieved the 0.0714 MW of network loss with the optimal integration of distributed generation and is comparable to the well-established optimization algorithms available in literature. From the simulation results, it is found that HC is 3322.31 kW, VDev is 0.4982 p.u and system losses is 1314.86 kWh with demand response program when all the objectives are simultaneously optimized. The simulation outcomes highlight the superiority of the MCSO over others. The application results show the benefits and the beauty of proposed research work. |
| format | Article |
| id | doaj-art-27fdd34c3c684452b6cfeb31412cbe10 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-27fdd34c3c684452b6cfeb31412cbe102025-01-05T12:15:14ZengNature PortfolioScientific Reports2045-23222025-01-0115112510.1038/s41598-024-82379-7Multiobjective distribution system operation with demand response to optimize solar hosting capacity, voltage deviation index and network lossKabulo Loji0Sachin Sharma1Gulshan Sharma2Tanuj Rawat3Department of Electrical Power Engineering, Durban University of TechnologyDepartment of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher EducationDepartment of Electrical Engineering Technology, University of JohannesburgGE Renewable EnergyAbstract In this research, demand response impact on the hosting capacity of solar photovoltaic for distribution system is investigated. The suggested solution model is formulated and presented as a tri-objective optimization that consider maximization of solar PV hosting capacity (HC), minimization of network losses (Loss) and maintaining node voltage deviation (VDev) within acceptable limits. These crucial objectives are optimized simultaneously as well as individually. To assess the efficacy of the solution, different multi-objective case studies are scrutinised based on the combinations of (i) HC and Loss, (ii) HC and VDev, (iii) Loss and VDev, (iv) HC Loss and VDev simultaneously with the effect of demand response. The multi-objective research problem is formulated as non-linear and non-convex programming approach. To solve this complex problem, the modified crow search optimization (MCSO) is proposed. The MCSO achieved the 0.0714 MW of network loss with the optimal integration of distributed generation and is comparable to the well-established optimization algorithms available in literature. From the simulation results, it is found that HC is 3322.31 kW, VDev is 0.4982 p.u and system losses is 1314.86 kWh with demand response program when all the objectives are simultaneously optimized. The simulation outcomes highlight the superiority of the MCSO over others. The application results show the benefits and the beauty of proposed research work.https://doi.org/10.1038/s41598-024-82379-7Multi-objective optimizationCrow search optimization algorithmSolar photovoltaicDemand response |
| spellingShingle | Kabulo Loji Sachin Sharma Gulshan Sharma Tanuj Rawat Multiobjective distribution system operation with demand response to optimize solar hosting capacity, voltage deviation index and network loss Scientific Reports Multi-objective optimization Crow search optimization algorithm Solar photovoltaic Demand response |
| title | Multiobjective distribution system operation with demand response to optimize solar hosting capacity, voltage deviation index and network loss |
| title_full | Multiobjective distribution system operation with demand response to optimize solar hosting capacity, voltage deviation index and network loss |
| title_fullStr | Multiobjective distribution system operation with demand response to optimize solar hosting capacity, voltage deviation index and network loss |
| title_full_unstemmed | Multiobjective distribution system operation with demand response to optimize solar hosting capacity, voltage deviation index and network loss |
| title_short | Multiobjective distribution system operation with demand response to optimize solar hosting capacity, voltage deviation index and network loss |
| title_sort | multiobjective distribution system operation with demand response to optimize solar hosting capacity voltage deviation index and network loss |
| topic | Multi-objective optimization Crow search optimization algorithm Solar photovoltaic Demand response |
| url | https://doi.org/10.1038/s41598-024-82379-7 |
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