Hybrid energy system optimization integrated with battery storage in radial distribution networks considering reliability and a robust framework
Abstract This research presents a robust optimization of a hybrid photovoltaic-wind-battery (PV/WT/Batt) system in distribution networks to reduce active losses and voltage deviation while also enhancing network customer reliability considering production and network load uncertainties. The best ins...
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| Format: | Article |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-73808-8 |
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| _version_ | 1850196208734175232 |
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| author | Mohammad Javad Aliabadi Masoud Radmehr |
| author_facet | Mohammad Javad Aliabadi Masoud Radmehr |
| author_sort | Mohammad Javad Aliabadi |
| collection | DOAJ |
| description | Abstract This research presents a robust optimization of a hybrid photovoltaic-wind-battery (PV/WT/Batt) system in distribution networks to reduce active losses and voltage deviation while also enhancing network customer reliability considering production and network load uncertainties. The best installation position and capacity of the hybrid system (HS) are found via an improved crow search algorithm with an inertia weight technique. The robust optimization issue, taking into account the risk of uncertainty, is described using the gap information decision theory method. The proposed approach is used with 33- and 69-bus networks. The results reveal that the HS optimization in the network reduces active losses and voltage variations, while improving network customer reliability. The robust optimization results show that in the 33-bus network, the system remains resilient to prediction errors under the worst-case uncertainty scenario, with a 44.53% reduction in production and a 22.18% increase in network demand for a 30% uncertainty budget. Similarly, in the 69-bus network, the system withstands a 36.22% reduction in production and a 16.97% increase in load for a 25% uncertainty budget. When comparing stochastic and robust methods, it was found that the stochastic Monte Carlo method could not consistently provide a reliable solution for all objectives under uncertainty, whereas the robust approach successfully managed the maximum uncertainty related to renewable generation and network demand across different uncertainty budgets. |
| format | Article |
| id | doaj-art-b5ecb17834e84cf78ae68905653a0aa4 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b5ecb17834e84cf78ae68905653a0aa42025-08-20T02:13:31ZengNature PortfolioScientific Reports2045-23222024-11-0114112410.1038/s41598-024-73808-8Hybrid energy system optimization integrated with battery storage in radial distribution networks considering reliability and a robust frameworkMohammad Javad Aliabadi0Masoud Radmehr1Department of Electrical Engineering, Aliabad Katoul Branch, Islamic Azad UniversityDepartment of Electrical Engineering, Aliabad Katoul Branch, Islamic Azad UniversityAbstract This research presents a robust optimization of a hybrid photovoltaic-wind-battery (PV/WT/Batt) system in distribution networks to reduce active losses and voltage deviation while also enhancing network customer reliability considering production and network load uncertainties. The best installation position and capacity of the hybrid system (HS) are found via an improved crow search algorithm with an inertia weight technique. The robust optimization issue, taking into account the risk of uncertainty, is described using the gap information decision theory method. The proposed approach is used with 33- and 69-bus networks. The results reveal that the HS optimization in the network reduces active losses and voltage variations, while improving network customer reliability. The robust optimization results show that in the 33-bus network, the system remains resilient to prediction errors under the worst-case uncertainty scenario, with a 44.53% reduction in production and a 22.18% increase in network demand for a 30% uncertainty budget. Similarly, in the 69-bus network, the system withstands a 36.22% reduction in production and a 16.97% increase in load for a 25% uncertainty budget. When comparing stochastic and robust methods, it was found that the stochastic Monte Carlo method could not consistently provide a reliable solution for all objectives under uncertainty, whereas the robust approach successfully managed the maximum uncertainty related to renewable generation and network demand across different uncertainty budgets.https://doi.org/10.1038/s41598-024-73808-8 |
| spellingShingle | Mohammad Javad Aliabadi Masoud Radmehr Hybrid energy system optimization integrated with battery storage in radial distribution networks considering reliability and a robust framework Scientific Reports |
| title | Hybrid energy system optimization integrated with battery storage in radial distribution networks considering reliability and a robust framework |
| title_full | Hybrid energy system optimization integrated with battery storage in radial distribution networks considering reliability and a robust framework |
| title_fullStr | Hybrid energy system optimization integrated with battery storage in radial distribution networks considering reliability and a robust framework |
| title_full_unstemmed | Hybrid energy system optimization integrated with battery storage in radial distribution networks considering reliability and a robust framework |
| title_short | Hybrid energy system optimization integrated with battery storage in radial distribution networks considering reliability and a robust framework |
| title_sort | hybrid energy system optimization integrated with battery storage in radial distribution networks considering reliability and a robust framework |
| url | https://doi.org/10.1038/s41598-024-73808-8 |
| work_keys_str_mv | AT mohammadjavadaliabadi hybridenergysystemoptimizationintegratedwithbatterystorageinradialdistributionnetworksconsideringreliabilityandarobustframework AT masoudradmehr hybridenergysystemoptimizationintegratedwithbatterystorageinradialdistributionnetworksconsideringreliabilityandarobustframework |