Low-carbon optimal scheduling for distribution networks under supply and demand uncertainty
This paper presents a low-carbon optimal scheduling model for distribution networks with wind and photovoltaic (PV), accounting for supply and demand uncertainties. The model optimizes thermal generation costs, wind and PV maintenance costs, and carbon emissions using a chance-constrained approach w...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Physics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2024.1514628/full |
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| author | Yu Nan Zhi Li Xin Gao Xiaoshi Kou |
| author_facet | Yu Nan Zhi Li Xin Gao Xiaoshi Kou |
| author_sort | Yu Nan |
| collection | DOAJ |
| description | This paper presents a low-carbon optimal scheduling model for distribution networks with wind and photovoltaic (PV), accounting for supply and demand uncertainties. The model optimizes thermal generation costs, wind and PV maintenance costs, and carbon emissions using a chance-constrained approach with fuzzy variables. The clear equivalent class method simplifies these constraints for easier problem-solving. Validation on the IEEE-30 node system shows the model reduces costs by 32.9% and carbon emissions by 19.2% compared to traditional scheduling, effectively lowering both costs and the carbon footprint. This real-world optimization approach tackles uncertainty in renewable energy supply and improves system efficiency and sustainability. |
| format | Article |
| id | doaj-art-b7506be2c3264fb59eafd48eb764a193 |
| institution | DOAJ |
| issn | 2296-424X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj-art-b7506be2c3264fb59eafd48eb764a1932025-08-20T02:50:34ZengFrontiers Media S.A.Frontiers in Physics2296-424X2024-12-011210.3389/fphy.2024.15146281514628Low-carbon optimal scheduling for distribution networks under supply and demand uncertaintyYu NanZhi LiXin GaoXiaoshi KouThis paper presents a low-carbon optimal scheduling model for distribution networks with wind and photovoltaic (PV), accounting for supply and demand uncertainties. The model optimizes thermal generation costs, wind and PV maintenance costs, and carbon emissions using a chance-constrained approach with fuzzy variables. The clear equivalent class method simplifies these constraints for easier problem-solving. Validation on the IEEE-30 node system shows the model reduces costs by 32.9% and carbon emissions by 19.2% compared to traditional scheduling, effectively lowering both costs and the carbon footprint. This real-world optimization approach tackles uncertainty in renewable energy supply and improves system efficiency and sustainability.https://www.frontiersin.org/articles/10.3389/fphy.2024.1514628/fulllow-carbon scheduling modeluncertaintychance-constrained approachdistribution networksreal-world optimization |
| spellingShingle | Yu Nan Zhi Li Xin Gao Xiaoshi Kou Low-carbon optimal scheduling for distribution networks under supply and demand uncertainty Frontiers in Physics low-carbon scheduling model uncertainty chance-constrained approach distribution networks real-world optimization |
| title | Low-carbon optimal scheduling for distribution networks under supply and demand uncertainty |
| title_full | Low-carbon optimal scheduling for distribution networks under supply and demand uncertainty |
| title_fullStr | Low-carbon optimal scheduling for distribution networks under supply and demand uncertainty |
| title_full_unstemmed | Low-carbon optimal scheduling for distribution networks under supply and demand uncertainty |
| title_short | Low-carbon optimal scheduling for distribution networks under supply and demand uncertainty |
| title_sort | low carbon optimal scheduling for distribution networks under supply and demand uncertainty |
| topic | low-carbon scheduling model uncertainty chance-constrained approach distribution networks real-world optimization |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2024.1514628/full |
| work_keys_str_mv | AT yunan lowcarbonoptimalschedulingfordistributionnetworksundersupplyanddemanduncertainty AT zhili lowcarbonoptimalschedulingfordistributionnetworksundersupplyanddemanduncertainty AT xingao lowcarbonoptimalschedulingfordistributionnetworksundersupplyanddemanduncertainty AT xiaoshikou lowcarbonoptimalschedulingfordistributionnetworksundersupplyanddemanduncertainty |