Low-carbon optimization planning method for integrated energy system based on DG uncertainty affine model
Aiming at the problem that the output of distributed generators (DG) of new energy sources such as wind power and photovoltaic is uncertain due to changes in environmental factors, and the transaction price of the existing carbon trading model is fixed, resulting in increased carbon reduction costs,...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2024-08-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024162/ |
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| _version_ | 1841528860859957248 |
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| author | JIANG Tao XU Cong JIA Shaohui WANG Shen ZHANG Yajian |
| author_facet | JIANG Tao XU Cong JIA Shaohui WANG Shen ZHANG Yajian |
| author_sort | JIANG Tao |
| collection | DOAJ |
| description | Aiming at the problem that the output of distributed generators (DG) of new energy sources such as wind power and photovoltaic is uncertain due to changes in environmental factors, and the transaction price of the existing carbon trading model is fixed, resulting in increased carbon reduction costs, an integrated energy system optimization planning method that takes into account dynamic carbon emission constraints and DG uncertainty was proposed. Firstly, a DG output model based on matrix affine algorithm was established according to environmental conditions to reduce the impact of DG output uncertainty on the optimization planning of the integrated energy system. Secondly, carbon emissions was introduced as a punitive measure into the optimization planning of the integrated energy system to improve the traditional carbon trading model and reduce the carbon emissions of the integrated energy system. Then, based on the differential evolution-particle swarm optimization algorithm, the established low-carbon planning model of the integrated energy system was solved to avoid the algorithm from falling into local optimality during the optimization process. Finally, the simulation results on an IEEE 33 node system show that the proposed planning method reduces the total investment cost by 8.68% and 2.93% respectively compared with the traditional stochastic optimization and interval optimization planning methods. Compared with the traditional fixed carbon trading price model, carbon emissions are reduced by 6.28%. |
| format | Article |
| id | doaj-art-75101bd97e1449dcb455e50edf71c1ae |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2024-08-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-75101bd97e1449dcb455e50edf71c1ae2025-01-15T03:33:49ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012024-08-014010812069875666Low-carbon optimization planning method for integrated energy system based on DG uncertainty affine modelJIANG TaoXU CongJIA ShaohuiWANG ShenZHANG YajianAiming at the problem that the output of distributed generators (DG) of new energy sources such as wind power and photovoltaic is uncertain due to changes in environmental factors, and the transaction price of the existing carbon trading model is fixed, resulting in increased carbon reduction costs, an integrated energy system optimization planning method that takes into account dynamic carbon emission constraints and DG uncertainty was proposed. Firstly, a DG output model based on matrix affine algorithm was established according to environmental conditions to reduce the impact of DG output uncertainty on the optimization planning of the integrated energy system. Secondly, carbon emissions was introduced as a punitive measure into the optimization planning of the integrated energy system to improve the traditional carbon trading model and reduce the carbon emissions of the integrated energy system. Then, based on the differential evolution-particle swarm optimization algorithm, the established low-carbon planning model of the integrated energy system was solved to avoid the algorithm from falling into local optimality during the optimization process. Finally, the simulation results on an IEEE 33 node system show that the proposed planning method reduces the total investment cost by 8.68% and 2.93% respectively compared with the traditional stochastic optimization and interval optimization planning methods. Compared with the traditional fixed carbon trading price model, carbon emissions are reduced by 6.28%.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024162/ladder carbon tradingintegrated energy systemaffine modeldifferential evolution particle swarm algorithminterval optimization |
| spellingShingle | JIANG Tao XU Cong JIA Shaohui WANG Shen ZHANG Yajian Low-carbon optimization planning method for integrated energy system based on DG uncertainty affine model Dianxin kexue ladder carbon trading integrated energy system affine model differential evolution particle swarm algorithm interval optimization |
| title | Low-carbon optimization planning method for integrated energy system based on DG uncertainty affine model |
| title_full | Low-carbon optimization planning method for integrated energy system based on DG uncertainty affine model |
| title_fullStr | Low-carbon optimization planning method for integrated energy system based on DG uncertainty affine model |
| title_full_unstemmed | Low-carbon optimization planning method for integrated energy system based on DG uncertainty affine model |
| title_short | Low-carbon optimization planning method for integrated energy system based on DG uncertainty affine model |
| title_sort | low carbon optimization planning method for integrated energy system based on dg uncertainty affine model |
| topic | ladder carbon trading integrated energy system affine model differential evolution particle swarm algorithm interval optimization |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024162/ |
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