Two-layer optimization model of distribution network line loss considering the uncertainty of new energy access
The integration of a distributed generator (DG) into the distribution network alters the topology structure and power flow distribution, subsequently causing changes in network loss. Moreover, existing distribution network optimization methods face high computational complexity, low efficiency, and...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1526693/full |
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| author | Xiping Ma Xiping Ma Xiaoyang Dong Haitao Xiao Yaxin Li Rui Xu Kai Wei Juanjuan Cai Juan Wei |
| author_facet | Xiping Ma Xiping Ma Xiaoyang Dong Haitao Xiao Yaxin Li Rui Xu Kai Wei Juanjuan Cai Juan Wei |
| author_sort | Xiping Ma |
| collection | DOAJ |
| description | The integration of a distributed generator (DG) into the distribution network alters the topology structure and power flow distribution, subsequently causing changes in network loss. Moreover, existing distribution network optimization methods face high computational complexity, low efficiency, and susceptibility to local optima. This article proposes a scenario generation method using a generative adversarial network (GAN) to handle the uncertainty associated with DGs and constructs a two-layer optimization model for the distribution network. The upper layer model determines the installation location and capacity of distributed power and energy storage systems with the lowest economic cost. The lower layer model establishes an optimization model, including wind, solar, and storage, with active power network loss and voltage deviation as objective functions. Both layers are solved using the Improved Whale Optimization algorithm (IWOA). Then, the IEEE-33 node distribution system was taken as a simulation example to verify the effectiveness and superiority of the proposed model and algorithm. |
| format | Article |
| id | doaj-art-a6a0f2b2fbcb4761a74345cda0db106c |
| institution | Kabale University |
| issn | 2296-598X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj-art-a6a0f2b2fbcb4761a74345cda0db106c2025-01-22T16:24:21ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2025-01-011210.3389/fenrg.2024.15266931526693Two-layer optimization model of distribution network line loss considering the uncertainty of new energy accessXiping Ma0Xiping Ma1Xiaoyang Dong2Haitao Xiao3Yaxin Li4Rui Xu5Kai Wei6Juanjuan Cai7Juan Wei8Electric Power Research Institute of State Grid Gansu Electric Power Company, Lanzhou, ChinaSchool of Electrical Engineering, Xi’an University of Technology, Xi’an, ChinaElectric Power Research Institute of State Grid Gansu Electric Power Company, Lanzhou, ChinaState Grid Gansu Electric Power Company, Lanzhou, ChinaElectric Power Research Institute of State Grid Gansu Electric Power Company, Lanzhou, ChinaElectric Power Research Institute of State Grid Gansu Electric Power Company, Lanzhou, ChinaElectric Power Research Institute of State Grid Gansu Electric Power Company, Lanzhou, ChinaSchool of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou, ChinaSchool of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou, ChinaThe integration of a distributed generator (DG) into the distribution network alters the topology structure and power flow distribution, subsequently causing changes in network loss. Moreover, existing distribution network optimization methods face high computational complexity, low efficiency, and susceptibility to local optima. This article proposes a scenario generation method using a generative adversarial network (GAN) to handle the uncertainty associated with DGs and constructs a two-layer optimization model for the distribution network. The upper layer model determines the installation location and capacity of distributed power and energy storage systems with the lowest economic cost. The lower layer model establishes an optimization model, including wind, solar, and storage, with active power network loss and voltage deviation as objective functions. Both layers are solved using the Improved Whale Optimization algorithm (IWOA). Then, the IEEE-33 node distribution system was taken as a simulation example to verify the effectiveness and superiority of the proposed model and algorithm.https://www.frontiersin.org/articles/10.3389/fenrg.2024.1526693/fullhigh proportion of new energyuncertainty modeltwo-layer optimizationImproved Whale Optimization algorithmline loss |
| spellingShingle | Xiping Ma Xiping Ma Xiaoyang Dong Haitao Xiao Yaxin Li Rui Xu Kai Wei Juanjuan Cai Juan Wei Two-layer optimization model of distribution network line loss considering the uncertainty of new energy access Frontiers in Energy Research high proportion of new energy uncertainty model two-layer optimization Improved Whale Optimization algorithm line loss |
| title | Two-layer optimization model of distribution network line loss considering the uncertainty of new energy access |
| title_full | Two-layer optimization model of distribution network line loss considering the uncertainty of new energy access |
| title_fullStr | Two-layer optimization model of distribution network line loss considering the uncertainty of new energy access |
| title_full_unstemmed | Two-layer optimization model of distribution network line loss considering the uncertainty of new energy access |
| title_short | Two-layer optimization model of distribution network line loss considering the uncertainty of new energy access |
| title_sort | two layer optimization model of distribution network line loss considering the uncertainty of new energy access |
| topic | high proportion of new energy uncertainty model two-layer optimization Improved Whale Optimization algorithm line loss |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1526693/full |
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