Multi-objective optimal dispatching of virtual power plants considering source-load uncertainty in V2G mode
To solve the risks brought by the uncertainty of renewable energy output and load demand to the virtual power plant dispatch, a multi-objective information gap decision theory (IGDT) dispatching model for virtual power plants considering source-load uncertainty under vehicle-to-grid (V2G) is propose...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2022.983743/full |
| _version_ | 1797950836322598912 |
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| author | Lan Ren Daogang Peng Danhao Wang Jianfang Li Huirong Zhao |
| author_facet | Lan Ren Daogang Peng Danhao Wang Jianfang Li Huirong Zhao |
| author_sort | Lan Ren |
| collection | DOAJ |
| description | To solve the risks brought by the uncertainty of renewable energy output and load demand to the virtual power plant dispatch, a multi-objective information gap decision theory (IGDT) dispatching model for virtual power plants considering source-load uncertainty under vehicle-to-grid (V2G) is proposed. With the lowest system operating cost and carbon emission as the optimization objectives, the multi-objective robust optimization model for virtual power plants is constructed based on the uncertainties of wind output, photovoltaic output and load demand guided by the time of use price. The weights of uncertainties quantify the effects of uncertainty factors. The adaptive reference vector based constrained multi-objective evolutionary algorithm is used to solve it. The weight coefficients, evasion coefficients of uncertainties and the penetration rate of electric vehicles are analyzed for the optimal dispatching of the virtual power plant. The algorithm results show that the method can effectively achieve load-side peak shaving and valley filling and has superiority in terms of economy, environmental benefits, robustness and stability. |
| first_indexed | 2024-04-10T22:21:25Z |
| format | Article |
| id | doaj.art-dc0c89e8b0d744baa00838148ed0d19d |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-10T22:21:25Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-dc0c89e8b0d744baa00838148ed0d19d2023-01-18T04:29:01ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-01-011010.3389/fenrg.2022.983743983743Multi-objective optimal dispatching of virtual power plants considering source-load uncertainty in V2G modeLan Ren0Daogang Peng1Danhao Wang2Jianfang Li3Huirong Zhao4College of Automation Engineering, Shanghai University of Electric Power, Shanghai, ChinaCollege of Automation Engineering, Shanghai University of Electric Power, Shanghai, ChinaCollege of Electric Power Engineering, Shanghai University of Electric Power, Shanghai, ChinaCollege of Automation Engineering, Shanghai University of Electric Power, Shanghai, ChinaCollege of Automation Engineering, Shanghai University of Electric Power, Shanghai, ChinaTo solve the risks brought by the uncertainty of renewable energy output and load demand to the virtual power plant dispatch, a multi-objective information gap decision theory (IGDT) dispatching model for virtual power plants considering source-load uncertainty under vehicle-to-grid (V2G) is proposed. With the lowest system operating cost and carbon emission as the optimization objectives, the multi-objective robust optimization model for virtual power plants is constructed based on the uncertainties of wind output, photovoltaic output and load demand guided by the time of use price. The weights of uncertainties quantify the effects of uncertainty factors. The adaptive reference vector based constrained multi-objective evolutionary algorithm is used to solve it. The weight coefficients, evasion coefficients of uncertainties and the penetration rate of electric vehicles are analyzed for the optimal dispatching of the virtual power plant. The algorithm results show that the method can effectively achieve load-side peak shaving and valley filling and has superiority in terms of economy, environmental benefits, robustness and stability.https://www.frontiersin.org/articles/10.3389/fenrg.2022.983743/fullvirtual power plants (VPP)information gap decision makingV2G (vehicle to grid)carbon emissionuncertainty |
| spellingShingle | Lan Ren Daogang Peng Danhao Wang Jianfang Li Huirong Zhao Multi-objective optimal dispatching of virtual power plants considering source-load uncertainty in V2G mode Frontiers in Energy Research virtual power plants (VPP) information gap decision making V2G (vehicle to grid) carbon emission uncertainty |
| title | Multi-objective optimal dispatching of virtual power plants considering source-load uncertainty in V2G mode |
| title_full | Multi-objective optimal dispatching of virtual power plants considering source-load uncertainty in V2G mode |
| title_fullStr | Multi-objective optimal dispatching of virtual power plants considering source-load uncertainty in V2G mode |
| title_full_unstemmed | Multi-objective optimal dispatching of virtual power plants considering source-load uncertainty in V2G mode |
| title_short | Multi-objective optimal dispatching of virtual power plants considering source-load uncertainty in V2G mode |
| title_sort | multi objective optimal dispatching of virtual power plants considering source load uncertainty in v2g mode |
| topic | virtual power plants (VPP) information gap decision making V2G (vehicle to grid) carbon emission uncertainty |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2022.983743/full |
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