A wind power curtailment reduction strategy using electric vehicles based on individual differential evolution quantum particle swarm optimization algorithm
A wind power curtailment consumption strategy using electric vehicles (EVs) based on individual differential evolution quantum particle swarm optimization algorithm (IDE-QPSO) is proposed, with the objective of reducing the system’s wind curtailment in order to further improve the wind power consump...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722023770 |
| _version_ | 1797901901990199296 |
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| author | Liang Zhang Qingbo Yin Zhihui Zhang Zheng Zhu Ling Lyu Koh Leong Hai Guowei Cai |
| author_facet | Liang Zhang Qingbo Yin Zhihui Zhang Zheng Zhu Ling Lyu Koh Leong Hai Guowei Cai |
| author_sort | Liang Zhang |
| collection | DOAJ |
| description | A wind power curtailment consumption strategy using electric vehicles (EVs) based on individual differential evolution quantum particle swarm optimization algorithm (IDE-QPSO) is proposed, with the objective of reducing the system’s wind curtailment in order to further improve the wind power consumption rate while effectively reducing wind power output fluctuation and amplitude. EV aggregators act as charging tariff setters, releasing dynamic time-of-use tariffs (DTOU) for EV clusters to respond to based on wind curtailment data accounted for by the dispatch center. This method first establishes an electric vehicle charging load model based on the travel chain theory and residents’ travel rules, then establishes an EV users autonomous response model based on the sensitivity of electric vehicle users to the charging prices. Second, a multi-objective optimization function is established based on the aforementioned model, which integrates wind power curtailment consumption and minimizes wind power output fluctuation and amplitude, and it is solved using an improved quantum particle swarm optimization algorithm. Finally, adequate simulation experiments show that this strategy can effectively smooth out the fluctuation of wind power output and improve the wind power consumption rate. |
| first_indexed | 2024-04-10T09:09:18Z |
| format | Article |
| id | doaj.art-ddd4e0937e6c4b3895e84b808b1b3a78 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T09:09:18Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-ddd4e0937e6c4b3895e84b808b1b3a782023-02-21T05:14:27ZengElsevierEnergy Reports2352-48472022-11-0181457814594A wind power curtailment reduction strategy using electric vehicles based on individual differential evolution quantum particle swarm optimization algorithmLiang Zhang0Qingbo Yin1Zhihui Zhang2Zheng Zhu3Ling Lyu4Koh Leong Hai5Guowei Cai6Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin 132012, ChinaKey Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin 132012, China; Corresponding author.State grid shanghai energy interconnection research institute Co., Ltd, Shanghai 201210, ChinaGlobal Energy Interconnection Development and Cooperation Organization, Beijing, 100006, ChinaKey Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin 132012, ChinaNational Metrology Centre, 138635, SingaporeKey Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin 132012, ChinaA wind power curtailment consumption strategy using electric vehicles (EVs) based on individual differential evolution quantum particle swarm optimization algorithm (IDE-QPSO) is proposed, with the objective of reducing the system’s wind curtailment in order to further improve the wind power consumption rate while effectively reducing wind power output fluctuation and amplitude. EV aggregators act as charging tariff setters, releasing dynamic time-of-use tariffs (DTOU) for EV clusters to respond to based on wind curtailment data accounted for by the dispatch center. This method first establishes an electric vehicle charging load model based on the travel chain theory and residents’ travel rules, then establishes an EV users autonomous response model based on the sensitivity of electric vehicle users to the charging prices. Second, a multi-objective optimization function is established based on the aforementioned model, which integrates wind power curtailment consumption and minimizes wind power output fluctuation and amplitude, and it is solved using an improved quantum particle swarm optimization algorithm. Finally, adequate simulation experiments show that this strategy can effectively smooth out the fluctuation of wind power output and improve the wind power consumption rate.http://www.sciencedirect.com/science/article/pii/S2352484722023770Electric vehicleReduce the wind curtailmentDynamic TOU tariffsIndividual differential evolution quantum particle swarm optimization algorithm |
| spellingShingle | Liang Zhang Qingbo Yin Zhihui Zhang Zheng Zhu Ling Lyu Koh Leong Hai Guowei Cai A wind power curtailment reduction strategy using electric vehicles based on individual differential evolution quantum particle swarm optimization algorithm Energy Reports Electric vehicle Reduce the wind curtailment Dynamic TOU tariffs Individual differential evolution quantum particle swarm optimization algorithm |
| title | A wind power curtailment reduction strategy using electric vehicles based on individual differential evolution quantum particle swarm optimization algorithm |
| title_full | A wind power curtailment reduction strategy using electric vehicles based on individual differential evolution quantum particle swarm optimization algorithm |
| title_fullStr | A wind power curtailment reduction strategy using electric vehicles based on individual differential evolution quantum particle swarm optimization algorithm |
| title_full_unstemmed | A wind power curtailment reduction strategy using electric vehicles based on individual differential evolution quantum particle swarm optimization algorithm |
| title_short | A wind power curtailment reduction strategy using electric vehicles based on individual differential evolution quantum particle swarm optimization algorithm |
| title_sort | wind power curtailment reduction strategy using electric vehicles based on individual differential evolution quantum particle swarm optimization algorithm |
| topic | Electric vehicle Reduce the wind curtailment Dynamic TOU tariffs Individual differential evolution quantum particle swarm optimization algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722023770 |
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