Optimal Configuration of Wind–Solar–Thermal-Storage Power Energy Based on Dynamic Inertia Weight Chaotic Particle Swarm
The proposed approach involves a method of joint optimization configuration for wind–solar–thermal-storage (WSTS) power energy bases utilizing a dynamic inertia weight chaotic particle swarm optimization (DIWCPSO) algorithm. The power generated from the combination of wind and solar energy is analyz...
| Asıl Yazarlar: | , , , , , , , |
|---|---|
| Materyal Türü: | Makale |
| Dil: | English |
| Baskı/Yayın Bilgisi: |
MDPI AG
2024-02-01
|
| Seri Bilgileri: | Energies |
| Konular: | |
| Online Erişim: | https://www.mdpi.com/1996-1073/17/5/989 |
| _version_ | 1827319706407665664 |
|---|---|
| author | Sile Hu Yuan Gao Yuan Wang Yuan Yu Yue Bi Linfeng Cao Muhammad Farhan Khan Jiaqiang Yang |
| author_facet | Sile Hu Yuan Gao Yuan Wang Yuan Yu Yue Bi Linfeng Cao Muhammad Farhan Khan Jiaqiang Yang |
| author_sort | Sile Hu |
| collection | DOAJ |
| description | The proposed approach involves a method of joint optimization configuration for wind–solar–thermal-storage (WSTS) power energy bases utilizing a dynamic inertia weight chaotic particle swarm optimization (DIWCPSO) algorithm. The power generated from the combination of wind and solar energy is analyzed quantitatively by using the average complementarity index (ACI) to determine the optimal ratio of wind and solar installations. We constructed a multi-objective optimization configuration model for the WSTS power generation systems, considering the equivalent annual income and the optimal energy consumption level as objective functions of the system. We solved the model using the chaotic particle swarm optimization algorithm with linearly decreasing dynamic inertia weight. To validate the effectiveness of the proposed approach, we conducted a simulation using the 2030 power energy base planning data of a particular region in Inner Mongolia. The results demonstrate that the proposed method significantly improves the annual income, enhances the consumption of wind–solar energy, and boosts the power transmission capacity of the system. |
| first_indexed | 2024-04-25T00:32:05Z |
| format | Article |
| id | doaj.art-4b03cc0c57f44900831c5da1e92fa73d |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-25T00:32:05Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-4b03cc0c57f44900831c5da1e92fa73d2024-03-12T16:42:56ZengMDPI AGEnergies1996-10732024-02-0117598910.3390/en17050989Optimal Configuration of Wind–Solar–Thermal-Storage Power Energy Based on Dynamic Inertia Weight Chaotic Particle SwarmSile Hu0Yuan Gao1Yuan Wang2Yuan Yu3Yue Bi4Linfeng Cao5Muhammad Farhan Khan6Jiaqiang Yang7College of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaCollege of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaInner Mongolia Electric Power Economic and Technical Research Institute Branch, Inner Mongolia Electric Power (Group) Co., Ltd., Hohhot 010020, ChinaInner Mongolia Power (Group) Co., Ltd., Hohhot 010020, ChinaCollege of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaCollege of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaCollege of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaCollege of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaThe proposed approach involves a method of joint optimization configuration for wind–solar–thermal-storage (WSTS) power energy bases utilizing a dynamic inertia weight chaotic particle swarm optimization (DIWCPSO) algorithm. The power generated from the combination of wind and solar energy is analyzed quantitatively by using the average complementarity index (ACI) to determine the optimal ratio of wind and solar installations. We constructed a multi-objective optimization configuration model for the WSTS power generation systems, considering the equivalent annual income and the optimal energy consumption level as objective functions of the system. We solved the model using the chaotic particle swarm optimization algorithm with linearly decreasing dynamic inertia weight. To validate the effectiveness of the proposed approach, we conducted a simulation using the 2030 power energy base planning data of a particular region in Inner Mongolia. The results demonstrate that the proposed method significantly improves the annual income, enhances the consumption of wind–solar energy, and boosts the power transmission capacity of the system.https://www.mdpi.com/1996-1073/17/5/989wind–solar–thermal storagepower energy baseaverage complementarity indexdynamic inertia weight chaotic particle swarm |
| spellingShingle | Sile Hu Yuan Gao Yuan Wang Yuan Yu Yue Bi Linfeng Cao Muhammad Farhan Khan Jiaqiang Yang Optimal Configuration of Wind–Solar–Thermal-Storage Power Energy Based on Dynamic Inertia Weight Chaotic Particle Swarm Energies wind–solar–thermal storage power energy base average complementarity index dynamic inertia weight chaotic particle swarm |
| title | Optimal Configuration of Wind–Solar–Thermal-Storage Power Energy Based on Dynamic Inertia Weight Chaotic Particle Swarm |
| title_full | Optimal Configuration of Wind–Solar–Thermal-Storage Power Energy Based on Dynamic Inertia Weight Chaotic Particle Swarm |
| title_fullStr | Optimal Configuration of Wind–Solar–Thermal-Storage Power Energy Based on Dynamic Inertia Weight Chaotic Particle Swarm |
| title_full_unstemmed | Optimal Configuration of Wind–Solar–Thermal-Storage Power Energy Based on Dynamic Inertia Weight Chaotic Particle Swarm |
| title_short | Optimal Configuration of Wind–Solar–Thermal-Storage Power Energy Based on Dynamic Inertia Weight Chaotic Particle Swarm |
| title_sort | optimal configuration of wind solar thermal storage power energy based on dynamic inertia weight chaotic particle swarm |
| topic | wind–solar–thermal storage power energy base average complementarity index dynamic inertia weight chaotic particle swarm |
| url | https://www.mdpi.com/1996-1073/17/5/989 |
| work_keys_str_mv | AT silehu optimalconfigurationofwindsolarthermalstoragepowerenergybasedondynamicinertiaweightchaoticparticleswarm AT yuangao optimalconfigurationofwindsolarthermalstoragepowerenergybasedondynamicinertiaweightchaoticparticleswarm AT yuanwang optimalconfigurationofwindsolarthermalstoragepowerenergybasedondynamicinertiaweightchaoticparticleswarm AT yuanyu optimalconfigurationofwindsolarthermalstoragepowerenergybasedondynamicinertiaweightchaoticparticleswarm AT yuebi optimalconfigurationofwindsolarthermalstoragepowerenergybasedondynamicinertiaweightchaoticparticleswarm AT linfengcao optimalconfigurationofwindsolarthermalstoragepowerenergybasedondynamicinertiaweightchaoticparticleswarm AT muhammadfarhankhan optimalconfigurationofwindsolarthermalstoragepowerenergybasedondynamicinertiaweightchaoticparticleswarm AT jiaqiangyang optimalconfigurationofwindsolarthermalstoragepowerenergybasedondynamicinertiaweightchaoticparticleswarm |