A Bilevel Optimization Model Based on Edge Computing for Microgrid
With the continuous progress of renewable energy technology and the large-scale construction of microgrids, the architecture of power systems is becoming increasingly complex and huge. In order to achieve efficient and low-delay data processing and meet the needs of smart grid users, emerging smart...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/20/7710 |
| _version_ | 1827648023897833472 |
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| author | Yi Chen Kadhim Hayawi Meikai Fan Shih Yu Chang Jie Tang Ling Yang Rui Zhao Zhongqi Mao Hong Wen |
| author_facet | Yi Chen Kadhim Hayawi Meikai Fan Shih Yu Chang Jie Tang Ling Yang Rui Zhao Zhongqi Mao Hong Wen |
| author_sort | Yi Chen |
| collection | DOAJ |
| description | With the continuous progress of renewable energy technology and the large-scale construction of microgrids, the architecture of power systems is becoming increasingly complex and huge. In order to achieve efficient and low-delay data processing and meet the needs of smart grid users, emerging smart energy systems are often deployed at the edge of the power grid, and edge computing modules are integrated into the microgrids system, so as to realize the cost-optimal control decision of the microgrids under the condition of load balancing. Therefore, this paper presents a bilevel optimization control model, which is divided into an upper-level optimal control module and a lower-level optimal control module. The purpose of the two-layer optimization modules is to optimize the cost of the power distribution of microgrids. The function of the upper-level optimal control module is to set decision variables for the lower-level module, while the function of the lower-level module is to find the optimal solution by mathematical methods on the basis of the upper-level and then feed back the optimal solution to the upper-layer. The upper-level and lower-level modules affect system decisions together. Finally, the feasibility of the bilevel optimization model is demonstrated by experiments. |
| first_indexed | 2024-03-09T19:30:36Z |
| format | Article |
| id | doaj.art-4aad1fc4cbd34d05bf25bd13c486369f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T19:30:36Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4aad1fc4cbd34d05bf25bd13c486369f2023-11-24T02:24:20ZengMDPI AGSensors1424-82202022-10-012220771010.3390/s22207710A Bilevel Optimization Model Based on Edge Computing for MicrogridYi Chen0Kadhim Hayawi1Meikai Fan2Shih Yu Chang3Jie Tang4Ling Yang5Rui Zhao6Zhongqi Mao7Hong Wen8College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Technological Innovation, Zayed University, Abu Dhabi 144534, United Arab EmiratesCollege of Communication Engineering, Chengdu University of Information Technology, Chengdu 610225, ChinaDepartment of Applied Data Science, San Jose State University, San Jose, CA 95192, USASchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaCollege of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, ChinaChina Mobile (Chengdu) Industrial Research, Chengdu 610041, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaWith the continuous progress of renewable energy technology and the large-scale construction of microgrids, the architecture of power systems is becoming increasingly complex and huge. In order to achieve efficient and low-delay data processing and meet the needs of smart grid users, emerging smart energy systems are often deployed at the edge of the power grid, and edge computing modules are integrated into the microgrids system, so as to realize the cost-optimal control decision of the microgrids under the condition of load balancing. Therefore, this paper presents a bilevel optimization control model, which is divided into an upper-level optimal control module and a lower-level optimal control module. The purpose of the two-layer optimization modules is to optimize the cost of the power distribution of microgrids. The function of the upper-level optimal control module is to set decision variables for the lower-level module, while the function of the lower-level module is to find the optimal solution by mathematical methods on the basis of the upper-level and then feed back the optimal solution to the upper-layer. The upper-level and lower-level modules affect system decisions together. Finally, the feasibility of the bilevel optimization model is demonstrated by experiments.https://www.mdpi.com/1424-8220/22/20/7710edge computingmicrogridpower distributioncostoptimization |
| spellingShingle | Yi Chen Kadhim Hayawi Meikai Fan Shih Yu Chang Jie Tang Ling Yang Rui Zhao Zhongqi Mao Hong Wen A Bilevel Optimization Model Based on Edge Computing for Microgrid Sensors edge computing microgrid power distribution cost optimization |
| title | A Bilevel Optimization Model Based on Edge Computing for Microgrid |
| title_full | A Bilevel Optimization Model Based on Edge Computing for Microgrid |
| title_fullStr | A Bilevel Optimization Model Based on Edge Computing for Microgrid |
| title_full_unstemmed | A Bilevel Optimization Model Based on Edge Computing for Microgrid |
| title_short | A Bilevel Optimization Model Based on Edge Computing for Microgrid |
| title_sort | bilevel optimization model based on edge computing for microgrid |
| topic | edge computing microgrid power distribution cost optimization |
| url | https://www.mdpi.com/1424-8220/22/20/7710 |
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