Providing a Control System for Charging Electric Vehicles Using ANFIS
Frequency control, especially when incorporating distributed generation units such as wind and solar power plants, is crucial for maintaining grid stability. To address this issue, a study proposes a method for controlling the connection status of electric vehicles (EVs) to prevent frequency fluctua...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2024/9921062 |
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| _version_ | 1849398049479065600 |
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| author | Zahra Mahdavi Tina Samavat Anita Sadat Jahani Javanmardi Mohammad Ali Dashtaki Mohammad Zand Morteza Azimi Nasab Mostafa Azimi Nasab Sanjeevikumar Padmanaban Baseem Khan |
| author_facet | Zahra Mahdavi Tina Samavat Anita Sadat Jahani Javanmardi Mohammad Ali Dashtaki Mohammad Zand Morteza Azimi Nasab Mostafa Azimi Nasab Sanjeevikumar Padmanaban Baseem Khan |
| author_sort | Zahra Mahdavi |
| collection | DOAJ |
| description | Frequency control, especially when incorporating distributed generation units such as wind and solar power plants, is crucial for maintaining grid stability. To address this issue, a study proposes a method for controlling the connection status of electric vehicles (EVs) to prevent frequency fluctuations. The method utilizes an adaptive neural-fuzzy inference system (ANFIS) and a whale optimization algorithm to regulate the charging or discharging of EV batteries based on frequency fluctuations. The objective is to minimize and adjust the frequency fluctuations to zero. The proposed method is evaluated using a real microgrid composed of a wind power plant, a solar power plant, a diesel generator, a large household load, an industrial load, and 711 electric vehicles. The ANFIS system serves as the primary controller, taking inputs such as electric vehicle and battery status and generating outputs that determine the charging or discharging of the electric vehicles. Several investigations are conducted to assess the effectiveness of this model, and the results obtained are compared with the normal state where electric vehicles only consume power. By implementing this method, it is expected that the connection status of electric vehicles can be optimized to help stabilize the grid and minimize frequency fluctuations caused by the integration of distributed renewable energy sources. This study highlights the importance of automatic frequency control in smart grids and offers a potential solution using ANFIS and the whale optimization algorithm. |
| format | Article |
| id | doaj-art-a2ad70fe25b249eca5602bd9976a5a34 |
| institution | Kabale University |
| issn | 2050-7038 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-a2ad70fe25b249eca5602bd9976a5a342025-08-20T03:38:44ZengWileyInternational Transactions on Electrical Energy Systems2050-70382024-01-01202410.1155/2024/9921062Providing a Control System for Charging Electric Vehicles Using ANFISZahra Mahdavi0Tina Samavat1Anita Sadat Jahani Javanmardi2Mohammad Ali Dashtaki3Mohammad Zand4Morteza Azimi Nasab5Mostafa Azimi Nasab6Sanjeevikumar Padmanaban7Baseem Khan8Department of Electrical EngineeringDepartment of Electrical EngineeringDepartment of Electrical EngineeringDepartment of Electrical and Computer EngineeringDepartment of Electrical EngineeringDepartment of Electrical EngineeringDepartment of Electrical EngineeringDepartment of Electrical EngineeringDepartment of Electrical and Computer EngineeringFrequency control, especially when incorporating distributed generation units such as wind and solar power plants, is crucial for maintaining grid stability. To address this issue, a study proposes a method for controlling the connection status of electric vehicles (EVs) to prevent frequency fluctuations. The method utilizes an adaptive neural-fuzzy inference system (ANFIS) and a whale optimization algorithm to regulate the charging or discharging of EV batteries based on frequency fluctuations. The objective is to minimize and adjust the frequency fluctuations to zero. The proposed method is evaluated using a real microgrid composed of a wind power plant, a solar power plant, a diesel generator, a large household load, an industrial load, and 711 electric vehicles. The ANFIS system serves as the primary controller, taking inputs such as electric vehicle and battery status and generating outputs that determine the charging or discharging of the electric vehicles. Several investigations are conducted to assess the effectiveness of this model, and the results obtained are compared with the normal state where electric vehicles only consume power. By implementing this method, it is expected that the connection status of electric vehicles can be optimized to help stabilize the grid and minimize frequency fluctuations caused by the integration of distributed renewable energy sources. This study highlights the importance of automatic frequency control in smart grids and offers a potential solution using ANFIS and the whale optimization algorithm.http://dx.doi.org/10.1155/2024/9921062 |
| spellingShingle | Zahra Mahdavi Tina Samavat Anita Sadat Jahani Javanmardi Mohammad Ali Dashtaki Mohammad Zand Morteza Azimi Nasab Mostafa Azimi Nasab Sanjeevikumar Padmanaban Baseem Khan Providing a Control System for Charging Electric Vehicles Using ANFIS International Transactions on Electrical Energy Systems |
| title | Providing a Control System for Charging Electric Vehicles Using ANFIS |
| title_full | Providing a Control System for Charging Electric Vehicles Using ANFIS |
| title_fullStr | Providing a Control System for Charging Electric Vehicles Using ANFIS |
| title_full_unstemmed | Providing a Control System for Charging Electric Vehicles Using ANFIS |
| title_short | Providing a Control System for Charging Electric Vehicles Using ANFIS |
| title_sort | providing a control system for charging electric vehicles using anfis |
| url | http://dx.doi.org/10.1155/2024/9921062 |
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