A Review on Application of Artificial Intelligence Techniques in Microgrids
A microgrid can be formed by the integration of different components such as loads, renewable/conventional units, and energy storage systems in a local area. Microgrids with the advantages of being flexible, environmentally friendly, and self-sufficient can improve the power system performance metri...
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| Published in | IEEE journal of emerging and selected topics in industrial electronics (Print) Vol. 3; no. 4; pp. 878 - 890 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | A microgrid can be formed by the integration of different components such as loads, renewable/conventional units, and energy storage systems in a local area. Microgrids with the advantages of being flexible, environmentally friendly, and self-sufficient can improve the power system performance metrics such as resiliency and reliability. However, the design and implementation of microgrids are always faced with different challenges considering the uncertainties associated with loads and renewable energy resources, sudden load variations, energy management of several energy resources, etc. Therefore, it is required to employ such rapid and accurate methods, as artificial intelligence (AI) techniques, to address these challenges and improve the MG's efficiency, stability, security, and reliability. Utilization of AI helps to develop systems as intelligent as humans to learn, decide, and solve problems. This article presents a review on different applications of AI-based techniques in microgrids such as energy management, load and generation forecasting, protection, power electronics control, and cyber security. Different AI tasks such as regression and classification in microgrids are discussed using methods including machine learning, artificial neural networks, fuzzy logic, support vector machines, etc. The advantages, limitation, and future trends of AI applications in microgrids are discussed. |
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| AbstractList | A microgrid can be formed by the integration of different components such as loads, renewable/conventional units, and energy storage systems in a local area. Microgrids with the advantages of being flexible, environmentally friendly, and self-sufficient can improve the power system performance metrics such as resiliency and reliability. However, the design and implementation of microgrids are always faced with different challenges considering the uncertainties associated with loads and renewable energy resources, sudden load variations, energy management of several energy resources, etc. Therefore, it is required to employ such rapid and accurate methods, as artificial intelligence (AI) techniques, to address these challenges and improve the MG's efficiency, stability, security, and reliability. Utilization of AI helps to develop systems as intelligent as humans to learn, decide, and solve problems. This article presents a review on different applications of AI-based techniques in microgrids such as energy management, load and generation forecasting, protection, power electronics control, and cyber security. Different AI tasks such as regression and classification in microgrids are discussed using methods including machine learning, artificial neural networks, fuzzy logic, support vector machines, etc. The advantages, limitation, and future trends of AI applications in microgrids are discussed. |
| Author | Mohammadi, Ebrahim Alizadeh, Mojtaba Wang, Xiaoyu Asgarimoghaddam, Mohsen Simoes, Marcelo Godoy |
| Author_xml | – sequence: 1 givenname: Ebrahim orcidid: 0000-0002-7960-5007 surname: Mohammadi fullname: Mohammadi, Ebrahim email: e.mohamadi66@gmail.com organization: Department of Electronics, Carleton University, Ottawa, ON, Canada – sequence: 2 givenname: Mojtaba surname: Alizadeh fullname: Alizadeh, Mojtaba email: mjt.alizadeh@gmail.com organization: Department of Electronics, Carleton University, Ottawa, ON, Canada – sequence: 3 givenname: Mohsen orcidid: 0000-0002-2005-6242 surname: Asgarimoghaddam fullname: Asgarimoghaddam, Mohsen email: mohsenasgarimoghadd@cmail.carleton.ca organization: Department of Electronics, Carleton University, Ottawa, ON, Canada – sequence: 4 givenname: Xiaoyu surname: Wang fullname: Wang, Xiaoyu email: xiaoyuwang3@cunet.carleton.ca organization: Department of Electronics, Carleton University, Ottawa, ON, Canada – sequence: 5 givenname: Marcelo Godoy orcidid: 0000-0003-4124-061X surname: Simoes fullname: Simoes, Marcelo Godoy email: marcelo.godoy.simoes@uwasa.fi organization: Department of Electrical Engineering, University of Vaasa, Vaasa, Finland |
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| SubjectTerms | Artificial intelligence Artificial intelligence (AI) Artificial neural networks control cyber security Cybersecurity Distributed generation Electrical loads Energy management Energy resources Energy sources Energy storage Fuzzy logic Load fluctuation load forecasting (LF) Machine learning machine learning (ML) microgrid (MG) Microgrids Performance measurement Power system reliability Power system stability Power systems protection Reliability Storage systems Support vector machines |
| Title | A Review on Application of Artificial Intelligence Techniques in Microgrids |
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