Energy management in networked microgrids: A comparative study of hierarchical deep learning and predictive analytics techniques
The management of renewable energy is one of the most important areas in the ability to use resources efficiently and ensure stability in the production of energy and the grid. This paper focuses on a networked microgrid (MG) system which is composed of multiple energy sources including biomass, pho...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Energy Conversion and Management: X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590174524003064 |
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| author | Nima Khosravi Adel Oubelaid Youcef Belkhier |
| author_facet | Nima Khosravi Adel Oubelaid Youcef Belkhier |
| author_sort | Nima Khosravi |
| collection | DOAJ |
| description | The management of renewable energy is one of the most important areas in the ability to use resources efficiently and ensure stability in the production of energy and the grid. This paper focuses on a networked microgrid (MG) system which is composed of multiple energy sources including biomass, photovoltaic (PV) solar panels, wind turbines (WTs), battery energy storage systems (BESS), and pumped hydro storage. The study analyzes an energy management method based on hierarchical deep learning (HDL) through several scenarios. These include normal operation, peak load, changes in renewable energy generation finding faults and odd events extreme weather, cost-effective energy distribution, and long-term planning. The HDL approach uses predictive analysis real-time data, and layered control algorithms to improve energy distribution strategies, make operations more flexible, and help provide grid support services. This provides a complete outlook on how efficient it is in different operating environments. Finally, the study employs the MATLAB/Simulink environment to validate the efficacy and accuracy of the proposed energy management systems (EMSs) strategy. |
| format | Article |
| id | doaj-art-4c05ef38890848229f7ea26a2d51e250 |
| institution | Kabale University |
| issn | 2590-1745 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Conversion and Management: X |
| spelling | doaj-art-4c05ef38890848229f7ea26a2d51e2502024-12-15T06:16:42ZengElsevierEnergy Conversion and Management: X2590-17452025-01-0125100828Energy management in networked microgrids: A comparative study of hierarchical deep learning and predictive analytics techniquesNima Khosravi0Adel Oubelaid1Youcef Belkhier2Department of Electrical and Instrumentation Engineering, R&D Management of NPC, Tehran, Iran; Centre of Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India; Corresponding author.Université de Bejaia, Faculté de Technologie, Laboratoire de Technologie Industrielle et de l’Information, Bejaia 06000, AlgeriaUMR CNRS 6027, University of Brest, 29238 Brest, FranceThe management of renewable energy is one of the most important areas in the ability to use resources efficiently and ensure stability in the production of energy and the grid. This paper focuses on a networked microgrid (MG) system which is composed of multiple energy sources including biomass, photovoltaic (PV) solar panels, wind turbines (WTs), battery energy storage systems (BESS), and pumped hydro storage. The study analyzes an energy management method based on hierarchical deep learning (HDL) through several scenarios. These include normal operation, peak load, changes in renewable energy generation finding faults and odd events extreme weather, cost-effective energy distribution, and long-term planning. The HDL approach uses predictive analysis real-time data, and layered control algorithms to improve energy distribution strategies, make operations more flexible, and help provide grid support services. This provides a complete outlook on how efficient it is in different operating environments. Finally, the study employs the MATLAB/Simulink environment to validate the efficacy and accuracy of the proposed energy management systems (EMSs) strategy.http://www.sciencedirect.com/science/article/pii/S2590174524003064Energy managementDistributed generationRenewable energy integrationNetworked microgridsHierarchical deep learningPredictive analytics techniques |
| spellingShingle | Nima Khosravi Adel Oubelaid Youcef Belkhier Energy management in networked microgrids: A comparative study of hierarchical deep learning and predictive analytics techniques Energy Conversion and Management: X Energy management Distributed generation Renewable energy integration Networked microgrids Hierarchical deep learning Predictive analytics techniques |
| title | Energy management in networked microgrids: A comparative study of hierarchical deep learning and predictive analytics techniques |
| title_full | Energy management in networked microgrids: A comparative study of hierarchical deep learning and predictive analytics techniques |
| title_fullStr | Energy management in networked microgrids: A comparative study of hierarchical deep learning and predictive analytics techniques |
| title_full_unstemmed | Energy management in networked microgrids: A comparative study of hierarchical deep learning and predictive analytics techniques |
| title_short | Energy management in networked microgrids: A comparative study of hierarchical deep learning and predictive analytics techniques |
| title_sort | energy management in networked microgrids a comparative study of hierarchical deep learning and predictive analytics techniques |
| topic | Energy management Distributed generation Renewable energy integration Networked microgrids Hierarchical deep learning Predictive analytics techniques |
| url | http://www.sciencedirect.com/science/article/pii/S2590174524003064 |
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