Optimal energy management for multi-energy microgrids using hybrid solutions to address renewable energy source uncertainty
Abstract Research in industrial grid energy management is essential due to increasing energy demands, rising costs, and the integration of renewable sources. Efficient energy management can reduce operational costs, enhance grid stability, and optimize resource allocation. Addressing these challenge...
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Nature Portfolio
2025-03-01
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| Online Access: | https://doi.org/10.1038/s41598-025-90062-8 |
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| author | M. Siva Ramkumar Jaganathan Subramani M. Sivaramkrishnan Arunkumar Munimathan Goh Kah Ong Michael Mohammad Mukhtar Alam |
| author_facet | M. Siva Ramkumar Jaganathan Subramani M. Sivaramkrishnan Arunkumar Munimathan Goh Kah Ong Michael Mohammad Mukhtar Alam |
| author_sort | M. Siva Ramkumar |
| collection | DOAJ |
| description | Abstract Research in industrial grid energy management is essential due to increasing energy demands, rising costs, and the integration of renewable sources. Efficient energy management can reduce operational costs, enhance grid stability, and optimize resource allocation. Addressing these challenges requires advanced techniques to balance supply, demand, and storage in dynamic industrial settings. In this study, a new hybrid algorithm is used for system modelling and low-cost, optimal management of Micro Grid (MG) networked systems. Optimizing micro-sources to reduce electricity production costs through hourly, day-ahead, and real-time scheduling was the process’ primary goal.This research proposes a Quadratic Interpolation and New Local Search for Greylag Goose Optimisation (QI-NLS-G2O) and Gaussian Radius Zone Perceptron Net (GRZPNet) technique based energy management scheme for Multi-Energy Microgrids (MEMG) to help the Energy Management System (EMS) formulate optimal dispatching strategies under Renewable Energy Source (RES) uncertainty. To precisely represent the MEMG’s operational state, the scheduling process incorporates an off-design performance model for energy conversion devices. Utilising MG inputs such as Wind Turbines (WT), Photovoltaic arrays (PV), and battery storage with associated cost functions, the GRZPNet learning phase based on QI-NLS-G2O is utilised to forecast load demand. The QI-NLS-G2O optimises the MG configuration according to the load demand. The MATLAB/Simulink working platform is used to implement the suggested hybrid technique, which is then contrasted with alternative approaches to solving problems.The proposed model significantly improves dispatching accuracy, reducing RES uncertainty impacts by approximately 15% and enhancing MEMG performance efficiency by up to 20% in simulations. |
| format | Article |
| id | doaj-art-73cd7ae0bfd246c5a352fee1f01b9f72 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-73cd7ae0bfd246c5a352fee1f01b9f722025-08-20T01:57:51ZengNature PortfolioScientific Reports2045-23222025-03-0115111410.1038/s41598-025-90062-8Optimal energy management for multi-energy microgrids using hybrid solutions to address renewable energy source uncertaintyM. Siva Ramkumar0Jaganathan Subramani1M. Sivaramkrishnan2Arunkumar Munimathan3Goh Kah Ong Michael4Mohammad Mukhtar Alam5Department Electronics and Communication Engineering, SNS College of TechnologyDepartment of Electrical and Electronics Engineering, Karpagam College of EngineeringDepartment of Electrical and Electronics Engineering, Karpagam College of EngineeringDepartment of Mechanical Engineering, University Centre for Research and Development, Chandigarh UniversityMultimedia UniversityDepartment of Industrial Engineering, College of Engineering, King Khalid UniversityAbstract Research in industrial grid energy management is essential due to increasing energy demands, rising costs, and the integration of renewable sources. Efficient energy management can reduce operational costs, enhance grid stability, and optimize resource allocation. Addressing these challenges requires advanced techniques to balance supply, demand, and storage in dynamic industrial settings. In this study, a new hybrid algorithm is used for system modelling and low-cost, optimal management of Micro Grid (MG) networked systems. Optimizing micro-sources to reduce electricity production costs through hourly, day-ahead, and real-time scheduling was the process’ primary goal.This research proposes a Quadratic Interpolation and New Local Search for Greylag Goose Optimisation (QI-NLS-G2O) and Gaussian Radius Zone Perceptron Net (GRZPNet) technique based energy management scheme for Multi-Energy Microgrids (MEMG) to help the Energy Management System (EMS) formulate optimal dispatching strategies under Renewable Energy Source (RES) uncertainty. To precisely represent the MEMG’s operational state, the scheduling process incorporates an off-design performance model for energy conversion devices. Utilising MG inputs such as Wind Turbines (WT), Photovoltaic arrays (PV), and battery storage with associated cost functions, the GRZPNet learning phase based on QI-NLS-G2O is utilised to forecast load demand. The QI-NLS-G2O optimises the MG configuration according to the load demand. The MATLAB/Simulink working platform is used to implement the suggested hybrid technique, which is then contrasted with alternative approaches to solving problems.The proposed model significantly improves dispatching accuracy, reducing RES uncertainty impacts by approximately 15% and enhancing MEMG performance efficiency by up to 20% in simulations.https://doi.org/10.1038/s41598-025-90062-8Micro gridMulti-energy microgridsEnergy management systemLoad demandRES uncertaintyWind turbine (WT) |
| spellingShingle | M. Siva Ramkumar Jaganathan Subramani M. Sivaramkrishnan Arunkumar Munimathan Goh Kah Ong Michael Mohammad Mukhtar Alam Optimal energy management for multi-energy microgrids using hybrid solutions to address renewable energy source uncertainty Scientific Reports Micro grid Multi-energy microgrids Energy management system Load demand RES uncertainty Wind turbine (WT) |
| title | Optimal energy management for multi-energy microgrids using hybrid solutions to address renewable energy source uncertainty |
| title_full | Optimal energy management for multi-energy microgrids using hybrid solutions to address renewable energy source uncertainty |
| title_fullStr | Optimal energy management for multi-energy microgrids using hybrid solutions to address renewable energy source uncertainty |
| title_full_unstemmed | Optimal energy management for multi-energy microgrids using hybrid solutions to address renewable energy source uncertainty |
| title_short | Optimal energy management for multi-energy microgrids using hybrid solutions to address renewable energy source uncertainty |
| title_sort | optimal energy management for multi energy microgrids using hybrid solutions to address renewable energy source uncertainty |
| topic | Micro grid Multi-energy microgrids Energy management system Load demand RES uncertainty Wind turbine (WT) |
| url | https://doi.org/10.1038/s41598-025-90062-8 |
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