Renewable Energy-Based DC Microgrid with Hybrid Energy Management System Supporting Electric Vehicle Charging System
Growing Electric vehicle (EV) ownership leads to an increase in charging stations, which raises load demand and causes grid outages during peak hours. Microgrids can significantly resolve these issues in the electrical distribution system by implementing an effective energy management approach. The...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/11/6/273 |
| _version_ | 1797592530829705216 |
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| author | Harin M. Mohan Santanu Kumar Dash |
| author_facet | Harin M. Mohan Santanu Kumar Dash |
| author_sort | Harin M. Mohan |
| collection | DOAJ |
| description | Growing Electric vehicle (EV) ownership leads to an increase in charging stations, which raises load demand and causes grid outages during peak hours. Microgrids can significantly resolve these issues in the electrical distribution system by implementing an effective energy management approach. The suggested hybrid optimization approach aims to provide constant power regardless of the generation discrepancy and should prevent the early deterioration of the storage devices. This study suggests using a dynamic control system based on the Fuzzy-Sparrow Search Algorithm (SSA) to provide a reliable power balance for microgrid (MG) operation. The proposed DC microgrid integrating renewable energy sources (RES) and battery storage system (BSS) as sources are designed and evaluated, and the findings are further validated using MATLAB Simulink simulation. In comparing the hybrid SSA strategy with the most widely used Particle Swarm Optimization (PSO)-based power management, it was observed that the hybrid SSA approach was superior in terms of convergence speed and stability. The effectiveness of the given energy management system is evaluated using two distinct modes, the variation of solar irradiation and the variation of battery state of charge, ensuring the microgrid’s cost-effective operation. The enhanced response characteristics indicate that the Fuzzy-SSA can optimise power management of the DC microgrid, making better use of energy resources. These results show the relevance of algorithm configuration for cost-effective power management in DC microgrids, as it saves approximately 7.776% in electricity expenses over a year compared to PSO. |
| first_indexed | 2024-03-11T01:52:27Z |
| format | Article |
| id | doaj.art-c8c834adc7034fdbaedd653a4a6213fa |
| institution | Directory Open Access Journal |
| issn | 2079-8954 |
| language | English |
| last_indexed | 2024-03-11T01:52:27Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj.art-c8c834adc7034fdbaedd653a4a6213fa2023-11-18T12:52:16ZengMDPI AGSystems2079-89542023-05-0111627310.3390/systems11060273Renewable Energy-Based DC Microgrid with Hybrid Energy Management System Supporting Electric Vehicle Charging SystemHarin M. Mohan0Santanu Kumar Dash1School of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, IndiaTIFAC-CORE, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, IndiaGrowing Electric vehicle (EV) ownership leads to an increase in charging stations, which raises load demand and causes grid outages during peak hours. Microgrids can significantly resolve these issues in the electrical distribution system by implementing an effective energy management approach. The suggested hybrid optimization approach aims to provide constant power regardless of the generation discrepancy and should prevent the early deterioration of the storage devices. This study suggests using a dynamic control system based on the Fuzzy-Sparrow Search Algorithm (SSA) to provide a reliable power balance for microgrid (MG) operation. The proposed DC microgrid integrating renewable energy sources (RES) and battery storage system (BSS) as sources are designed and evaluated, and the findings are further validated using MATLAB Simulink simulation. In comparing the hybrid SSA strategy with the most widely used Particle Swarm Optimization (PSO)-based power management, it was observed that the hybrid SSA approach was superior in terms of convergence speed and stability. The effectiveness of the given energy management system is evaluated using two distinct modes, the variation of solar irradiation and the variation of battery state of charge, ensuring the microgrid’s cost-effective operation. The enhanced response characteristics indicate that the Fuzzy-SSA can optimise power management of the DC microgrid, making better use of energy resources. These results show the relevance of algorithm configuration for cost-effective power management in DC microgrids, as it saves approximately 7.776% in electricity expenses over a year compared to PSO.https://www.mdpi.com/2079-8954/11/6/273microgridenergy managementfuzzy logicsparrow search algorithm |
| spellingShingle | Harin M. Mohan Santanu Kumar Dash Renewable Energy-Based DC Microgrid with Hybrid Energy Management System Supporting Electric Vehicle Charging System Systems microgrid energy management fuzzy logic sparrow search algorithm |
| title | Renewable Energy-Based DC Microgrid with Hybrid Energy Management System Supporting Electric Vehicle Charging System |
| title_full | Renewable Energy-Based DC Microgrid with Hybrid Energy Management System Supporting Electric Vehicle Charging System |
| title_fullStr | Renewable Energy-Based DC Microgrid with Hybrid Energy Management System Supporting Electric Vehicle Charging System |
| title_full_unstemmed | Renewable Energy-Based DC Microgrid with Hybrid Energy Management System Supporting Electric Vehicle Charging System |
| title_short | Renewable Energy-Based DC Microgrid with Hybrid Energy Management System Supporting Electric Vehicle Charging System |
| title_sort | renewable energy based dc microgrid with hybrid energy management system supporting electric vehicle charging system |
| topic | microgrid energy management fuzzy logic sparrow search algorithm |
| url | https://www.mdpi.com/2079-8954/11/6/273 |
| work_keys_str_mv | AT harinmmohan renewableenergybaseddcmicrogridwithhybridenergymanagementsystemsupportingelectricvehiclechargingsystem AT santanukumardash renewableenergybaseddcmicrogridwithhybridenergymanagementsystemsupportingelectricvehiclechargingsystem |