Robust Optimization for Microgrid Management With Compensator, EV, Storage, Demand Response, and Renewable Integration
Navigating the complex terrain of microgrid energy management is challenging due to the uncertainties linked with abundant renewable resources, fluctuating demand, and a wide range of devices including batteries, distributed energy sources, electric vehicles, and compensatory devices. This paper pre...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10531707/ |
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| author | Hamid Hematian Mohamad Tolou Askari Meysam Amir Ahmadi Mahmood Sameemoqadam Majid Babaei Nik |
| author_facet | Hamid Hematian Mohamad Tolou Askari Meysam Amir Ahmadi Mahmood Sameemoqadam Majid Babaei Nik |
| author_sort | Hamid Hematian |
| collection | DOAJ |
| description | Navigating the complex terrain of microgrid energy management is challenging due to the uncertainties linked with abundant renewable resources, fluctuating demand, and a wide range of devices including batteries, distributed energy sources, electric vehicles, and compensatory devices. This paper presents an advanced two-stage robust day-ahead optimization model designed specifically for MG operations. The model primarily addresses challenges arising from the integration of power electronics-based generation units, the unpredictable nature of demand in microgrids, and the integration of small-scale renewable energy sources. The proposed model includes detailed formulations for MG energy management, covering optimal battery usage, efficient EV energy management, compensator usage, and strategic dispatching of DG resources. The multi-objective function aims to minimize various costs related to energy losses, power purchases, load curtailment, DG operation, and battery/EV expenses over a 24-hour period. To efficiently solve this optimization problem, the C&CG algorithm is utilized. Numerical simulations on a test system validate the effectiveness of the proposed model and solution algorithm, showing a significant reduction in the operating costs of the microgrid. This approach offers a robust framework to enhance the resilience and efficiency of microgrid energy management. The results conclusively demonstrate that the proposed approach surpasses comparable methods by at least 5%, highlighting its effectiveness in improving key indicators within the microgrid system. |
| format | Article |
| id | doaj-art-2ed2660c9dc54535bf5185fb375df151 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2ed2660c9dc54535bf5185fb375df1512025-08-20T02:27:50ZengIEEEIEEE Access2169-35362024-01-0112734137342510.1109/ACCESS.2024.340183410531707Robust Optimization for Microgrid Management With Compensator, EV, Storage, Demand Response, and Renewable IntegrationHamid Hematian0Mohamad Tolou Askari1https://orcid.org/0009-0005-4742-2432Meysam Amir Ahmadi2Mahmood Sameemoqadam3https://orcid.org/0009-0006-6719-0226Majid Babaei Nik4https://orcid.org/0009-0009-0959-2149Department of Electrical Engineering, Semnan Branch,, Islamic Azad University, Semnan, IranDepartment of Electrical Engineering, Semnan Branch,, Islamic Azad University, Semnan, IranDepartment of Electrical Engineering, Semnan Branch,, Islamic Azad University, Semnan, IranDepartment of Electrical Engineering, Shahrood Branch,, Islamic Azad University, Shahrood, IranDepartment of Electrical Engineering, Semnan Branch,, Islamic Azad University, Semnan, IranNavigating the complex terrain of microgrid energy management is challenging due to the uncertainties linked with abundant renewable resources, fluctuating demand, and a wide range of devices including batteries, distributed energy sources, electric vehicles, and compensatory devices. This paper presents an advanced two-stage robust day-ahead optimization model designed specifically for MG operations. The model primarily addresses challenges arising from the integration of power electronics-based generation units, the unpredictable nature of demand in microgrids, and the integration of small-scale renewable energy sources. The proposed model includes detailed formulations for MG energy management, covering optimal battery usage, efficient EV energy management, compensator usage, and strategic dispatching of DG resources. The multi-objective function aims to minimize various costs related to energy losses, power purchases, load curtailment, DG operation, and battery/EV expenses over a 24-hour period. To efficiently solve this optimization problem, the C&CG algorithm is utilized. Numerical simulations on a test system validate the effectiveness of the proposed model and solution algorithm, showing a significant reduction in the operating costs of the microgrid. This approach offers a robust framework to enhance the resilience and efficiency of microgrid energy management. The results conclusively demonstrate that the proposed approach surpasses comparable methods by at least 5%, highlighting its effectiveness in improving key indicators within the microgrid system.https://ieeexplore.ieee.org/document/10531707/Microgridtwo-stage robust optimizationdemand responsestorageelectric vehicleuncertainty |
| spellingShingle | Hamid Hematian Mohamad Tolou Askari Meysam Amir Ahmadi Mahmood Sameemoqadam Majid Babaei Nik Robust Optimization for Microgrid Management With Compensator, EV, Storage, Demand Response, and Renewable Integration IEEE Access Microgrid two-stage robust optimization demand response storage electric vehicle uncertainty |
| title | Robust Optimization for Microgrid Management With Compensator, EV, Storage, Demand Response, and Renewable Integration |
| title_full | Robust Optimization for Microgrid Management With Compensator, EV, Storage, Demand Response, and Renewable Integration |
| title_fullStr | Robust Optimization for Microgrid Management With Compensator, EV, Storage, Demand Response, and Renewable Integration |
| title_full_unstemmed | Robust Optimization for Microgrid Management With Compensator, EV, Storage, Demand Response, and Renewable Integration |
| title_short | Robust Optimization for Microgrid Management With Compensator, EV, Storage, Demand Response, and Renewable Integration |
| title_sort | robust optimization for microgrid management with compensator ev storage demand response and renewable integration |
| topic | Microgrid two-stage robust optimization demand response storage electric vehicle uncertainty |
| url | https://ieeexplore.ieee.org/document/10531707/ |
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