Optimizing Distribution Grid Performance through Electric Vehicle Integration and Stochastic Modeling in Extreme Weather Conditions
This paper presents an innovative method for operational planning of micro grids, focusing on improving economic performance and enhancing resilience. The proposed approach addresses key uncertainties, including weather conditions, probabilistic charging/discharging behavior of electric vehicles (EV...
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| Format: | Article |
| Language: | English |
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Babol Noshirvani University of Technology
2025-07-01
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| Series: | Iranica Journal of Energy and Environment |
| Subjects: | |
| Online Access: | https://www.ijee.net/article_213026_daa88cb78bdb45f277a17ee15236a819.pdf |
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| author | A. Niknami M. Askari M. Amirahmadi M. Babaeinik |
| author_facet | A. Niknami M. Askari M. Amirahmadi M. Babaeinik |
| author_sort | A. Niknami |
| collection | DOAJ |
| description | This paper presents an innovative method for operational planning of micro grids, focusing on improving economic performance and enhancing resilience. The proposed approach addresses key uncertainties, including weather conditions, probabilistic charging/discharging behavior of electric vehicles (EVs), and integration of renewable energy sources, energy price fluctuations, and load conditions. Additionally, it considers EV owners' satisfaction and demand-side management. A key innovation of this research is the development of a comprehensive framework for simultaneously managing network topology reconfiguration, EV movement within the network, and mitigating the impacts of adverse weather conditions. Monte Carlo simulation is employed to model uncertainties, while a multi-objective optimization algorithm is used to solve the problem. This algorithm aims to maximize the profits of network operators and the private sector while minimizing unsupplied energy and its associated penalties. The proposed method demonstrates significant improvements, including a 37.1% reduction in unsupplied energy costs, a 5% increase in network operators' profits, and a 23.1% boost in EV charging station profits. Overall, the method outperforms existing approaches by approximately 8%. The proposed method offers an effective and robust solution for improving micro grid resilience and operational efficiency under extreme weather conditions, showcasing its superiority over traditional approaches. |
| format | Article |
| id | doaj-art-0a3ee543115b4bb998f1101008eb1566 |
| institution | Kabale University |
| issn | 2079-2115 2079-2123 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Babol Noshirvani University of Technology |
| record_format | Article |
| series | Iranica Journal of Energy and Environment |
| spelling | doaj-art-0a3ee543115b4bb998f1101008eb15662025-01-22T10:46:58ZengBabol Noshirvani University of TechnologyIranica Journal of Energy and Environment2079-21152079-21232025-07-0116355556910.5829/ijee.2025.16.03.15213026Optimizing Distribution Grid Performance through Electric Vehicle Integration and Stochastic Modeling in Extreme Weather ConditionsA. Niknami0M. Askari1M. Amirahmadi2M. Babaeinik3Department of Electrical and Electronic Engineering, Semnan Branch, Islamic Azad University, Semnan, IranDepartment of Electrical and Electronic Engineering, Semnan Branch, Islamic Azad University, Semnan, IranDepartment of Electrical and Electronic Engineering, Semnan Branch, Islamic Azad University, Semnan, IranDepartment of Electrical and Electronic Engineering, Semnan Branch, Islamic Azad University, Semnan, IranThis paper presents an innovative method for operational planning of micro grids, focusing on improving economic performance and enhancing resilience. The proposed approach addresses key uncertainties, including weather conditions, probabilistic charging/discharging behavior of electric vehicles (EVs), and integration of renewable energy sources, energy price fluctuations, and load conditions. Additionally, it considers EV owners' satisfaction and demand-side management. A key innovation of this research is the development of a comprehensive framework for simultaneously managing network topology reconfiguration, EV movement within the network, and mitigating the impacts of adverse weather conditions. Monte Carlo simulation is employed to model uncertainties, while a multi-objective optimization algorithm is used to solve the problem. This algorithm aims to maximize the profits of network operators and the private sector while minimizing unsupplied energy and its associated penalties. The proposed method demonstrates significant improvements, including a 37.1% reduction in unsupplied energy costs, a 5% increase in network operators' profits, and a 23.1% boost in EV charging station profits. Overall, the method outperforms existing approaches by approximately 8%. The proposed method offers an effective and robust solution for improving micro grid resilience and operational efficiency under extreme weather conditions, showcasing its superiority over traditional approaches.https://www.ijee.net/article_213026_daa88cb78bdb45f277a17ee15236a819.pdfadverse weather conditioncharge stationelectric vehiclemulti-objective optimizationresilience oriented operation |
| spellingShingle | A. Niknami M. Askari M. Amirahmadi M. Babaeinik Optimizing Distribution Grid Performance through Electric Vehicle Integration and Stochastic Modeling in Extreme Weather Conditions Iranica Journal of Energy and Environment adverse weather condition charge station electric vehicle multi-objective optimization resilience oriented operation |
| title | Optimizing Distribution Grid Performance through Electric Vehicle Integration and Stochastic Modeling in Extreme Weather Conditions |
| title_full | Optimizing Distribution Grid Performance through Electric Vehicle Integration and Stochastic Modeling in Extreme Weather Conditions |
| title_fullStr | Optimizing Distribution Grid Performance through Electric Vehicle Integration and Stochastic Modeling in Extreme Weather Conditions |
| title_full_unstemmed | Optimizing Distribution Grid Performance through Electric Vehicle Integration and Stochastic Modeling in Extreme Weather Conditions |
| title_short | Optimizing Distribution Grid Performance through Electric Vehicle Integration and Stochastic Modeling in Extreme Weather Conditions |
| title_sort | optimizing distribution grid performance through electric vehicle integration and stochastic modeling in extreme weather conditions |
| topic | adverse weather condition charge station electric vehicle multi-objective optimization resilience oriented operation |
| url | https://www.ijee.net/article_213026_daa88cb78bdb45f277a17ee15236a819.pdf |
| work_keys_str_mv | AT aniknami optimizingdistributiongridperformancethroughelectricvehicleintegrationandstochasticmodelinginextremeweatherconditions AT maskari optimizingdistributiongridperformancethroughelectricvehicleintegrationandstochasticmodelinginextremeweatherconditions AT mamirahmadi optimizingdistributiongridperformancethroughelectricvehicleintegrationandstochasticmodelinginextremeweatherconditions AT mbabaeinik optimizingdistributiongridperformancethroughelectricvehicleintegrationandstochasticmodelinginextremeweatherconditions |