Utilization of EV Charging Station in Demand Side Management Using Deep Learning Method
<p dir="ltr">Conventional energy sources are a major source of pollution. Major efforts are being made by global organizations to reduce CO<sub>2</sub> emissions. Research shows that by 2030, EVs can reduce CO<sub>2</sub> emissions by 28%. However, two major o...
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2023
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| _version_ | 1864513527891361792 |
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| author | Abdul Hafeez (3134514) |
| author2 | Rashid Alammari (16855428) Atif Iqbal (5504636) |
| author2_role | author author |
| author_facet | Abdul Hafeez (3134514) Rashid Alammari (16855428) Atif Iqbal (5504636) |
| author_role | author |
| dc.creator.none.fl_str_mv | Abdul Hafeez (3134514) Rashid Alammari (16855428) Atif Iqbal (5504636) |
| dc.date.none.fl_str_mv | 2023-01-23T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3238667 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Utilization_of_EV_Charging_Station_in_Demand_Side_Management_Using_Deep_Learning_Method/25202369 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Load modeling Renewable energy sources Electric vehicle charging Mathematical models Costs Charging stations Batteries Deep learning CO2 emission data-driven approach demand-side management peak clipping |
| dc.title.none.fl_str_mv | Utilization of EV Charging Station in Demand Side Management Using Deep Learning Method |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Conventional energy sources are a major source of pollution. Major efforts are being made by global organizations to reduce CO<sub>2</sub> emissions. Research shows that by 2030, EVs can reduce CO<sub>2</sub> emissions by 28%. However, two major obstacles affect the widespread adoption of electric vehicles: the high cost of EVs and the lack of charging stations. This paper presents a comprehensive data-driven approach based demand-side management for a solar-powered electric vehicle charging station connected to a microgrid. The proposed approach utilizes a solar-powered electric vehicle charging station to compensate for the energy required during peak demand, which reduces the utilization of conventional energy sources and shortens the problem of fewer EVCS in the current scenario. PV power stations, commercial loads, residential loads, and electric vehicle charging stations were simulated using the collected real-time data. Furthermore, a deep learning approach was developed to control the energy supply to the microgrid and to charge the electric vehicle from the grid during off-peak hours. Furthermore, two different machine learning approaches were compared to estimate the state of charge estimation of an energy storage system. Finally, the proposed framework of the demand management system was executed for a case study of 24 hours. The results reflect that peak demand has been compensated with the help of an electric vehicle charging station during peak hours.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3238667" target="_blank">https://dx.doi.org/10.1109/access.2023.3238667</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_7f496ec05def7e87d9ebe61d45381de6 |
| identifier_str_mv | 10.1109/access.2023.3238667 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25202369 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Utilization of EV Charging Station in Demand Side Management Using Deep Learning MethodAbdul Hafeez (3134514)Rashid Alammari (16855428)Atif Iqbal (5504636)EngineeringElectrical engineeringElectronics, sensors and digital hardwareMaterials engineeringLoad modelingRenewable energy sourcesElectric vehicle chargingMathematical modelsCostsCharging stationsBatteriesDeep learningCO2 emissiondata-driven approachdemand-side managementpeak clipping<p dir="ltr">Conventional energy sources are a major source of pollution. Major efforts are being made by global organizations to reduce CO<sub>2</sub> emissions. Research shows that by 2030, EVs can reduce CO<sub>2</sub> emissions by 28%. However, two major obstacles affect the widespread adoption of electric vehicles: the high cost of EVs and the lack of charging stations. This paper presents a comprehensive data-driven approach based demand-side management for a solar-powered electric vehicle charging station connected to a microgrid. The proposed approach utilizes a solar-powered electric vehicle charging station to compensate for the energy required during peak demand, which reduces the utilization of conventional energy sources and shortens the problem of fewer EVCS in the current scenario. PV power stations, commercial loads, residential loads, and electric vehicle charging stations were simulated using the collected real-time data. Furthermore, a deep learning approach was developed to control the energy supply to the microgrid and to charge the electric vehicle from the grid during off-peak hours. Furthermore, two different machine learning approaches were compared to estimate the state of charge estimation of an energy storage system. Finally, the proposed framework of the demand management system was executed for a case study of 24 hours. The results reflect that peak demand has been compensated with the help of an electric vehicle charging station during peak hours.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3238667" target="_blank">https://dx.doi.org/10.1109/access.2023.3238667</a></p>2023-01-23T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3238667https://figshare.com/articles/journal_contribution/Utilization_of_EV_Charging_Station_in_Demand_Side_Management_Using_Deep_Learning_Method/25202369CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252023692023-01-23T03:00:00Z |
| spellingShingle | Utilization of EV Charging Station in Demand Side Management Using Deep Learning Method Abdul Hafeez (3134514) Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Load modeling Renewable energy sources Electric vehicle charging Mathematical models Costs Charging stations Batteries Deep learning CO2 emission data-driven approach demand-side management peak clipping |
| status_str | publishedVersion |
| title | Utilization of EV Charging Station in Demand Side Management Using Deep Learning Method |
| title_full | Utilization of EV Charging Station in Demand Side Management Using Deep Learning Method |
| title_fullStr | Utilization of EV Charging Station in Demand Side Management Using Deep Learning Method |
| title_full_unstemmed | Utilization of EV Charging Station in Demand Side Management Using Deep Learning Method |
| title_short | Utilization of EV Charging Station in Demand Side Management Using Deep Learning Method |
| title_sort | Utilization of EV Charging Station in Demand Side Management Using Deep Learning Method |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Load modeling Renewable energy sources Electric vehicle charging Mathematical models Costs Charging stations Batteries Deep learning CO2 emission data-driven approach demand-side management peak clipping |