Simulation of Electric Vehicle Charging Stations Load Profiles in Office Buildings Based on Occupancy Data
Transportation vehicles are a large contributor of the carbon dioxide emissions to the atmosphere. Electric Vehicles (EVs) are a promising solution to reduce the CO<sub>2</sub> emissions which, however, requires the right electric power production mix for the largest impact. The increase...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/21/5700 |
| _version_ | 1797549221302239232 |
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| author | Semen Uimonen Matti Lehtonen |
| author_facet | Semen Uimonen Matti Lehtonen |
| author_sort | Semen Uimonen |
| collection | DOAJ |
| description | Transportation vehicles are a large contributor of the carbon dioxide emissions to the atmosphere. Electric Vehicles (EVs) are a promising solution to reduce the CO<sub>2</sub> emissions which, however, requires the right electric power production mix for the largest impact. The increase in the electric power consumption caused by the EV charging demand could be matched by the growing share of Renewable Energy Sources (RES) in the power production. EVs are becoming a popular sustainable mean of transportation and the expansion of EV units due to the stochastic nature of charging behavior and increasing share of RES creates additional challenges to the stability in the power systems. Modeling of EV charging fleets allows understanding EV charging capacity and demand response (DR) potential of EV in the power systems. This article focuses on modeling of daily EV charging profiles for buildings with various number of chargers and daily events. The article presents a modeling approach based on the charger occupancy data from the local charging sites. The approach allows one to simulate load profiles and to find how many chargers are necessary to suffice the approximate demand of EV charging from the traffic characteristics, such as arrival time, duration of charging, and maximum charging power. Additionally, to better understand the potential impact of demand response, the modeling approach allows one to compare charging profiles, while adjusting the maximum power consumption of chargers. |
| first_indexed | 2024-03-10T15:10:35Z |
| format | Article |
| id | doaj.art-05439855a9604d0a9d364d130fdbd24c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T15:10:35Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-05439855a9604d0a9d364d130fdbd24c2023-11-20T19:18:43ZengMDPI AGEnergies1996-10732020-10-011321570010.3390/en13215700Simulation of Electric Vehicle Charging Stations Load Profiles in Office Buildings Based on Occupancy DataSemen Uimonen0Matti Lehtonen1School of Electrical Engineering, Aalto University, P.O. Box 15500, 00076 Espoo, FinlandSchool of Electrical Engineering, Aalto University, P.O. Box 15500, 00076 Espoo, FinlandTransportation vehicles are a large contributor of the carbon dioxide emissions to the atmosphere. Electric Vehicles (EVs) are a promising solution to reduce the CO<sub>2</sub> emissions which, however, requires the right electric power production mix for the largest impact. The increase in the electric power consumption caused by the EV charging demand could be matched by the growing share of Renewable Energy Sources (RES) in the power production. EVs are becoming a popular sustainable mean of transportation and the expansion of EV units due to the stochastic nature of charging behavior and increasing share of RES creates additional challenges to the stability in the power systems. Modeling of EV charging fleets allows understanding EV charging capacity and demand response (DR) potential of EV in the power systems. This article focuses on modeling of daily EV charging profiles for buildings with various number of chargers and daily events. The article presents a modeling approach based on the charger occupancy data from the local charging sites. The approach allows one to simulate load profiles and to find how many chargers are necessary to suffice the approximate demand of EV charging from the traffic characteristics, such as arrival time, duration of charging, and maximum charging power. Additionally, to better understand the potential impact of demand response, the modeling approach allows one to compare charging profiles, while adjusting the maximum power consumption of chargers.https://www.mdpi.com/1996-1073/13/21/5700electric vehiclesload modelingload profilingdemand responseload aggregation |
| spellingShingle | Semen Uimonen Matti Lehtonen Simulation of Electric Vehicle Charging Stations Load Profiles in Office Buildings Based on Occupancy Data Energies electric vehicles load modeling load profiling demand response load aggregation |
| title | Simulation of Electric Vehicle Charging Stations Load Profiles in Office Buildings Based on Occupancy Data |
| title_full | Simulation of Electric Vehicle Charging Stations Load Profiles in Office Buildings Based on Occupancy Data |
| title_fullStr | Simulation of Electric Vehicle Charging Stations Load Profiles in Office Buildings Based on Occupancy Data |
| title_full_unstemmed | Simulation of Electric Vehicle Charging Stations Load Profiles in Office Buildings Based on Occupancy Data |
| title_short | Simulation of Electric Vehicle Charging Stations Load Profiles in Office Buildings Based on Occupancy Data |
| title_sort | simulation of electric vehicle charging stations load profiles in office buildings based on occupancy data |
| topic | electric vehicles load modeling load profiling demand response load aggregation |
| url | https://www.mdpi.com/1996-1073/13/21/5700 |
| work_keys_str_mv | AT semenuimonen simulationofelectricvehiclechargingstationsloadprofilesinofficebuildingsbasedonoccupancydata AT mattilehtonen simulationofelectricvehiclechargingstationsloadprofilesinofficebuildingsbasedonoccupancydata |