TY - GEN
T1 - A Novel Charging Load Prediction and Carbon Emission Calculation Method for Electric Vehicle
AU - Liu, Wenli
AU - Liu, Wensi
AU - Han, Te
AU - Zhao, Weigang
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - With the continuous development and promotion of the new energy vehicle market, the management of charging loads for new energy vehicles has emerged as one of the focal issues in the energy domain. Rational planning for the layout of charging facilities for these vehicles, combined with effective load management, serves as a crucial approach to foster the sustainable development of the new energy vehicle sector, and also to reduce energy consumption and environmental pollution. This paper proposes a reliable methodology and system for load characteristic analysis and carbon emission computation, tailored to new energy vehicle charging scenarios. Initially, diverse charging scenarios are contemplated, encompassing residential communities, corporate campuses, public parking spaces, and highway service areas. Subsequently, depending on varied dates and meteorological conditions, models characterizing the charging load features of new energy vehicles are sequentially constructed. These models predominantly consist of an evaluation module for the time distribution of electric vehicle charging sessions and a multi-state charging demand assessment module for electric vehicles. Utilizing Monte Carlo simulations, the characteristic load curves for the targeted new energy vehicle charging scenarios are derived. Lastly, based on these characteristic curves, the charging modality, and associated carbon emission factors, the carbon emissions are calculated. The methodology presented here can effectively facilitate a comprehensive analysis and assessment of charging loads for electric vehicles across diverse scenarios, factoring in carbon emissions.
AB - With the continuous development and promotion of the new energy vehicle market, the management of charging loads for new energy vehicles has emerged as one of the focal issues in the energy domain. Rational planning for the layout of charging facilities for these vehicles, combined with effective load management, serves as a crucial approach to foster the sustainable development of the new energy vehicle sector, and also to reduce energy consumption and environmental pollution. This paper proposes a reliable methodology and system for load characteristic analysis and carbon emission computation, tailored to new energy vehicle charging scenarios. Initially, diverse charging scenarios are contemplated, encompassing residential communities, corporate campuses, public parking spaces, and highway service areas. Subsequently, depending on varied dates and meteorological conditions, models characterizing the charging load features of new energy vehicles are sequentially constructed. These models predominantly consist of an evaluation module for the time distribution of electric vehicle charging sessions and a multi-state charging demand assessment module for electric vehicles. Utilizing Monte Carlo simulations, the characteristic load curves for the targeted new energy vehicle charging scenarios are derived. Lastly, based on these characteristic curves, the charging modality, and associated carbon emission factors, the carbon emissions are calculated. The methodology presented here can effectively facilitate a comprehensive analysis and assessment of charging loads for electric vehicles across diverse scenarios, factoring in carbon emissions.
KW - carbon emission calculation
KW - charging load prediction
KW - electric vehicle
UR - http://www.scopus.com/inward/record.url?scp=85191451330&partnerID=8YFLogxK
U2 - 10.1109/ICSMD60522.2023.10490644
DO - 10.1109/ICSMD60522.2023.10490644
M3 - Conference contribution
AN - SCOPUS:85191451330
T3 - ICSMD 2023 - International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, Proceedings
BT - ICSMD 2023 - International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2023
Y2 - 2 November 2023 through 4 November 2023
ER -