This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Impact of EV Charging on Power System with High Penetration of EVs: Simulation and Quantitative Analysis Based on Real World Usage Data
Technical Paper
2020-01-0531
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
The adoption of electric vehicles (EVs) has been announced worldwide with the aim of reducing CO2 emissions. However, a significant increase in electricity demand by EVs might impact the stable operation of the existing power grid. Meanwhile, EV charging is acceptable to most users if it is completed by the time of the next driving event. From the viewpoint of power grid operators, flexibility for shifting the timing of EV charging would be advantageous, including making effective use of renewable energy.
In this work, an EV model and simulation tool were developed to make clear how the total charging demand of all EVs in use will be influenced by future EV specifications (e.g., charge power) and installation of charging infrastructure. Among the most influential factors, EV charging behavior according to use cases and regional characteristics were statistically analyzed based on the real-world usage data of over 14, 000 EVs and incorporated in the simulation tool. Using the resultant statistical model, the Monte Carlo method was applied to conduct a parameter simulation study of continuous EV usage. The impact of EV charging demand on the power grid in a future scenario and also the benefit of controlling EV charging were evaluated quantitatively in the study. Important findings indicated that active smart charging will be necessary rather than passive control based on time-of-use tariffs.
Authors
Topic
Citation
Suzuki, K., Kobayashi, Y., Murai, K., and Ikezoe, K., "Impact of EV Charging on Power System with High Penetration of EVs: Simulation and Quantitative Analysis Based on Real World Usage Data," SAE Technical Paper 2020-01-0531, 2020, https://doi.org/10.4271/2020-01-0531.Data Sets - Support Documents
| Title | Description | Download |
|---|---|---|
| [Unnamed Dataset 1] | ||
| [Unnamed Dataset 2] |
Also In
References
- Ikegami, T., Ogimoto, K., Yano, H., Kudo, K. and Iguchi, H. , “Balancing Power Supply-Demand by Controlled Charging of Numerous Electric Vehicles,” in 2012 IEEE International Electric Vehicle Conference, Greenville, SC, 2012, 1-8, 10.1109/IEVC.2012.6183216.
- Gong, L., Cao, W., and Zhao, J. , “Load Modeling Method for Ev Charging Stations Based on Trip Chain,” in 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), Beijing, 2017, 1-5, 10.1109/EI2.2017.8245572.
- Darabi, Z. and Ferdowsi, M. , “Aggregated Impact of Plug-in Hybrid Electric Vehicles on Electricity Demand Profile,” IEEE Transactions on Sustainable Energy 2(4):501-508, Oct. 2011, doi:10.1109/TSTE.2011.2158123.
- Guo, Y. and Bashash, S. , “Analyzing The Impacts Of Plug-In Evs on The California Power Grid Using Quadratic Programming and Fixed-Point Iteration,” in 2017 American Control Conference (ACC), Seattle, WA, 2017, 2060-2065, 10.23919/ACC.2017.7963256.
- Lin, C., Chang, Y., Liu, M., and Wu, Q. , “Estimating EV Integration Patterns Considering Spatial Dispersion in Distribution Systems,” in 2015 IEEE Power & Energy Society General Meeting, Denver, CO, 2015, 1-5, 10.1109/PESGM.2015.7286443.
- U.S. Department of Transportation, Federal Highway Administration , “National Household Travel Survey,” 2009, https://nhts.ornl.gov.
- California Independent System Operator , “Open Access Same-Time Information System Open Access Same-Time Information System,” http://www.caiso.com/.
- Bloomberg New Energy Finance , “Electric Vehicle Outlook 2018,” https://about.bnef.com/electric-vehicle-outlook/.