Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
This paper develops a probabilistic assessment of the derived demand for transportation to support the recharging of electric vehicles after an earthquake in Los Angeles. This analysis is developed using 4,212 risk-adjusted damage scenarios based on 351 earthquake scenarios that represent the seismic hazard in Los Angeles. By analyzing each of these damage scenarios via a dc load flow model at the bus-level, we calculate the unserved demand in each damage scenario and evaluate the reduction in unserved demand when electric vehicles are allowed to be charged from other operational substations (via aggregation of demand to the substation level). Our findings indicate that by 2030, we can expect that about 3% of unserved electric power demand during the recovery process after an earthquake will be satisfied via electric vehicles utilizing power from recharging stations not supported by their home substations. The average and median distance traveled are estimated to be 8.6 km and 6.8 km, respectively. Furthermore, 95% of all trips motivated in this manner are less than 24.1 km in length; suggesting that many of the trips are rather short. Hence, this analysis suggests that electric vehicles can contribute to the post-earthquake resilience of the electric power system via flexibility in the selection of recharging locations.
Recommended Citation
Cheng, Boyu; Liu, Dahui; and Nozick, Linda, "Understanding Derived Demand for Transportation to Support Electric Vehicle Recharging After Earthquake Events" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 8.
https://aisel.aisnet.org/hicss-57/es/renewable_resources/8
Understanding Derived Demand for Transportation to Support Electric Vehicle Recharging After Earthquake Events
Hilton Hawaiian Village, Honolulu, Hawaii
This paper develops a probabilistic assessment of the derived demand for transportation to support the recharging of electric vehicles after an earthquake in Los Angeles. This analysis is developed using 4,212 risk-adjusted damage scenarios based on 351 earthquake scenarios that represent the seismic hazard in Los Angeles. By analyzing each of these damage scenarios via a dc load flow model at the bus-level, we calculate the unserved demand in each damage scenario and evaluate the reduction in unserved demand when electric vehicles are allowed to be charged from other operational substations (via aggregation of demand to the substation level). Our findings indicate that by 2030, we can expect that about 3% of unserved electric power demand during the recovery process after an earthquake will be satisfied via electric vehicles utilizing power from recharging stations not supported by their home substations. The average and median distance traveled are estimated to be 8.6 km and 6.8 km, respectively. Furthermore, 95% of all trips motivated in this manner are less than 24.1 km in length; suggesting that many of the trips are rather short. Hence, this analysis suggests that electric vehicles can contribute to the post-earthquake resilience of the electric power system via flexibility in the selection of recharging locations.
https://aisel.aisnet.org/hicss-57/es/renewable_resources/8