The Economics of Electric Vehicles with Application to Electricity Grids
Abstract
1. Introduction
2. Economic Effects of Electric Vehicles
2.1. Direct Effects
2.2. Indirect Economic Effects
2.3. Electric Vehicle and Electricity Grids
3. Results: An Application to Canada
3.1. Electric Vehicle Registrations vs. Total Registrations
3.2. Potential Energy Sources
- Renewables include solar, wind, biomass, biofuels, and municipal solid waste sources.
- Hydro refers to run-of-river hydro, ‘storage hydro’ (hydraulics with large reservoir), wave, and tidal sources.
- Natural gas and oil refer to natural gas, biogas, oil, and diesel sources.
- Coal refers to coke and coal.
- Nuclear simply refers to nuclear power generation.
3.3. Monte Carlo Simulation
3.4. Discussion: Impact on Electricity Grids
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. R File Used in Monte Carlo Analysis
- library(extraDistr) #Load this library
- # To obtain random variates from a triangular distribution use:
- # rtriang(n, a = −1, b = 1, c = (a + b)/2)
- # a, b and c are minimum, maximum, and midpoint (mode, average, most likely)
- # of the distribution and n is the number of observations (iterations).
- set.seed(6) # Set random number seed to duplicate results if desired
- iter <- 10000 # Number of iterations
- jurisdiction <- c(‘Canada’, ‘British columbia’, ‘Alberta’, ‘Ontario’, ‘Quebec’)
- # Number of vehicles by jurisdiction plus average, min and max
- vehicles <- c(26302526, 3615356, 3519123, 9429566, 6007063)
- avAuto <- vehicles
- minAuto <- 0.75*vehicles
- maxAuto <- 1.25*vehicles
- # Distance traveled by vehicles in jurisdiction (km)
- avDistance <- c(15200, 15600, 13100, 16000, 14300)
- minDistance <- 0.9*avDistance
- maxDistance <- 1.2*avDistance
- # Battery efficiency energy (Wh/km)
- BatEfficiency <- rtriang(iter, 150.0, 295.0, 199.3)
- Region <- rep(0, 5); ElecMean<-Region; ElecStDev<-Region
- HydroPlants <- Region; GasPlants <- Region; Hrly <- Region
- turbines <- Region; turbcap <- Region
- for (i in 1:length(jurisdiction)){
- Region[i] <- jurisdiction[i]
- Number <- rtriang(iter, minAuto[i], maxAuto[i], avAuto[i])
- Distance <- rtriang(iter, minDistance[i], maxDistance[i], avDistance[i])
- Watts <- 0.000001*Number*Distance*BatEfficiency # MWh/8760
- ElecMean[i] <- mean(Watts) # GWh per year or MW per hour
- ElecStDev[i] <- sd(Watts) # Standard deviation
- HydroPlants[i] <- (ElecMean[i])/5100000
- GasPlants[i] <- (ElecMean[i])/5913000
- Hrly[i] <- ElecMean[i]/8760
- turbcap[i] <- 4*Hrly[i]
- turbines[i] <- (ElecMean[i])/7665
- }
- Info <- c(‘Item’, ‘Mean’, ‘Standard Deviation’)
- Output <- data.frame(rbind(jurisdiction, ElecMean, ElecStDev), row.names = Info)
- print(Output)
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Statistic | Capacity (kWh) | Energy (Wh/km) 1 | Range (km) | Weight (kg) |
---|---|---|---|---|
Mean | 70.2 | 199.3 | 357.5 | 1226.5 |
Maximum | 123.0 | 295.0 | 685.0 | 2500.0 |
Minimum | 16.7 | 150.0 | 95.0 | 300.0 |
Median | 71.0 | 192.0 | 365.0 | 1000.0 |
Observations | 299 | 299 | 299 | 188 |
Canada | BC | Alberta | Ontario | Quebec | ||||||
---|---|---|---|---|---|---|---|---|---|---|
Year | All Fuel Types | BEV & PHEV | All Fuel Types | BEV & PHEV | All Fuel Types | BEV & PHEV | All Fuel Types | BEV & PHEV | All Fuel Types | BEV & PHEV |
2017 | 24,618,831 | 43,807 | 3,268,655 | 8887 | 3,480,263 | 1349 | 8,711,241 | 13,547 | 5,575,518 | 18,876 |
2018 | 25,043,044 | 76,431 | 3,327,929 | 14,940 | 3,530,021 | 1996 | 8,875,296 | 26,252 | 5,706,344 | 31,780 |
2019 | 25,426,285 | 126,563 | 3,381,707 | 29,023 | 3,583,685 | 3224 | 9,036,981 | 37,294 | 5,801,503 | 54,880 |
2020 | 25,744,196 | 180,729 | 3,369,266 | 45,016 | 3,549,362 | 4601 | 9,335,112 | 46,253 | 5,913,745 | 81,507 |
2021 | 26,223,871 | 249,245 | 3,512,196 | 65,647 | 3,554,592 | 6765 | 9,456,317 | 60,369 | 5,987,358 | 110,903 |
2022 | 26,302,526 | 346,534 | 3,615,356 | 91,829 | 3,519,123 | 10,468 | 9,429,566 | 87,299 | 6,007,063 | 147,321 |
Annual increase | 1.33% | 51.23% | 2.04% | 59.53% | 0.22% | 50.65% | 1.60% | 45.16% | 1.50% | 50.82% |
PHEV as % of EVs | 40.9% | 27.7% | 42.3% | 43.2% | 46.4% |
Capacity | Generation | |
---|---|---|
Coal | 6.03% | 4.99% |
Natural Gas | 15.25% | 11.93% |
Oil | 2.45% | 0.56% |
Hydro | 54.97% | 61.87% |
Nuclear | 9.01% | 13.37% |
Wind | 8.92% | 5.67% |
Solar | 1.85% | 0.36% |
Biomass | 1.53% | 1.24% |
TOTAL | 100.0% | 100.0% |
Level | 148.9 GW | 624 TWh |
Jurisdiction | Domestic Electricity Supply (MWh) 1 | Average Distance Driven (km/year) 2 | Average Energy per EV Annually (kWh) 3 | Total Energy Use by EVs in Jurisdiction (MWh) 4 | % of Electricity Use in Jurisdiction |
---|---|---|---|---|---|
Canada | 578,273,577 | 15,200 | 3030 | 1,545,468 | 0.27% |
Alberta | 79,531,379 | 15,600 | 3030 | n.a. | n.a. |
BC | 67,053,704 | 13,100 | 2611 | 314,233 | 0.47% |
Ontario | 135,308,943 | 16,000 | 3189 | 476,357 | 0.35% |
Quebec | 210,693,634 | 14,300 | 2850 | 571,187 | 0.27% |
Item | Canada | BC | Alberta | Ontario | Quebec |
---|---|---|---|---|---|
Annual increase (GWh) | |||||
Mean | 88,787.7 | 12,543.2 | 10,245.4 | 33,558.0 | 19,071.9 |
Standard deviation | 16,177.4 | 2313.8 | 1882.1 | 6258.5 | 3488.4 |
Hourly increase (MW) | |||||
Mean | 10,135.6 | 1431.9 | 1169.6 | 3830.8 | 2177.2 |
Proportional increase | 15.4% | 15.8% | 15.3% | 24.8% | 9.1% |
Potential hydro facilities needed 2 | |||||
Based on mean increase | 17 | 3 | 2 | 7 | 4 |
95% guarantee 1 | 24 | 4 | 3 | 9 | 5 |
Potential number of 750-MW capacity gas plants needed 3 | |||||
Based on mean increase | 15 | 3 | 2 | 6 | 4 |
95% guarantee 1 | 21 | 3 | 3 | 8 | 5 |
Wind power capacity and required turbines (3.5 MW cap) | |||||
Capacity (MW) | 40,542 | 5727 | 4678 | 15,323 | 8709 |
Number of turbines | 11,584 | 1636 | 1337 | 4378 | 2488 |
95% guarantee 1 | 15,805 | 2240 | 1828 | 6011 | 3398 |
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van Kooten, G.C.; Stobbe, T.E. The Economics of Electric Vehicles with Application to Electricity Grids. Energies 2024, 17, 4109. https://doi.org/10.3390/en17164109
van Kooten GC, Stobbe TE. The Economics of Electric Vehicles with Application to Electricity Grids. Energies. 2024; 17(16):4109. https://doi.org/10.3390/en17164109
Chicago/Turabian Stylevan Kooten, G. Cornelis, and Tracy E. Stobbe. 2024. "The Economics of Electric Vehicles with Application to Electricity Grids" Energies 17, no. 16: 4109. https://doi.org/10.3390/en17164109
APA Stylevan Kooten, G. C., & Stobbe, T. E. (2024). The Economics of Electric Vehicles with Application to Electricity Grids. Energies, 17(16), 4109. https://doi.org/10.3390/en17164109