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