Future EV Demand Impact on Energy and Emissions

Authors: Daniel Huynh, Duy Lam, Yagna Patel, and Kevin Yu

Problem Statement

This project analyzes how the Electric Vehicles (EVs) affects carbon emissions across the United States. By projecting future EV adoption rates and their corresponding energy demands, we identify optimal solutions to implement renewable energy infrastructure to support a carbon neutral world. This information can be used to develop and implement Renewable Energy policies that encourage a carbon neutral world. Governments can use this model to plan their budgets and see where more investments into renewable energy may be needed to meet challenges created from increasing EV demand.

Project Impact

Policy Development

• Support creation of effective renewable energy policies
• Identify regions requiring immediate infrastructure investment

Resource Allocation

• Help governments optimize budget allocation for renewable and EV infrastructure
• Prioritize high-impact areas for renewable energy development
• Plan strategic grid and EV charging upgrades based on projected EV demand

Environmental Planning

• Track progress toward carbon neutrality goals
• Identify regions where emissions may increase without intervention

Usage

To use this program, follow these steps:

• In terminal, run the command pip install -r -requirements.txt to install required libraries

• run dashboard.py and click the local host link outputted in the terminal starting with "https://..."

• Adjust the slider to change the prediction year for plots and state maps

• Select from a drop down menu to see specific data for an individual state

• For 3d map, you can rotate the map via command + click or ctrl + click

Additional Details

• The program extracts relevant CSV files. From these relevant CSV files, two CSV files are created where data analysis will be performed.
• Based on this data, various operations will be performed to extract relevant information.
• The relevant information will be projected in a plot or a map. This map or plot will be projected onto interactive dashboard widgets.

Limits

• Data is calculated using data from 2016-2022 specifically for fully battery electric vehicles.

Strengths

• The program can be scaled quickly to use larger datasets by adding more rows.
• The visual dashboard allows for a comprehensive analysis and overall understanding the impact of EVs through a friendly user interface

Exapansions

• Adding more data related to other industries that the production and charging of EVs affect such as rare earth minerals.
• Considering county by county data to create a more comprehensive analysis of areas in need of potential energy infrastructure intervention to accomodate EV growth.
• Consider EV charger count and location in consideration with EV demand.

Complexity

Time Complexity and Space Complexity for Each Function and Operation //Approximates

• Most of the functions are time complexity of O(n) with half coming from data analysis and half coming from plotting functions dynamically

data1Extraction.py:

• extract_co2_emissions function: Time: O(n) Space: O(n)

• extract_annual_generation function: Time: O(n) Space: O(n)

• calculate_co2_per_wh function: Time: O(n²) Space: O(n)

• process_ev_sales_data function: Time: O(n) Space: O(n)

data2Regressions.py: k = prediction year

• calculate_registration_trend function: Time: O(n + k) Space: O(n + k)

• calculate_growth_rate function: Time: O(n + k) Space: O(n + k)

• calculate_co2_per_wh function: Time: O(n + k) Space: O(n + k)

• calculate_ev_demand function: Time: O(n + k) Space: O(n + k)

• calculate_ev_emissions function: Time: O(n + k) Space: O(n + k)

data3Models.py:

• plot_registration_trend function function: Time: O(n), Space: O(n)

• plot_growth_rate function: Time: O(n), Space: O(n)

• plot_co2_emissions function: Time O(n), Space: O(n)

• plot_ev_demand function: Time: O(n), Space: O(n)

• plot_ev_emissions function: Time: O(n), Space: O(n)

• plot_ev_gas_proportion function: Time: O(n), Space: O(n)

GeoData1Analysis.py:

• generate_emissions_projection function: Time: O(n) Space: O(n)

GeoData2Models.py:

• create_3d_state_map function: Time: O(n) Space: O(n)

• create_3d_county_map function: Time: O(n) Space: O(n)

GeoData3Generation.py:

• load_state_data function: Time: O(n) Space: O(n)

• calculate_state_emissions_data function: Time: O(n) Space: O(n)

Dashboard.py:

• generate_emissions_projection function: Time: O(n) Space: O(n)

• generate_ev_emissions_percent_change_projection function: Time: O(n) Space: O(n)

• create_3d_state_map function: Time: O(n) Space: O(n)

• ev_registration_plot function: Time: O(n) Space: O(n)

• co2_plot function: Time: O(n) Space: O(n)

• ev_demand_plot function: Time: O(n) Space: O(n)

• ev_emissions_plot function: Time: O(n) Space: O(n)

• map_view function: Time: O(n) Space: O(n)

• percent_map_view function: Time: O(n) Space: O(n)

Citations

https://www.eia.gov/environment/emissions/carbon/

https://www.eia.gov/energyexplained/electricity/electricity-in-the-us-generation-capacity-and-sales.php?os=http

https://www.epa.gov/greenvehicles/greenhouse-gas-emissions-typical-passenger-vehicle

https://afdc.energy.gov/vehicle-registration