Fleet Electrification BC

Warning

This documentation is under active development. Some sections may be incomplete.

Welcome to the Fleet Electrification BC documentation! This project provides a comprehensive Python-based workflow for simulating electric vehicle (EV) fleet patterns and their impact on the British Columbia power system.

• Workflow Overview:

Charging Strategies

The framework supports multiple EV charging strategies:

• Coordinated: Optimized charging timing for grid benefits

• Uncoordinated: Real-time charging patterns based on user behavior

• V2G: Vehicle-to-grid capabilities for grid support

• Hybrid: Mixed coordinated/uncoordinated charging scenarios

Overview

This workflow simulates fleet patterns, translates vehicle fleets to electric vehicle loads based on user-defined EV penetration and charging strategies, and integrates the EV load into the PyPSA-BC power system model.

Key Features

🚗 Fleet Pattern Simulation - Translates vehicle populations and travel patterns to fleet patterns using ICBC data
⚡ EV Load Simulation - Converts fleet patterns to EV loads with multiple charging strategies
🔌 Power System Integration - Integrates EV loads into PyPSA-BC for capacity planning and operational analysis
📊 Comprehensive Analysis - Provides detailed visualization and scenario comparison tools

Three-Stage Workflow

Workflow Components

1. Fleet Pattern Simulator - Translates vehicle populations and travel patterns to fleet patterns
2. EV Fleet Load Simulator - Converts fleet patterns to EV loads with user-defined penetration and charging strategies
3. PyPSA-BC Integration - Integrates EV loads into the power system model for analysis

Quick Start

Installation

Requirements: Anaconda/Miniconda on Linux/WSL

# Clone the repository
git clone --branch combined https://github.com/DeltaE/Fleet_Electrification.git
cd Fleet_Electrification

# Create conda environment
conda env create -f env/environment.yaml

# Activate environment
conda activate ev_fleet_bc

# Run the workflow
python fleet_electrification_BC.py

Using Makefile (recommended)

# Setup environment
make setup

# Run fleet simulation
make fleet

# Generate plots
make plots

# View all options
make help

Documentation Structure

Contents:

• Getting Started
  • Prerequisites
  • Installation
  • Basic Usage
  • Configuration
  • Workflow Components
  • Troubleshooting
  • Next Steps
• Charging Scenarios
  • Scenario Overview
  • Hybrid Coordinated-Uncoordinated Strategy
  • Expected System Response
  • Elbow Curve Analysis
  • Charging Strategy Configurations
  • Scenario Implementation
  • Research Questions
  • Visualization Outputs
• API Reference
  • Core Modules
  • Utility Functions
  • Configuration Parameters
  • Data Structures
  • Input Data Requirements
  • Output Data Formats
  • Error Handling
  • Performance Considerations
  • Extensions and Customization
  • Integration Examples
• Examples
  • Basic Usage Examples
  • Scenario Comparison Examples
  • Advanced Configuration Examples
  • Visualization Examples
  • Integration with Notebooks
  • Error Handling Examples

Sample Results

The framework generates comprehensive visualizations and analysis including:

• Capacity scenario comparisons

• Load profile analysis

• Generation profile optimization

• Load duration curves

• System cost analysis

Links

• GitHub Repository

• Issue Tracker

• DeltaE Research Group

License

This project is open source under the MIT License.