EV Charging Simulation (Python)

Learn and practice EV charging simulation for a fleet of vehicles using hourly time steps. Current scope covers:

• Synthetic EV profile generation (Day 1)
• Uncontrolled charging simulation + fleet load aggregation (Day 2)

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What’s implemented

Day 1 — Synthetic EV profile generation

Generates realistic EV charging profiles for a single day:

• arrival_hour, departure_hour
• battery_kwh
• initial_soc, target_soc
• derived: energy_needed_kwh, available_hours

Outputs:

• results/ev_profiles_day1.csv
• results/figures/arrival_hour_histogram.png
• results/figures/energy_needed_histogram.png

Day 2 — Uncontrolled charging simulation (baseline)

Implements uncontrolled charging:

• Charging starts immediately at arrival_hour
• Constant charging power (default: 7 kW)
• Stops when energy target is reached or when departure_hour is reached

Outputs:

• results/fleet_load_uncontrolled.csv (hourly aggregate fleet load, kW)
• results/ev_results_uncontrolled.csv (per-EV delivered energy, shortfall, completion)
• results/figures/aggregate_load_uncontrolled.png

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Results (current run)

Configuration:

• Fleet size: 50 EVs
• Random seed: 42
• Charging power: 7 kW
• Time resolution: 1 hour (0–23)

Metrics:

• Peak load: 82.67 kW
• Total energy delivered: 1073.94 kWh
• Total energy needed: 1330.32 kWh
• Completion rate: 84%

Completion rate < 100% is expected in uncontrolled charging because some EVs have insufficient available time (arrival–departure window) to reach the target SOC at fixed charging power.

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Figures

EV arrival distribution

Energy needed distribution

Uncontrolled charging fleet load

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Assumptions

• Single-day simulation with 24 hourly slots (0–23)
• departure_hour is not inclusive
  • Example: arrival=10, departure=13 → charging can occur at hours 10, 11, 12
• Constant charging power (no CC–CV tapering yet)
• No grid constraints, tariffs, or V2G/V2H (planned)