EV Charging Optimizer

The EVChargingScheduler determines the optimal charging power for each vehicle at each time slot, minimizing total energy cost while respecting site capacity, charger limits, vehicle availability, and priority ordering.

How It Works

The optimizer uses a greedy LP-relaxation approach:

Availability matrix — For each session × time slot, determine if the vehicle is present
Effective cost — Combine energy price and carbon intensity with configurable weights
Priority ordering — Process urgent vehicles first, then by earliest departure
Slot filling — For each vehicle, fill cheapest available slots until energy need is met
Capacity tracking — Deduct allocated power from remaining site headroom

Configuration

from optimizer.base import OptimizationConfig
from optimizer.ev.scheduler import EVChargingScheduler

config = OptimizationConfig(
    horizon="24h",       # Scheduling window
    resolution="1h",     # Time slot granularity (supports 15min, 1h)
    objective="minimize_cost",
    weights={
        "cost": 1.0,     # Energy cost weight
        "carbon": 0.0,   # Carbon intensity weight
        "peak": 0.0      # Peak reduction weight
    }
)

scheduler = EVChargingScheduler(
    config=config,
    site_capacity_kw=200.0,    # Max total site power draw
    num_chargers=10,            # Number of charging ports
    charger_capacity_kw=22.0    # Max power per charger (AC Level 2)
)

Charging Sessions

Each EV is described by a ChargingSession:

from optimizer.ev.scheduler import ChargingSession
from datetime import datetime, timezone

session = ChargingSession(
    vehicle_id="ev_001",
    arrival_time=datetime(2025, 1, 15, 8, 0, tzinfo=timezone.utc),
    departure_time=datetime(2025, 1, 15, 17, 0, tzinfo=timezone.utc),
    energy_needed_kwh=30.0,        # Energy to deliver
    max_charge_rate_kw=22.0,       # Vehicle onboard charger limit
    min_charge_rate_kw=0.0,        # Minimum (0 = can pause)
    current_soc_percent=20.0,      # Current battery level
    target_soc_percent=80.0,       # Desired departure SOC
    battery_capacity_kwh=60.0,     # Vehicle battery size
    priority=1,                     # 1=normal, 2=high, 3=urgent
    v2g_enabled=False,             # Vehicle-to-grid capable
    max_discharge_rate_kw=0.0      # V2G discharge limit
)

Priority Levels

Priority	Description	Scheduling Behavior
1	Normal	Scheduled after higher priorities, cost-optimized
2	High	Preferred slot allocation over normal priority
3	Urgent	First access to cheapest available slots

Tariff Integration

The scheduler natively consumes Qubit tariff schemas:

tariff = {
    "energy_rates": [
        {
            "name": "off_peak",
            "rate": 0.08,
            "schedule": {"start_time": "00:00", "end_time": "06:59"}
        },
        {
            "name": "peak",
            "rate": 0.25,
            "schedule": {"start_time": "07:00", "end_time": "22:59"}
        },
        {
            "name": "off_peak_night",
            "rate": 0.08,
            "schedule": {"start_time": "23:00", "end_time": "23:59"}
        }
    ]
}

Tariff schedules support:

Time-of-use windows — start_time / end_time in HH:MM format
Weekday filtering — weekdays array (e.g., ["monday", "tuesday"])
Monthly filtering — months array (e.g., [6, 7, 8] for summer)
Carbon intensity — carbon_intensity_gco2_kwh per rate period

Running an Optimization

import numpy as np

# Multiple vehicles
sessions = [
    ChargingSession(vehicle_id="ev_001", arrival_hour=8, departure_hour=17, energy_needed_kwh=30.0, max_charge_rate_kw=22.0, priority=1),
    ChargingSession(vehicle_id="ev_002", arrival_hour=9, departure_hour=15, energy_needed_kwh=20.0, max_charge_rate_kw=22.0, priority=2),
    ChargingSession(vehicle_id="ev_003", arrival_hour=10, departure_hour=18, energy_needed_kwh=40.0, max_charge_rate_kw=22.0, priority=3),
]

# Optional: background site load and solar generation
load_forecast = np.full(24, 100.0)   # 100 kW constant background load
solar_forecast = np.zeros(24)
solar_forecast[8:17] = 50.0          # 50 kW solar during day

result = scheduler.optimize(
    sessions=sessions,
    tariff=tariff,
    start_time=datetime(2025, 1, 15, 0, 0, tzinfo=timezone.utc),
    load_forecast=load_forecast,
    solar_forecast=solar_forecast
)

Result Structure

The OptimizationResult contains:

# Status
result.status           # "optimal" or "feasible"
result.is_optimal       # True if all constraints met
result.solve_time_ms    # Solve time in milliseconds

# Metrics
result.total_cost       # Total energy cost ($)
result.total_energy_kwh # Total energy delivered (kWh)
result.peak_demand_kw   # Maximum net load (kW)
result.carbon_kg        # Total carbon emissions (kg CO2)

# Constraints
result.constraints_satisfied
# {"energy_ev_001": True, "energy_ev_002": True, "site_capacity": True}

# Schedule DataFrame
result.schedule.columns
# ["ev_ev_001_kw", "ev_ev_002_kw", "ev_ev_003_kw",
#  "total_ev_kw", "background_load_kw", "solar_generation_kw",
#  "net_load_kw", "price_per_kwh", "slot_cost"]

Schedule Columns

Column	Description
`ev_{vehicle_id}_kw`	Charging power allocated to each vehicle per slot
`total_ev_kw`	Sum of all EV charging power per slot
`background_load_kw`	Site background load (from forecast)
`solar_generation_kw`	Solar generation (from forecast)
`net_load_kw`	Total net load: background + EV - solar
`price_per_kwh`	Energy price at each slot
`slot_cost`	Cost for each time slot

Constraint Enforcement

The scheduler enforces several hard constraints:

Site Capacity

Total EV charging power plus background load minus solar never exceeds the site transformer rating. The optimizer tracks remaining headroom at each slot and caps allocation accordingly.

Charger Capacity

Each vehicle’s charge rate is capped at the minimum of its onboard charger limit and the EVSE port capacity (charger_capacity_kw).

Vehicle Availability

Charging only occurs during the arrival-to-departure window. No power is allocated outside these bounds.

Energy Delivery

The optimizer attempts to deliver the full energy_needed_kwh for each session. If constraints prevent this (e.g., too many vehicles competing for limited capacity), the result status changes from “optimal” to “feasible”.

Carbon-Aware Scheduling

Enable carbon-conscious scheduling by adjusting objective weights:

config = OptimizationConfig(
    weights={"cost": 0.5, "carbon": 0.5, "peak": 0.0}
)

tariff = {
    "energy_rates": [
        {"name": "night_clean", "rate": 0.08, "carbon_intensity_gco2_kwh": 200,
         "schedule": {"start_time": "00:00", "end_time": "06:59"}},
        {"name": "day_dirty", "rate": 0.25, "carbon_intensity_gco2_kwh": 450,
         "schedule": {"start_time": "07:00", "end_time": "22:59"}}
    ]
}

The effective cost becomes: 0.5 * price + 0.5 * carbon_intensity, shifting charging toward lower-carbon periods.

Get Started

Layer 1: Data

Layer 2: Prediction

Layer 3: Optimization

Layer 4: Coordination

Layer 5: Settlement

Layer 6: Interface

EV Charging Optimizer

EV Charging Optimizer

How It Works

Configuration

Charging Sessions

Priority Levels

Tariff Integration

Running an Optimization

Result Structure

Schedule Columns

Constraint Enforcement

Carbon-Aware Scheduling

Next Steps

Peak Shaving

Getting Started

​EV Charging Optimizer

​How It Works

​Configuration

​Charging Sessions

​Priority Levels

​Tariff Integration

​Running an Optimization

​Result Structure

​Schedule Columns

​Constraint Enforcement

​Carbon-Aware Scheduling

​Next Steps

Peak Shaving

Getting Started

EV Charging Optimizer

How It Works

Configuration

Charging Sessions

Priority Levels

Tariff Integration

Running an Optimization

Result Structure

Schedule Columns

Constraint Enforcement

Carbon-Aware Scheduling

Next Steps