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Layer 3: Optimization Engine

Production Ready Python License: MIT Layer 3 provides mathematical optimization algorithms that determine optimal resource allocation, scheduling, and control strategies for energy systems. Building on forecasts from Layer 2 and tariff data from Layer 1, this layer delivers cost-optimal dispatch schedules in milliseconds.

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Available Features

EV Charging Optimization

Fleet charging schedule optimization with tariff-aware, priority-based, carbon-conscious scheduling

Peak Shaving Controller

Battery dispatch optimization to minimize peak demand and demand charges using a two-pass algorithm

Multi-Objective Optimization

Configurable objective weights for cost, carbon, and peak reduction trade-offs

Tariff Integration

Native consumption of Qubit tariff schemas — time-of-use, demand charges, and carbon intensity

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Quick Start

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Installation

pip install qubit-energy-optimizer

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Optimize an EV Charging Schedule

from optimizer.ev.scheduler import EVChargingScheduler, ChargingSession
from optimizer.base import OptimizationConfig
from datetime import datetime, timezone

config = OptimizationConfig(horizon="24h", resolution="1h")
scheduler = EVChargingScheduler(
    config=config,
    site_capacity_kw=200.0,
    charger_capacity_kw=22.0
)

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,
    max_charge_rate_kw=22.0,
    priority=1
)

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"}}
    ]
}

result = scheduler.optimize(sessions=[session], tariff=tariff)
print(f"Total cost: ${result.total_cost:.2f}")
print(f"Peak demand: {result.peak_demand_kw:.1f} kW")

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Architecture

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Technology Stack

  • Python 3.9+ — Primary language
  • NumPy / pandas — Numerical computation and schedule DataFrames
  • Pydantic — Configuration validation
  • Greedy LP — Cost-sorted slot filling for EV scheduling
  • Two-pass dispatch — Peak identification + cheapest-slot charging

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Optimization Algorithms

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EV Charging — Greedy LP

The EV scheduler uses a greedy linear-programming relaxation:
  1. Build an availability matrix — which vehicles are present at each time slot
  2. Compute effective cost per slot — cost_weight * price + carbon_weight * carbon_intensity
  3. Process sessions by priority (urgent first), then deadline (earliest departure first)
  4. For each session, fill cheapest available slots until energy requirement is met
  5. Enforce site capacity by tracking remaining headroom at each slot

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Peak Shaving — Two-Pass Dispatch

The peak shaving controller uses a two-pass algorithm:
  1. Pass 1 (Discharge): Walk forward through time. At each slot where load exceeds the peak target, discharge the battery (respecting SOC limits and max discharge rate)
  2. Pass 2 (Charge): Calculate total energy discharged, then schedule recharging in the cheapest off-peak slots that have headroom below the peak target
This approach naturally produces schedules that:
  • Shave peak demand below the target threshold
  • Minimize charging cost by exploiting TOU tariff structure
  • Respect all battery physical constraints (SOC, charge/discharge rates, efficiency)
  • Account for battery degradation cost

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Integration with Qubit Stack

1

Data Input

Consumes tariff schemas from Layer 1 and forecast arrays from Layer 2
2

Optimization

Runs cost-optimal scheduling algorithms with configurable objectives and constraints
3

Schedule Output

Produces time-indexed DataFrames with per-slot dispatch commands, SOC profiles, and cost breakdowns
4

Coordination

Outputs feed into Layer 4 for real-time dispatch and multi-site coordination

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Next Steps

Get Started

Install and run your first optimization in 5 minutes

EV Charging

Deep dive into fleet charging optimization

Peak Shaving

Deep dive into battery dispatch for demand reduction

GitHub Repository

Explore source code and contribute
Layer 3 Optimization Engine is production-ready, powering EV charging fleets and battery dispatch across commercial and industrial energy sites.