Powerflow energy optimization simulator

Overview

A Python-based simulator for optimizing solar PV systems with battery storage. The simulator allows testing different battery management strategies to maximize cost savings and efficiency by analyzing historical generation and consumption data.

Important

Some metrics are yet to be validated, this is more of a proof of concept than anything else. Code is messy and there are no tests, so use at your own risk. 💃😄

Why

The project aims to optimize a PV installation with battery by exploring different charging strategies:

• Self-consume strategy: Battery charges from solar and discharges when load exceeds production
• Force charge at night strategy: Battery charges from solar and discharges when load exceeds production, but it also forces the battery to charge at night to take advantage of lower prices
• Other strategies are possible like predictive charging, weather-aware charging, etc.

You can also explore what would happen if you increased the number of batteries, in my case, adding another battery increases the maximum power of the battery system from 2kW to 5kW. Or what happens when export limit is changed...

Simulation vs Ground Truth

Inputs of the simulation are solar power and house load, outputs are battery SoC (kWh) and import/export grid (kW). Simulation granularity is 1 minute, and the shown scenario is force charging the battery at night.

Estimated metrics are almost spot on when compared with Home Assistant's energy dashboard. Some discrepancies might arise because we are not simulating battery behavior in detail, but rather a simplified model. End to end efficiency of solar panels, battery, and inverter isn't modeled, but rather a simplified model.

Simulation

Ground Truth

Simulation	Ground Truth

Installation

# Clone repository
git clone https://github.com/leandroalbero/powerflow-simulator.git
cd powerflow-simulator

# Create virtual environment
python -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

Usage

1. Prepare input data:

   • Solar generation CSV with datetime index and 'state' column measured in W
   • Load consumption CSV with datetime index and 'state' column measured in W
   • Ensure data is in the correct timezone, you have examples in the data folder, 1y worth of real data! high-res version is 1m resolution, low-res version is 1h resolution.

2. Configure system parameters:

tariff = PowerTariff(
    import_rate_schedule={(0, 8): 0.085, (8, 10): 0.134, ...},
    export_rate_schedule={(0, 24): 0.08}
)

battery = Battery(capacity=5, max_charge_rate=2.0, max_discharge_rate=2.0)

3. Run simulation (theres an example in the use_cases folder):

sim = EnergySimulator(battery, load, grid, tariff, solar)
for timestamp in load_data.index:
    sim.step(timestamp, prev_timestamp, strategy='self_consume')

4. Analyze results:

results = sim.get_metrics()
print(f"Total cost: {results['total_cost']:,.2f}€")
print(f"Solar self-consumption rate: {results['self_consumption_rate']:.1f}%")

Future Plans (with no particular order)

1. Implement additional battery management strategies:
   • Time-of-use optimization
   • Weather-aware charging
   • Peak demand reduction
2. Create Home Assistant integration for strategy execution
3. Implement predictive modeling capabilities
4. Add support for varying solar capacity
5. Better UX/UI for data visualization
6. Better modeling of entities in the code, code cleanup/refactor is needed

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.