Initializing energy systems...

Hybrid Microgrid
Simulation & Optimization Platform

Open-source Python platform built on NREL's HOPP framework for hybrid microgrid optimization. Supports multi-location processing, predictive battery dispatch, and comprehensive economic analysis.

Get Started View on GitHub
Solar PV
Wind
Battery
Genset
Load
System Optimization
Multi-component sizing & configuration
Economic Analysis
LCOE, NPC, and CO2 emissions
Multi-Location
Batch processing for multiple sites

Powerful Features for Microgrid Optimization

Comprehensive tools for renewable energy system design and analysis

System Optimization

Advanced algorithms optimize PV, wind, battery, and genset components for maximum efficiency and cost-effectiveness.

  • Multi-objective optimization
  • Component sizing
  • Configuration analysis

Predictive Battery Dispatch

Intelligent battery management with predictive dispatch algorithms and up to 20% load reduction capability.

  • Predictive dispatch
  • Load management
  • Demand response

Economic Analysis

Comprehensive financial modeling including LCOE, Net Present Cost, CO2 emissions, and payback analysis.

  • LCOE calculation
  • NPC analysis
  • CO2 emissions tracking

Multi-Location Processing

Batch processing capabilities for analyzing multiple sites simultaneously with location-specific resource data.

  • Batch optimization
  • Resource data management
  • Comparative analysis

HOPP Integration

Built on NREL's HOPP framework for comprehensive renewable energy system simulation and optimization.

  • NREL data integration
  • Weather modeling
  • Performance simulation

Flexible Configuration

YAML-based configuration system with extensive customization options for all system parameters.

  • YAML configuration
  • Parameter tuning
  • Custom constraints

Code Examples

Learn how to use Py-Microgrid with practical examples

Basic Optimization Example 
# Set up NREL API key and initialize system
from py_microgrid.utilities.keys import set_developer_nrel_gov_key
from py_microgrid.tools.optimization import SystemOptimizer
from py_microgrid.simulation.resource_files import ResourceDataManager

# Set your NREL API key (get free at: developer.nrel.gov/signup)
set_developer_nrel_gov_key('your_api_key_here')

# Initialize resource data manager and download data
resource_manager = ResourceDataManager()
resource_manager.download_solar_data(lat=39.7392, lon=-104.9903)
resource_manager.download_wind_data(lat=39.7392, lon=-104.9903)

# Initialize optimizer with configuration
config_path = 'config/site_config.yaml'
optimizer = SystemOptimizer(config_path)

# Run optimization
results = optimizer.optimize()

# Access comprehensive results
print(f'LCOE: ${results.lcoe:.3f}/kWh')
print(f'NPC: ${results.npc:,.0f}')
print(f'Renewable Fraction: {results.renewable_fraction:.1f}%')
Multi-Location Processing 
from py_microgrid.utilities import ConfigManager
from py_microgrid.tools.optimization import SystemOptimizer
from py_microgrid.simulation.resource_files import ResourceDataManager

# Define multiple locations for analysis
locations = [
    {'name': 'Denver', 'lat': 39.7392, 'lon': -104.9903},
    {'name': 'Phoenix', 'lat': 33.4484, 'lon': -112.0740},
    {'name': 'Seattle', 'lat': 47.6062, 'lon': -122.3321}
]

# Process multiple locations
results = {}
for location in locations:
    # Download resource data for each location
    resource_manager = ResourceDataManager()
    resource_manager.download_solar_data(lat=location['lat'], lon=location['lon'])
    resource_manager.download_wind_data(lat=location['lat'], lon=location['lon'])
    
    # Update config for this location
    config_manager = ConfigManager('config/site_config.yaml')
    config_manager.update_location(location['lat'], location['lon'])
    
    # Run optimization
    optimizer = SystemOptimizer(config_manager.config_path)
    result = optimizer.optimize()
    results[location['name']] = result

# Compare results across locations
for location, result in results.items():
    print(f'{location}: LCOE=${result.lcoe:.3f}/kWh, Renewable: {result.renewable_fraction:.1f}%')
YAML Configuration 
# site_config.yaml - Based on actual Py-Microgrid examples
config:
  dispatch_options:
    battery_dispatch: heuristic_load_following
    grid_charging: true
    include_lifecycle_count: false
    n_look_ahead_periods: 48
    pv_charging_only: false
    solver: glpk

site:
  data:
    lat: -33.5265
    lon: 149.1588
    elev: 1099
    tz: -6
    year: 2020
    urdb_label: 5ca4d1175457a39b23b3d45e
    
  desired_schedule:
    # Load profile data (hourly values)
    # [250, 280, 320, 340, ...]

technologies:
  pv:
    system_capacity_kw: 500
  wind:
    system_capacity_kw: 200
  battery:
    system_capacity_kw: 300
    system_capacity_kwh: 1200
  grid:
    interconnect_kw: 1000
View Full Examples on GitHub

Quick Installation

Get started with Py-Microgrid in minutes

1

Install Dependencies

pip install HOPP
conda install -c conda-forge glpk -y
conda install -c conda-forge coin-or-cbc -y
2

Clone Repository

git clone https://github.com/Hanrong-Huang/Py-Microgrid.git
cd Py-Microgrid
3

Get NREL API Key

# Get your free API key from:
# https://developer.nrel.gov/signup/
# Add to your environment variables
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