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GEO-INFER-ENERGY

README.md

title GEO-INFER-ENERGY: Energy Systems and Grid Analysis
description Renewable energy siting, grid optimization, and energy systems modeling
purpose Provide geospatial analysis for energy planning, renewable resource assessment, and grid infrastructure
module_type Domain Application
status Alpha
last_updated 2026-02-25
dependencies
SPACE
TIME
DATA
CLIMATE
compatibility
GEO-INFER-SPACE
GEO-INFER-TIME
GEO-INFER-DATA
GEO-INFER-CLIMATE
tags
energy
renewable
solar
wind
grid
power-systems
difficulty Intermediate
estimated_time 55

GEO-INFER-ENERGY: Energy Systems and Grid Analysis

Overview

GEO-INFER-ENERGY provides comprehensive geospatial capabilities for energy systems analysis and planning. The module enables:

  • Renewable Resource Assessment: Solar, wind, and hydro potential mapping
  • Site Selection: Optimal location for energy infrastructure
  • Grid Analysis: Transmission network modeling and optimization
  • Demand Forecasting: Spatiotemporal energy demand prediction
  • Energy Transition Planning: Decarbonization pathway analysis

Features

Solar Resource Assessment

from geo_infer_energy import SolarAnalyzer

# Analyze solar potential
solar = SolarAnalyzer()

potential = solar.assess_potential(
    area=study_region,
    data_source="nsrdb",
    resolution="1km",
    metrics=["ghi", "dni", "capacity_factor"]
)

print(f"Annual GHI: {potential.ghi_avg} kWh/m²/year")
print(f"Optimal tilt: {potential.optimal_tilt}°")
print(f"Capacity factor: {potential.capacity_factor}%")

Wind Resource Assessment

from geo_infer_energy import WindAnalyzer

# Analyze wind potential
wind = WindAnalyzer()

assessment = wind.assess(
    area=wind_farm_area,
    hub_height=100,  # meters
    turbine_model="generic_3mw",
    wake_model="jensen"
)

print(f"Mean wind speed: {assessment.mean_speed} m/s")
print(f"Power density: {assessment.power_density} W/m²")
print(f"Annual energy: {assessment.annual_energy} GWh")

Site Suitability Analysis

from geo_infer_energy import SiteSuitability

# Find optimal sites for solar farms
suitability = SiteSuitability()

sites = suitability.analyze(
    energy_type="solar_pv",
    region=county_boundary,
    criteria={
        "slope": {"max": 5},  # degrees
        "land_use": {"exclude": ["wetland", "forest", "urban"]},
        "distance_to_grid": {"max": 10},  # km
        "solar_resource": {"min": 4.5}  # kWh/m²/day
    }
)

print(f"Suitable area: {sites.total_area_km2} km²")
print(f"Potential capacity: {sites.potential_mw} MW")

Grid Analysis

from geo_infer_energy import GridAnalyzer

# Analyze transmission grid
grid = GridAnalyzer()

# Load grid data
grid.load_network(transmission_lines, substations)

# Analyze capacity constraints
constraints = grid.analyze_capacity(
    new_generation=proposed_solar_farms,
    scenario="2030_load_forecast"
)

print(f"Constrained segments: {constraints.bottlenecks}")
print(f"Upgrade needed: {constraints.upgrade_cost}M")

Energy Resources

Resource Analysis Capabilities
Solar PV GHI/DNI mapping, shading, rooftop potential
Solar Thermal CSP suitability, DNI requirements
Wind Resource mapping, wake modeling, noise setbacks
Hydropower Stream flow, head calculation, run-of-river
Geothermal Heat flow, reservoir assessment
Biomass Feedstock availability, logistics

Grid Modeling

from geo_infer_energy import PowerFlow

# Run power flow analysis
pf = PowerFlow()

# Load system data
pf.load_system(network_model)

# Run analysis
results = pf.run(
    method="ac_power_flow",
    scenario="peak_summer"
)

print(f"Total losses: {results.losses_mw} MW")
print(f"Voltage violations: {results.voltage_violations}")
print(f"Overloaded lines: {results.overloads}")

Integration Points

Module Integration
GEO-INFER-CLIMATE Weather data, climate projections
GEO-INFER-SPACE Spatial analysis, terrain
GEO-INFER-TIME Temporal demand patterns
GEO-INFER-ECON Economic analysis, LCOE
GEO-INFER-RISK Infrastructure vulnerability

Installation

# Install energy module
uv pip install -e "./GEO-INFER-ENERGY"

# With all analysis tools
uv pip install -e "./GEO-INFER-ENERGY[full]"

Use Cases

Renewable Energy Planning

from geo_infer_energy import RenewablePlanner

planner = RenewablePlanner(region="state_california")

# Plan renewable portfolio
plan = planner.optimize(
    target={"renewable_percent": 100, "year": 2045},
    technologies=["solar", "wind", "storage"],
    constraints={"land_use": land_constraints}
)

print(f"Optimal mix: {plan.technology_mix}")
print(f"Required investment: ${plan.investment_billions}B")

Related Documentation

Status: Alpha - Core functionality implemented

Last Updated: 2026-02-25

Documentation Hub

Full framework documentation, guides, and tutorials are available in the GEO-INFER-INTRA documentation hub.

Resource Description
Getting Started Installation, first steps, quick start guides
Module Overview All 44 modules with descriptions and use cases
Integration Patterns How modules work together
Testing Guide Testing standards, fixtures, CI integration
API Standards Code conventions and contribution guidelines