Geoskill: Solar Energy Potential

Solar Energy Potential

Assess solar photovoltaic (PV) energy potential using NASA POWER solar radiation data. Computes annual GHI, optimal tilt, estimated PV output, and economic metrics.

Features

• Annual GHI: Global Horizontal Irradiance from NASA POWER
• Optimal tilt angle: Based on latitude
• PV output estimation: kWh/kWp/year
• Economic analysis: Simple payback, LCOE estimate
• Single point + batch: CSV input for multiple locations
• No API key required: NASA POWER is free and open

Key Parameters

Usage

Assess a single location

python scripts\solar-energy-potential.py assess \
  --lat 39.9 --lon 116.4 \
  --output solar_assessment.json

Batch process from CSV

python scripts\solar-energy-potential.py batch \
  --input locations.csv --lat-col lat --lon-col lon \
  --output solar_batch.json

Economic analysis

python scripts\solar-energy-potential.py economic \
  --lat 39.9 --lon 116.4 \
  --capacity 5.0 --cost-per-kwp 800 --electricity-price 0.12 \
  --output economic.json

Installation

pip install requests>=2.28.0 numpy>=1.21.0
# Or: pip install -r scripts/requirements.txt

Parameters

• --lat: Latitude (-90 to 90)
• --lon: Longitude (-180 to 180)
• --input: Input CSV file for batch mode
• --lat-col: Latitude column name in CSV
• --lon-col: Longitude column name in CSV
• --output: Output JSON file
• --efficiency: PV panel efficiency (0.15-0.25, default: 0.18)
• --performance-ratio: Performance ratio (0.70-0.90, default: 0.80)
• --capacity: Installed capacity in kWp (default: 1.0)
• --cost-per-kwp: System cost per kWp in USD (default: 1000)
• --electricity-price: Electricity price in USD/kWh (default: 0.10)
• --year: Year for NASA POWER data (default: 2023)
• --json: Output as JSON

Output

• Annual GHI: kWh/m²/year
• Optimal tilt: degrees
• Annual PV output: kWh/kWp/year
• Capacity factor: %
• Economic metrics: Payback period, LCOE, annual savings

Optimal Tilt Angle Formula

The optimal tilt angle for fixed-mount PV systems is estimated as:

tilt ≈ latitude × 0.87

For more precise estimation, the tool uses the PVWatts method:

Note: This is a simplified estimate. Actual optimal tilt depends on local climate, albedo, and shading.

LCOE Formula Documentation

Levelized Cost of Energy is calculated as:

LCOE = (CAPEX × CRF + O&M) / Annual_energy

Where:

Use --discount-rate and --system-lifetime to adjust CRF parameters.

Temporal Resolution

NASA POWER data supports three temporal resolutions:

Specify with --temporal-resolution monthly. Default is daily.

CSV Output Format

In addition to JSON, output results as CSV:

python scripts\solar-energy-potential.py assess \
  --lat 39.9 --lon 116.4 \
  --output solar_assessment.csv --format csv

Batch mode outputs CSV by default (one row per location).

API Error Handling and Retry Logic

The tool handles NASA POWER API errors:

Use --max-retries 5 and --retry-delay 120 to customize.

Known Limitations

This tool provides estimates only. Known limitations include:

• No shading analysis: Does not account for terrain or building shadows
• No soiling losses: Does not model dust/pollution on panels
• No terrain effects: Assumes flat surface; no slope/aspect correction
• Simplified PV model: Uses performance ratio; does not model inverter efficiency curves
• NASA POWER resolution: ~0.5°×0.5° grid; local microclimate not captured
• Economic assumptions: Simple LCOE; does not model degradation, financing, incentives

For detailed system design, use PVsyst, SAM, or HOMER.

Batch Output Format

Batch mode produces structured output:

{
  "locations": [
    {"lat": 39.9, "lon": 116.4, "ghi": 1450, "tilt": 34.7, "pv_output": 1320},
    {"lat": 31.2, "lon": 121.5, "ghi": 1380, "tilt": 27.2, "pv_output": 1250}
  ],
  "summary": {
    "mean_ghi": 1415,
    "total_potential_kwp": 2570
  }
}

CSV output has one row per location with all metrics as columns.

Visualization

• GHI map: Interpolate point results to create spatial raster (use QGIS or Python scipy.interpolate)
• Bar chart: Compare PV output across locations
• Monthly profile: Plot monthly GHI to show seasonal variation
• Economic scatter: LCOE vs GHI for site comparison

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('solar_batch.csv')
plt.scatter(df['ghi'], df['pv_output'], c=df['latitude'], cmap='coolwarm')
plt.colorbar(label='Latitude')
plt.xlabel('Annual GHI (kWh/m²/year)')
plt.ylabel('PV Output (kWh/kWp/year)')
plt.show()

Citation

Please cite NASA POWER data:

@misc{nasa_power,
  author       = {{NASA Langley Research Center}},
  title        = {NASA POWER Project},
  howpublished = {\url{https://power.larc.nasa.gov}},
  year         = {2024},
  note         = {SSE-R6}
}

@software{solar_energy_potential,
  author  = {ruiduobao},
  title   = {Solar Energy Potential Assessment Tool},
  url     = {https://github.com/ruiduobao/solar-energy-potential},
  version = {0.1.0},
  year    = {2024},
}

Troubleshooting

API Information

• Endpoint: https://power.larc.nasa.gov/api/temporal/daily/point
• No API key required
• Data: NASA POWER Project (SSE-R6)
• License: Public Domain

Dependencies

requests>=2.28.0
numpy>=1.21.0

Data Source

NASA POWER (Prediction Of Worldwide Energy Resources) API.

---

Advanced Usage

Batch Assessment from CSV

python scripts\solar-energy-potential.py assess   --input locations.csv --output solar_assessment.json

CI/CD Integration (GitHub Actions)

# .github/workflows/solar-assessment.yml
name: Solar Potential Update
on:
  schedule:
    - cron: '0 0 1 1 *'  # Yearly
jobs:
  assess:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - run: pip install requests
      - run: |
          python scripts\solar-energy-potential.py assess \
            --input data/solar_sites.csv \
            --output data/solar_latest.json

PostgreSQL Import

python scripts\solar-energy-potential.py assess   --input sites.csv --output solar.json

# Parse JSON and import
python -c "
import json, csv
data = json.load(open('solar.json'))
with open('solar.csv', 'w', newline='') as f:
    w = csv.DictWriter(f, fieldnames=data[0].keys())
    w.writeheader(); w.writerows(data)
"
psql -d gis_db -c "\COPY solar_assessment FROM 'solar.csv' CSV HEADER"

Performance Tips

• Use --temporal climatology for feasibility studies (fastest, pre-computed)
• Add sleep 1 between batch locations to avoid rate limits
• --json output is machine-readable; use --csv for direct spreadsheet import

---

中文说明

使用 NASA POWER 太阳辐射数据评估太阳能光伏潜力。计算年 GHI、最佳倾角、预估发电量及经济分析。

功能特性

• 年 GHI：全球水平辐照度
• 最佳倾角：基于纬度计算
• 发电量估算：kWh/kWp/年
• 经济分析：简单回收期、LCOE 估算
• 单点 + 批量：CSV 输入多地点
• 无需 API 密钥：NASA POWER 免费开放

关键参数

使用示例

单点评估

python scripts\solar-energy-potential.py assess \
  --lat 39.9 --lon 116.4 \
  --output solar_assessment.json