Solar Energy Potential

Calculate solar PV energy potential from NASA POWER solar radiation data. Computes annual GHI, optimal tilt angle, estimated PV output, and economic analysis.

ruiduobao@ruiduobao

Install

openclaw skills install @ruiduobao/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

Parameter	Description	Default
System efficiency	PV panel efficiency (%)	18%
Performance ratio	System losses factor	0.80
Installed capacity	kWp per assessment	1.0
Electricity price	$/kWh for economic analysis	0.10
System cost	$/kWp installed	1000

Usage

Assess a single location

bash

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

Batch process from CSV

bash

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

Economic analysis

bash

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

bash

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:

text

tilt ≈ latitude × 0.87

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

Mount Type	Tilt Formula
Fixed	`latitude × 0.87`
Seasonal adjust	`latitude − 15°` (summer), `latitude + 15°` (winter)
Tracking	0° (horizontal axis), latitude (tilted axis)

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:

text

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

Where:

Variable	Description	Default
CAPEX	Initial investment ($/kWp)	1000
CRF	Capital recovery factor = r(1+r)ⁿ / ((1+r)ⁿ − 1)	r=0.06, n=25
O&M	Annual O&M cost ($/kWp/year)	20
Annual_energy	kWh/kWp/year from PV output	computed

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

Temporal Resolution

NASA POWER data supports three temporal resolutions:

Resolution	Parameter	Use Case
Daily	`daily`	Detailed analysis, day-to-day variation
Monthly	`monthly`	Seasonal patterns, resource mapping
Climatology	`climatology`	Long-term average, feasibility studies

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

CSV Output Format

In addition to JSON, output results as CSV:

bash

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:

Error	Cause	Tool Behavior
HTTP 500	Server error	Waits 30s, retries up to 3 times
HTTP 503	Service unavailable	Waits 60s, retries
Timeout	Slow response	Increases timeout, retries
No data	Invalid coordinates	Reports error, suggests valid range

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:

json

{
  "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

python

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:

bibtex

@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

Error	Cause	Solution
`ConnectionError`	Network issue	Check internet, retry
`HTTP 429`	Rate limit	Wait 60s, retry
`ValueError`	Invalid coordinates	Check lat (-90 to 90), lon (-180 to 180)
Empty output	No data for location	Try nearby coordinates
`ModuleNotFoundError`	Missing dep	Run pip install
`HTTP 500/503`	NASA server issue	Wait and retry later
Unrealistic GHI	Ocean/coastland grid cell	Move point inland or check grid resolution

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

text

requests>=2.28.0
numpy>=1.21.0

Data Source

NASA POWER (Prediction Of Worldwide Energy Resources) API.

Advanced Usage

Batch Assessment from CSV

bash

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

CI/CD Integration (GitHub Actions)

yaml

# .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

bash

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

参数	说明	默认值
系统效率	光伏板效率 (%)	18%
性能比	系统损耗因子	0.80
装机容量	kWp	1.0
电价	$/kWh	0.10
系统成本	$/kWp	1000

使用示例

单点评估

bash

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

批量处理

bash

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

经济分析

bash

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

安装

bash

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

参数说明

--lat: 纬度 (-90 到 90)
--lon: 经度 (-180 到 180)
--input: 批量模式输入 CSV
--lat-col: CSV 中纬度列名
--lon-col: CSV 中经度列名
--output: 输出 JSON 文件
--efficiency: 光伏板效率 (0.15-0.25)
--performance-ratio: 性能比 (0.70-0.90)
--capacity: 装机容量 kWp
--cost-per-kwp: 每 kWp 系统成本 USD
--electricity-price: 电价 USD/kWh
--year: NASA POWER 数据年份
--json: 以 JSON 输出

输出结果

年 GHI：kWh/m²/年
最佳倾角：度
年发电量：kWh/kWp/年
容量因子：%
经济指标：回收期、LCOE、年节省

API 信息

端点: https://power.larc.nasa.gov/api/temporal/daily/point
无需 API 密钥
数据: NASA POWER Project
许可: Public Domain

依赖库

text

requests>=2.28.0
numpy>=1.21.0

最佳倾角公式

固定支架光伏系统的最佳倾角估算：

text

倾角 ≈ 纬度 × 0.87

更精确的估算使用 PVWatts 方法：

支架类型	倾角公式
固定支架	`纬度 × 0.87`
季节可调	`纬度 − 15°`（夏季），`纬度 + 15°`（冬季）
跟踪支架	0°（水平轴），纬度（倾斜轴）

注意：此为简化估算。实际最佳倾角取决于当地气候、反照率和遮挡情况。

LCOE 公式文档

平准化度电成本（LCOE）计算公式：

text

LCOE = (CAPEX × CRF + O&M) / 年发电量

其中：

变量	说明	默认值
CAPEX	初始投资 ($/kWp)	1000
CRF	资本回收因子 = r(1+r)ⁿ / ((1+r)ⁿ − 1)	r=0.06, n=25
O&M	年运维成本 ($/kWp/年)	20
年发电量	kWh/kWp/年（来自光伏输出）	计算得出

使用 --discount-rate 和 --system-lifetime 调整 CRF 参数。

时间分辨率

NASA POWER 数据支持三种时间分辨率：

分辨率	参数	用途
日	`daily`	详细分析、逐日变化
月	`monthly`	季节模式、资源制图
气候态	`climatology`	长期平均、可行性研究

使用 --temporal-resolution monthly 指定。默认 daily。

CSV 输出格式

除 JSON 外，还支持 CSV 输出：

bash

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

批量模式默认输出 CSV（每个地点一行）。

API 错误处理与重试逻辑

工具处理 NASA POWER API 错误：

错误	原因	工具行为
HTTP 500	服务器错误	等待 30 秒，最多重试 3 次
HTTP 503	服务不可用	等待 60 秒，重试
超时	响应慢	增加超时，重试
无数据	坐标无效	报告错误，建议有效范围

使用 --max-retries 5 和 --retry-delay 120 自定义。

已知限制

本工具仅提供估算。已知限制包括：

无遮挡分析：不考虑地形或建筑阴影
无污染损失：不建模灰尘/污染对面板的影响
无地形效应：假设平面；无坡度/坡向校正
简化光伏模型：使用性能比；不建模逆变器效率曲线
NASA POWER 分辨率：~0.5°×0.5° 网格；无法捕捉局地小气候
经济假设：简单 LCOE；不建模衰减、融资、补贴

详细系统设计请使用 PVsyst、SAM 或 HOMER。

批量输出格式

批量模式产生结构化输出：

json

{
  "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 输出每个地点一行，所有指标为列。

可视化

GHI 制图：插点结果生成空间栅格（使用 QGIS 或 Python scipy.interpolate）
柱状图：比较不同地点的光伏输出
月际分布：绘制月 GHI 展示季节变化
经济散点图：LCOE vs GHI 用于选址对比

引用格式

使用 NASA POWER 数据时请引用：

bibtex

@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},
}

故障排除

错误	原因	解决方案
`ConnectionError`	网络问题	检查网络，重试
`HTTP 429`	速率限制	等待 60 秒后重试
`ValueError`	坐标无效	检查纬度 (-90 到 90)，经度 (-180 到 180)
无输出	该位置无数据	尝试附近坐标
`ModuleNotFoundError`	缺少依赖	运行 pip install
`HTTP 500/503`	NASA 服务器问题	稍后重试
GHI 不现实	海洋/海岸网格单元	移入内陆或检查网格分辨率

数据来源

NASA POWER (Prediction Of Worldwide Energy Resources) API。