
Energy Systems
- 16 installs
- 869 repo stars
- Updated June 8, 2026
- beita6969/scienceclaw
energy-systems is a Claude skill that analyzes energy systems, including renewable resource assessment, grid modeling, storage optimization, and techno-economic analysis.
About
This skill analyzes energy systems, covering renewable resource assessment, power-grid modeling, battery-storage optimization, and techno-economic analysis. A developer uses it when evaluating solar or wind projects, sizing storage, or computing levelized cost of energy. It references tools like NREL SAM, PVWatts, HOMER, and PyPSA.
- Runs renewable resource assessment for solar and wind
- Models grid integration, storage sizing, and dispatch optimization
- Computes LCOE, NPV, IRR, and payback with sensitivity analysis
Energy Systems by the numbers
- 16 all-time installs (skills.sh)
- Ranked #1,318 of 2,065 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 2, 2026 (Skillselion catalog sync)
energy-systems capabilities & compatibility
Free; uses open NREL and energy datasets and open-source tools.
- Capabilities
- exploratory data analysis · economics analysis
- Use cases
- data analysis · research
- Pricing
- Free
What energy-systems says it does
Calculate LCOE (levelized cost of energy) with discount rate, capital costs, O&M, fuel costs, and lifetime.
For wind: analyze wind speed distributions (Weibull), compute power curves, assess turbulence intensity.
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| Installs | 16 |
|---|---|
| repo stars | ★ 869 |
| Last updated | June 8, 2026 |
| Repository | beita6969/scienceclaw ↗ |
What it does
Use it to analyze energy systems: renewable resource assessment, grid modeling, storage sizing, and techno-economic evaluation.
Who is it for?
Assessing renewable energy resources and running techno-economic analysis (LCOE, payback) of energy technologies.
Skip if: Building energy-trading applications or work outside energy-system analysis.
When should I use this skill?
The user discusses solar, wind, grid integration, energy storage, or power-system design.
What you get
Produces a sized energy system with an LCOE breakdown, NPV/IRR, and sensitivity analysis.
- Resource assessment tables
- LCOE breakdown
- Economic results with sensitivity tornado chart
By the numbers
- 7-step methodology
- 8-item quality checklist
- 6 tools/databases listed
Files
When to Trigger
Activate this skill when the user mentions:
- Solar energy, photovoltaic, wind power, hydropower
- Power grid, load balancing, dispatch optimization
- Battery storage, lithium-ion, energy density, cycling
- Energy efficiency, HVAC, building energy modeling
- Techno-economic analysis, LCOE, payback period
- Electric vehicles, charging infrastructure, V2G
- Hydrogen economy, fuel cells, electrolysis
Step-by-Step Methodology
1. Define the energy system scope - Specify system boundaries: single building, microgrid, regional grid, or national scale. Identify energy sources (solar, wind, fossil, nuclear, hydro), storage technologies, and demand profiles. 2. Resource assessment - For solar: analyze irradiance data (GHI, DNI, DHI), calculate capacity factor, account for degradation and soiling. For wind: analyze wind speed distributions (Weibull), compute power curves, assess turbulence intensity. Use TMY (Typical Meteorological Year) data or site-specific measurements. 3. System modeling - Size components: panels/turbines (capacity), inverters, batteries (energy and power), converters. Model energy balance: generation - consumption - storage - curtailment = grid exchange. Use hourly or sub-hourly time resolution. 4. Grid integration - Analyze grid interconnection requirements: voltage, frequency, power factor. Assess variability and ramping impacts. Model dispatch optimization (merit order, economic dispatch, unit commitment). Evaluate ancillary services potential. 5. Storage analysis - Characterize storage technology: energy density (Wh/kg), power density (W/kg), round-trip efficiency, cycle life, calendar life, self-discharge rate. Optimize sizing based on arbitrage value, peak shaving, or reliability requirements. 6. Economic analysis - Calculate LCOE (levelized cost of energy) with discount rate, capital costs, O&M, fuel costs, and lifetime. Compute NPV, IRR, and payback period. Include incentives (ITC, PTC, feed-in tariffs). Conduct sensitivity analysis on key assumptions. 7. Environmental assessment - Calculate avoided CO2 emissions using grid emission factors. Perform lifecycle emissions analysis (cradle-to-gate). Compare with conventional alternatives.
Key Databases and Tools
- NREL (SAM, PVWatts, NSRDB) - Solar and renewable energy tools
- Global Wind Atlas - Wind resource data
- EIA / IEA - Energy statistics and projections
- HOMER Energy - Microgrid optimization
- OpenDSS - Distribution system simulation
- PyPSA - Open-source power system analysis
Output Format
- Resource assessment as tables: annual/monthly capacity factor, energy yield (kWh/kWp).
- System diagram with component sizes, power flows, and energy balance.
- LCOE breakdown: capital, O&M, fuel, financing costs per kWh.
- Economic results as NPV, IRR, payback period with sensitivity tornado chart.
- Time series plots: generation, demand, storage state-of-charge, grid exchange.
Quality Checklist
- [ ] Resource data source and time resolution specified
- [ ] System losses itemized (inverter, wiring, degradation, soiling, curtailment)
- [ ] Discount rate and financial assumptions documented
- [ ] Storage degradation and replacement costs included in economics
- [ ] Grid emission factor source and year specified
- [ ] Sensitivity analysis covers key uncertainties (resource, cost, discount rate)
- [ ] Units consistent (kW vs. kWh, AC vs. DC clearly distinguished)
- [ ] Comparison with alternatives provided for context
Related skills
FAQ
What economic metrics does it compute?
LCOE, NPV, IRR, and payback period, with incentives like ITC, PTC, and feed-in tariffs, plus sensitivity analysis.
What tools does it reference?
NREL SAM/PVWatts/NSRDB, Global Wind Atlas, EIA/IEA, HOMER Energy, OpenDSS, and PyPSA.