📋 Case Study

Hawaii Island Microgrid Solar Forecast Hardening

Volcanic haze and trade-wind cloud variability caused 30–50% forecast error spikes during monsoon season

🏗️ Project Overview

Maui Smart Grid Initiative — 100% renewable target by 2030

🎯 Challenge

Volcanic haze and trade-wind cloud variability caused 30–50% forecast error spikes during monsoon season

🔧 Design Approach

Multi-source uncertainty quantification: Bayesian neural net for haze opacity estimation + optical flow + physics-informed dropout ensembles; outputs fed to real-time reconciliation engine compliant with IEEE 1547-2018 Annex J

📐 Design Diagram

AI-generated project design illustration

📐 Key Calculations

Haze Attenuation Coefficient

σ = −ln(I/I₀)/L

Result: σ = 0.42 km⁻¹

Drives irradiance correction factor

Reconciliation Safety Margin

SM = 2 × σ_forecast

Result: SM = 187 W/m²

Guarantees IEEE 1547-2018 voltage ride-through compliance

📊 Results

Peak error reduced from 47% → 9.3%; microgrid islanding stability improved (Δf < 0.05 Hz); achieved IEEE 1547-2018 Annex J certification

💡 Lessons Learned

• •Volcanic aerosol index must be ingested from NOAA GOES-R AOD product
• •Physics-informed dropout requires custom gradient clipping to preserve conservation laws

✅ Key Takeaways

• 1Volcanic aerosol index must be ingested from NOAA GOES-R AOD product
• 2Physics-informed dropout requires custom gradient clipping to preserve conservation laws