AI Decarbonization Pathway Planning
AI-driven modeling and optimization of corporate decarbonization strategies
The Problem
“Optimize least-cost energy decarbonization pathways at scale”
Organizations face these key challenges:
1
Too many interacting variables (load growth, DER adoption, fuel/carbon prices, policy incentives, interconnection delays) to evaluate comprehensively with manual scenario planning
2
Slow, infrequent planning updates that cannot keep pace with market volatility and regulatory changes, increasing risk of stranded assets and missed interim targets
3
Difficulty balancing emissions reductions with reliability constraints (LOLE/reserve margins), transmission bottlenecks, and operational realities (ramping, outages, heat-rate degradation)
Impact When Solved
Faster, more frequent pathway refreshes (monthly/quarterly) enabling earlier course-corrections and improved regulatory defensibilityLower abatement cost through optimized sequencing of retirements, repowering, PPAs, storage, hydrogen/CCUS, and transmission upgrades under uncertainty