SolarScenario Optimizer
Evaluates solar and storage scenarios to quantify how technology choices and policy-sensitive inputs impact LCOE, generation mix, emissions, and storage economics for energy planning and procurement.
The Problem
“Slow, spreadsheet-driven solar and storage scenario analysis limits energy planning decisions”
Organizations face these key challenges:
Scenario assumptions are scattered across spreadsheets, PDFs, and analyst notes
Policy-sensitive inputs such as tax credits and incentives change frequently
Storage value depends on multiple interacting variables that are hard to model manually
Teams struggle to compare financial, operational, and emissions outcomes in one workflow
Impact When Solved
The Shift
Human Does
- •Collect scenario assumptions from spreadsheets, policy documents, tariffs, and analyst notes
- •Update solar, storage, financing, and load inputs for each case manually
- •Run separate scenario comparisons for LCOE, generation mix, emissions, and storage economics
- •Review outputs, reconcile inconsistencies, and prepare planning or procurement recommendations
Automation
- •No meaningful AI support in the legacy workflow
- •Calculations follow static spreadsheet formulas and manual planning logic
- •Scenario comparisons depend on analyst-created case sets and hand-built summaries
Human Does
- •Set planning objectives, scenario constraints, and decision criteria
- •Review ranked scenarios and approve assumptions for policy, financing, and operational cases
- •Investigate flagged outliers, exceptions, or implausible recommendations
AI Handles
- •Extract and normalize assumptions from uploaded documents and prior scenario materials
- •Generate and evaluate hundreds to thousands of solar, battery, and hybrid scenarios
- •Rank configurations by LCOE, emissions, generation mix, curtailment, and storage value
- •Flag inconsistent inputs, estimate missing parameters, and explain key scenario tradeoffs
Operating Intelligence
How it works
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not approve final planning recommendations or procurement choices without review by an energy planner, procurement lead, or utility analyst [S1].
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in SolarScenario Optimizer implementations:
Key Players
Companies actively working on SolarScenario Optimizer solutions: