Off-Grid Solar Sizing
Operators need to configure hybrid storage that is economical, low-carbon, and flexible, but traditional planning often ignores hydrogen’s broader value and the operational impact of ramping shortages.
The Problem
“Optimize off-grid hybrid solar, battery, and hydrogen sizing under carbon and ramping constraints”
Organizations face these key challenges:
Hydrogen value is underestimated when only short-duration balancing is modeled
Battery-only designs become uneconomic for long-duration or seasonal balancing needs
Ramping shortages create hidden reliability risks not captured in static sizing methods
Stepped carbon trading introduces nonlinear cost behavior that spreadsheets handle poorly
Weather and load uncertainty make deterministic sizing fragile
Engineering teams lack a unified tool for techno-economic and operational co-optimization
Manual scenario analysis is slow and difficult to audit
Asset interactions across PV, battery, electrolyzer, hydrogen tank, and fuel cell are complex
Impact When Solved
The Shift
Human Does
- •Collect customer appliance lists, site survey notes, and rough daily energy estimates
- •Estimate PV, battery, inverter, and generator sizes using rules of thumb and spreadsheet calculations
- •Apply safety factors, autonomy assumptions, and component selection based on engineer judgment
- •Review design tradeoffs for cost versus reliability and prepare the proposal or quote
Automation
- •No meaningful AI support in the legacy sizing workflow
- •No automated load profile inference from sparse customer inputs
- •No probabilistic solar or seasonal scenario analysis
- •No automated optimization of lifecycle cost against reliability targets
Human Does
- •Confirm customer requirements, critical loads, uptime targets, and site constraints
- •Review AI-generated sizing recommendations and approve the preferred design option
- •Handle exceptions where site conditions, logistics, or customer priorities differ from model assumptions
AI Handles
- •Infer realistic hourly load profiles from sparse inputs such as appliance lists, occupancy, and historical usage signals
- •Generate site-specific solar, temperature, and seasonal demand scenarios from available weather and location data
- •Optimize PV, battery, inverter, and generator sizing to meet reliability targets at lowest lifecycle cost
- •Produce auditable design options with expected CAPEX, LCOE, diesel use, and battery replacement implications
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 finalize a customer design without review and approval by an energy system planner or design engineer. [S1][S2][S3]
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 Off-Grid Solar Sizing implementations:
Key Players
Companies actively working on Off-Grid Solar Sizing solutions: