Generation Mix Planning

Evaluates and compares energy sourcing options for data centres—including fuel cells, nuclear, CCS and grid supply—to support fast, reliable and cost-effective capacity planning decisions.

The Problem

“Data-centre energy sourcing decisions are slow, fragmented and hard to defend”

Organizations face these key challenges:

1

Power technology options have different cost structures, deployment timelines and reliability profiles

2

Grid availability and interconnection timelines are uncertain and location-specific

3

Permitting, regulatory and fuel supply risks are difficult to compare consistently

4

Stakeholders disagree on weighting of cost, carbon, resilience and speed-to-power

Impact When Solved

Cuts energy sourcing evaluation cycles from weeks to hoursStandardizes comparison of grid, fuel cell, nuclear and CCS-backed optionsImproves transparency of trade-offs across cost, reliability and deployment speedSupports faster go/no-go decisions for data-centre expansion

The Shift

Before AI~85% Manual

Human Does

  • •Gather technology, grid, regulatory and commercial inputs from separate sources
  • •Build spreadsheet comparisons and consultant-style scenario decks for each power option
  • •Review trade-offs in workshops and debate weighting of cost, carbon, resilience and speed-to-power
  • •Decide shortlist, go/no-go direction and next-step studies for each site or expansion plan

Automation

  • •No AI-driven analysis in the legacy process
  • •No automated normalization of assumptions across regions and technologies
  • •No continuous sensitivity testing when prices, timelines or policy inputs change
With AI~75% Automated

Human Does

  • •Set project priorities, decision criteria and weighting across cost, uptime, carbon and deployment speed
  • •Approve assumptions, source data boundaries and scenario inputs for each site or capacity tranche
  • •Review AI-ranked options and decide shortlist, go/no-go actions and escalation of exceptions

AI Handles

  • •Normalize technical, commercial, regulatory and deployment inputs across grid, fuel cell, nuclear and CCS options
  • •Score and rank power mix scenarios against cost, reliability, emissions, timing and risk criteria
  • •Run sensitivity analysis on fuel prices, interconnection delays, carbon prices and incentive changes
  • •Generate grounded summaries, trade-off explanations and recommendation narratives for stakeholders

Operating Intelligence

How it works

AI runs the first three steps autonomously.

Humans own every decision.

The system gets smarter each cycle.

Confidence96%
ArchetypeRecommend & Decide
Shape6-step converge
Human gates1
Autonomy
67%AI controls 4 of 6 steps

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.

Loop shapeconverge

Step 1

Assemble Context

Step 2

Analyze

Step 3

Recommend

Step 4

Human Decision

Step 5

Execute

Step 6

Feedback

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

The Loop

6 steps

1 operating angles mapped

Operational Depth

Free access to this report