NEXLYTIQ  ·  PORTFOLIO INTELLIGENCE POC

Cross-Fleet Portfolio Optimization

A unified, OEM-agnostic view across sites and turbine vendors — the view no single OEM portal gives a multi-vendor operator. Built on synthetic data modeled on a 6-site, 3-OEM portfolio (306 MW).

$0
Identified annual value — curtailment optimization + targeted maintenance allocation
Curtailment savings
Maintenance-driven uplift
Budget deployed

01 · Fleet performance rail

PERFORMANCE RATIO — ACTUAL VS. THEORETICAL OUTPUT

Every site normalized to a common metric regardless of OEM power-curve spec — surfacing the two GE sites underperforming their own theoretical output, invisible when each OEM only reports against its own baseline.

Vestas GE Siemens Gamesa │ white marker = fleet-best target (95%)

02 · Curtailment allocation

400 MWh GRID CURTAILMENT ORDER — SINGLE EVENT

When the grid orders a fixed curtailment volume, price-aware allocation across sites and hours (LP-optimized) beats spreading it pro-rata by capacity — because not every site/hour has equal market value.

Naive (pro-rata) cost
Optimized allocation cost
Savings / event
Annualized (12 events/yr)

03 · Maintenance budget allocation

$250K BUDGET · KNAPSACK-OPTIMIZED SITE SELECTION

Ranked by return per dollar of O&M spend to close each site's gap toward fleet-best performance — the budget is deployed where it buys the most annual revenue, not spread evenly.

SiteOEMPerf. gapFix costAnnual upliftROIFunded