IRP-grade portfolio optimization for data centers. The same rigor utilities apply to their own resource plans—applied to your behind-the-meter decision.
Data center developers evaluating on-site generation face economics that spreadsheets can't handle. Fuel costs fluctuate. Maintenance schedules interact with each other. Equipment fails at rates that depend on age, load, and weather. Grid outages follow statistical distributions that change by region. Regulatory compliance adds constraints that vary by jurisdiction. All of this creates an incredibly dynamic and complex environment where choosing the right generation stack can make or break a project.
The standard approach is a deterministic spreadsheet that assumes everything works perfectly, then applies a "risk factor" at the end. This produces a single number that gives false confidence. The real question isn't "what's the expected cost?"—it's "what's the range of outcomes, and how bad can it get?"
BTM-Optimize is a two-layer optimization platform. Layer 1 is a simple cost calculator—it tells you whether the economics pencil under ideal conditions. Layer 2 is where the real analysis lives: a Monte Carlo simulation that breaks things on purpose to show you the full distribution of outcomes.
Why Monte Carlo matters: A deterministic model tells you the expected value. Monte Carlo tells you the shape of the risk. A project might have a great expected return but a fat left tail—meaning a small but real chance of catastrophic underperformance. You can't see that in a spreadsheet.
Per-unit Bernoulli outage sampling | NERC GADS forced outage rates | Poisson grid outage modeling | Cold-weather performance multipliers | Full distribution of cost outcomes across N simulations
Simple cost calculator: does this pencil? Capital costs, fuel, O&M, grid charges, revenue streams. Assumes everything works perfectly—the baseline before risk enters.
Technology cost benchmarks
Real generator performance
Forced outage rates
OEM-specific performance, maintenance, and reliability data
Each profile traces a provenance chain: NREL benchmark → EIA actuals → NERC reliability → manufacturer specs. No magic numbers.
Fuel prices & generation
Emissions factors
Solar & wind resource
OEM equipment profiles with full provenance
Reference data layers (NREL, EIA, NERC, OEM)
Years of weather data (NREL NSRDB)
Run the Layer 1 financial screener on a real scenario: a 50MW Phoenix data center with gas, solar, and battery storage. No login required.
Try the Interactive DemoBTM-Optimize demonstrates the kind of tool we build for clients: purpose-built, rigorously tested, grounded in real data, and architected so your team can understand and maintain it. The methodology—stochastic modeling, provenance-tracked data, layered architecture—applies to any complex energy analysis problem.
Explore ResourcesIf your team is solving complex energy economics with spreadsheets, let's talk about building the right tools.
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