Will the lights stay on? Show your working.

Loss-of-load expectation, capacity value and capacity-market clearing — the numbers that decide what a battery, wind farm or peaker is worth to system reliability. Today they take a six-month consultancy study against last year’s fleet. CESentinel makes them reproducible runs on the live one.

LOLE / EENS reliability metricsELCC per class & per assetCM auction clearing

What it does

Reliability, made auditable.

Adequacy metrics

Loss-of-load expectation and expected energy not served, by Monte-Carlo over weather, demand and outage states — on the same fleet model the rest of the platform runs.

Capacity value (ELCC)

How much firm capacity is this asset really worth? Computed per technology class and per asset, with the marginal-vs-average distinction explicit — because both mislead alone.

Capacity-market clearing

GB, French, Italian and PJM-style mechanisms simulated as auctions over the derated fleet — clearing price, awarded capacity, per-asset outcomes.

For the quants

The method.

SamplingSequential Monte-Carlo over decades of historical weather years, with demand and renewables conditioned on temperature, wind and irradiance, and outages as Markov chains.
Storage adequacyBattery state-of-charge carried hour by hour through each replication — a 2-hour and a 4-hour battery get honestly different capacity values.
ELCCEquivalent-firm-capacity matching: solve for the firm injection with the same reliability as the asset. Marginal at the current fleet point; class-average by re-solve.
Mechanism templatesAdministrative demand curves and per-market obligation rules built in; user-defined mechanisms via a declarative spec.

Status: in active build. Researchers and academics: free access and pre-submission drafts on request.

Early access

Get in early.

Early-access partners run pilot adequacy studies with our engineers and lock founding terms.

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