Your battery isn't earning what it could.
Pareto Grid measures the gap between what a storage asset actually earns and the most it could have earned with perfect foresight, then shows where it is lost and how to close it. Independent, and with no operational risk.
Measure the gap. Then close it.
Two halves of the same problem: knowing exactly how much revenue an asset is leaving behind, and showing how to capture more of it.
The Percentage of Perfect
Every asset has a ceiling: the revenue it would have earned if it had known prices in advance. Pareto Grid computes that ceiling with a full-window mixed-integer optimisation and scores your real operation against it, interval by interval. No cherry-picked weeks, no dry-run estimates. Just how much of the achievable revenue you're actually capturing.
Perfect-foresight MILPA digital twin that shows the play
A rolling-horizon optimiser, a live twin of your asset, that plans charge, discharge and bids across energy and FCAS together and re-solves every interval as prices and forecasts move. It shows the dispatch and bidding strategy that captures more of the ceiling, and it sits alongside your operation as a decision layer: it does not touch your EMS or submit bids for you.
Decision layer, no operational riskFrom dispatch data to a gap you can act on.
A benchmark run is four steps, and none of it is a black box: every number traces back to an optimisation you could check by hand.
- 01
Bring the data
Actual dispatch, prices and your asset's specs: power, usable energy, round-trip efficiency, SoC limits. Real operation, not indicative figures.
- 02
Solve for perfect
A full-window MILP finds the most revenue the asset could have earned with perfect price foresight, co-optimising energy and all ten FCAS markets.
- 03
Score the actual
Your real operation is measured against that ceiling at every five-minute interval, so the comparison is like-for-like rather than against a softer baseline.
- 04
Read the gap
One Percentage of Perfect, plus where the gap opens: spikes held through or missed, charging timed around negative prices, short one or two interval bursts.
The optimiser runs that same logic as a rolling loop, re-solving every dispatch interval as forecasts and prices move.
Tested against a real NEM battery, across the full window.
517 days, scored against the asset's actual metered dispatch and a perfect-foresight ceiling. Energy only, because that is the number that holds up.
Over 517 days the model captured 56% of the energy perfect-foresight ceiling. The asset's actual metered dispatch captured 41%.
More energy revenue on about 14% less throughput and slightly fewer cycles. The edge is timing, not volume.
Swapping the in-house forecast for AEMO pre-dispatch, with nothing else changed, lifts capture from 47% to 56% of perfect.
The edge holds on 70% of days and survives removing the 10 biggest price-spike days. Structural, not a handful of cap days.
Energy only, gross, full 517-day window, scored against the asset's actual metered dispatch. FCAS is held back as indicative; the optimiser is a price-taker, so cap-day figures are an upper bound; and the real asset's dispatch reflects its own operating objective, not merchant maximisation. The figures above are the ones that survive those caveats.
Measured, not asserted.
The market is full of revenue claims. Pareto Grid is built around the opposite instinct: a number isn't real until it survives a full-window backtest against the ceiling.
- →Full window, or it doesn't count
Gains are validated across the whole period, not a flattering slice. Spike-day economics are asymmetric, so a pilot week proves nothing.
- →Benchmarked against perfect, not a baseline
The reference is the theoretical best, computed by optimisation, not last month's numbers or a vendor's default strategy.
- →If a signal doesn't replicate, it doesn't ship
Anything whose uplift is near-zero, negative, or can't be reproduced out of sample is rejected and documented, not quietly kept.
One number, read across the table.
The Percentage of Perfect is a single benchmark, but it answers a different question for everyone with a stake in a battery's revenue.
Audit your Percentage of Perfect, see where revenue leaks by hour and condition, and what to change.
Anchor tolling fees and floor prices to a hard revenue ceiling, not a vendor's estimate.
Validate merchant revenue before FID and test a project's strategy on historical years before it is built.
An independent upper bound for bankability, sensitivity and downside cases in the model.
Score any bidder or platform against a neutral ceiling, for diligence, sale or certification.
Quantify cap-contract value and spot exposure through the spikes that move the book.
Abdollah Ahmadi
A decade of quantitative work in the National Electricity Market, building automated bidding and optimisation systems that have run grid-scale batteries across energy and FCAS. Pareto Grid is where that work becomes a product: the benchmarking and bidding engine, packaged for asset owners and operators.
LinkedIn ↗Want to know your Percentage of Perfect?
Send the asset and a window of dispatch data, and Pareto Grid will tell you how much of the achievable revenue it's capturing, and where the gap is.
hello@paretogrid.com.au