A battery's value is realised almost entirely in the extreme hours — the evening peak, the negative-price afternoon, the constrained winter week. Average those hours away and the business case averages away with them.
That is why price and demand now enter the model at full hourly resolution, not as a smoothed daily shape.
01 / WHAT'S NEWTwo new inputs, built for the extremes
Bring your real price and demand story into the model. Both new profile types ship with built-in analytics, so you can see exactly when your value sits before you run anything.
PPA Price Profiles
Upload a full hourly price timeseries in your own timezone — we align it for you — or paste a quick 24-hour average shape for early screening. Negative prices and escalation are fully supported.
Each profile carries daily and seasonal price shapes, a 12×24 hour/month heatmap, a distribution and a monthly summary.
Grid Dispatch Profiles
The companion to PPA: upload an hourly demand profile (MW) with your grid connection capacity, so the battery and export logic respect a real interconnection limit hour by hour.
Built-in demand analytics report connection capacity, mean demand and constrained hours per year.
02 / THE EVIDENCEWhy it matters — a measured result
On the same one-year PPA dataset, we computed NPV two ways: (A) a 24-point profile built from the averaged prices, and (B) the full price timeseries. Same plant, same financing — only the price resolution changed.
The averaged profile smooths away the price extremes; the full timeseries preserves them, capturing the seasonal and year-to-year revenue that actually drives the business case. The gap is not noise — it is the value that lives in the tails.
Seasonal price distribution, resolved monthly
Each box spans the interquartile range of hourly prices for that month; whiskers mark the 5th–95th percentiles and the ticks above mark the outlier hours an average would discard.
03 / THE CASEThe hours that averages hide
During the European heatwave, German wholesale electricity prices swung from €86 to €566/MWh — in a single evening.
Averaging collapses the price and load distributions onto their central tendency and discards the tails — yet a battery's value is realised almost entirely in those tail hours.
That is not a market anomaly. It is physics. The solar ramp-down at dusk was visible in ensemble forecasts well before the first red alert was issued: the temperature peak, the timing, the geographic footprint — all foreseeable to the hour. What the market lacked was storage positioned to act on it.
"The average smooths away the price extremes; the timeseries keeps them — and the extremes are where the value sits."
04 / THE MODELFour explicit, locked inputs
Runs now record exactly what produced them. Every simulation is defined by four explicit inputs — so configs stay lean and every result is fully reproducible.
Create a PPA profile and a Grid Dispatch profile in each project before running new simulations. Existing results are untouched.
05 / ALSO SHIPPEDAlso new this month
06 / ON THE ROADMAPWhat's next — smart battery dispatch
Dispatch Strategies
Landing soon · opt-inWe're building an optimising engine that lets the battery control system look ahead. Selectable per run and fully opt-in — today's dispatch stays the default.
Capture price
Release energy into your highest-value hours.
Match demand
Shape output to a real load profile hour by hour.
Hold a reserve
Keep firming capacity in hand for reliability.