Calibration to Index

To ensure that our forecast provides realistic revenues, we run our battery dispatch model on historical prices to model what revenues we would expect to be achieved. We then compare that with the revenues actually achieved by batteries from our historical benchmarking data, and calculate what adjustments need to be made so that our model is aligned.

Realistic Price Visibility

Our dispatch model gives the battery a realistic level of price visibility. More specifically, the battery can see the next hour of prices in full detail, but beyond that it is given visibility of smoothed prices. This means it knows the overall price shape and makes informed charge/discharge decisions, while reflecting the real-world uncertainty that operators face when trading against volatile intraday prices. The model then iterates throughout the day, hour by hour, with the battery locking in charge/discharge decisions each time. This level of price visibility reflects observed battery trading behaviour, so that modelled dispatch closely replicates real-world trading outcomes.

Calibration

After this smoothing, we still want to make sure that battery revenues are as closely aligned as possible with actual historical revenues. We do this by simulating the revenues of each asset with this realistic price visibility, and comparing that with their actual revenue performance, using a representative sample of merchant batteries. Below you can see a comparison of modelled vs historical revenues for each of these assets.

We then compare modelled and actual revenues to derive the calibration applied in the forecast. Calibration is applied in two price tiers: typical market conditions and high-price periods are calibrated separately, reflecting that batteries capture extreme price events differently from day-to-day trading. Further adjustments account for battery duration and region, so that forecast revenues align with observed performance across the fleet.