A forward curve is today's price for future trading. It indicates the market’s current view of the future and can be a useful tool in determining the current, fair market value of an investment.

Batteries make money in wholesale markets by taking advantage of short-term price spreads, e.g. purchasing power and charging when prices are low and selling power and discharging when prices are high. This occurs on the scale of hours. Therefore, to determine wholesale battery revenues, we need an hourly forward view of prices. For this, we use our hourly forward power curve.

Two futures products are available to trade on Intercontinental Exchange (ICE): baseload and peakload. Since the delivery hours of these contracts overlap, we can derive the future value of an off-peak product, even though it is not directly traded on the exchange.

Off-peak prices are calculated as the weighted difference between baseload and peakload prices, where the weighting is determined by the number of peak-load hours each month.

The futures curves are then resampled to hourly granularity. This, however, results in a problem: discontinuous changes in prices between successive months.

To rectify this, we apply a smoothing algorithm to the resampled curves to avoid large jumps at the beginning and end of each month. Importantly, this must preserve the average value of the curve for each month to ensure the new, smoothed forward curves are consistent with the original, market forward curves.

Next, we need to add a daily shape to the smoothed curves. To do this, we characterize daily price profiles via ‘hourly price scalars’. Hourly price scalars are defined as the ratio of historical day-ahead prices to the monthly peak or off-peak prices. Median scalars are then used to characterize an indicative price profile across a 24h period and normalized to ensure they average to 1 across the day (more on this later).

This process is repeated across different types of days -namely summer and winter, weekdays and weekends. Prices (and resultant scalars) are taken from the Nordpool day-ahead hourly auctions.

To apply historical daily profiles, we multiply the daily price profiles by the smoothed hourly forward curves. Since the daily profiles have an average value of one, the resultant hourly curve is consistent with market futures curves.

The above assumes a consistent daily price shape for all similar days (e.g., winter weekdays). In reality, however, certain system conditions see prices move away from their average behavior resulting in very high or very low prices. These are the days when batteries stand to make the most money - therefore, we model them separately.

Using a historical view of wind generation, residual demand, available dispatchable generation, and the resulting system margin, we train a machine learning model to predict the system conditions that result in extreme pricing events. Then, using forecasted values for demand, renewable generation, and available capacity, we identify when these extreme prices are most likely to occur. For future days identified as ‘extreme pricing days’ by our model, we apply a daily profile shape derived from historical extreme price days instead of using the average profiles.

From this hourly curve, we calculate and include in our Signal forecast a forward view of battery revenues by modeling optimal dispatch against varying wholesale prices. These resulting revenues represent the current forward market value of battery energy storage.