In Signal, we model both average battery fleet revenues and asset-specific revenues. The former represents the average "Benchmark" revenues, across the fleet, in each market. In this article, we give a high-level overview of how we produce our 3-year forward view of these revenues.

Batteries make money in two ways - capturing power price spreads (wholesale trading) and participating in ancillary services (frequency response). Signal considers both of these.

Historically, frequency response has been the main revenue stream for batteries. But, with the increasing saturation of these markets and increasing renewable penetration, there will be causing a shift in monetization strategy toward the merchant case. To forecast fleet revenues, we must consider the merchant and ancillary side of the revenue stack.

Here we give a high-level overview of the main steps involved in producing Signal's fleet average revenue projections.

Firstly we calculate the value available to a battery participating exclusively in wholesale by finding the within-day price spread using our hourly forward power curve. Not only does this inform how much a battery could make in merchant activity, but it also informs the value of frequency response markets via opportunity cost.

When we talk about opportunity cost, we mean the revenues foregone should a battery choose to exit a market. For example, if a battery could earn £100 in the wholesale market, when tendering into frequency response, it should bid to recover at least the loss of revenues from not participating in wholesale. We, therefore, use the wholesale revenues, determined in step 1, as a basis to model frequency response revenues. You can read in more detail how we modeled Dynamic Containment revenues here.

To model revenues from batteries in the Balancing Mechanism, we find the historical relationship between day-ahead prices and bid and offer prices. We use these to generate bid and offer price projections with our hourly forward power curve. Then we model battery acceptance and participation rates in the Balancing Mechanism and combine these with bid and offer prices to quantify revenues. For more details, read our Balancing Mechanism methodology article here.

Finally, having calculated the revenues from the merchant and ancillary markets, we combine them to estimate the fleet-wide average revenues. This is based on several factors, including the expected battery build-out, average duration, and requirements for each of the ancillary services.

There you have it, a step-by-step guide to producing Signal's fleet average revenue projections. If you have any questions, comments, or feedback, please leave a message in the chat box below - we'd love to hear it!