Financial software integrates distributed storage software


Because there are 50+2 unique solar power markets in the United States, each with their own unique financial modeling requirements due to unique laws, different utilities and oh so many other nuances, it makes sense that a company like Energy Toolbase exists for solar power developers. It is hard to make a spreadsheet that covers those markets, and it is even harder to keep it updated.

And since we now live in a growing distributed energy market, in which energy storage resources are to be placed where the end customer prefers versus where the centralized utility chooses, it makes sense that a company like Stem exists with its software platform to tie together these disparate units to then work closely with the existing electric utilities.

In a bid to pull together the advanced data that is provided by Stem’s machine learning techniques and Energy Toolbase’s ability to financially model what happens when solar+storage is combined at a customer’s site, the two companies hope to offer an integrated solution that will help project developers better communicate the economic benefits to site owners.

The above image is of a project in Southern California making use of a demand charge management energy storage solution plus solar power (30 kW / 40 kWh of energy storage + 50 kWdc of solar power). Energy Toolbase combined the 8760 data of projected rooftop solar power production, and a year of the customer’s 15 minute interval data from the utility allowing for the customer to clearly “see” where their money has been spent historically, and where they can save money.

Below is a projected model from Stem’s PowerScope that shows how a solar + energy storage system was able to to lower the overall demand charges of a customer in Hawaii.

Stem has approximately 1,000 energy storage systems under contract across six U.S. states, Ontario, and Japan. Since 2012, Stem has captured data from its systems on a one-second basis to feed Stem’s predictive analytics, machine-learning, and grid-edge computing AI.

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