How Gridmatic uses Modo Energy as the scoreboard and the playbook

Everyone claims to be the best optimizer

The BESS optimization market has a credibility problem. Every optimizer pitching an asset owner claims top-quartile performance. But definitions differ. One company measures against a theoretical perfect battery. Another runs a back-test and picks the best result. A third defines its own benchmark entirely.

For asset owners and lenders, comparing these claims is nearly impossible.

"There's a general issue in battery optimization," says Marc Alvarez, a member of the business development and origination team at Gridmatic. "A lot of people make different marketing claims that are very hard to validate and very hard to compare."

For Gridmatic, the problem was particularly frustrating. The company believed its AI-driven approach delivered strong results. But demonstrating that credibly meant producing 40-page annual storage reports and asking counterparties to trust the numbers.

Meanwhile, the company was expanding. After launching storage operations in ERCOT, Gridmatic moved into CAISO, one of the most complex and opaque electricity markets in the US. Austin Park, a machine learning engineer and data science team lead, was tasked with building the automated bidding system.

"It was a big job," Park says. "Thousands of pages of business practice manuals and market tariffs. CAISO discloses less market data publicly than ERCOT, which makes market evaluation and understanding a challenge."

The playbook

For Austin Park, Modo Energy's value came from a different direction entirely. As lead developer of the CAISO storage bidder, Park needed more than raw market data. He needed clear, expert explanations of how CAISO actually works.

"Modo Energy gave the clearest and most concise interpretation of any particular policy topic," Park says. "It gave us orientation, a base of knowledge that helped us figure out what questions to ask next."

The specifics mattered. A Modo Energy article on CAISO's flex ramp product resolved a question that had hung over Park's work for months. The resource adequacy series explained how slice-of-day changes would affect battery economics. Both fed directly into the design of Gridmatic's automated scheduler.

One insight came through Modo Energy's competitive data in ERCOT. Park observed a shift in how some operators were bidding certain ancillary services, and used that signal to investigate further. He traced the behavior to a specific market rule and incorporated it into Gridmatic’s models. The added visibility helped validate and refine Gridmatic’s strategy in a rapidly evolving market.

A shared standard for the market

Modo Energy has not just helped Gridmatic. It has changed how the ERCOT battery energy storage market operates. Before Modo Energy, performance data was scattered and inconsistent. Operators defined their own benchmarks. Asset owners had no way to verify claims.

Gridmatic previously produced its own annual ERCOT storage report, pulling ISO data, calculating performance metrics, and publishing the results. When Modo Energy began covering the same ground with greater depth and frequency, Gridmatic happily stopped writing those reports and re-allocated its resources to new R&D tasks.

Gridmatic’s development team also benefits from staying current. In ERCOT alone, ancillary service state-of-charge requirements have changed four times in recent years. Each change requires the decision-making engine to be updated. Modo Energy's research helps the team track these shifts before they hit production.