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Gridmatic: How Gridmatic uses Modo Energy as the scoreboard and the playbook

How Gridmatic uses Modo Energy as the scoreboard and the playbook

Featuring:

Marc Alvarez, Business Development & Origination

Austin Park, Machine Learning Engineer & Data Science Team Lead

case-study-photo

"We welcome the transparency Modo Energy brings. When everyone's looking at the same scoreboard, you stop arguing over definitions and start comparing what people have actually done."

Marc Alvarez

Business Development & Origination

case-study-photo

Austin Park

Machine Learning Engineer & Data Science Team Lead


7

US markets traded

800 MWh

Storage under management

100%

Automated battery operation

The customer

Customer: Gridmatic
Industry: AI-enabled power company: battery energy storage, trading, retail
Portfolio: 300 MW / 800 MWh of battery energy storage under management across ERCOT and CAISO
Use cases: Performance benchmarking, competitive analysis, market research, new market entry

Gridmatic is a new kind of power company. Founded by former Google engineer Matt Wytock and powered by a foundational AI model for the grid, Gridmatic enables automated forecasting and risk management — predicting weather, renewable generation, electricity prices, and demand to optimize how energy is procured, stored, and delivered.

The company operates grid-scale batteries in Texas and California, with over 300 MW and 800 MWh of storage under management. It trades in every major US wholesale market and serves commercial and industrial retail customers in ERCOT and PJM.

The challenge

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 solution

The scoreboard

When Gridmatic discovered Modo Energy's benchmarking platform, the value was immediate. Instead of producing internal reports, Gridmatic could point to an independent leaderboard showing exactly where Gridmatic's batteries ranked against the fleet.

"We share the Modo Energy leaderboard with asset owners to show that the batteries we operate are in the top five out of a hundred," Alvarez says. "That's a much more powerful visual than sending someone a 40-page report we put together ourselves."

The independence is the point. Modo Energy pulls data directly from the ISOs and applies standardized revenue metrics. Every operator is measured the same way. For a company that stakes its reputation on operational results, having a neutral third party publish the numbers changed the dynamic in commercial conversations.

Modo Energy's coverage also raised the bar across the market. Rather than each optimizer defining its own version of "perfect," the industry now has a shared reference point. For Gridmatic, that is a competitive advantage, not a threat.

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.

"It would easily take three to four times as long to understand a particular topic without Modo Energy," Park says. "Sometimes there are things you don't even know to look for. Modo Energy takes you from an unknown unknown to something you can actually work with."

The impact

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.

"Modo Energy has brought a lot of sunlight to the battery energy storage ecosystem," Alvarez says. "Without it, it was much less clear how people were doing and how battery energy storage as an asset class was performing."

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.

Going further

Gridmatic is expanding. The company has signed offtake agreements for additional battery capacity in both ERCOT and CAISO, and its retail business is growing rapidly across Texas and PJM. As it enters new storage markets, Modo Energy is the first resource the team reaches for.

"We read everything Modo Energy publishes on the markets we're looking at," Alvarez says. "Battery energy storage is a new and fast-moving space. A huge number of the assets being built are owned by first-time operators. Having high-quality, independent research is essential, and Modo Energy is doing that better than anyone."

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