Gridmatic Releases 2022 Annual Energy Storage Report

May 18, 2023
Reading Time: 3 minutes

AI forecasting can boost revenue by 46% for Texas battery owners, data shows

CUPERTINO, Calif.–(BUSINESS WIRE)–Gridmatic, the industry-leading AI-enabled power marketer, today published its second annual Energy Storage Report. The report analyzes the financial performance of all commercial storage systems operating in the Electric Reliability Council of Texas’ (ERCOT) territory and serves as a guide to help energy operators and developers plan the next course of action for their storage assets.

Revenue opportunities for energy storage are constantly evolving and becoming increasingly complex, creating a greater need for optimization strategies backed by Artificial Intelligence (AI). Grid-tied storage systems in ERCOT earned only about half (51%) of what is theoretically possible due to difficulties in predicting revenue opportunities, the 2022 report found, utilizing methodologies validated by DNV for the 2021 report.

In tests with Gridmatic’s AI-based forecasting models, revenue generation would have increased to almost 75% of the potential revenue, an increase of 46%, by combining proprietary market price forecasts with a storage bid optimizer. These increases are due to the AI-based forecasting and optimization improving and learning from previous outcomes.

Other key highlights from the report include:

  • New ERCOT rules will drastically affect 1-hour storage systems, causing potential revenue decreases exceeding 20%
  • The two days of extreme weather during Winter Storm Elliott in December 2022 comprised over 40% of total annual revenue for more than 1 in 10 systems
  • Revenue opportunities from Ancillary Services are decreasing, driven largely by energy storage growth in ERCOT, a trend expected to continue into 2023
  • To fill this gap, energy storage systems need to tap into difficult-to-capture revenue sources, such as Real Time Energy, which grew from 3% of system revenue in 2021 to 10% in 2022

“As Ancillary Services revenue opportunities decrease, energy storage asset owners are faced with the challenge of drawing on new revenue sources that are far more challenging to optimize,” said David Miller, vice president of business development at Gridmatic. “AI helps operators optimize the performance of their assets and capture revenue that would otherwise be lost due to the limitations of manual forecasting.”

In 2022 alone the US deployed 4.8 GW of storage, and the US Energy Storage Monitor predicts an additional deployment of 75 GW between 2023 and 2027. With this rapid growth, it becomes imperative to optimize operations by capitalizing on arbitrage opportunities, minimizing temporal discrepancies between renewable energy assets and demand, and actively participating in Ancillary Services markets.

Gridmatic, founded in 2017, is a power marketer powered by AI, leveraging machine learning to forecast energy supply, demand, and pricing within wholesale energy markets. With a successful track record of five years in market engagement, the company currently operates in all seven US Independent System Operators (ISO). The company plans to share a comparable report for California ISO (CAISO) later this year.

About Gridmatic
Unlike traditional power marketers, Gridmatic uses AI to optimize renewable energy participation in wholesale markets by forecasting energy supply, demand and pricing. Leveraging market-proven algorithms, Gridmatic is able to provide stability, predictability and automation for energy buyers, sellers, and storage owners amid increasing volatility. With Gridmatic Retail, the company offers advanced solutions for businesses with complex energy needs to hit carbon reduction goals, including time matched, variable load and carbon-free energy products. With its industry-leading AI, Gridmatic is working to accelerate the transition to net zero and balance the renewable-powered grid. For more, visit https://www.gridmatic.com.

Contacts

Media

For Gridmatic:
Leo Traub
646-883-3562
gridmatic@antennagroup.com

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