Next-Generation
Energy Management
for Grid-Scale Batteries
Better performance. Better returns.
A leap forward in capability.
We’re developing new bidding technology that transforms how batteries in the grid buy and sell energy.
A Completely New Approach to Bidding Optimization
The grid is getting more complex and dynamic every day. Yet, today’s approaches to energy management remain static and simple, relying on what’s worked in the past.
First Generation
Trading Desks
• Simple strategies limited by human ideas and experience
• Suboptimal performance compared to today’s computer bidders
Today’s State of the Art:
AI Price Prediction,
Rule-Based Solver
• Pre-determined strategies for bidding that don’t change with the grid, only prices
• Bidding decisions made based on imperfect models of grid dynamics
Next Generation:
Self-Optimizing,
Self-Adapting
• System teaches itself a dynamic, novel strategy tailored to each battery
• Continuously adjusts as the grid changes, all without relying on imprecise grid models
How It Works:
Bid, Learn, Update, Repeat
Bid
Our system teaches itself a dynamic, optimized bidding strategy specifically calibrated to each battery, its location, and ever-evolving grid landscape.
Learn
As our system bids in the power market, it progressively refines its strategy based on actual bid outcomes. This continuous learning process allows it to constantly adjust and respond to fluctuating market conditions.
Update
Leveraging its learnings, our system then bids with an updated strategy, ensuring that it stays at the forefront of market dynamics to deliver maximized, sustained value to our clients.
Energy Trading in CAISO:
A Proving Ground for Our Approach
While building our full energy management product, we’ve been learning and iterating on our models by doing actual energy trading in CAISO.
Redefining the Grid, Supported by Exceptional Investors
Self-optimizing, self-adapting systems are the future of optimization in the grid
Coming to U.S. ISOs in 2025
