StackEase develops algorithms that optimize bidding strategies for grid-connected battery storage systems by analyzing five energy markets to maximize revenue while minimizing operational risks. Their technology provides detailed financial forecasts and real-time bid optimization, addressing the need for stable profits and efficient market participation for battery asset owners.
Funding
$120K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Grid-connected battery storage systems require sophisticated bidding strategies to maximize revenue across multiple energy markets. Battery asset owners need accurate financial forecasts and real-time bid optimization to ensure stable profits while minimizing operational risks like battery degradation and grid fees.
Solution
StackEase offers algorithms that optimize bidding strategies for grid-connected battery storage systems by analyzing five energy markets: FCR, aFRR, day-ahead, intraday, and imbalance. The technology provides detailed financial forecasts and real-time bid optimization, enabling robust and efficient cross-market operations. StackEase's API provides 24/7 optimized bids, taking into account specific battery costs and operating on both ancillary services and short-term markets to ensure stable and positive profit. The company's approach considers factors like price, volatility, transfer fees, and battery degradation cost, ensuring a high return on investment throughout the battery’s lifespan.
Target Audience
StackEase targets battery project developers needing operational profit forecasts, battery operation managers seeking optimal bidding strategies, and battery asset owners looking for a turnkey operation service.
Features
- Backtests and financial forecast simulations providing detailed insights on revenues and costs.
- API for real-time, 24/7 optimized bidding strategies across multiple markets.
- Algorithms that account for specific battery costs, including grid fees, NEMO commission, and battery degradation.
- Optimization across ancillary services and short-term markets.
- Advanced mathematical optimization and machine learning techniques, including deep reinforcement learning.