Director- Product Management Qcells San Francisco, California, United States
We evaluated profits from battery operations in ERCOT merchant (wholesale) markets using automated bidding Software. This study used a digital twin simulation of an actual project in operation in North hub of Texas, USA. Proprietary optimization and price forecasting techniques were used to maximize profits from the battery asset. All available ERCOT wholesale market products were targeted, and a risk-vs-reward sensitivity analysis was performed to understand the value associated with taking higher risk in merchant market.
Risk appetite of Asset owner directly impacts profits from bidding the battery asset in merchant markets, until a point of no-return is reached. Automated bidding strategies for storage assets can be customized per needs of the Asset owner. Getting high IRR’s (>10%) from projects are possible without taking significant risk during project operations via superior technology.