PREDICTING AND OPTIMIZING ENERGY STORAGE LIFETIME PERFORMANCE WITH ADAPTIVE AUTOMATION CONTROL SOFTWARE

摘要

A transactive energy system design is linked to an energy automation control process. A design process provides a predictive analytics engine at its core. This design process includes three models: application modeling, health/asset modeling, and revenue modeling. An energy storage system health model is the combination of the application model with storage life characteristic data that comprises electrical efficiency, effective capacity, and capacity fade as a function of temperature, voltage range, and calendar life. These models enable a predictive analytics engine to inform energy automation control software how to operate. The inventive concept involves utilization of various core data communication methods. The predictive analysis uses the same algorithms and processes as those used in the actual eACS and energy operating system. The continuity from analytics to operations improves the accuracy of the economic models, which reduces risk to financial planning and system financing.