White Paper by Argonne National Lab
The U.S. national electric grid is facing significant changes due to aggressive federal and state targets to decrease emissions while improving grid efficiency and reliability. Additional challenges include supply/demand imbalances, transmission constraints, and aging infrastructure. A significant number of technologies are emerging under this environment including renewable generation, distributed storage, and energy management systems. In this paper, we claim that predictive energy management systems can play a significant role in achieving federal and state targets. These systems can merge sensor data and predictive statistical models, thereby allowing for a more proactive modulation of building energy usage as external weather and market signals change. A key observation is that these predictive capabilities, coupled with the fast responsiveness of air handling units and storage devices, can enable participation in several markets such as the day-ahead and real-time pricing markets, demand and reserves markets, and ancillary services markets. Participation in these markets has implications for both market prices and reliability and can help balance the integration of intermittent renewable resources. In addition, these emerging predictive energy management systems are inexpensive and easy to deploy, allowing for broad building participation in utility centric programs.