Implementation of Learning-Based Dynamic Demand Response on a Campus Micro-Grid.

Demand Response (DR) allows utilities to curtail electricity consumption during peak demand periods. Real time automated DR can offer utilities a scalable solution for fine grained control of curtailment over small intervals for the duration of the entire DR event. In this work, we demonstrate a system for a real time automated Dynamic DR (D2R). Our system has already been integrated with the electrical infrastructure of the University of Southern California, which offers a unique environment to study the impact of automated DR in a complex social and cultural environment including 170 buildings in a \"city-within-a-city\" scenario. Our large scale information processing system coupled with accurate forecasting models for sparse data and fast polynomial time optimization algorithms for curtailment maximization provide the ability to adapt and respond to changing curtailment requirements in near real-time. Our D2R algorithms automatically and dynamically select customers for load curtailment to guarantee the achievement of a curtailment target over a given DR interval.