Proceedings Abstracts of the Twenty-Fifth International Joint Conference on Artificial Intelligence

Implementation of Learning-Based Dynamic Demand Response on a Campus Micro-Grid / 4250
Sanmukh R. Kuppannagari, Rajgopal Kannan, Charalampos Chelmis, Viktor K. Prasanna

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 D2 R algorithms automatically and dynamically select customers for load curtailment to guarantee the achievement of a curtailment target over a given DR interval.