A bottom-up approach to sustained curtailment and comfort for controlled demand response

Curtailment prediction and efficient demand response (DR) strategy selection challenge the effectiveness of developing smart grid applications. Here we present solutions to both challenges, taking a bottom-up approach to demonstrate that curtailment at the equipment-level determined based on the equipment mechanical properties and models can be used to efficiently estimate curtailment and to optimize human comfort during a DR event across a subset of energy consumers. We focus on a controlled microgrid environment present on the University of Southern California campus and address two HVAC oriented DR strategies: variable frequency drive and global temperature reset. Strong correlation between equipment and building level consumption was found to exist in less than half the cases, showing that building level consumption can be successfully used as well for those scenarios. Several fast heuristics aiming to optimize human comfort during equipment level DR proved to be successful and showed that the best method depends on the DR strategy. Finally, relying on the equipment specifications and consumption models for our analyses circumvents the common challenges associated with statistical and machine-learning algorithm approaches that rely on large amounts of historical data.