Optimal HVAC Control for Demand Response via Chance-Constrained Two-Stage Stochastic Program

This study is interested in the application of demand response (DR) to building energy management via heating, ventilation and air conditioning (HVAC) controls. The main objective of this study is to develop an optimization model to appropriately control HVAC operations to minimize cost in response to time-varying electricity prices and intermittent solar power generation. The main idea is to adjust electricity consumption associated with HVAC operations while ensuring the thermal comfort of occupants. In particular, we propose a two-stage stochastic program formulated to find an optimal HVAC operations schedule that comprises proactive and reactive controls to minimize energy cost against the stochastic nature in electricity prices, solar power generation, and weather conditions. Specifically, we introduce a chance constraint that can be appropriately integrated with the proposed optimization model to ensure thermal comfort at the desired level. To validate and evaluate the proposed approach, numerical experiments are designed to compare the proposed approach with the benchmark control policies designed to simulate the practice of the general purpose thermostat and the algorithm proposed by relevant study with various parameter settings. The numerical experiments show that the proposed approach results in significant cost savings while capturing optimal HVAC operations.