Model predictive control of thermal storage for demand response

Buildings with thermal storage use it mainly to shift cooling loads. Ice or chilled water is produced when electricity prices are low and stored to provide cooling when prices are high. While this price-based load shifting has value for power system operators, buildings with thermal storage could provide more direct grid services by reacting to demand charges and demand response calls. In this paper, we consider the problem of cooling a building under these incentives. The context is a New York City office building with passive and active thermal storage, subject to Consolidated Edison´s (ConEd´s) default rate plan for large commercial buildings. This rate plan includes a three-tiered demand charge and hourly energy prices determined by the system operator´s day-ahead dispatch. We also model a ConEd demand response program, and consider the thermal comfort of building occupants. The problem is formulated in the language of stochastic optimal control and solved approximately using model predictive control (MPC). Extending previous work on MPC of thermal storage, which has focused on dynamic energy prices, we include the full set of economic incentives directly in the stage and terminal costs. Simulations of the hottest day of 2013 demonstrate the value of realistic economic modeling. They also highlight an interesting tension between the various incentives, which all compete for shiftable load.