“Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory”

Authors

Gregor P. Henze, University of Nebraska - Lincoln
Moncef Krarti, University of Colorado at Boulder

Document Type

Article

Date of this Version

2003

Comments

Topical Report for Phase I: Analysis, Modeling, and Simulation of Cooperative Agreement DE-FC26-01NT41255
Project Title: “Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory”

Abstract

Cooling of commercial buildings contributes significantly to the peak demand placed on an electrical utility grid. Time-of-use electricity rates encourage shifting of electrical loads to off-peak periods at night and weekends. Buildings can respond to these pricing signals by shifting cooling-related thermal loads either by precooling the building’s massive structure or the use of active thermal energy storage systems such as ice storage. While these two thermal batteries have been engaged separately in the past, this project investigates the merits of harnessing both storage media concurrently in the context of predictive optimal control.
The analysis, modeling, and simulation research presented in this topical report covers the first of three project phases. Based on the new dynamic building simulation program EnergyPlus, we added a utility rate module, two thermal energy storage models, and incorporated a sequential optimization approach to the cost minimization problem using direct search, gradient-based, and dynamic programming methods. The objective function is the total utility bill including the cost of heating and a time-of-use electricity rate with demand charges. The evaluation of the combined optimal control assumes perfect weather prediction and match between the building model and the actual building counterpart.
The analysis shows that the combined utilization leads to cost savings that is significantly greater than either storage but less than the sum of the individual savings. The findings reveal that the cooling-related on-peak electrical demand of commercial buildings can be drastically reduced and justify the development of a predictive optimal controller that accounts for uncertainty in predicted variables and modeling mismatch in real time.