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Thermal load shifting in commercial buildings has previously been employed, in anticipation of costly time-of-use (TOU) and peak electrical-demand utility charges, by precooling a building's massive structure to take advantage of its inherent thermal capacitance. In both theoretical and experimental studies, supervisory control has mostly been evaluated heuristically to determine cost reduction from a typical night- time setup strategy However, the applicability of these results are limited. This paper describes the development of an optimal passive thermal-mass control simulation environment, in which a dynamic building simulation program is coupled to a popular technical computing environment for total utility cost minimization, including both energy and demands charges. The relative alignment of utility and occupancy schedules defines how the optimal control trajectory is calculated, and is characterized by: (1) a precooling duration and temperature setpoint during the unoccupied and off-peak period, (2) an occupied period temperature setpoint before on-peak, (3) an occupied, on-peak postcooling duration and temperature setpoint, and finally (4) an exponential controlled release of storage to the upper comfort limit described by a time constant. With demand commonly charged over a monthly billing period, the environment features an outer loop, where a target demand limit is enforced in the underlying energy cost optimization. The underlying monthly optimization is a closed-loop certainty-equivalent model predictive control optimization, with a 48-hour planning and a 24-hour execution-time horizon, and the Nelder-Mead Simplex method as the solver. The environment was formulated to evaluate factors that drive the cost savings potential in building thermal mass control and to allow for model-based predictive control in future work. [ABSTRACT FROM AUTHOR]

This paper identifies the primary factors that impact the optimal control of passive thermal storage. Optimal control strategies are determined with the objective of minimizing total energy and demand costs using an integrated optimization and building simulation tool. A fractional factorial analysis is employed to investigate how cost savings are affected by several building and system characteristics, utility rate structures, and climates. Utility rates, internal loads, building mass level, and equipment efficiency are found to have the largest impacts on cost savings, whereas building envelope characteristics do not have a significant impact. Although the magnitude of the savings is affected by climate, the relative impacts of each of these factors are largely independent of weather [ABSTRACT FROM AUTHOR]