Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory

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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. This topical report describes the demonstration of ... continued below

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Henze, Gregor P. & Krarti, Moncef December 17, 2003.

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Description

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. This topical report describes the demonstration of the model-based predictive optimal control for active and passive building thermal storage inventory in a test facility in real-time using time-of-use differentiated electricity prices without demand charges. The laboratory testing findings presented in this topical report cover the second of three project phases. The novel supervisory controller successfully executed a three-step procedure consisting of (1) short-term weather prediction, (2) optimization of control strategy over the next planning horizon using a calibrated building model, and (3) post-processing of the optimal strategy to yield a control command for the current time step that can be executed in the test facility. The primary and secondary building mechanical systems were effectively orchestrated by the model-based predictive optimal controller in real-time while observing comfort and operational constraints. The findings reveal that when the optimal controller is given imperfect weather fore-casts and when the building model used for planning control strategies does not match the actual building perfectly, measured utility costs savings relative to conventional building operation can be substantial. This requires that the facility under control lends itself to passive storage utilization and the building model includes a realistic plant model. The savings associated with passive building thermal storage inventory proved to be small be-cause the test facility is not an ideal candidate for the investigated control technology. Moreover, the facility's central plant revealed the idiosyncratic behavior that the chiller operation in the ice-making mode was more energy efficient than in the chilled-water mode. Field experimentation (Phase III) is now required in a suitable commercial building with sufficient thermal mass, an active TES system, and a climate conducive to passive storage utilization over a longer testing period to support the laboratory findings presented in this topical report.

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  • Report No.: None
  • Grant Number: FC26-01NT41255
  • DOI: 10.2172/894510 | External Link
  • Office of Scientific & Technical Information Report Number: 894510
  • Archival Resource Key: ark:/67531/metadc878339

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  • December 17, 2003

Added to The UNT Digital Library

  • Sept. 22, 2016, 2:13 a.m.

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  • Jan. 8, 2018, 11:08 p.m.

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Henze, Gregor P. & Krarti, Moncef. Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory, report, December 17, 2003; United States. (digital.library.unt.edu/ark:/67531/metadc878339/: accessed October 19, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.