Advisor

Luck, Rogelio

Committee Member

Felicelli, Sergio D.

Committee Member

Mago, Pedro J.

Committee Member

Steele, W. Glenn, Jr.

Date of Degree

1-1-2012

Document Type

Dissertation - Open Access

Major

Mechanical Engineering

Degree Name

Doctor of Philosophy

College

College of Engineering

Department

Department of Mechanical Engineering

Abstract

Combined Cooling Heating and Power (CCHP) systems have been recognized as a viable alternative to conventional electrical and thermal energy generation in buildings because of their high efficiency, low environmental impact, and power grid independence. Many researchers have presented models for comparing CCHP systems to conventional systems and for optimizing CCHP systems. However, many of the errors and uncertainties that affect these modeling efforts have not been adequately addressed in the literature. This dissertation will focus on the following key issues related to errors and uncertainty in CCHP system modeling: (a) detailed uncertainty analysis of a CCHP system model with novel characterization of weather patterns, fuel prices and component efficiencies; (b) sensitivity analysis of a method for estimating the hourly energy demands of a building using Department of Energy (DOE) reference building models in combination with monthly utility bills; (c) development of a practical technique for selecting the optimal Power Generation Unit (PGU) for a given building that is robust with respect to fuel cost and weather uncertainty; (d) development of a systematic method for integrated calibration and parameter estimation of thermal system models. The results from the detailed uncertainty analysis show that CCHP operational strategies can effectively be assessed using steady state models with typical year weather data. The results of the sensitivity analysis reveal that the DOE reference buildings can be adjusted using monthly utility bills to represent the hourly energy demands of actual buildings. The optimal PGU sizing study illustrates that the PGU can be selected for a given building in consideration of weather and fuel cost uncertainty. The results of the integrated parameter estimation study reveal that using the integrated approach can reduce the effect of measurement error on the accuracy of predictive thermal system models.

URI

https://hdl.handle.net/11668/18973

Comments

Optimal Operation||Optimal Sizing||Parameter Estimation||Uncertainty Analysis||CCHP||CHP

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