General methodology combining engineering optimization of primary hvac&r plants with decision analysis methods—part II: Uncertainty and decision analysis

A companion paper (Jiang and Reddy 2007) presents a general and computationally efficient methodology for dynamic scheduling and optimal control of complex primary HVAC&R plants using a deterministic engineering optimization approach. The objective of this paper is to complement the previous work by proposing a methodology by which the robustness of the optimal deterministic strategy to various sources of uncertainties can be evaluated against non-optimal but risk averse alternatives within a formal decision analysis framework. This specifically involves performing a sensitivity analysis on the effect of various stochastic factors that impact primary HVAC&R plant optimization, such as the uncertainty in load prediction and the uncertainties associated with various component models of the equipment. This is achieved through Monte Carlo simulations on the deterministic outcome, which allow additional attributes, such as its variability and the probability of insufficient cooling, to be determined along with the minimum operating cost. The entire analysis is then repeated for a specific non-optimal but risk-averse operating strategy. Finally, a formal decision analysis model using linear multi-attribute utility functions is suggested for comparing both these strategies in a framework that explicitly models the risk perception of the plant operator in terms of the three attributes. The methodology is demonstrated using the same illustrative case study as the companion paper.