General methodology combining engineering optimization of primary hvac&r plants with decision analysis methods—part I: Deterministic analysis

This paper is the first of a two-part sequence that proposes a general methodology for dynamic scheduling and optimal control of complex primary HVAC&R plants, which combines engineering analyses within a practical decision analysis framework by modeling risk attitudes of the operator. The methodology involves a computationally efficient, deterministic engineering optimization phase for scheduling and controlling primary systems over the planning horizon, followed by a systematic and comprehensive stochastic sensitivity and decision analysis phase, where various sources of uncertainties are evaluated along with alternative non-optimal but risk-averse operating strategies. This paper describes the deterministic component of the analysis methodology, which essentially involves the development of response surface models for different combinations of system configurations to be used for static optimization and then using them in conjunction with the modified Dijkstra's algorithm for dynamic scheduling and optimal control under different operating conditions and pricing signals. The proposed methodology is illustrated for a semi-real hybrid cooling plant operated under two different pricing schemes: real-time pricing and time-of-use with electricity demand. We feel that the general methodology framework proposed sacrifices very little in accuracy while being much more efficient computationally than the more complicated optimization methods proposed in thegeneral literature. Moreover, this approach is suitable for online implementation, and it is also general enough to be relevant to other energy systems.