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Please use this identifier to cite or link to this item: http://hdl.handle.net/1860/663

Title: Framework combining static optimization, dynamic scheduling and decision analysis applicable to complex primary HVAC&R systems
Authors: Jiang, Wei
Keywords: Mechanical engineering;Heating and ventilation industry;Quadratic programming
Issue Date: 2-Feb-2006
Abstract: The primary objective of this work is to propose a general and computationally efficient methodology for dynamic scheduling and optimal control of primary HVAC&R systems that would be of practical benefit to plant operators and can be implemented on-line. This objective is achieved in several discrete steps. First, a generalized static optimization scheme is developed using Sequential Quadratic Programming along with physical models for each component. Next, in order to enhance computational efficiency, an experimental design technique, namely response surface methodology, is adopted to predict expected minimal cost (or energy consumption) for different plant operating modes. Then the optimal dynamic scheduling strategy over several hours of the planning horizon is determined using the computationally efficient Dijkstra’s algorithm which results in minimal operating cost. The fourth phase involves analyzing the effect of various stochastic factors that impact the optimal operation, such as the uncertainty in load prediction, and the uncertainties associated with various component models and response surface models using Monte Carlo methods. Finally, a decision analysis framework is adopted to study the robustness of the determined optimal operating strategy in terms of its benefit as against a riskaverse operating strategy. This methodology is illustrated for a hybrid cooling plant operated under two electric rate structures, namely, real-time pricing (RTP) and time-of-use (TOU) with electric demand. We find that under the RTP rate, least-cost paths are equally least-energy paths, while this is not so under TOU with electricity demand rate. Also under RTP rate, the relative benefit of optimal scheduling and control during a hot day is not as significant as during a mild day; while under TOU with electricity demand, it can yield very significant benefit. The decision analysis involves formulating an additive multi-attribute utility function which includes expected value and variability of the predicted operating cost as well as the penalty of possible loss of cooling capability. Though several risk-averse operating strategies can be defined, we select a simple one involving operating an additional chiller than the optimal deterministic strategy. How the assumption of different weights results in different scheduling decisions to be made is studied.
URI: http://hdl.handle.net/1860/663
Appears in Collections:Drexel Theses and Dissertations

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