This study presents a multi-attribute decision making (MADM) framework in support of strategic resource planning of electric utilities. Study efforts have focused on four technical issues identified to be essentially important to the process of strategic resource development, i.e., decision data expansion, MADM analysis with imprecise information, MADM analysis under uncertainty and screening applications. Main contributions from this study are summarized as follows. First, an automatic learning method is introduced for decision data expansion aiming at reducing the amount of computations involved in the creation of decision database. Test results have shown that the proposed method is feasible, easy to implement, and more accurate than the techniques available in the existing literature. Second, an interval-based MADM methodology is developed, which extends the traditional utility function model with the measure of composite utility variance, accounting for individual errors from inaccurate attribute measurements and inconsistent priority judgments. This enhanced decision approach would help the decision-maker (DM) gain insight into how the imprecise data may affect the choice toward the best solution and how a range of acceptable alternatives may be identified with certain confidence. Third, an integrated MADM framework is developed for multi-attribute planning under uncertainty which combines attractive features of utility function, tradeoff/risk analysis and analytical hierarchy process and thus provides a structured decision analysis platform accommodating both probabilistic evaluation approach and risk evaluation approach. Fourth, the application of screening models is investigated in the context of integrated resource planning of electric utilities as to identify cost effective demand-side options and robust generation expansion planning schemes. / Ph. D.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/30138 |
| Date | 31 December 1999 |
| Creators | Pan, Jiuping |
| Contributors | Electrical and Computer Engineering, Rahman, Saifur, Liu, Yilu, Broadwater, Robert P., de Castro, Arnulfo, Sherali, Hanif D., VanLandingham, Hugh F. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | Chapts.pdf |
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