Title page for ETD etd-121799-153600

Type of Document Dissertation
Author Pan, Jiuping
Author's Email Address jipan@vt.edu,jiuping.pan@us.abb.com
URN etd-121799-153600
Title MADM Framework for Strategic Resource Planning of Electric Utilities
Degree PhD
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Rahman, Saifur Committee Chair
Broadwater, Robert P. Committee Member
de Castro, Arnulfo Committee Member
Liu, Yilu Committee Member
Sherali, Hanif D. Committee Member
VanLandingham, Hugh F. Committee Member
  • Power System Planning
  • Strategic Planning
  • Power Generation
  • Decision Making
  • Decision Support System
  • Electric Utilities
Date of Defense 1999-12-16
Availability unrestricted
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


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