A hierarchical approach to electric utility planning

The analytic hierarchy process (AHP), designed to examine the mutual dependence of various factors that lead to a decision, is applied to deal with the uncertainty in electric utility planning. This is a procedure useful for quantifying various divergencies of opinions, practices and events that lead to the planning uncertainty. A brief summary of AHP is presented in this paper, which includes the basic formulation and intuitive justification for AHP. AHP is compared with the Delphi technique which has also been used for planning with ill-defined parameters. Finally, an example problem showing the application of AHP in electric utility planning under uncertainty is presented.     

The NSF Foundation Coalition: The First Five Years

The Foundation Coalition was funded in 1993 as the fifth coalition in the National Science Foundation's Engineering Education Coalitions Program. The member institutions—Arizona State University, Maricopa Community College District, Rose-Hulman Institute of Technology, Texas A&M University, Texas A&M University - Kingsville, Texas Woman's University, and the University of Alabama—have developed improved curricula and learning environment models that are based on four primary thrusts: integration of subject matter within the curriculum, cooperative and active learning, technology-enabled learning, and continuous improvement through assessment and evaluation. This paper discusses the first five years of Coalition activities and major accomplishments to date.     

Optimization of System Reliability via Redundancy and/or Design Considerations

The two traditional methods used in improving the reliability of a multi-stage system are examined. The first method is the creation of redundancy in system components, whereas the second method consists of overdesigning the system components. An Integrated Reliability Optimization Model that includes both of these two methods for improving the reliability of a system is presented. It is shown that under certain assumptions the integrated model reduces to a Redundancy Optimization Model and under certain other assumptions reduces to a Design Optimization Model. Several methods that have been previously suggested for obtaining solutions to the Integrated Optimization Model are reviewed and a generalized solution procedure for such a model is presented. This solution procedure involves the successive solution of two subproblems a number of times. The first subproblem is a design optimization problem that is solved by the Davidon-Fletcher-Powell optimization algorithm. The second subproblem is a reliability redundancy optimization problem that is solved with the heuristic approach of Aggarwal, et al.