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by Yung Kai-man. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves 90-92. / ABSTRACT --- p.ii / ACKNOWLEDGMENTS --- p.iii / TABLE OF CONTENTS --- p.iv / LIST OF ILLUSTRATIONS --- p.vi / LIST OF TABLES --- p.vii / Chapter / Chapter I. --- INTRODUCTION --- p.1 / Background --- p.1 / Methodology Review --- p.6 / Chapter II. --- DATA BASE AND VARIABLES --- p.8 / The Data Base --- p.8 / The Dependent Variables --- p.9 / The Independent Variables --- p.14 / Chapter III. --- METHODOLOGY --- p.24 / Regression Analysis --- p.24 / Selection of the Predictors --- p.25 / Regression Studies Using Moving Data --- p.29 / Programming Aids --- p.32 / Chapter IV. --- RESULTS AND DISCUSSIONS --- p.35 / Validity of the Assumptions for the Regression Model --- p.35 / Prediction Power of the Model --- p.37 / Utility of the Prediction Model --- p.39 / A Practical View of the Model Prediction --- p.47 / Representation of the Predictors --- p.48 / Chapter V. --- CONCLUSION AND RECOMMENDATIONS --- p.51 / Evaluation of the Prediction Model --- p.51 / Extension of the Project --- p.53 / APPENDICES --- p.55 / REFERENCES --- p.90

Many electric utilities have been tapping such energy sources as wind energy or conservation for years. However, the literature shows few attempts to incorporate such non-dispatchable energy sources as decision variables into the long-range planning methodology. In this dissertation, efficient algorithms for electric utility capacity expansion planning with renewable energy are developed. The algorithms include a deterministic phase which quickly finds a near-optimal expansion plan using derating and a linearized approximation to the time-dependent availability of non-dispatchable energy sources. A probabilistic second phase needs comparatively few computer-time consuming probabilistic simulation iterations to modify this solution towards the optimal expansion plan. For the deterministic first phase, two algorithms, based on a Lagrangian Dual decomposition and a Generalized Benders Decomposition, are developed. The Lagrangian Dual formulation results in a subproblem which can be separated into single-year plantmix problems that are easily solved using a breakeven analysis. The probabilistic second phase uses a Generalized Benders Decomposition approach. A depth-first Branch and Bound algorithm is superimposed on the two-phase algorithm if conventional equipment types are only available in discrete sizes. In this context, computer time savings accrued through the application of the two-phase method are crucial. Extensive computational tests of the algorithms are reported. Among the deterministic algorithms, the one based on Lagrangian Duality proves fastest. The two-phase approach is shown to save up to 80 percent in computing time as compared to a purely probabilistic algorithm. The algorithms are applied to determine the optimal expansion plan for the Tijuana-Mexicali subsystem of the Mexican electric utility system. A strong recommendation to push conservation programs in the desert city of Mexicali I results from this implementation. / Ph. D.