Multicriteria analysis of power generation expansion planning

Abstract

This thesis describes and evaluates a set of multiobjective generation expansion planning models that include four objectives and importance given to renewable generation technologies while considering location of generation units. Using multicriteria decision making theory, these models provide results which indicate the most recommendable amount of each type of generating technology to install at each location. A framework to solve and generate alternative solutions is provided for each model, and representative case studies from the Mexican Electric Power System are used to show the performance of the proposed models and solution methods. The models include a single-period model, a multi-period model, single-period mixed-integer non-linear model, and a fuzzy multi-criteria model. Among the attributes considered are the investment and operation cost of the units, the environmental impact, the amount of imported fuel, and the portfolio investment risk. The approaches to solve the models are based on multiobjective linear programming, analytical hierarchy process, and evolutionary algorithms. The incorporation of more than three criteria to generate the expansion alternatives, the importance given to renewable generation technologies, and the geographical location of the new generation units are some features of the proposed models which have not been considered simultaneously in the literature. A novel multiobjective evolutionary programming algorithm has been proposed in this thesis.