MULTI-OBJECTIVE STOCHASTIC MODELS FOR ELECTRICITY GENERATION EXPANSION PLANNING PROBLEMS CONSIDERING RISK ABSTRACT OF THE DISSERTATION Multi-Objective Stochastic Models for Electricity Generation Expansion Planning Problems Considering Risk release_pjhncji43fd3lfng6cjakypvsy

by Hatice Tekiner, Hatice Tekiner

Released as a article-journal .

(2010)

Abstract

This dissertation is focused on the development of mathematical models to solve electricity generation expansion planning problems where important problem objectives, such as cost, greenhouse gas and pollutant emissions and reliability are explicitly considered under an uncertain environment. Generation expansion planning problems are solved to determine what, when and where to built the new technologies. The main objective of the power grid is to provide an economic and reliable energy supply to consumers. Due to the increasing awareness for clean air and global warming, the power grid should also be designed to be environmental friendly. In this research, an approach is proposed to determine critical components for the grid with regard to reliability, cost and gas emissions, and an optimization approach is proposed to select a set of availability scenarios which represent the stochastic characteristics of the system and to determine the associated probabilities. The problem is formulated as a two stage multi-objective stochastic optimization problem considering the generated scenarios. There are also some iii other technological developments, called "Smart Grid Technologies" which can affect the grid. The impacts of "Smart Grid Technologies" on the grid are that (i) shift/reduce energy demand, (ii) increase the effective availability of the system components, and (iii) reduce the energy loss during transmission. This research is the first comprehensive attempt to include the Smart Grid technologies, affecting the availabilities and transmission loss, into the generation expansion planning problem. This research also leads to the contributions for developing models where risk aversion is incorporated into the model, improving solution efficiency by extending Benders decomposition and improving solution techniques for multi-objective optimization problems. iv
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