An adaptive and iterative approach to dynamic modelling of bicriterion optimization in power systems expansion decisions

Abstract

In this study,power systems expansion decisions are modelled, using Bicriterion Linear Programing techniques. The model consists of: energy and power demand forecasts, production capacity, build-up rate, nuclear, hyifro (both base and peak), fossil and fuel energy constraints in an adaptive process of 5-yr. increments for a 25-yr. planning horizon. The model, as applied to the Turkish Power System, contains 40 decision variables and 45 constraints. Efficient (nondominated) solutions are obtained at each iteration with respect to the economic cost objective and one of the following objectives: pollution, space occupied or risk involved. The aim of this study is to derive a framework for the power systems expansion decisions based on trade-off functions between pairs of objectives. A particular combination of objective functions and changes in some parameters result in different scenarios. A fast algorithm develpped by Kızıltan [ 8], is adapted and used as a subroutine in order to obtain the preliminary solutions to the problem. According to the adaptive nature of the study, the overall solutions are grouped together to form decision alternatives and to show the trade-offs between the objectives. Special effort was expended on the generation of the computer programs so that minimum handling of data for a particular scenario is achieved together with a self explanatory output design.