Model of Long Term Electricity Generation Expansion Planning in Thailand by Load Demand Forecasting

Abstract

This paper presents a mathematical model to an application of a least-cost generation expansion planning (GEP) problem in Thailand. Least-cost GEP problem is concerned with a highly constrained non-linear dynamic optimization problem that can only be solved by complete enumeration. The model consists of the cost function that minimizes the construction and operating costs. The genetic algorithm (GA) is employed for optimization algorithm to determine the types of generation which meet the forecasted demand within a pre-specified criterion over the planning horizon from 2007-2021. The proposed model indicates that 53% (47% natural gas, 2% lignite, and 4% turbine) of electricity must be supplied from the Electricity Generating Authority of Thailand (EGAT), and 47% purchased from private enterprises (40%) and neighboring countries (9%).