Modeling hydraulic and thermal electricity production based on genetic algorithm-time series (GATS)

Abstract

This study deals with the estimation of electricity production from hydraulic and thermal sources using the Genetic Algorithm (GA) with time series (TS) approach. Two forms of the mathematical models are developed, of which one is exponential and the second is polynomial. The power form of the Genetic Algorithm-Time Series (GATS) model is used for the thermal electricity production. The polynomial form of the GATS is used for the electricity production from the hydraulic sources. The GATS weighting parameters are obtained by minimizing the Sum of Squared Error (SSE) between observed and estimated electricity production from both sources. Therefore, the fitness function adapted is the minimization of the SSE for use in the GA process. The application of the GATS model is correspondingly presented. Some future scenarios are made to increase the electricity production from hydraulic sources. Variations of the electricity production from thermal and hydraulic energy sources are analyzed. Future prospects of electricity production are dealt with in terms of policy changes. The GATS models are used for making scenarios for future electricity planning policy. Results also show if current trend continues, the thermal electricity production amounts to 75% of the total electricity production, which is undesirable for environmental concerns. Results also shows that if new policy is to move from the thermal to hydraulic electricity production, the hydraulic sources will meet the demand until 2020.