Application of genetic algorithm in short term hydrothermal scheduling for cost and emission

Abstract

The Electric power service providing firms have been subjected to many restrictions for the supply of electric power to its customers. The restrictions are based on the consistency, excellence, cost effectiveness, customer concern and pollution limits. Recent technologies and intelligent algorithms of a multi-objective dynamic programming and genetic algorithm are applied for the solution for short-term hydrothermal scheduling problem. The efficient scheduling preparation of existing electric power resources for satisfying load demand has become significant task in the modern power systems. The generation scheduling problem is consists of determining the optimal operation strategy for the next subsequent scheduling period, subject to a variety of limitations. For the hydrothermal systems, the inadequate energy storage capability of water reservoirs, along with the stochastic nature of their availability, make its solution a more complex job than for purely thermal systems. The well-timed allocation of hydro energy resources is a difficult task, that requires probabilistic analysis and long-term considerations, because water may not be available in the future when the water is used in the present period and the operation costs are increasing due this reason. So, the Hydrothermal Generation Scheduling Problem is usually decomposed into smaller problems in order to solve it. In this way, the hydrothermal generation scheduling problem involves three main decision stages, usually separated using a time hierarchical decomposition such as Hydrothermal power generation demand Problem, total power generation cost for demands and the emission rate for the particular power demands newline