Artificial intelligence based Algorithms for the optimal solution Of security constrained thermal unit N Commitment and short term Hydrothermal scheduling problem

Abstract

The main objective of this research is to develop Genetic Algorithm newline GA based algorithms for solving the Unit Commitment UC problems newlinehydrothermal scheduling and hydrothermal scheduling with pumped hydro newlineplant Efforts are taken to make the proposed algorithms simple reliable and newlineefficient with excellent convergence characteristics newlineThe problems investigated with the proposed algorithms include newlineUC problem with improved lambda iteration technique and security newlineConstraints hydrothermal scheduling with security constraints and newlinehydrothermal scheduling with pumped hydro plant for the operational benefits newlinesuch as peak load management and spinning reserve unit with security newlineconstraints An Enhanced Genetic Algorithm EGA is proposed to solve the newlineUC problem In addition to Simple Genetic Algorithm SGA advanced and newlineproblem specific operators like two level crossovers swap mutation repair newlineand approximate operators are introduced in order to enhance the algorithms newlineefficiency and accuracy newline newline