Some Studies on Application of Different Optimization Techniques in Power Systems

Abstract

This thesis briefly represents the Some Studies on Application of Different Optimization newlineTechniques in Power Systems network. In this work, the applications have been considered newlinemainly in Economic Load Dispatch (ELD) which is also one of the basic requirements of Unit newlineCommitment. Unit commitment is a quadratic programming optimization problem. During the newlinegeneration of various units, the sets of different possible combinations of units, which means newlineeither kVA loading of the generators and#8730;and#119875;2 + and#119876;2 newline or generator outputs in kW (kW = kVA × newlinepower factor) are recorded periodically. This recorded data satisfies a polynomial equation newlineamong generation cost and output of generators (kW). newlineHere, the different optimization techniques are applied to the polynomial equation and the newlineoptimal generation dispatch among the operating units is obtained, which satisfies the system newlineload demand without violating the operational constraints of generators. newlineIn earlier days feasible solutions to the ELD problem were achieved by solving various newlineoptimization techniques such as Genetic Algorithms (GA), participation factor method, newlinesimulated annealing (SA), etc. Throughout the previous decennium, generally, the GA newlinetechnique has been accepted due to its faster convergence, but modern research has identified newlinea few deficiencies of the Genetic Algorithms (GA). Those deficiencies are newline1. newline2. newlineLong execution time to converge towards the global best feasible outcome, newlineDifficulty increases with the enhancement of the bus numbers (ELD) or variables. newlineThe problem related to the large convergence time is possibly resolved by using a parallel newlinecomputing platform method (PCPM). PCPM is dependent on a system of inter-connected newlinepersonal computers (PC) with the help of TCP/IP socket communication features. The recently newlinedeveloped various heuristic or probabilistic searching optimization techniques such as the newlineParticle Swarm Optimization (PSO) method, Differential Evolutionary (DE) method, and newlineHarmony Search (HS) have overcome the defic