Novel clustering based evolutionary algorithm approach for solving optimal scheduling and placement issues in power system

Abstract

A system operator in a power system utility is always concerned with the economic and reliable operation of generation system. The optimal use of the available resources is gaining importance, both monetarily and due to the depleting irreplaceable natural resources. Thus, this area has warranted a great deal of attention from power system engineers across the globe. Unit Commitment Problem (UCP) in power systems refers to the optimization problem for determining the ON/OFF status of generating units that minimize the operating cost for a given time horizon. Once the status of the generating unit for particular hour is determined, Economic Dispatch Problem (EDP), a subproblem is then solved to determine the optimal dispatch of the generator to satisfy the hourly demand. Furthermore, another important development that has been practiced in the power system arena is the integration of renewable energy resources with thermal power plant using Distributed Generators (DG) on the load side. Integration of renewable energy with conventional power generation presents apool of technical issues which has to be addressed very seriously by the power engineers. Thereby, it provides more scope for the researchers working in this area and one such interesting problem is optimal location and sizing of DG in the distributed system. Emerging evolutionary algorithms such as Firefly Algorithm (FFA) and Gravitational Search Algorithm (GSA) under cluster based environment such as Clustered Firefly Algorithm (CFFA) and Clustered Gravitational Search Algorithm (CGSA) are implemented in this dissertation and it is employed to solve economic dispatch problem, dynamic economic dispatch problem, unit newlinecommitment problem using parallel computing methodology, security constrained unit commitment problem and optimal sizing and placement of DG in distribution system. newline newline