STOCHASTIC HYBRID ALGORITHMIC SOLUTIONS FOR UNIT COMMITMENT PROBLEM
COMPARATIVE OPTIMAL COST ANALYSIS OF META HEURISTIC ALGORITHMS FOR CONSTRAINED UNIT COMMITMENT PROBLEM IN POWER SECTOR
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In a restructured electrical power system market, there is a need for planned, secured and reliable hour based optimal power generation to provide a cost effective power generating entities of a standard designed market. The solution of constrained unit commitment (CUC) is a challenging task to the researchers, which interlines two optimization problems named as unit scheduling problem and economic load dispatch problem. The traditional mathematical techniques and stochastic optimization techniques poses drawbacks of highly heuristic in nature, high operational cost & computational time. In th...
In a restructured electrical power system market, there is a need for planned, secured and reliable hour based optimal power generation to provide a cost effective power generating entities of a standard designed market. The solution of constrained unit commitment (CUC) is a challenging task to the researchers, which interlines two optimization problems named as unit scheduling problem and economic load dispatch problem. The traditional mathematical techniques and stochastic optimization techniques poses drawbacks of highly heuristic in nature, high operational cost & computational time. In this book, main objective is to develop a meta-heuristic hybridized algorithm for finding a feasible solution of the minimized fuel cost with better accuracy, less computational effort and minimum computational time in exploring the global search space. The book comprises of the development of meta-heuristic algorithms such as Bat, Hybrid Bat-Artificial Bee Colony (Bat-ABC) and Hybrid Bat-Genetic Algorithm (Bat-GA) tested for the standard 3 unit, 10 unit and 45 unit systems and proper comparative analysis of operational costs are mentioned for easy emphasis.
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