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Security-constrained unit commitment (SCUC), as one of key components in power system operation, is being widely applied in vertically integrated utilities and restructured power systems. The efficient solution framework is to implement iterations between a master problem (unit commitment) and subproblems (network security evaluations). In industrial applications, both Lagrangian relaxation and mixed-integer programming are commonly applied for the unit commitment problem, and both linear sensitivity factor and Benders cut methods are used to generate additional constraints in the phase of network security evaluations. This paper evaluates capabilities and performances of each algorithm through technical discussion and numerical testing. Special topics on the large-scale SCUC engine development are also discussed in this paper, such as input data screening, inactive constrains elimination, contingency management, infeasibility handling, parallel computing, and model simplification. This paper will benefit academic researchers, software developers, and system operators when they design, develop and assess effective models and algorithms for solving large-scale SCUC problems. [ABSTRACT FROM PUBLISHER]

This paper presents both application and comparison of the metaheuristic techniques to Generation Expansion Planning (GEP) problem. The Metaheuristic techniques such as the Genetic Algorithm, Differential Evolution, Evolutionary Programming, Evolutionary Strategy, Ant Colony Optimization, Particle Swarm Optimization, Tabu Search, Simulated Annealing, and Hybrid Approach are applied to solve GEP problem. The original GEP problem is modified using the proposed methods Virtual Mapping Procedure (VMP) and Penalty Factor Approach (PFA), to improve the efficiency of the metaheuristic techniques. Further, Intelligent Initial Population Generation (IIPG), is introduced in the solution techniques to reduce the computational time. The VMP, PFA, and IIPG are used in solving all the three test systems. The GEP problem considered synthetic test systems for 6-year, 14-year, and 24-year planning horizon having five types of candidate units. The results obtained by all these proposed techniques are compared and validated against conventional Dynamic Programming and the effectiveness of each proposed methods has also been illustrated in detail. [ABSTRACT FROM AUTHOR]