Comparing For Algorithms On Researching Of Active Power Optimization In The Power Systems

Active power optimization is one of the core power system operation management. The scheduling strategy with better efficiency and economy is particularly critical in modern power system. Active optimization algorithm is deeply conducted in this paper.The development of algorithms for active power optimization method is analyzed in this paper. Then the methods and verification by IEEE examples for calculating power-flow and active incremental transmission losses are presented. On the base, the paper introduces classical active power optimization, develop active power optimization model and derive the computational formula. This paper also study the application of improved genetic algorithm in the active power optimization system, introducing the basic principle and setting of basic parameter of genetic algorithm and tabu search algorithm, developing a hybrid genetic algorithm which applies tabu search algorithm to improve the crossover operator and the mutation operator of genetic algorithm. As well as applying the hybrid genetic algorithm in active power system optimization. Finally through the examples of IEEE-14node and IEEE-30node, feasibility verification and comparison of classic method and the improved genetic algorithm are carried out. It is found that it is feasible to apply the improved genetic algorithm to the active power optimization in power system.