Research on Daily Optimal Scheduling of Cascade Power Plant Based on Improved Genetic Algorithm
This paper points out that the optimization operation of cascade hydropower stations is often accompanied by multi constraint,nonlinear,and multi-stage combination optimization problems.When solving,traditional genetic algorithms and other similar intelligent algorithms have poor local search ability and low search efficiency in the later stage of evolution,which often leads to premature convergence problems.In response to this issue,this paper makes appropriate improvements to the genetic algorithm and applies it to the daily optimization scheduling model of the Xiluodu-Xiangjiaba cascade power station with the goal of maximizing daily economic benefits in the context of time of use electricity prices.By comparing the results obtained by the improved genetic algorithm with traditional genetic algorithm and other optimization algorithms,it is found that the improved genetic algorithm converges quickly and has good stability,overcoming the shortcomings of other algorithms such as early convergence and falling into local optima.This can provide certain reference value for reservoir optimization scheduling.
NETL
NSTL
万方数据
