Improvement Of Artificial Fish Swarm Algorithm And Its Application In Unit Commitment Of Power Grid

Large-scale power system contains a large number of generating units,and many factors need to be considered during its operation.Its unit commitment optimization is a multi-objective and multi-constrained nonlinear large-scale optimization problem,which has many shortcomings in existing methods.The artificial fish swarm algorithm has good performance in solving nonlinear optimization problems,but it has disadvantages such as low optimization efficiency and may fall into local extremes.Therefore,this paper designs an improved artificial fish swarm algorithm,builds a power grid unit commitment model and applies the improved algorithm to the model,which achieves the multi-objective unit commitment optimization of large-scale units.In order to overcome the deficiencies of the artificial fish swarm algorithm,this paper improves the algorithm from three aspects.By adopting the variable vision,adjusting the movement strategy and combining the mutation operation in the genetic algorithm,the convergence performance and global search ability of the algorithm are improved.In order to verify the effectiveness of the improved method,experiments are carried out with a test function,and the experimental results prove that the improved algorithm makes up for the shortcomings of the original algorithm.A power grid unit commitment model is built to solve the problem of large-scale multi-objective unit commitment.The model sets two objectives of economy and environmental protection,and uses linear weighting to deal with multi-objective.In order to avoid the overlong calculation time caused by the increase of unit scale,the model adopts a phased optimization method.The optimization process is devided into two phases:status arrangement and load distribution.The objectives are set and the constraints are processed respectively at each phase,so that the algorithm does not have to distribute the load repeatedly,which greatly shortens the solution time.The improved algorithm is applied to the status arrangement phase of the model,and simulation experiments are implemented.In order to verify the feasibility of the algorithm and the model,simulation experiments of economic dispatch and multi-objective optimization are carried out for a large-scale example with up to 1000units.The simulation results of the economic dispatch experiments show that the improved algorithm has stronger ability in searching optimal solution,the phased optimization method can effectively shorten the calculation time of the problem,and the designed algorithm is more applicable to large-scale unit commitment problems compared with other optimization methods.The multi-objective optimization simulation results indicate that the model can significantly reduce the CO₂ and SO₂emissions of power system without greatly prolonging the solution time,which achieves the environmental protection objective,and has a good performance in solving ramp constraints.