Optimal Allocation Method of Factory-Level AGC Based on Improved Particle Swarm Algorithm
Aiming at the problem that the deep peak shaving cost of units is not taken into account in the factory-level AGC command allocation of power plants and the conventional algorithm is difficult to obtain the optimal results,a deep peak shaving cost model based on coal consumption cost and oil input cost is proposed.The chaotic variable is introduced into the optimization process of particle swarm optimization algorithm.The ability of the algorithm to jump out of the local optimal solution is improved by chaotic processing of the excellent individuals in each generation of particles.The convergence speed of the algorithm is improved by changing the inertia weight and learning factor,which makes the particle swarm optimization algorithm more suitable for solving multi parameter optimization problems.An improved particle swarm optimization algorithm is used to simulate the factory-level AGC instruction decomposition process of a power plant dispatching.The results show that this algorithm can achieve better optimization effect in the optimal allocation of factory-level AGC commands at the plant level,reduce the overall energy consumption level of the power plant,and improve the overall economic benefits.
万方数据
维普
