Application of intelligent algorithms in optimal operation of pumping stations

Abstract An optimal operation model of pumping stations is established in this paper, with the minimum energy consumption of the pumping station as the optimization objective, considering the constraints of the pumping station operating head, flowrate of the pumping station and so on. Simulated annealing is introduced into genetic algorithm, particle swarm optimization, wolf pack algorithm and grey wolf optimization to solve the model. The minimum value, average value, relative standard deviation of the objective function and infeasibility of constraint conditions are selected as the evaluation indexes of the algorithms performance. The calculation results show that simulated annealing ensures that the intelligent algorithms can find the optimal solution in the feasible solution space and avoid them falling into local optimization. The operation schemes obtained by solving the example model with the improved algorithm can reduce the energy consumption of the pumping station by 5.759%∼6.682%, which provides guidance for the operation schemes of the example pumping station. The results can be further applied in other pumping stations.