Combination improved particle swarm optimization algorithm for single unit optimal scheduling of pumping station
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Abstract:
With the continuous increase of the application scale of pumping station engineering and the improvement of the complexity of operation and management, it has become an important research field to carry out the optimal scheduling of pumping station units and realize economic operation. Many scholars in China explore and study the construction and solution methods of pumping station optimization model. The traditional pumping station optimization scheduling methods mainly include nonlinear programming, dynamic programming, experimental optimization and large-scale system optimization methods. Among them, the dynamic programming method is widely used in the optimal scheduling of pumping stations. In the solution process, the decision variables are usually discretized with a certain step size, and the size of the discrete step size has a certain degree of influence on the accuracy of the optimal solution of the model target. Therefore, this paper attempts to find a particle swarm optimization algorithm that decision variables are randomly generated in the feasible region and can be continuously updated to further discuss the influence of different value methods of decision variables on the optimization results. According to the shortcomings of particle swarm optimization algorithm, which is easy to fall into local optimal solution and low precision, a combined improvement method of multi-strategy fusion of "Sobol sequence optimization initial population & real-time adjustment of inertia weight & sine and cosine substitution learning factor" was proposed. (1) Sobol sequence was applied to initialize the population, which made the initial population distribution more uniform and laid a good foundation for the global search of the algorithm. (2) A real-time nonlinear decreasing adjustment of inertia weight with number of iterations was adopted to improve the search ability of the algorithm at different stages. (3) The sine-cosine factor in the position update formula of SCA (sine-cosine algorithm) was introduced to replace the learning factor. Each particle could search and move between the best position of the individual and the best position of the population, so that it could carry out multi-directional search and enhance its search ability. At the same time, combined with the real-time nonlinear decreasing adjustment of inertia weight, the collaborative improvement of search ability of particle swarm optimization algorithm was realized in different stages. Through the performance test of four benchmark functions, it was verified that the improved particle swarm optimization algorithm had a significant improvement in search ability and accuracy compared with the basic particle swarm algorithm. On this basis, the improved particle swarm optimization algorithm was applied to the solution of the single unit variable speed optimization model with the minimum power consumption cost as objective function in a large-scale pump station. The optimal decision-making scheme and the corresponding optimal objective value were obtained, then compared with the calculation results of the dynamic programming method and basic PSO algorithm. The results showed that: (1) Compared with the basic PSO algorithm, the multi-strategy fusion improvement effect of the improved PSO algorithm is more significant (the water-pumping cost is reduced by 11.4 % at 60 % load operation, and the unit water-pumping cost is reduced by 11.9 %). (2) The optimal decision-making process of the two methods (improved PSO algorithm and dynamic programming method) was basically consistent, that was, in the process of variable speed operation of single unit, the unit was generally shutdown when the electricity price and lift head were relatively higher. Even if the unit was turned on, the speed was generally smaller, the amount of water was relatively less; However, when the electricity price and lift head were relatively lower, the unit was turned on, and the speed was generally larger, and the amount of water was relatively more. (3) The accuracy of the optimal objective values of two methods (improved PSO algorithm and dynamic programming method) was comparable. Under three different operating load scenarios, the unit cost of water-pumping of two methods were very close, and the absolute value of the deviation rate was not more than 0.1%. It can be seen that the combination improvement strategy of particle swarm optimization proposed was feasible and effective, and the solution result was satisfactory. Therefore, the combined improved particle swarm optimization algorithm can be used as an effective method to solve the unit variable speed optimization model of pumping station.
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