Study On Optimal Operation Of Cascade Reservoirs Based On Adaptive Progressive Particle Swarm Optimization

In recent years,China is developing hydropower construction in full swing.With the improvement of hydropower infrastructure in China,how to develop and utilize hydropower energy reasonably,especially for the operation and operation of cascade hydropower stations,has gradually become the focus of the study of hydropower energy workers in China.Traditionally,dynamic programming algorithm is used to solve the optimal operation problem of cascade reservoirs,but with the increase of solution dimension,the problem of"dimension disaster"often appears.Therefore,this paper uses particle swarm optimization to solve such problems.However,traditional particle swarm optimization（PSO）is slow to search and easy to fall into the local optimal solution when solving the reservoir cascade optimal operation problem.This kind of problems can be effectively avoided by reasonably improving particle swarm optimization.Based on the analysis of the standard PSO algorithm,this paper proposes an improved Particle Swarm Optimization Algorithm,Adaptive Progressive Particle Swarm Optimization Algorithm（APPSO）,to solve the cascade reservoir scheduling problem.Then,taking Shuibuya,Geheyan and Gaobazhou hydropower stations in Qingjiang cascade basin as the research objects,the single reservoir power generation optimal operation model and cascade reservoir power generation optimal operation model are established respectively.Finally,the operation scheme is obtained by using APPSO algorithm,PSO algorithm and an improved particle swarm optimization algorithm based on adaptive strategy（SAPSO）.The research contents and achievements are as follows:（1）According to the characteristics of the PSO algorithm,the APPSO algorithm is proposed.On the one hand,an adaptive adjustment strategy is made for the inertia weight value,the learning factors(8₁ and(8₂.On the other hand,the worst individual optimal value in the particle swarm is replaced by the average of the individual optimal value of all particles,which is used in the speed update formula of the algorithm.Four classical standard test functions are selected to test the optimization ability of APPSO algorithm for complex functions.The results show that the APPSO algorithm has some advantages in the calculation of multidimensional optimization problems.（2）Combined with the example of Shuibuya Hydropower Station,the optimal performance of APPSO algorithm in the long-term optimal operation of a single reservoir is tested.The water inflow in typical wet year,normal year and dry year of Shuibuya hydropower station is selected as the experimental object,and the APPSO algorithm is compared with the scheduling scheme of standard PSO algorithm and SAPSO algorithm.The results show that the APPSO algorithm has the best results and good stability,and it has satisfactory applicability in solving the medium and long-term single reservoir optimal operation problem.（3）The APPSO algorithm is applied to solve the maximum power generation model of long-term operation of cascade reservoirs in Qingjiang River Basin,and the final operation scheme is compared with the solution results of other algorithms and the optimal operation schemes of three reservoirs.The results show that the APPSO algorithm has better stability and search accuracy in solving the long-term optimal generation scheduling problem of cascade reservoirs,and the cascade joint operation is better than the single reservoir optimal operation.