Due to the stochastic and correlated attributes of natural inflows, the mid-term generation scheduling problem for cascaded hydro systems is a very challenging issue.This paper proposes a novel stochastic optimization...Due to the stochastic and correlated attributes of natural inflows, the mid-term generation scheduling problem for cascaded hydro systems is a very challenging issue.This paper proposes a novel stochastic optimization algorithm using Latin hypercube sampling and Cholesky decomposition combined with scenario bundling and sensitivity analysis(LC-SB-SA) to address this problem.To deal with the uncertainty of natural inflows, Latin hypercube sampling is implemented to provide an adequate number of sampling scenarios efficiently, and Cholesky decomposition is introduced to describe the correlated natural inflows among cascaded stations.In addition, to overcome the difficulties in solving the objectives of all the scenarios, scenario bundling and sensitivity analysis algorithms are developed to improve the computational efficiency.Simulation results from both two-station and tenstation systems indicate that the proposed method has the merits in accuracy as well as calculation speed for the midterm cascaded hydro generation scheduling.The consideration of natural inflow correlation makes the formulated problem more realistic.展开更多
基金supported in part by National Natural Science Foundation of China (No.51507100)in part by Shanghai Sailing Program (No.15YF1404600)+1 种基金in part by ‘‘Chen Guang’’ project supported by Shanghai Municipal Education CommissionShanghai Education Development Foundation (No.14CG55)
文摘Due to the stochastic and correlated attributes of natural inflows, the mid-term generation scheduling problem for cascaded hydro systems is a very challenging issue.This paper proposes a novel stochastic optimization algorithm using Latin hypercube sampling and Cholesky decomposition combined with scenario bundling and sensitivity analysis(LC-SB-SA) to address this problem.To deal with the uncertainty of natural inflows, Latin hypercube sampling is implemented to provide an adequate number of sampling scenarios efficiently, and Cholesky decomposition is introduced to describe the correlated natural inflows among cascaded stations.In addition, to overcome the difficulties in solving the objectives of all the scenarios, scenario bundling and sensitivity analysis algorithms are developed to improve the computational efficiency.Simulation results from both two-station and tenstation systems indicate that the proposed method has the merits in accuracy as well as calculation speed for the midterm cascaded hydro generation scheduling.The consideration of natural inflow correlation makes the formulated problem more realistic.
