Parallel improved DPSA algorithm for medium-term optimal scheduling of large-scale cascade hydropower plants

Medium-term optimal scheduling of hydropower plants (MOSHPP) should be frequently updated for recurring extreme precipitation events in China to reduce spillage and increase power generation; however, the curse of dimensionality makes obtaining a satisfactory solution in an acceptable time difficult. In this paper, a parallel improved dynamic programming with successive approximation (PIDPSA) for MOSHPPs is proposed to improve the solution quality and meet time requirements. The solution quality is improved by successive approximation of multiple plants instead of one plant for considering more hydraulic connections, a state space reduction strategy combined with constraint preprocessing is adopted to reduce unnecessary calculations and the fine-grained parallelism based on the fork/join framework is employed to greatly shorten the solution time. The proposed method was applied to 11 hydropower cascade plants in the Lancang River with a time horizon of 30 days. The results showed that higher quality solutions can be obtained by IDPSA than conventional methods and DPSA, and the space reduction strategy can effectively improve the solution efficiency. The computation time of PIDPSA decreased from 81345 s for the single-core environment to 6073 s for the 32-core environment, which proves the high efficiency and practical value of PIDPSA for solving MOSHPP problems.

Automated summary

This paper proposes a parallel improved dynamic programming with successive approximation (PIDPSA) method for medium-term optimal scheduling of hydropower plants in China. The method improves the solution quality by considering more hydraulic connections and reducing unnecessary calculations, and it greatly shortens the solution time through fine-grained parallelism. The results show that PIDPSA can obtain higher quality solutions and provides high efficiency and practical value for solving MOSHPP problems.