Parallel discrete differential dynamic programming for multireservoir

Abstract

The curse of dimensionality and computational time cost are a great challenge to operation of large-scale hydropower systems (LSHSs) in China because computer memory and computational time increase exponentially with increasing number of reservoirs. Discrete differential dynamic programming (DDDP) is one of the most classical algorithms for alleviating the dimensionality problem for operation of LSHSs. However, the computational time performed on DDDP still increases exponentially with increasing number of reservoirs. Therefore, a fine-grained parallel DDDP (PDDDP) algorithm, which is based on Fork/Join parallel framework in multi-core environment, is proposed to improve the computing efficiency for long-term operation of multireservoir hydropower systems. The proposed algorithm is tested using a huge cascaded hydropower system located on the Lancang River in China. The results demonstrate that the PDDDP algorithm enhances the computing efficiency significantly and takes full advantage of multi-core resources, showing its potential practicability and validity for operation of LSHSs in future.