Parallel Discrete Differential Dynamic Programming for Multireservoir Operation. Chenga, C., Wanga, S., Chaub, K., & Wu, X.
Parallel Discrete Differential Dynamic Programming for Multireservoir Operation [link]Paper  doi  abstract   bibtex   
[Highlights] • Time cost is crucial to operations of large-scale hydropower systems (LSHSs). • Parallelism of discrete differential dynamic programming (PDDDP) is proposed. • PDDDP enhances the computing efficiency and makes full use of multi-core resources. • PDDDP shows its potential practicability and validity for operations of LSHSs. [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.
@article{chengaParallelDiscreteDifferential2014,
  title = {Parallel Discrete Differential Dynamic Programming for Multireservoir Operation},
  author = {Chenga, Chuntian and Wanga, Sen and Chaub, Kwok-Wing and Wu, Xinyu},
  date = {2014},
  journaltitle = {Environmental Modelling \& Software},
  issn = {1364-8152},
  doi = {10.1016/j.envsoft.2014.02.018},
  url = {https://doi.org/10.1016/j.envsoft.2014.02.018},
  abstract = {[Highlights] 

 • Time cost is crucial to operations of large-scale hydropower systems (LSHSs). • Parallelism of discrete differential dynamic programming (PDDDP) is proposed. • PDDDP enhances the computing efficiency and makes full use of multi-core resources. • PDDDP shows its potential practicability and validity for operations of LSHSs.

[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.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-13165615,complexity,curse-of-dimensionality,dynamic-programming,integrated-water-resources-management,mitigation,water-reservoir-management}
}

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