Parallel discrete differential dynamic programming for multireservoir operation

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) i...

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Vydáno v:Environmental modelling & software : with environment data news Ročník 57; s. 152 - 164
Hlavní autoři: Cheng, Chuntian, Wang, Sen, Chau, Kwok-Wing, Wu, Xinyu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Elsevier Ltd 01.07.2014
Elsevier
Témata:
algorithms
Animal, plant and microbial ecology
Biological and medical sciences
China
computer hardware
computer software
Discrete differential dynamic programming
dynamic programming
environmental models
Fundamental and applied biological sciences. Psychology
General aspects. Techniques
Hydropower systems
Long-term operation
Methods and techniques (sampling, tagging, trapping, modelling...)
Optimization
Parallel
rivers
water power
China
Discrete differential dynamic programming
Parallel
Hydropower systems
Optimization
Long-term operation
Dynamics
Long term
ISSN:1364-8152
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Popis
Shrnutí: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. •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.
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ISSN:1364-8152
DOI:10.1016/j.envsoft.2014.02.018