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    Comparing Model-Based and Model-Free Streamflow Simulation Approaches to Improve Hydropower Reservoir Operations

    Source: Journal of Water Resources Planning and Management:;2018:;Volume ( 144 ):;issue: 003
    Author:
    Desreumaux Quentin;Côté Pascal;Leconte Robert
    DOI: 10.1061/(ASCE)WR.1943-5452.0000860
    Publisher: American Society of Civil Engineers
    Abstract: This paper presents a comparison between an inflow model-based and an inflow model-free optimization method applied to a hydropower system. Widely used stochastic dynamic programming (SDP) and the evolutionary multiobjective direct policy search (EMODPS) methods are used, respectively, as model-based and model-free methods. Main results show that the model-free approach provides a better representation of the complex inflow correlations. Stochastic dynamic programming suffers from the temporal decomposition of the problem that allows only autoregressive exogenous (ARX) models to be used. However, because inflow uncertainty is implicitly represented through simulation with EMODPS, long time series are required to accurately characterize the probability of all possible events. To tackle this drawback and to avoid only learning the data set, a new regularization framework is introduced to improve the policy robustness on unseen data sets. Moreover, this study highlights how the preselected family of functions defining the policy can reduce the performance of the EMODPS method. This study is based on the real-world case of Kemano, located in British Columbia, Canada. This system is challenging because of the long-term inflow correlation due to long snow accumulation periods and its multiple objectives.
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      Comparing Model-Based and Model-Free Streamflow Simulation Approaches to Improve Hydropower Reservoir Operations

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4249409
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    contributor authorDesreumaux Quentin;Côté Pascal;Leconte Robert
    date accessioned2019-02-26T07:47:28Z
    date available2019-02-26T07:47:28Z
    date issued2018
    identifier other%28ASCE%29WR.1943-5452.0000860.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4249409
    description abstractThis paper presents a comparison between an inflow model-based and an inflow model-free optimization method applied to a hydropower system. Widely used stochastic dynamic programming (SDP) and the evolutionary multiobjective direct policy search (EMODPS) methods are used, respectively, as model-based and model-free methods. Main results show that the model-free approach provides a better representation of the complex inflow correlations. Stochastic dynamic programming suffers from the temporal decomposition of the problem that allows only autoregressive exogenous (ARX) models to be used. However, because inflow uncertainty is implicitly represented through simulation with EMODPS, long time series are required to accurately characterize the probability of all possible events. To tackle this drawback and to avoid only learning the data set, a new regularization framework is introduced to improve the policy robustness on unseen data sets. Moreover, this study highlights how the preselected family of functions defining the policy can reduce the performance of the EMODPS method. This study is based on the real-world case of Kemano, located in British Columbia, Canada. This system is challenging because of the long-term inflow correlation due to long snow accumulation periods and its multiple objectives.
    publisherAmerican Society of Civil Engineers
    titleComparing Model-Based and Model-Free Streamflow Simulation Approaches to Improve Hydropower Reservoir Operations
    typeJournal Paper
    journal volume144
    journal issue3
    journal titleJournal of Water Resources Planning and Management
    identifier doi10.1061/(ASCE)WR.1943-5452.0000860
    page5018002
    treeJournal of Water Resources Planning and Management:;2018:;Volume ( 144 ):;issue: 003
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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