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Efficient multi-scenario Model Predictive Control for water resources management with ensemble streamflow forecasts.

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Title: Efficient multi-scenario Model Predictive Control for water resources management with ensemble streamflow forecasts.
Authors: Tian, Xin1 x.tian@nuist.edu.cn, Negenborn, Rudy R.2 r.r.negenborn@tudelft.nl, Van Overloop, Peter-Jules3 p.j.a.t.m.vanOverloop@tudelft.nl, María Maestre, José4 pepemaestre@us.es, Sadowska, Anna5 a.d.Sadowska@slb.com, Van De Giesen, Nick6 n.c.vandegiesen@tudelft.nl
Source: Advances in Water Resources. Nov2017, Vol. 109, p58-68. 11p.
Subjects: Water resources development, Streamflow, Forecasting, Uncertainty, Adaptive control systems
Abstract: Model Predictive Control (MPC) is one of the most advanced real-time control techniques that has been widely applied to Water Resources Management (WRM). MPC can manage the water system in a holistic manner and has a flexible structure to incorporate specific elements, such as setpoints and constraints. Therefore, MPC has shown its versatile performance in many branches of WRM. Nonetheless, with the in-depth understanding of stochastic hydrology in recent studies, MPC also faces the challenge of how to cope with hydrological uncertainty in its decision-making process. A possible way to embed the uncertainty is to generate an Ensemble Forecast (EF) of hydrological variables, rather than a deterministic one. The combination of MPC and EF results in a more comprehensive approach: Multi-scenario MPC (MS-MPC). In this study, we will first assess the model performance of MS-MPC, considering an ensemble streamflow forecast. Noticeably, the computational inefficiency may be a critical obstacle that hinders applicability of MS-MPC. In fact, with more scenarios taken into account, the computational burden of solving an optimization problem in MS-MPC accordingly increases. To deal with this challenge, we propose the Adaptive Control Resolution (ACR) approach as a computationally efficient scheme to practically reduce the number of control variables in MS-MPC. In brief, the ACR approach uses a mixed-resolution control time step from the near future to the distant future. The ACR-MPC approach is tested on a real-world case study: an integrated flood control and navigation problem in the North Sea Canal of the Netherlands. Such an approach reduces the computation time by 18% and up in our case study. At the same time, the model performance of ACR-MPC remains close to that of conventional MPC. [ABSTRACT FROM AUTHOR]
Copyright of Advances in Water Resources is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 126392406
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  Data: Efficient multi-scenario Model Predictive Control for water resources management with ensemble streamflow forecasts.
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  Data: <searchLink fieldCode="AR" term="%22Tian%2C+Xin%22">Tian, Xin</searchLink><relatesTo>1</relatesTo><i> x.tian@nuist.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Negenborn%2C+Rudy+R%2E%22">Negenborn, Rudy R.</searchLink><relatesTo>2</relatesTo><i> r.r.negenborn@tudelft.nl</i><br /><searchLink fieldCode="AR" term="%22Van+Overloop%2C+Peter-Jules%22">Van Overloop, Peter-Jules</searchLink><relatesTo>3</relatesTo><i> p.j.a.t.m.vanOverloop@tudelft.nl</i><br /><searchLink fieldCode="AR" term="%22María+Maestre%2C+José%22">María Maestre, José</searchLink><relatesTo>4</relatesTo><i> pepemaestre@us.es</i><br /><searchLink fieldCode="AR" term="%22Sadowska%2C+Anna%22">Sadowska, Anna</searchLink><relatesTo>5</relatesTo><i> a.d.Sadowska@slb.com</i><br /><searchLink fieldCode="AR" term="%22Van+De+Giesen%2C+Nick%22">Van De Giesen, Nick</searchLink><relatesTo>6</relatesTo><i> n.c.vandegiesen@tudelft.nl</i>
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  Data: <searchLink fieldCode="JN" term="%22Advances+in+Water+Resources%22">Advances in Water Resources</searchLink>. Nov2017, Vol. 109, p58-68. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Water+resources+development%22">Water resources development</searchLink><br /><searchLink fieldCode="DE" term="%22Streamflow%22">Streamflow</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Uncertainty%22">Uncertainty</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+control+systems%22">Adaptive control systems</searchLink>
– Name: Abstract
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  Data: Model Predictive Control (MPC) is one of the most advanced real-time control techniques that has been widely applied to Water Resources Management (WRM). MPC can manage the water system in a holistic manner and has a flexible structure to incorporate specific elements, such as setpoints and constraints. Therefore, MPC has shown its versatile performance in many branches of WRM. Nonetheless, with the in-depth understanding of stochastic hydrology in recent studies, MPC also faces the challenge of how to cope with hydrological uncertainty in its decision-making process. A possible way to embed the uncertainty is to generate an Ensemble Forecast (EF) of hydrological variables, rather than a deterministic one. The combination of MPC and EF results in a more comprehensive approach: Multi-scenario MPC (MS-MPC). In this study, we will first assess the model performance of MS-MPC, considering an ensemble streamflow forecast. Noticeably, the computational inefficiency may be a critical obstacle that hinders applicability of MS-MPC. In fact, with more scenarios taken into account, the computational burden of solving an optimization problem in MS-MPC accordingly increases. To deal with this challenge, we propose the Adaptive Control Resolution (ACR) approach as a computationally efficient scheme to practically reduce the number of control variables in MS-MPC. In brief, the ACR approach uses a mixed-resolution control time step from the near future to the distant future. The ACR-MPC approach is tested on a real-world case study: an integrated flood control and navigation problem in the North Sea Canal of the Netherlands. Such an approach reduces the computation time by 18% and up in our case study. At the same time, the model performance of ACR-MPC remains close to that of conventional MPC. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Advances in Water Resources is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.advwatres.2017.08.015
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      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 58
    Subjects:
      – SubjectFull: Water resources development
        Type: general
      – SubjectFull: Streamflow
        Type: general
      – SubjectFull: Forecasting
        Type: general
      – SubjectFull: Uncertainty
        Type: general
      – SubjectFull: Adaptive control systems
        Type: general
    Titles:
      – TitleFull: Efficient multi-scenario Model Predictive Control for water resources management with ensemble streamflow forecasts.
        Type: main
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            NameFull: Tian, Xin
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            NameFull: Negenborn, Rudy R.
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            NameFull: Van Overloop, Peter-Jules
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            NameFull: María Maestre, José
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            NameFull: Sadowska, Anna
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            – D: 01
              M: 11
              Text: Nov2017
              Type: published
              Y: 2017
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              Value: 03091708
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              Value: 109
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            – TitleFull: Advances in Water Resources
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