Multi-Scenario Model Predictive Control Based on Genetic Algorithms for Level Regulation of Open Water Systems under Ensemble Forecasts

Xin Tian (), Yuxue Guo (), Rudy R. Negenborn (), Lingna Wei (), Nay Myo Lin () and José María Maestre ()
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Xin Tian: Nanjing University of Information Science & Technology
Yuxue Guo: Delft University of Technology
Rudy R. Negenborn: Delft University of Technology
Lingna Wei: Nanjing University of Information Science & Technology
Nay Myo Lin: Delft University of Technology
José María Maestre: University of Seville

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2019, vol. 33, issue 9, No 4, 3025-3040

Abstract: Abstract Operational water resources management needs to adopt operational strategies to re-allocate water resources by manipulating hydraulic structures. Model Predictive Control (MPC) has been shown to be a promising technique in this context. However, we still need to advance MPC in the face of hydrological uncertainties. This study makes the first attempt to combine Multi-Scenario MPC (MSMPC) with a Genetic Algorithm (GA) to find Pareto optimal solutions for a multi-scenario operational water resources management problem. Then three performance metrics are adopted to select the solution to be implemented. In order to assess the performance of the proposed approach, a case study of the North Sea Canal in the Netherlands is carried out, in which ensemble discharge forecasts are used. Compared with classic MSMPC approaches that deal with uncertainty by the weighted sum approach, GA-MSMPC can better fulfill management goals although it may also be computationally expensive. With the rapid development of multi-objective evolutionary algorithms, our study suggests the potential of GA-MSMPC to deal with a wide range of operational water management problems in the future.

Keywords: Model predictive control; Genetic algorithms; Multiple scenarios; Water level regulation; Ensemble forecasts (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11269-019-02284-x

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