Print Email Facebook Twitter Multi-scenario Model Predictive Control based on genetic algorithms for level regulation of open water systems under ensemble forecasts Title Multi-scenario Model Predictive Control based on genetic algorithms for level regulation of open water systems under ensemble forecasts Author Tian, X. (TU Delft Water Resources; Nanjing University of Information Sciences and Technology) Guo, Y. (TU Delft Water Resources; Hohai University) Negenborn, R.R. (TU Delft Transport Engineering and Logistics) Wei, Lingna (Nanjing University of Information Sciences and Technology) Lin, N.M. (TU Delft Water Resources) Maestre, José María (University of Seville) Date 2019 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. Subject Ensemble forecastsGenetic algorithmsModel predictive controlMultiple scenariosWater level regulation To reference this document use: http://resolver.tudelft.nl/uuid:0140c86a-52df-4353-9089-6d1531d6ca9a DOI https://doi.org/10.1007/s11269-019-02284-x Embargo date 2020-06-05 ISSN 0920-4741 Source Water Resources Management, 33 (9), 3025-3040 Bibliographical note Accepted Author Manuscript Part of collection Institutional Repository Document type journal article Rights © 2019 X. Tian, Y. Guo, R.R. Negenborn, Lingna Wei, N.M. Lin, José María Maestre Files PDF GAMPC.pdf 2.34 MB Close viewer /islandora/object/uuid:0140c86a-52df-4353-9089-6d1531d6ca9a/datastream/OBJ/view