Multi-scenario Model Predictive Control based on genetic algorithms for level regulation of open water systems under ensemble forecasts
X Tian (Nanjing University of Information Science and Technology, TU Delft - Water Resources)
Y. Guo (Hohai University, TU Delft - Water Resources)
Rudy R. Negenborn (TU Delft - Transport Engineering and Logistics)
Lingna Wei (Nanjing University of Information Science and Technology)
N.M. Lin (TU Delft - Water Resources)
J. M. Maestre (University of Seville)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.