Next Article in Journal
Groundwater Parameter Inversion Using Topographic Constraints and a Zonal Adaptive Multiscale Procedure: A Case Study of an Alluvial Aquifer
Previous Article in Journal
Antibiotic Resistance Genes Occurrence in Wastewaters from Selected Pharmaceutical Facilities in Nigeria
Article

Multi-Objective Model Predictive Control for Real-Time Operation of a Multi-Reservoir System

1
Department of Water Management, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands
2
Department of Irrigation and Water Utilization Management, Thitsar Road, Yankin Township, Yangon 11081, Myanmar
3
KWR Water Research Institute, Groningenhaven 7, 3433 PE Nieuwegein, The Netherlands
4
Department of Engineering of Systems and Automatics, University of Seville, 41092 Seville, Spain
*
Author to whom correspondence should be addressed.
Water 2020, 12(7), 1898; https://doi.org/10.3390/w12071898
Received: 17 April 2020 / Revised: 13 June 2020 / Accepted: 29 June 2020 / Published: 3 July 2020
(This article belongs to the Section Hydraulics and Hydrodynamics)
This paper presents an extended Model Predictive Control scheme called Multi-objective Model Predictive Control (MOMPC) for real-time operation of a multi-reservoir system. The MOMPC approach incorporates the non-dominated sorting genetic algorithm II (NSGA-II), multi-criteria decision making (MCDM) and the receding horizon principle to solve a multi-objective reservoir operation problem in real time. In this study, a water system is simulated using the De Saint Venant equations and the structure flow equations. For solving multi-objective optimization, NSGA-II is used to find the Pareto-optimal solutions for the conflicting objectives and a control decision is made based on multiple criteria. Application is made to an existing reservoir system in the Sittaung river basin in Myanmar, where the optimal operation is required to compromise the three operational objectives. The control objectives are to minimize the storage deviations in the reservoirs, to minimize flood risks at a downstream vulnerable place and to maximize hydropower generation. After finding a set of candidate solutions, a couple of decision rules are used to access the overall performance of the system. In addition, the effect of the different decision-making methods is discussed. The results show that the MOMPC approach is applicable to support the decision-makers in real-time operation of a multi-reservoir system. View Full-Text
Keywords: real-time control; multi-objective Model Predictive Control; genetic algorithm; multi-criteria decision making; multi-reservoir system real-time control; multi-objective Model Predictive Control; genetic algorithm; multi-criteria decision making; multi-reservoir system
Show Figures

Figure 1

MDPI and ACS Style

Myo Lin, N.; Tian, X.; Rutten, M.; Abraham, E.; Maestre, J.M.; van de Giesen, N. Multi-Objective Model Predictive Control for Real-Time Operation of a Multi-Reservoir System. Water 2020, 12, 1898. https://doi.org/10.3390/w12071898

AMA Style

Myo Lin N, Tian X, Rutten M, Abraham E, Maestre JM, van de Giesen N. Multi-Objective Model Predictive Control for Real-Time Operation of a Multi-Reservoir System. Water. 2020; 12(7):1898. https://doi.org/10.3390/w12071898

Chicago/Turabian Style

Myo Lin, Nay, Xin Tian, Martine Rutten, Edo Abraham, José M. Maestre, and Nick van de Giesen. 2020. "Multi-Objective Model Predictive Control for Real-Time Operation of a Multi-Reservoir System" Water 12, no. 7: 1898. https://doi.org/10.3390/w12071898

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop