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

• Main Authors: Nay Myo Lin, Xin Tian, Martine Rutten, Edo Abraham, José M. Maestre, Nick van de Giesen
• Format: Article
• Language: English
• Published: MDPI AG 2020-07-01
• Series: Water
• Subjects: real-time control; multi-objective Model Predictive Control; genetic algorithm; multi-criteria decision making; multi-reservoir system
• Online Access: https://www.mdpi.com/2073-4441/12/7/1898

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.