Abstract
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.
Highlights
Reservoirs are important water retaining structures for management and sustainable development of the world’s water resources
NLP can deal with non–separable objective functions and nonlinear constraints, it is much more complicated and takes time to solve the optimization process compared with the other methods [13]
The most effective approach for solving reservoir operation problems is a combination of optimization and simulation model [21,22,23] in which the control decisions are made by optimizing the control objectives and a simulation model is used to estimate the response of the system for certain control decisions
Summary
Reservoirs are important water retaining structures for management and sustainable development of the world’s water resources. Optimization, simulation and combined optimization–simulation approaches have been commonly applied to reservoir operation studies. NLP can deal with non–separable objective functions and nonlinear constraints, it is much more complicated and takes time to solve the optimization process compared with the other methods [13]. The most effective approach for solving reservoir operation problems is a combination of optimization and simulation model [21,22,23] in which the control decisions are made by optimizing the control objectives and a simulation model is used to estimate the response of the system for certain control decisions. Various combinations of optimization–simulation models are available for real-time operations of a reservoir system and the choice of a method depends on the characteristic of a certain reservoir system, for example, number of reservoirs, types of objective functions and constraints
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