Multireservoir Modeling with Dynamic Programming and Neural Networks
01 Apr 2001-Journal of Water Resources Planning and Management (American Society of Civil Engineers)-Vol. 127, Iss: 2, pp 89-98
TL;DR: The multireservoir model based on the dynamic programming-neural network algorithm gives improved performance in this study.
Abstract: For optimal multireservoir operation, a dynamic programming-based neural network model is developed in this study. In the suggested model, multireservoir operating rules are derived using a feedforward neural network from the results of three state variables' dynamic programming algorithm. The training of the neural network is done using a supervised learning approach with the back-propagation algorithm. A multireservoir system called the Parambikulam Aliyar Project system is used for this study. The performance of the new multireservoir model is compared with (1) the regression-based approach used for deriving the multireservoir operating rules from optimization results; and (2) the single-reservoir dynamic programming-neural network model approach. The multireservoir model based on the dynamic programming-neural network algorithm gives improved performance in this study.
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TL;DR: Application of heuristic programming methods using evolutionary and genetic algorithms are described, along with application of neural networks and fuzzy rule-based systems for inferring reservoir system operating rules, to assess the state of the art in optimization of reservoir system management and operations.
Abstract: With construction of new large-scale water storage projects on the wane in the U.S. and other developed countries, attention must focus on improving the operational effectiveness and efficiency of existing reservoir systems for maximizing the beneficial uses of these projects. Optimal coordination of the many facets of reservoir systems requires the assistance of computer modeling tools to provide information for rational management and operational decisions. The purpose of this review is to assess the state-of-the-art in optimization of reservoir system management and operations and consider future directions for additional research and application. Optimization methods designed to prevail over the high-dimensional, dynamic, nonlinear, and stochastic characteristics of reservoir systems are scrutinized, as well as extensions into multiobjective optimization. Application of heuristic programming methods using evolutionary and genetic algorithms are described, along with application of neural networks and fuzzy rule-based systems for inferring reservoir system operating rules.
1,484 citations
TL;DR: Simulation, optimization and combined simulation–optimization modeling approach are discussed and an overview of their applications reported in literature is provided to help system managers decide appropriate methodology for application to their systems.
Abstract: This paper presents a survey of simulation and optimization modeling approaches used in reservoir systems operation problems. Optimization methods have been proved of much importance when used with simulation modeling and the two approaches when combined give the best results. The main objective of this review article is to discuss simulation, optimization and combined simulation–optimization modeling approach and to provide an overview of their applications reported in literature. In addition to classical optimization techniques, application and scope of computational intelligence techniques, such as, evolutionary computations, fuzzy set theory and artificial neural networks, in reservoir system operation studies are reviewed. Conclusions and suggestive remarks based on this survey are outlined, which could be helpful for future research and for system managers to decide appropriate methodology for application to their systems.
428 citations
TL;DR: Uncertainty of the upstream boundary can have significant impact on the model results, exceeding the importance of model parameter uncertainty in some areas, however, this depends on the hydraulic conditions in the reach e.g. internal boundary conditions and, for example, the amount of backwater within the modelled region.
345 citations
Cites methods from "Multireservoir Modeling with Dynami..."
...Other approaches include the use of weir equations [39]; theoretical relationships derived from flume experiments [57,58]; neural networks [59–63]; or M5 regression trees, which approximate the data by a set of linear equations [63]....
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TL;DR: The comprehensive reviews on the use of various programming techniques for the solution of different optimization problems have been provided and conclusions are drawn where gaps exist and more research needs to be focused.
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References
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TL;DR: In this paper, a nonlinear disaggregation technique for the operation of multireservoir systems is described, where the disaggregation is done by training a neural network to give, for an aggregated storage level, the storage level of each reservoir of the system.
Abstract: This paper describes a nonlinear disaggregation technique for the operation of multireservoir systems. The disaggregation is done by training a neural network to give, for an aggregated storage level, the storage level of each reservoir of the system. The training set is obtained by solving the deterministic operating problem of a large number of equally likely flow sequences. The training is achieved using the back propagation method, and the minimization of the quadratic error is computed by a variable step gradient method. The aggregated storage level can be determined by stochastic dynamic programming in which all hydroelectric installations are aggregated to form one equivalent reservoir. The results of applying the learning disaggregation technique to Quebec's La Grande river are reported, and a comparison with the principal component analysis disaggregation technique is given.
79 citations
TL;DR: In this paper, the authors developed a heuristic and neural network technique to reduce the computational time required to solve the multiple realization model, through identification and utilization of only potentially important stream and water quality information that influence the optimal solution.
Abstract: Traditional chance-constrained programming (CCP) and simulation-optimization methods of incorporating input information uncertainty in pollution management models are unsuitable for complex river systems with several critical water quality segments. Using the CCP method, characterization of the joint probability distribution of coefficients of the management model is often difficult because stream information is limited and the model formulation is generally difficult to understand and solve. For the simulation-optimization method most of the solutions produced are inferior. The multiple realization model, which includes several scenarios of design conditions simultaneously in an optimization model, overcomes such weaknesses by not requiring the joint probability distribution of the stochastic model coefficients and by producing noninferior solutions. Heuristic and neural network techniques are developed to reduce the computational time required to solve the multiple realization model, through identification and utilization of only potentially important stream and water quality information that influence the optimal solution. These techniques are applied to develop trade-off relationships between waste treatment cost and reliability of achieving dissolved oxygen objectives for an example river basin. Results show that the heuristic technique is computationally efficient when <1000 realizations are included in the model, while the neural network method is suitable when several thousand realizations are needed to adequately represent the stochastic water quality system.
74 citations
TL;DR: In this article, the authors describe a dual approach called "constructive dynamic programming" (CDP) which has been successfully applied to optimize releases in a stochastic two-reservoir model of the New Zealand power system.
Abstract: The reservoir management problem for a hydrothermal power system is well suited to modeling via dynamic programing. In this paper we describe a dual approach which we term “constructive dynamic programming” (CDP) which has been successfully applied to optimize releases in a stochastic two-reservoir model of the New Zealand power system. That model ignores serial correlations of inflows, though, and hence assumes that current inflow observations do not have any impact on future release decisions. Tests show, however, that better decision rules can be produced by accounting for inflow correlation. Hence we have developed an extension to the standard CDP to explicitly deal with serial correlation of reservoir inflows, and we report on those extensions also.
13 citations