Optimal Reservoir Operation Using Stochastic Model Predictive Control
Optimal Reservoir Operation Using Stochastic Model Predictive Control

Author: Reetik Kumar Sahu

Publisher:

Published: 2016

Total Pages: 65

ISBN-13:

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Dynamical systems are subjected to various random external forcings that complicate theie control. In order to achieve optimal performance, these systems need to continually adapt to external disturbances in real time. This capability is provided by feedback based control strategies that derive an optimal control from the current state of the system. Model Predictive Control(MPC) is one such feedback-based technique. This thesis explores the application of a stochastic version of MPC to a reservoir system. The reservoir system is designed to maximize the revenue generated from the hydroelectricity while simultaneously obeying several exogenous constraints. An ensemble based version of the stochastic MPC technique is studied and applied to the reservoir to determine the optimal water release strategies. Further analysis is performed to understand the sensitivity of different parameters in the MPC technique.

Optimal Reservoir Operation Under Inflow Uncertainty
Optimal Reservoir Operation Under Inflow Uncertainty

Author: Jinshu Li

Publisher:

Published: 2021

Total Pages: 126

ISBN-13:

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Stochastic programming is a mathematical model used to resolve the uncertainty of random variables in optimization problems. In reservoir management and operation, the reservoir inflow is typically regarded as a random variable as it brings most of the operation uncertainty. Although stochastic programming has been successfully applied to many reservoir managements cases, the pursuit of the improvement on its accuracy, efficiency, and applicability never ceases. This dissertation consists of five chapters. The first introductory presents the classical stochastic model and describes the challenges. Then, the second chapter develops a statistical model that focuses on improving the distribution fitting accuracy for the monthly average inflow as the random variable. The third chapter discusses a method aiming at streamflow scenario tree reduction, which is essential for alleviating the computational burden of a two-stage stochastic programming with recourse model. The fourth chapter expands the applicability of stochastic programming model, by introducing a multi-objective, multi-stage stochastic programming with recourse model. The final chapter offers conclusions, discussions, and potential future research opportunities.

Multi-agent Real-time Decision Making in Water Resources Systems
Multi-agent Real-time Decision Making in Water Resources Systems

Author: Reetik Kumar Sahu

Publisher:

Published: 2018

Total Pages: 83

ISBN-13:

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Optimal utilization of natural resources such as water, wind and land over extended periods of time requires a carefully designed framework coupling decision making and a mathematical abstraction of the physical system. On one hand, the choice of the decision-strategy can set limits/bounds on the maximum benefit that can be extracted from the physical system. On the other hand the mathematical formulation of the physical system determines the limitations of such strategies when applied to real physical systems. The nuances of decision making and abstraction of the physical system are illustrated with two classical water resource problems: optimal hydropower reservoir operation and competition for a common pool groundwater source. Reservoir operation is modeled as a single agent stochastic optimal control problem where the operator (agent) negotiates a firm power contract before operations begin and adjusts the reservoir release during operations. A probabilistic analysis shows that predictive decision strategies such as stochastic dynamic programming and model predictive control give better performance than standard deterministic operating rules. Groundwater competition is modeled as a multi-agent dynamic game where each farmer (agent) aims to maximize his/her personal benefit. The game analysis shows that uncooperative competition for the resource reduces economic efficiency somewhat with respect to the cooperative socially optimum behavior. However, the efficiency reduction is relatively small compared to what might be expected from incorrect assumptions about uncertain factors such as future energy and crop prices. Spatially lumped and distributed models of the groundwater system give similar pictures of the inefficiencies that result from uncooperative behavior. The spatially distributed model also reveals the important roles of the geometry and density of the pumping well network. Overall, the game analysis provides useful insight about the factors that make cooperative groundwater management beneficial in particular situations.

Adaptive Catchment Management and Reservoir Operation
Adaptive Catchment Management and Reservoir Operation

Author: Guangtao Fu

Publisher: MDPI

Published: 2019-04-09

Total Pages: 498

ISBN-13: 3038977381

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River catchments and reservoirs play a central role in water security, food supply, flood risk management, hydropower generation, and ecosystem services; however, they are now under increasing pressure from population growth, economic activities, and changing climate means and extremes in many parts of the world. Adaptive management of river catchments and reservoirs requires an in-depth understanding of the impacts of future uncertainties and thus the development of robust, sustainable solutions to meet the needs of various stakeholders and the environment. To tackle the huge challenges in moving towards adaptive catchment management, this book presents the latest developments in cutting-edge knowledge, novel methodologies, innovative management strategies, and case studies, focusing on the following themes: reservoir dynamics and impact analysis of dam construction, optimal reservoir operation, climate change impacts on hydrological processes and water management, and integrated catchment management.

Nested algorithms for optimal reservoir operation and their embedding in a decision support platform
Nested algorithms for optimal reservoir operation and their embedding in a decision support platform

Author: Blagoj Delipetrev

Publisher: CRC Press

Published: 2020-04-30

Total Pages: 157

ISBN-13: 0429611552

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Reservoir operation is a multi-objective optimization problem, and is traditionally solved with dynamic programming (DP) and stochastic dynamic programming (SDP) algorithms. The thesis presents novel algorithms for optimal reservoir operation, named nested DP (nDP), nested SDP (nSDP), nested reinforcement learning (nRL) and their multi-objective (MO) variants, correspondingly MOnDP, MOnSDP and MOnRL. The idea is to include a nested optimization algorithm into each state transition, which reduces the initial problem dimension and alleviates the curse of dimensionality. These algorithms can solve multi-objective optimization problems, without significantly increasing the algorithm complexity or the computational expenses. It can additionally handle dense and irregular variable discretization. All algorithms are coded in Java and were tested on the case study of the Knezevo reservoir in the Republic of Macedonia. Nested optimization algorithms are embedded in a cloud application platform for water resources modeling and optimization. The platform is available 24/7, accessible from everywhere, scalable, distributed, interoperable, and it creates a real-time multiuser collaboration platform. This thesis contributes with new and more powerful algorithms for an optimal reservoir operation and cloud application platform. All source codes are available for public use and can be used by researchers and practitioners to further advance the mentioned areas.

Model Predictive Control in the Process Industry
Model Predictive Control in the Process Industry

Author: Eduardo F. Camacho

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 250

ISBN-13: 1447130081

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Model Predictive Control is an important technique used in the process control industries. It has developed considerably in the last few years, because it is the most general way of posing the process control problem in the time domain. The Model Predictive Control formulation integrates optimal control, stochastic control, control of processes with dead time, multivariable control and future references. The finite control horizon makes it possible to handle constraints and non linear processes in general which are frequently found in industry. Focusing on implementation issues for Model Predictive Controllers in industry, it fills the gap between the empirical way practitioners use control algorithms and the sometimes abstractly formulated techniques developed by researchers. The text is firmly based on material from lectures given to senior undergraduate and graduate students and articles written by the authors.

Optimizing Reservoir Resources
Optimizing Reservoir Resources

Author: Charles ReVelle

Publisher: John Wiley & Sons

Published: 1999-05-10

Total Pages: 234

ISBN-13: 9780471188773

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Introducing the next generation in optimization modeling. This book presents a dynamic new approach to optimization modeling in the design and management of surface water reservoirs. More accurate in its representation of critical flows than traditional synthetic hydrology approaches, ReVelle's model utilizes both deterministic and stochastic constraints to derive a unified water supply, allocation, and reallocation optimization model. Dr. ReVelle begins with an in-depth presentation of conventional approaches to optimizing various reservoir functions and services-including water supply, flood control, hydropower, irrigation, navigation, and recreation. He then describes a method to enhance performance by allocating services and functions. Finally, he develops a unified optimization model that can be applied both to existing reservoirs and in the design of a new generation of efficient surface water reservoirs. A valuable working resource for water resources analysts, engineers, and managers, Optimizing Reservoir Resources features: * A new model to achieve desired levels of water supply reliability. * A multiobjective framework for exploring reservoir services. * Storage rule curves derived with anti-zigzagging methods. * Improved cost allocation models. * Original techniques for linearizing the nonlinear hydropower equation and for the derivation of the storage-energy curve. * Techniques for allocation and reallocation of reservoir services through the derivation of an economic supply curve.

Stochastic Hydrology and its Use in Water Resources Systems Simulation and Optimization
Stochastic Hydrology and its Use in Water Resources Systems Simulation and Optimization

Author: J.B. Marco

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 470

ISBN-13: 9401116970

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Stochastic hydrology is an essential base of water resources systems analysis, due to the inherent randomness of the input, and consequently of the results. These results have to be incorporated in a decision-making process regarding the planning and management of water systems. It is through this application that stochastic hydrology finds its true meaning, otherwise it becomes merely an academic exercise. A set of well known specialists from both stochastic hydrology and water resources systems present a synthesis of the actual knowledge currently used in real-world planning and management. The book is intended for both practitioners and researchers who are willing to apply advanced approaches for incorporating hydrological randomness and uncertainty into the simulation and optimization of water resources systems. (abstract) Stochastic hydrology is a basic tool for water resources systems analysis, due to inherent randomness of the hydrologic cycle. This book contains actual techniques in use for water resources planning and management, incorporating randomness into the decision making process. Optimization and simulation, the classical systems-analysis technologies, are revisited under up-to-date statistical hydrology findings backed by real world applications.