Multi Objective Optimization Of Reservoir Operation Under Uncertainty With Robust And Flexible Decision Variables
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Multi Objective Optimization Of Reservoir Operation Under Uncertainty With Robust And Flexible Decision Variables
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Author : Parnian Hosseini
language : en
Publisher:
Release Date : 2017
Multi Objective Optimization Of Reservoir Operation Under Uncertainty With Robust And Flexible Decision Variables written by Parnian Hosseini and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Franklin D. Roosevelt Lake (Wash.) categories.
Optimization of reservoir operation is involves various competing objectives for a scarce resource (water). To find the optimal operation of reservoirs, it is essential to consider multiple objectives simultaneously. There are various sources of uncertainty associated with the reservoir operation problem that should be considered as well. The overarching goal of this research is to develop a framework for finding flexible and reliable solutions to the reservoir operation problem with competing objectives. Because some sources of uncertainty are not well quantified, providing flexible decision variables lets the decision maker choose accordingly from a range of options knowing that all the flexible decision variables are feasible with a specified probability of failure and that are relatively optimal. To accomplish this goal, each flexible decision variable is represented by a random variable within a specific range instead of a single deterministic decision variable. An additional objective is added to the optimization problem, in order to maximize the flexibility of decision variables. The proposed methodology is tested for two mathematical test problems and the operation of the Grand Coulee reservoir, which is located on the Columbia River in the Northwestern United States. The Stochastic Collocation (SC) method is used to sample the random variables and approximate the expected values of the objectives. For the Grand Coulee reservoir, the decision variables are the daily turbine outflows. The first objective of the optimization is to minimize the forebay elevation deviation at the end of the optimization period. The second objective is to maximize the revenue from the hydropower production. The results show that the proposed methodology could find some flexible decision variables with 45% coefficient of variation. The corresponding expected objectives have less than 20% deterioration from the deterministic Pareto solutions. However, the number of function evaluations increases exponentially with the number of decision variables. Therefore, this methodology is suggested for problems with a few decision variables. For finding flexible decision variables in problems with many decision variables, a dimension reduction method called Karhunen Loeve (KL) expansion is implemented in the optimization problem. By extracting useful information from the decision variables, the decision space can be represented with merely a few random variables using a set of deterministic decision variables. The results show that three random variables are sufficient to generate decision variable realizations which have mean and variance less than 1% and 5% different from the original decision variable realizations, respectively. The proposed methodology is capable of efficiently finding flexible decision variables that lead to expected objective values close to the Pareto deterministic solutions. To force the generated decision variable realizations to stay within the feasible bounds and therefore reduce the number of constraints that need to be checked, the data is transformed to be within bounds first, and then the KL-expansion is performed. Using the transformed data decreases the computational time but the decrease in computational time is not significant. The inflow uncertainty is also considered as the only source of input uncertainty. Forecast inflow ensembles can be used as the source of inflow uncertainty. However in this study due to lack of information, historical inflows are used instead. The inflow uncertainties are represented using the KL-expansion. Robust optimization is performed by optimizing the weighted sum of the expectation and standard deviation of the objective due to uncertain inflows. The weights in the robust objective formulation can be changed based on the decision maker’s preference of robustness versus performance. Finally, the combined framework to find robust and flexible decision variables is tested on a reservoir operation problem and the results were compared to the deterministic case.
Robust Optimization
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Author : Aharon Ben-Tal
language : en
Publisher: Princeton University Press
Release Date : 2009-08-10
Robust Optimization written by Aharon Ben-Tal and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-08-10 with Mathematics categories.
Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject. Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution. The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations. An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject.
Optimal Reservoir Operation Under Inflow Uncertainty
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Author : Jinshu Li
language : en
Publisher:
Release Date : 2021
Optimal Reservoir Operation Under Inflow Uncertainty written by Jinshu Li and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
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 Objective Optimization Of The Steam Alternating Solvent Sas Process Using Pareto Based Multi Objective Evolutionary Algorithm
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Author : Israel Mayo Molina
language : en
Publisher:
Release Date : 2021
Multi Objective Optimization Of The Steam Alternating Solvent Sas Process Using Pareto Based Multi Objective Evolutionary Algorithm written by Israel Mayo Molina and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Bitumen categories.
The Steam Alternating Solvent (SAS) process is a relatively new auspicious alternative recovery process to produce heavy oil and bitumen resources. This process consists of injecting steam and solvent (i.e. propane) alternatively using the same well configuration as the widely adopted Steam-Assisted Gravity Drainage (SAGD) process. The SAS and other solvent-based processes have gained popularity as they aim to reduce the environmental footprint by reducing water usage and Greenhouse Gas (GHG) emissions. However, to successfully apply these processes in the field, vast knowledge and a proper design of all controllable parameters that intervene in each process and their operational ranges that might conflict with multiple objectives (especially in reservoirs with heterogeneities such as shale barriers) are needed. This study proposes a robust Multi-Objective Optimization (MOO) workflow based on Pareto optimality to determine the optimal operational ranges to implement the SAS process in homogenous and various heterogeneous reservoirs. The MOO is carried out by constructing different simulation models under the following steps. First, a 2-D homogeneous reservoir model is built based on the Fort McMurray formation in the Athabasca region in Alberta, Canada. Then, for the heterogeneous case, multiple model sets superimposing shale barriers at different locations and geometries (shale proportions and lengths) are constructed and subjected to simulation to assess the impacts of heterogeneities according to those characteristics. After, a detailed sensitivity analysis is performed on the most impactful models 1) to determine the controllable operational parameter (decision variables) that impact the most in each model and 2) to select the targets (objective functions) to be optimized. Subsequently, three different Multi-Objective Evolutionary Algorithms (MOEAs) such as Multi-Objective Particle Swarm Optimization (MOPSO), Pareto Envelope-Based Selection Algorithm (PESA-II) and Strength Pareto Evolutionary Algorithm II (SPEA-II) are applied. This is to 1) obtain the Pareto optimal set of decision variables and 2) identify the most suitable algorithm for each problem. Finally, Response Surface Methodology (RSM) to build proxy models is incorporated to estimate each objective function from the chosen decision variables to reduce the computational effort. For the homogenous case, the results indicate that high propane concentration injected over short cycles, coupled with more extended steam injection, is more optimal for the first period. The bottom-hole pressure in the injector and producer should be kept low to reduce the steam and solvent injection and to allow the fluids to be produced, respectively. In contrast, lower solvent concentration and longer cycles are preferred for the second period, and higher steam injection is more optimal to achieve a higher reservoir temperature. In heterogeneous reservoirs was observed that the steam-solvent chamber growth and production profiles are highly impacted by the location and geometry of these heterogeneities. This impact, especially in the area near the wells, is more representative. Conversely, in areas away from the wells pair, just longer and thicker shale barriers are relevant; this conclusion is consistent with other processes studies such as SA-SAGD (Al-Gosayir et al., 2012). The controllable parameters in heterogeneous reservoirs such as solvent composition (i.e. %Propane,%Methane), cycle duration (when either steam or solvent are injected), bottom-hole pressure (BHP) and some production constraints such as steam trap and Bottom-Hole Gas (BHG) have a significant impact on the SAS performance. Since this is a MOO, some trade-offs and relationships among the controllable variables in the process are observed. The robust and detailed optimization workflow presented in this study accounts for multiple targets (objective functions) involving many controllable operational parameters (decision variables). Also, by using different MOEAs to optimize the process, the results might be more accurate and reliable. Thus, this study intends to give a more profound analysis of the SAS process to facilitate field-scale decisions, minimizing the risk that this new technology might have.
Multi Objective Optimization Using Evolutionary Algorithms
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Author : Kalyanmoy Deb
language : en
Publisher: John Wiley & Sons
Release Date : 2001-07-05
Multi Objective Optimization Using Evolutionary Algorithms written by Kalyanmoy Deb and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-07-05 with Mathematics categories.
Optimierung mit mehreren Zielen, evolutionäre Algorithmen: Dieses Buch wendet sich vorrangig an Einsteiger, denn es werden kaum Vorkenntnisse vorausgesetzt. Geboten werden alle notwendigen Grundlagen, um die Theorie auf Probleme der Ingenieurtechnik, der Vorhersage und der Planung anzuwenden. Der Autor gibt auch einen Ausblick auf Forschungsaufgaben der Zukunft.
Evolutionary Multiobjective Optimization
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Author : Ajith Abraham
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-09-05
Evolutionary Multiobjective Optimization written by Ajith Abraham and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-09-05 with Computers categories.
Evolutionary Multi-Objective Optimization is an expanding field of research. This book brings a collection of papers with some of the most recent advances in this field. The topic and content is currently very fashionable and has immense potential for practical applications and includes contributions from leading researchers in the field. Assembled in a compelling and well-organised fashion, Evolutionary Computation Based Multi-Criteria Optimization will prove beneficial for both academic and industrial scientists and engineers engaged in research and development and application of evolutionary algorithm based MCO. Packed with must-find information, this book is the first to comprehensively and clearly address the issue of evolutionary computation based MCO, and is an essential read for any researcher or practitioner of the technique.
Metaheuristics Outlines Matlab Codes And Examples
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Author : Ali Kaveh
language : en
Publisher: Springer
Release Date : 2019-03-29
Metaheuristics Outlines Matlab Codes And Examples written by Ali Kaveh and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-29 with Technology & Engineering categories.
The book presents eight well-known and often used algorithms besides nine newly developed algorithms by the first author and his students in a practical implementation framework. Matlab codes and some benchmark structural optimization problems are provided. The aim is to provide an efficient context for experienced researchers or readers not familiar with theory, applications and computational developments of the considered metaheuristics. The information will also be of interest to readers interested in application of metaheuristics for hard optimization, comparing conceptually different metaheuristics and designing new metaheuristics.
Confronting Climate Uncertainty In Water Resources Planning And Project Design
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Author : Patrick A. Ray
language : en
Publisher: World Bank Publications
Release Date : 2015-08-20
Confronting Climate Uncertainty In Water Resources Planning And Project Design written by Patrick A. Ray and has been published by World Bank Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-08-20 with Business & Economics categories.
Confronting Climate Uncertainty in Water Resources Planning and Project Design describes an approach to facing two fundamental and unavoidable issues brought about by climate change uncertainty in water resources planning and project design. The first is a risk assessment problem. The second relates to risk management. This book provides background on the risks relevant in water systems planning, the different approaches to scenario definition in water system planning, and an introduction to the decision-scaling methodology upon which the decision tree is based. The decision tree is described as a scientifically defensible, repeatable, direct and clear method for demonstrating the robustness of a project to climate change. While applicable to all water resources projects, it allocates effort to projects in a way that is consistent with their potential sensitivity to climate risk. The process was designed to be hierarchical, with different stages or phases of analysis triggered based on the findings of the previous phase. An application example is provided followed by a descriptions of some of the tools available for decision making under uncertainty and methods available for climate risk management. The tool was designed for the World Bank but can be applicable in other scenarios where similar challenges arise.
The Engineering Index Annual
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Author :
language : en
Publisher:
Release Date : 1992
The Engineering Index Annual written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Engineering categories.
Since its creation in 1884, Engineering Index has covered virtually every major engineering innovation from around the world. It serves as the historical record of virtually every major engineering innovation of the 20th century. Recent content is a vital resource for current awareness, new production information, technological forecasting and competitive intelligence. The world?s most comprehensive interdisciplinary engineering database, Engineering Index contains over 10.7 million records. Each year, over 500,000 new abstracts are added from over 5,000 scholarly journals, trade magazines, and conference proceedings. Coverage spans over 175 engineering disciplines from over 80 countries. Updated weekly.
Mastering Uncertainty In Mechanical Engineering
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Author : Peter F. Pelz
language : en
Publisher: Springer Nature
Release Date : 2021-10-11
Mastering Uncertainty In Mechanical Engineering written by Peter F. Pelz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-11 with Technology & Engineering categories.
This open access book reports on innovative methods, technologies and strategies for mastering uncertainty in technical systems. Despite the fact that current research on uncertainty is mainly focusing on uncertainty quantification and analysis, this book gives emphasis to innovative ways to master uncertainty in engineering design, production and product usage alike. It gathers authoritative contributions by more than 30 scientists reporting on years of research in the areas of engineering, applied mathematics and law, thus offering a timely, comprehensive and multidisciplinary account of theories and methods for quantifying data, model and structural uncertainty, and of fundamental strategies for mastering uncertainty. It covers key concepts such as robustness, flexibility and resilience in detail. All the described methods, technologies and strategies have been validated with the help of three technical systems, i.e. the Modular Active Spring-Damper System, the Active Air Spring and the 3D Servo Press, which have been in turn developed and tested during more than ten years of cooperative research. Overall, this book offers a timely, practice-oriented reference guide to graduate students, researchers and professionals dealing with uncertainty in the broad field of mechanical engineering.