Download or read book Scalable Energy System Expansion Under Uncertainty Using Multi Stage Stochastic Optimization written by and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The intermittent nature of power from renewable energy sources poses new challenges for electrical grids. This is due to the variable and uncertain nature of the power output from these resources. These features of renewable generation are becoming more relevant to energy system planning as grids reach higher penetration levels of renewable energy. In this presentation we present approaches for energy system planning based on scalable computational approaches which enable the explicit consideration of operational uncertainties in the planning process. Using multi-stage stochastic programming and the progressive hedging algorithm, we compute energy system expansion decisions on modified versions of the RTS-GMLC test system augmented with large amounts of renewable generation.

Download or read book Transmission Expansion Planning The Network Challenges of the Energy Transition written by Sara Lumbreras and published by Springer Nature. This book was released on 2020-11-19 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a panoramic look at the transformation of the transmission network in the context of the energy transition. It provides readers with basic definitions as well as details on current challenges and emerging technologies. In-depth chapters cover the integration of renewables, the particularities of planning large-scale systems, efficient reduction and solution methods, the possibilities of HVDC and super grids, distributed generation, smart grids, demand response, and new regulatory schemes. The content is complemented with case studies that highlight the importance of the power transmission network as the backbone of modern energy systems. This book will be a comprehensive reference that will be useful to both academics and practitioners.

Download or read book Scalable Stochastic Transmission Expansion written by Jonathan Maack and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Reinforcement Learning and Stochastic Optimization written by Warren B. Powell and published by John Wiley & Sons. This book was released on 2022-03-15 with total page 1090 pages. Available in PDF, EPUB and Kindle. Book excerpt: REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a “diary problem” that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.

Download or read book Multistage Stochastic Optimization written by Georg Ch. Pflug and published by Springer. This book was released on 2014-11-16 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multistage stochastic optimization problems appear in many ways in finance, insurance, energy production and trading, logistics and transportation, among other areas. They describe decision situations under uncertainty and with a longer planning horizon. This book contains a comprehensive treatment of today’s state of the art in multistage stochastic optimization. It covers the mathematical backgrounds of approximation theory as well as numerous practical algorithms and examples for the generation and handling of scenario trees. A special emphasis is put on estimation and bounding of the modeling error using novel distance concepts, on time consistency and the role of model ambiguity in the decision process. An extensive treatment of examples from electricity production, asset liability management and inventory control concludes the book.

Download or read book Optimization Approaches for Electricity Generation Expansion Planning Under Uncertainty written by Yiduo Zhan and published by . This book was released on 2017 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: The last problem concerns a multistage adaptive investment planning problem while considering uncertain future demand at various locations. To solve this multi-level optimization problem, we take advantage of affine policies to transform it to a single-level optimization problem. Our numerical examples show the benefits of using this multistage adaptive robust planning model over both traditional stochastic programming and single-level robust optimization approaches. Based on numerical studies in the three projects, we conclude that our approaches provide effective and efficient modeling and computational tools for advanced power systems expansion planning.

Download or read book Optimization and Decision Making Under Uncertainty for Distributed Generation Technologies written by Carlos Antonio Marino and published by . This book was released on 2016 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation studies two important models in the field of the distributed generation technologies to provide resiliency to the electric power distribution system. In the first part of the dissertation, we study the impact of assessing a Combined Cooling Heating Power system (CCHP) on the optimization and management of an on-site energy system under stochastic settings. These mathematical models propose a scalable stochastic decision model for large-scale microgrid operation formulated as a two-stage stochastic linear programming model. The model is solved enhanced algorithm strategies for Benders decomposition are introduced to find an optimal solution for larger instances efficiently. Some observations are made with different capacities of the power grid, dynamic pricing mechanisms with various levels of uncertainty, and sizes of power generation units. In the second part of the dissertation, we study a mathematical model that designs a Microgrid (MG) that integrates conventional fuel based generating (FBG) units, renewable sources of energy, distributed energy storage (DES) units, and electricity demand response. Curtailment of renewable resources generation during the MG operation affects the long-term revenues expected and increases the greenhouses emission. Considering the variability of renewable resources, researchers should pay more attention to scalable stochastic models for MG for multiple nodes. This study bridges the research gap by developing a scalable chance-constrained two-stage stochastic program to ensure that a significant portion of the renewable resource power output at each operating hour will be utilized. Finally, some managerial insights are drawn into the operation performance of the Combined Cooling Heating Power and a Microgrid.

Download or read book Stochastic Multi Stage Optimization written by Pierre Carpentier and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The focus of the present volume is stochastic optimization of dynamical systems in discrete time where - by concentrating on the role of information regarding optimization problems - it discusses the related discretization issues. There is a growing need to tackle uncertainty in applications of optimization. For example the massive introduction of renewable energies in power systems challenges traditional ways to manage them. This book lays out basic and advanced tools to handle and numerically solve such problems and thereby is building a bridge between Stochastic Programming and Stochastic Control. It is intended for graduates readers and scholars in optimization or stochastic control, as well as engineers with a background in applied mathematics.

Download or read book Stochastic Optimization in Energy Systems written by Steffen Rebennack and published by Springer Vieweg. This book was released on 2016-09-07 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview on stochastic optimization and its application in the Energy Industry. Special focus is given on hydro-thermal scheduling and, more generally, storage systems. The book begins by illuminating several approaches to deal with uncertainties (e.g., robust optimization, chance-constraints, stochastic optimization) and discusses their relations, advantages and disadvantages. Special focus is given on GAMS coded examples and the usage of the GAMS software. The book is very suitable for courses in business schools, system engineering, applied mathematics, operations research and energy producing industry.

Download or read book Long Term Planning Under Uncertainty written by Vijay Kumar and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: An increase in renewable energy portfolio in the electric grid to reduce emissions from the energy sector has changed grid requirements in terms of flexibility, reliability, and resiliency. Power system planning should consider all these essential characteristics of the grid to ensure a reliable grid in the future. This thesis aims to develop tools and techniques to address power system planning with all the characteristics in a computationally tractable framework. The methods developed are inspired by a framework called approximate dynamic programming or reinforcement learning. Approximate Dynamic Programming(ADP) is a Monte Carlo-based simulation technique that uses low order approximation of the objective function and uses dynamic programming principles to obtain future policies for planning under uncertainty. We exploit the stage-wise and problem decomposition framework present to do adaptive system planning under uncertainty with hourly resolution in simulating operations of the power system. We develop variations of the hourly power system operations, out of which one of them is integrated into long-term planning. Chapter 2 focuses on developing hourly power system operations models, including integrality constraints to represent specific units and other operational constraints such as uptime, downtime, ramping, and reserve constraints, along with transmission constraints. To ensure computational tractability for large systems like Western Electricity Coordinating Council (WECC), we decompose into two separate models with operational constraints and transmission constraints. This approximation was required to integrate the power system model into other physical system models such as water, land, and economy in a computationally tractable framework. We compare this proposed model with different variations of the models used in the power system to estimate the impacts of water stress in the future on the power system and other parts of the economy. Chapter 3 introduces the approximate dynamic programming framework for future power system planning. ADP is a sampling-based optimization technique, and like other techniques within these classes of methods, it suffers from the 'explore vs. exploit' problem. It needs to balance the trade-off between exploring new areas of the search space to improve estimates where the variance could be significant or exploiting the current approximation to obtain better policies. We propose a new algorithm called Q-Importance Sampling (QIS), where importance sampling is defining the sampling policy than weighing costs from some other policy. The disproportionate sampling characteristic in importance sampling addresses the explore vs. exploit problem as the approximation improves. Chapter 4 integrates the hourly power system operations model in Chapter 2 without integrality and operational constraints into long term system planning model based on approximate dynamic programming developed in Chapter 3. We compare the proposed model with other multi-stage stochastic optimization methods such as progressive hedging and stochastic dual dynamic programming for solution quality and computational effort.

Download or read book Extensions of Multistage Stochastic Optimization with Applications in Energy and Healthcare written by Ludwig Charlemagne Kuznia and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation focuses on extending solution methods in the area of stochastic optimization. Attention is focused to three specific problems in the field. First, a solution method for mixed integer programs subject to chance constraints is discussed. This class of problems serves as an effective modeling framework for a wide variety of applied problems. Unfortunately, chance constrained mixed integer programs tend to be very challenging to solve. Thus, the aim of this work is to address some of these challenges by exploiting the structure of the deterministic reformulation for the problem. Second, a stochastic program for integrating renewable energy sources into traditional energy systems is developed. As the global push for higher utilization of such green resources increases, such models will prove invaluable to energy system designers. Finally, a process for transforming clinical medical data into a model to assist decision making during the treatment planning phase for palliative chemotherapy is outlined. This work will likely provide decision support tools for oncologists. Moreover, given the new requirements for the usage electronic medical records, such techniques will have applicability to other treatment planning applications in the future.

Download or read book Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers written by Stephen Boyd and published by Now Publishers Inc. This book was released on 2011 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others.

Download or read book Modeling Uncertainty Processes for Multi Stage Optimization of Strategic Energy Planning written by Frédéric Babonneau and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper deals with the modeling of stochastic processes in long-term multi-stage energy planning problems when little information is available on the degree of uncertainty of such processes. Starting from simple estimates of variation intervals for uncertain parameters, such as energy demands and costs, we model the temporal correlation of these parameters through carefully constructed autoregressive (AR) models that respect the intervals defined in each period. We introduce a coefficient for 'zigzag' effects in the evolution of uncertain processes that controls the correlation across periods and also the likelihood of extreme scenarios. To preserve the convexity of the stochastic problem, we discretize the AR models associated with the cost parameters involved in the objective function by Markov chains. The resulting formulation is then solved with a Stochastic Dual Dynamic Programming (SDDP) algorithm available in the literature that handles finite-state Markov chains. Our numerical experiments, performed on the Swiss energy system, show a very desirable adaptation strategy of investment decisions to uncertainty scenarios, a behavior that is not observed when the temporal correlation is ignored. Moreover, the solutions lead to better out-of-sample cost performances, especially on extreme scenario realizations, than the non-correlated ones which usually yield overcapacities to protect against high, but unlikely, parameter variations over time.

Download or read book A Multi stage Stochastic Integer Programming Approach for Capacity Expansion Under Uncertainty written by Shabbir Ahmed and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multiscale Control of Energy Systems written by Ranjeet Kumar and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Energy networks are operated at multiple timescales (from hours to milliseconds) to ensure that supply and demands are matched in real time. There is a great interest in designing automation architectures to exploit the fast timescale electricity markets using energy storage systems. Generating revenue in electricity markets, however, is challenging because of significant uncertainty on electricity prices and on revenue streams from different markets. A major accomplishment of this research is the development of multiscale stochastic model predictive control (MPC) framework that can be applied to systems such as energy storage systems like batteries or central HVAC plants for a large campus participating in the electricity markets and performing peak shaving applications. This computational framework integrates forecasting, uncertainty quantification, and MPC to benchmark the performance of deterministic and stochastic MPC. Through exhaustive closed-loop simulations with real data from ISOs and a typical university campus, we illustrate that off-the-shelf deterministic MPC implementations suffer significant losses in performance and constraint violations. We also developed a hierarchical MPC framework using stochastic programming to handle long horizon problems for periodic systems. We show that if the state policy of an infinite-horizon problem is periodic, the problem can be cast as a retroactive stochastic program (SP) that progressively accumulates historical data to deliver the optimal periodic states. The retroactive problem can be seen as a high-level hierarchical layer that provides targets to guide a low-level MPC controller operating over a short period at high time resolution. We derive a retroactive incremental cutting-plane scheme tailored to linear systems and suggest strategies to handle nonlinear systems and to analyze stability properties. Finally, to provide a scalable approach to handle complex MPC applications with uncertainties evolving over long time horizons and with fine time resolutions, we derived a stochastic dual dynamic programming (SDDP) framework from the perspective of MPC. Scalability is enabled by using a nested cutting-plane scheme, which uses forward and backward sweeps along the time horizon to adaptively construct and refine cost-to-go functions. We also leverage the nested decomposition scheme of SDDP to develop a dual dynamic programming scheme for long-horizon mixed-integer MPC problems.

Download or read book Stochastic Optimization for Integrated Energy System with Reliability Improvement Using Decomposition Algorithm written by Yuping Huang and published by . This book was released on 2014 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: With strong support of NG and electric facilities, the second strategy provides an optimal day-ahead scheduling for electric power generation system incorporating with non-generation resources, i.e. demand response and energy storage. Because of risk aversion, this generation scheduling enables a power system qualified with higher reliability and promotes non-generation resources in smart grid. To take advantage of power generation sources, the third strategy strengthens the change of the traditional energy reserve requirements to risk constraints but ensuring the same level of systems reliability. In this way we can maximize the use of existing resources to accommodate internal or/and external changes in power system. All problems are formulated by stochastic mixed integer programming, particularly considering the uncertainties from fuel price, renewable energy output and electricity demand over time. Taking the benefit of models structure, new decomposition strategies are proposed to decompose the stochastic unit commitment problems which are then solved by an enhanced Benders Decomposition algorithm. Compared to the classic Benders Decomposition, this proposed solution approach is able to increase convergence speed and thus reduce 25% of computation times on the same cases.

Download or read book ECAI 2016 written by G.A. Kaminka and published by IOS Press. This book was released on 2016-08-24 with total page 1860 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence continues to be one of the most exciting and fast-developing fields of computer science. This book presents the 177 long papers and 123 short papers accepted for ECAI 2016, the latest edition of the biennial European Conference on Artificial Intelligence, Europe’s premier venue for presenting scientific results in AI. The conference was held in The Hague, the Netherlands, from August 29 to September 2, 2016. ECAI 2016 also incorporated the conference on Prestigious Applications of Intelligent Systems (PAIS) 2016, and the Starting AI Researcher Symposium (STAIRS). The papers from PAIS are included in this volume; the papers from STAIRS are published in a separate volume in the Frontiers in Artificial Intelligence and Applications (FAIA) series. Organized by the European Association for Artificial Intelligence (EurAI) and the Benelux Association for Artificial Intelligence (BNVKI), the ECAI conference provides an opportunity for researchers to present and hear about the very best research in contemporary AI. This proceedings will be of interest to all those seeking an overview of the very latest innovations and developments in this field.