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Many managerial decisions impact the energy consumption of discrete manufacturing firms. Since an energy amount to be consumed in manufacturing systems is closely connected to energy costs and environmental consequences, these managerial decisions can have long-lasting effects. Hence, making informed decisions with the aid of energy estimation tools is important to manufacturing firms. Estimating energy consumption in manufacturing is not, however, straightforward. There are a number of different manufacturing processes, and the energy consumption of each process is dependent on many operational parameters. Thus, for a better manufacturing energy analysis, the power profiles need to be collected and analyzed from real manufacturing machines, and various methods including analytical and simulation approaches should be proposed and tested based on the collected data. Furthermore, since many previous studies are focusing mainly on mean power demands for evaluating energy consumption, variability of manufacturing power demands also has to be investigated for exploring how the uncertainty impacts on manufacturing energy.In order to address the issues, this dissertation proposes various but useful methods. At the beginning, this study shows an analytical manufacturing energy model based on queueing network theory. Considering Markovian and Non-Markovian assumptions, we present the manufacturing energy consumption in a closed form equation. Then, our analysis develops the previous model further for energy efficiency benchmarking. Comparing the manufacturing energy in a hypothetical system with peers in the U.S., the proposed model shows how to assess the energy efficiency in a manufacturing plant based on the simulation and stochastic frontier analysis approaches. After the energy estimation and energy efficiency assessment are discussed, our study transcends the previous studies by considering uncertainty and variability on the manufacturing electrical demands. Our approach presents the benefits of considering uncertainty in the manufacturing power demands, and proposes a systemic method to estimate the mean and uncertainty by applying probabilistic techniques. At each discussion, the proposed method is validated and verified in a suitable manner, and the accuracy of the proposed method is also checked in detail.
Now in a thoroughly revised and expanded second edition, this classroom-tested text demonstrates and illustrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability, statistics, experimental design, regression, optimization, parameter estimation, inverse modeling, risk analysis, decision-making, and sustainability assessment methods to energy processes and systems. It provides a formal structure that offers a broad and integrative perspective to enhance knowledge, skills, and confidence to work in applied data analysis and modeling problems. This new edition also reflects recent trends and advances in statistical modeling as applied to energy and building processes and systems. It includes numerous examples from recently published technical papers to nurture and stimulate a more research-focused mindset. How the traditional stochastic data modeling approaches are complemented by data analytic algorithmic models such as machine learning and data mining are also discussed. The important societal issues related to the sustainability of energy systems are presented, and a formal structure is proposed meant to classify the various assessment methods found in the literature. Applied Data Analysis and Modeling for Energy Engineers and Scientists is designed for senior-level undergraduate and graduate instruction in energy engineering and mathematical modeling, for continuing education professional courses, and as a self-study reference book for working professionals. In order for readers to have exposure and proficiency with performing hands-on analysis, the open-source Python and R programming languages have been adopted in the form of Jupyter notebooks and R markdown files, and numerous data sets and sample computer code reflective of real-world problems are available online.
Most industrial and natural materials exhibit a macroscopic behaviour which results from the existence of microscale inhomogeneities. The influence of such inhomogeneities is commonly modelled using probabilistic methods. Most of the approaches to the evaluation of the safety of structures according to probabilistic criteria are somewhat scattered, however, and it is time to present such material in a coherent and up-to-date form. Probabilities and Materials undertakes this task, and also defines the great tasks that must be tackled in coming years. For engineers and researchers dealing with materials, geotechnics, solid mechanics, soil mechanics, statistics and stochastic processes. The expository nature of the book means that no prior knowledge of statistics or probability is required of the reader. The book can thus serve as an excellent introduction to the nature of applied statistics and stochastic modelling.
Applied Data Analysis and Modeling for Energy Engineers and Scientists fills an identified gap in engineering and science education and practice for both students and practitioners. It demonstrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability,statistics, experimental design, regression, model building, optimization, risk analysis and decision-making to actual engineering processes and systems. The text provides a formal structure that offers a basic, broad and unified perspective,while imparting the knowledge, skills and confidence to work in data analysis and modeling. This volume uses numerous solved examples, published case studies from the author’s own research, and well-conceived problems in order to enhance comprehension levels among readers and their understanding of the “processes”along with the tools.
This textbook provides an introduction to probabilistic reliability analysis of power systems. It discusses a range of probabilistic methods used in reliability modelling of power system components, small systems and large systems. It also presents the benefits of probabilistic methods for modelling renewable energy sources. The textbook describes real-life studies, discussing practical examples and providing interesting problems, teaching students the methods in a thorough and hands-on way. The textbook has chapters dedicated to reliability models for components (reliability functions, component life cycle, two-state Markov model, stress-strength model), small systems (reliability networks, Markov models, fault/event tree analysis) and large systems (generation adequacy, state enumeration, Monte-Carlo simulation). Moreover, it contains chapters about probabilistic optimal power flow, the reliability of underground cables and cyber-physical power systems. After reading this book, engineering students will be able to apply various methods to model the reliability of power system components, smaller and larger systems. The textbook will be accessible to power engineering students, as well as students from mathematics, computer science, physics, mechanical engineering, policy & management, and will allow them to apply reliability analysis methods to their own areas of expertise.
The roles and applications of various modeling approaches, aimed at improving the usefulness of energy policy models in public decision making, are covered by this book. The development, validation, and applications of system dynamics and agent-based models in service of energy policy design and assessment in the 21st century is a key focus. A number of modeling approaches and models for energy policy, with a particular focus on low-carbon economic development of regions and states are covered. Chapters on system dynamics methodology, model-based theory, fuzzy system dynamics frame-work, and optimization modeling approach are presented, along with several chapters on future research opportunities for the energy policy modeling community. The use of model-based analysis and scenarios in energy policy design and assessment has seen phenomenal growth during the past several decades. In recent years, renewed concerns about climate change and energy security have posed unique modeling challenges. By utilizing the validation techniques and procedures which are effectively demonstrated in these contributions, researchers and practitioners in energy systems domain can increase the appeal and acceptance of their policy models.
Production cost, generation expansion, and reliability models are used extensively by utilities in the planning process. However, many of these models do not provide adequate means for representing the full range of potential variation in wind power plants. In order to properly account for expected variation in wind-generated electricity in these models, we describe an enumerated probabilistic approach that can be performed outside the production cost model, compare it with a reduced enumerated approach, and present some selected utility results. Our technique can be applied to any model, and can result in a considerable reduction in model runs. We use both a load duration curve model and a chronological model to measure wind plant capacity credit, and also present some other selected results.
The markets for electricity, gas and temperature have distinctive features, which provide the focus for countless studies. For instance, electricity and gas prices may soar several magnitudes above their normal levels within a short time due to imbalances in supply and demand, yielding what is known as spikes in the spot prices. The markets are also largely influenced by seasons, since power demand for heating and cooling varies over the year. The incompleteness of the markets, due to nonstorability of electricity and temperature as well as limited storage capacity of gas, makes spot-forward hedging impossible. Moreover, futures contracts are typically settled over a time period rather than at a fixed date. All these aspects of the markets create new challenges when analyzing price dynamics of spot, futures and other derivatives. This book provides a concise and rigorous treatment on the stochastic modeling of energy markets. OrnsteinOCoUhlenbeck processes are described as the basic modeling tool for spot price dynamics, where innovations are driven by time-inhomogeneous jump processes. Temperature futures are studied based on a continuous higher-order autoregressive model for the temperature dynamics. The theory presented here pays special attention to the seasonality of volatility and the Samuelson effect. Empirical studies using data from electricity, temperature and gas markets are given to link theory to practice. Sample Chapter(s). A Survey of Electricity and Related Markets (331 KB). Contents: A Survey of Electricity and Related Markets; Stochastic Analysis for Independent Increment Processes; Stochastic Models for the Energy Spot Price Dynamics; Pricing of Forwards and Swaps Based on the Spot Price; Applications to the Gas Markets; Modeling Forwards and Swaps Using the HeathOCoJarrowOCoMorton Approach; Constructing Smooth Forward Curves in Electricity Markets; Modeling of the Electricity Futures Market; Pricing and Hedging of Energy Options; Analysis of Temperature Derivatives. Readership: Researchers in energy and commodity markets, and mathematical finance.
This book explores the principles of probabilistic decision theory and shows how they work in real world situations. Decision theory is the calculus of uncertain outcomes and preferences and values. The great mathematician and economist Thomas Schelling defines decision theory as the science of choosing in accordance with one's existing preferences, maximizing the satisfaction of one's values. As the science of choosing, decision theory is the natural quantitative foundation of medical care and research and policy making. Decision theory is not fundamental. Unlike physics decision theory is not a set of basic laws and fundamental truths; it discovers no natural phenomena or concepts hidden from us and waiting to be found. Decision theory is a practical instrument which helps translate one's observations and objectives and values and preferences into one's actions. As a practical instrument decision theory is a set of invented rules and useful mathematical methods and tools. The role of these rules and methods and tools is to link human knowledge and assumptions (models) with captured data (observables) and with human preferences and values (valuables) and translate them into choices and, ultimately, actions. We invent these rules and methods and tools and we keep them only because they make sense and because they work. This book is a primer of probabilistic decision theory written for medical professionals, scientists and policy makers. It is a collection of mostly independent essays. Except for a few relatively tough spots which are marked as advanced, mathematics in this book is reasonably accessible.