This addition to the ISOR series is a readable yet rigorous advanced text/reference on models and decision-making under uncertainty in the growing area of electricity markets. It is the first book to show how to use stochastic programming procedures to carry out in-depth analysis of decision-making models under uncertainty in these markets, including formulation issues and solution techniques. Due to the recent creation of futures markets for electricity in the past decade, much of the book is groundbreaking and reflects the most recent advances in operations research and its application in energy markets in general.

An electricity market is simply a system for effecting the purchase and sale of electricity using supply and demand to set the price. These markets are competitive, and have been a growing worldwide trend since the 1980 s, and coming to prominence (and notoriety) in 2001 when both the California electricity crisis and the Enron scandal occurred. Though the phenomenon of the electricity market grew from deregulation, and will likely continue to move toward increased openness, the situation in California resulted entirely from faulty regulation, particularly in modeling risk. The fact is, there are so many constraints to consider in modeling these markets, with so many possible points of failure, that it s a wonder it s taken this long for a rigorous text on stochastic programming to appear.

This is an advanced expository book on solving the most current and relevant short- and medium-term decision-making problems pertaining to producers, consumers, retailers, and market operators. Among its unique features: it addresses essentially all operational problems that arise in electricity markets; practical applications are developed up to the stage of working algorithms, coded in the GAMS (General Algebraic Modeling System) so that practitioners can put the book to use immediately; applications encompass areas in applied mathematics and business, as well as electrical and energy engineering; it presents a unified treatment of risk; it includes two chapters on wind power; and it provides an appropriate blend of theoretical background and practical applications. It can be used in graduate level courses (or Conejo s own PhD course in electricity markets) in a broad range of programs, whether economic, mathematic, or engineering, and will also be well-suited for the practitioner.

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The study of animal movement has always been a key element in ecological science, because it is inherently linked to critical processes that scale from individuals to populations and communities to ecosystems. Rapid improvements in biotelemetry data collection and processing technology have given rise to a variety of statistical methods for characterizing animal movement. The book serves as a comprehensive reference for the types of statistical models used to study individual-based animal movement. Animal Movement is an essential reference for wildlife biologists, quantitative ecologists, and statisticians who seek a deeper understanding of modern animal movement models. A wide variety of modeling approaches are reconciled in the book using a consistent notation. Models are organized into groups based on how they treat the underlying spatio-temporal process of movement. Connections among approaches are highlighted to allow the reader to form a broader view of animal movement analysis and its associations with traditional spatial and temporal statistical modeling. After an initial overview examining the role that animal movement plays in ecology, a primer on spatial and temporal statistics provides a solid foundation for the remainder of the book. Each subsequent chapter outlines a fundamental type of statistical model utilized in the contemporary analysis of telemetry data for animal movement inference. Descriptions begin with basic traditional forms and sequentially build up to general classes of models in each category. Important background and technical details for each class of model are provided, including spatial point process models, discrete-time dynamic models, and continuous-time stochastic process models. The book also covers the essential elements for how to accommodate multiple sources of uncertainty, such as location error and latent behavior states. In addition to thorough descriptions of animal movement models, differences and connections are also emphasized to provide a broader perspective of approaches.

This addition to the ISOR series addresses the analytics of the operations of electric energy systems with increasing penetration of stochastic renewable production facilities, such as wind- and solar-based generation units. As stochastic renewable production units become ubiquitous throughout electric energy systems, an increasing level of flexible backup provided by non-stochastic units and other system agents is needed if supply security and quality are to be maintained. Within the context above, this book provides up-to-date analytical tools to address challenging operational problems such as: * The modeling and forecasting of stochastic renewable power production. * The characterization of the impact of renewable production on market outcomes. * The clearing of electricity markets with high penetration of stochastic renewable units. * The development of mechanisms to counteract the variability and unpredictability of stochastic renewable units so that supply security is not at risk. * The trading of the electric energy produced by stochastic renewable producers. * The association of a number of electricity production facilities, stochastic and others, to increase their competitive edge in the electricity market. * The development of procedures to enable demand response and to facilitate the integration of stochastic renewable units. This book is written in a modular and tutorial manner and includes many illustrative examples to facilitate its comprehension. It is intended for advanced undergraduate and graduate students in the fields of electric energy systems, applied mathematics and economics. Practitioners in the electric energy sector will benefit as well from the concepts and techniques explained in this book.

The study of animal movement has always been a key element in ecological science, because it is inherently linked to critical processes that scale from individuals to populations and communities to ecosystems. Rapid improvements in biotelemetry data collection and processing technology have given rise to a variety of statistical methods for characterizing animal movement. The book serves as a comprehensive reference for the types of statistical models used to study individual-based animal movement. Animal Movement is an essential reference for wildlife biologists, quantitative ecologists, and statisticians who seek a deeper understanding of modern animal movement models. A wide variety of modeling approaches are reconciled in the book using a consistent notation. Models are organized into groups based on how they treat the underlying spatio-temporal process of movement. Connections among approaches are highlighted to allow the reader to form a broader view of animal movement analysis and its associations with traditional spatial and temporal statistical modeling. After an initial overview examining the role that animal movement plays in ecology, a primer on spatial and temporal statistics provides a solid foundation for the remainder of the book. Each subsequent chapter outlines a fundamental type of statistical model utilized in the contemporary analysis of telemetry data for animal movement inference. Descriptions begin with basic traditional forms and sequentially build up to general classes of models in each category. Important background and technical details for each class of model are provided, including spatial point process models, discrete-time dynamic models, and continuous-time stochastic process models. The book also covers the essential elements for how to accommodate multiple sources of uncertainty, such as location error and latent behavior states. In addition to thorough descriptions of animal movement models, differences and connections are also emphasized to provide a broader perspective of approaches.

This addition to the ISOR series addresses the analytics of the operations of electric energy systems with increasing penetration of stochastic renewable production facilities, such as wind- and solar-based generation units. As stochastic renewable production units become ubiquitous throughout electric energy systems, an increasing level of flexible backup provided by non-stochastic units and other system agents is needed if supply security and quality are to be maintained. Within the context above, this book provides up-to-date analytical tools to address challenging operational problems such as: * The modeling and forecasting of stochastic renewable power production. * The characterization of the impact of renewable production on market outcomes. * The clearing of electricity markets with high penetration of stochastic renewable units. * The development of mechanisms to counteract the variability and unpredictability of stochastic renewable units so that supply security is not at risk. * The trading of the electric energy produced by stochastic renewable producers. * The association of a number of electricity production facilities, stochastic and others, to increase their competitive edge in the electricity market. * The development of procedures to enable demand response and to facilitate the integration of stochastic renewable units. This book is written in a modular and tutorial manner and includes many illustrative examples to facilitate its comprehension. It is intended for advanced undergraduate and graduate students in the fields of electric energy systems, applied mathematics and economics. Practitioners in the electric energy sector will benefit as well from the concepts and techniques explained in this book.

Decision Making Under Uncertainty in Electricity Markets provides models and procedures to be used by electricity market agents to make informed decisions under uncertainty. These procedures rely on well established stochastic programming models, which make them efficient and robust. Particularly, these techniques allow electricity producers to derive offering strategies for the pool and contracting decisions in the futures market. Retailers use these techniques to derive selling prices to clients and energy procurement strategies through the pool, the futures market and bilateral contracting. Using the proposed models, consumers can derive the best energy procurement strategies using the available trading floors. The market operator can use the techniques proposed in this book to clear simultaneously energy and reserve markets promoting efficiency and equity. The techniques described in this book are of interest for professionals working on energy markets, and for graduate students in power engineering, applied mathematics, applied economics, and operations research.