Around the world, liberalization and privatization in the electricity industry have lead to increased competition among utilities. At the same time, utilities are now exposed more than ever to risk and uncertainties, which they cannot pass on to their customers through price increases as in a regulated environment. Especially electricity-generating companies have to face volatile wholesale prices, fuel price uncertainty, limited long-term hedging possibilities and huge, to a large extent, sunk investments. In this context, Uncertainty in the Electric Power Industry: Methods and Models for Decision Support aims at an integrative view on the decision problems that power companies have to tackle. It systematically examines the uncertainties power companies are facing and develops models to describe them - including an innovative approach combining fundamental and finance models for price modeling. The optimization of generation and trading portfolios under uncertainty is discussed with particular focus on CHP and is linked to risk management. Here the concept of integral earnings at risk is developed to provide a theoretically sound combination of value at risk and profit at risk approaches, adapted to real market structures and market liquidity. Also methods for supporting long-term investment decisions are presented: technology assessment based on experience curves and operation simulation for fuel cells and a real options approach with endogenous electricity prices.

The electric power industry is undergoing the greatest transformation in its 100-year history. In readable, concise fashion, author Denise Warkentin explains how the electric industry works and what changes are in store. After briefly tracing the history of the industry, she details how different segments are structured and work together. Investor-owned, consumer-owned, and government-owned utilities are explained, as are rural cooperatives and independent power producers. Other issues addressed include deregulation, the emergence of energy marketers, and the impact of ongoing mergers, acquisitions, and consolidations.

Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add in developing data-oriented computational programs in the power industry, with a particular focus on thoughtfully chosen real-world case studies. It provides a set of realistic pathways for organizations seeking to develop machine learning methods, with a discussion on data selection and curation as well as organizational implementation in terms of staffing and continuing operationalization. It articulates a body of case study–driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, and forecasting. - Provides best practices on how to design and set up ML projects in power systems, including all nontechnological aspects necessary to be successful - Explores implementation pathways, explaining key ML algorithms and approaches as well as the choices that must be made, how to make them, what outcomes may be expected, and how the data must be prepared for them - Determines the specific data needs for the collection, processing, and operationalization of data within machine learning algorithms for power systems - Accompanied by numerous supporting real-world case studies, providing practical evidence of both best practices and potential pitfalls

This book makes intelligible the wide range of electricity generating technologies available today, as well as some closely allied technologies such as energy storage. The book opens by setting the many power generation technologies in the context of global energy consumption, the development of the electricity generation industry and the economics involved in this sector. A series of chapters are each devoted to assessing the environmental and economic impact of a single technology, including conventional technologies, nuclear and renewable (such as solar, wind and hydropower). The technologies are presented in an easily digestible form.Different power generation technologies have different greenhouse gas emissions and the link between greenhouse gases and global warming is a highly topical environmental and political issue. With developed nations worldwide looking to reduce their emissions of carbon dioxide, it is becoming increasingly important to explore the effectiveness of a mix of energy generation technologies.Power Generation Technologies gives a clear, unbiased review and comparison of the different types of power generation technologies available. In the light of the Kyoto protocol and OSPAR updates, Power Generation Technologies will provide an invaluable reference text for power generation planners, facility managers, consultants, policy makers and economists, as well as students and lecturers of related Engineering courses.· Provides a unique comparison of a wide range of power generation technologies - conventional, nuclear and renewable· Describes the workings and environmental impact of each technology· Evaluates the economic viability of each different power generation system

How the interplay between government regulation and the private sector has shaped the electric industry, from its nineteenth-century origins to twenty-first-century market restructuring. For more than a century, the interplay between private, investor-owned electric utilities and government regulators has shaped the electric power industry in the United States. Provision of an essential service to largely dependent consumers invited government oversight and ever more sophisticated market intervention. The industry has sought to manage, co-opt, and profit from government regulation. In The Power Brokers, Jeremiah Lambert maps this complex interaction from the late nineteenth century to the present day. Lambert's narrative focuses on seven important industry players: Samuel Insull, the principal industry architect and prime mover; David Lilienthal, chairman of the Tennessee Valley Authority (TVA), who waged a desperate battle for market share; Don Hodel, who presided over the Bonneville Power Administration (BPA) in its failed attempt to launch a multi-plant nuclear power program; Paul Joskow, the MIT economics professor who foresaw a restructured and competitive electric power industry; Enron's Ken Lay, master of political influence and market-rigging; Amory Lovins, a pioneer proponent of sustainable power; and Jim Rogers, head of Duke Energy, a giant coal-fired utility threatened by decarbonization. Lambert tells how Insull built an empire in a regulatory vacuum, and how the government entered the electricity marketplace by making cheap hydropower available through the TVA. He describes the failed overreach of the BPA, the rise of competitive electricity markets, Enron's market manipulation, Lovins's radical vision of a decentralized industry powered by renewables, and Rogers's remarkable effort to influence cap-and-trade legislation. Lambert shows how the power industry has sought to use regulatory change to preserve or secure market dominance and how rogue players have gamed imperfectly restructured electricity markets. Integrating regulation and competition in this industry has proven a difficult experiment.

`Electric energy must be treated as a commodity which can be bought, sold, and traded, taking into account its time- and space-varying values and costs.` Spot Pricing of Electricity, Schweppe et al, 1988. Computational Auction Mechanisms for Restructured Power Industry Operation outlines the application of auction methods for all aspects of power system operation, primarily for a competitive environment. A complete description of the industry structure as well as the various markets now being formed is given. A thorough introduction to auction basics is included to explain how auctions have grown in other industries. Auction methods are compared to classical techniques for power system analysis, operations, and planning. The traditional applications of economic dispatch, optimal power flow and unit commitment are compared to auction mechanisms. Algorithms for auctions using linearized power flow equations, DC power flow equations, and AC power flow equations are included. The bundling of supportive services, known as ancillary services within the United States, is discussed. Extensions to the basic auction algorithms for inclusion of supportive services as well as algorithms for scheduling and bidding on generation for GENCOs or independent power producers are presented. Algorithms for scheduling and contracting with customers are also presented for energy service companies. An introduction to the various commodity and financial market products includes the use of futures and options for GENCOs. The material is useful for students performing research on the new business environment based on competition. Regulators will find information on initial methods of designing and evaluating market systems, and power exchange and financial analysts will find information on the interdependence of markets and power system-based techniques for risk management. This information compares the new business environment solutions with old business environment solutions. Computational Auction Mechanisms for Restructured Power Industry Operation provides a first introduction to how electricity will be traded as a commodity in the future.

This publication provides industry data on electric power, including generating capability, generation, fuel consumption, cost of fuels, and retail sales and revenue.