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This report includes several papers on modeling and forecasting electricity demands by time -of -day that were presented at a workshop in San Diego, June 11-14, 1978.The papers and the accompanying discussants' comments present a cross section of the state of the art in research on the responsiveness of electricity demands to time -of -day rates. Preliminary analyses of several residential peak -load -pricing experiments present diverse estimates of the responsiveness of household electricity demand to time -of -day prices. As yet, there are few results that are directly applicable to utility forecasting and planning, however these analyses undoubtedly lay the foundation for useful results in the near future. There is only a small amount of data and even less analysis on the price responsiveness of load patterns in the commercial and industrial sectors. The volume is concluded with several insightful commentators' overviews of where the state of the art is and where it ought to be extended.
The fIrst oil crisis of 1973-74 and the questions it raised in the economic and social fIelds drew attention to energy issues. Industrial societies, accustomed for two decades or more to energy sufficiently easy to produce and cheap to consume that it was thought to be inexhaustible, began to question their energy future. The studies undertaken at that time, and since, on a national, regional, or world level were over-optimistic. The problem seemed simple enough to solve. On the one hand, a certain number of resources: coal, the abundance of which was discovered, or rather rediscovered oil, source of all the problems ... In fact, the problems seemed to come, if not from oil itself (an easy explanation), then from those who produced it without really owning it, and from those who owned it without really control ling it natural gas, second only to oil and less compromised uranium, all of whose promises had not been kept, but whose resources were not in question solar energy, multiform and really inexhaustible thermonuclear fusion, and geothermal energy, etc. On the other hand, energy consumption, though excessive perhaps, was symbolic of progress, development, and increased well being. The originality of the energy policies set up since 1974 lies in the fact they no longer aimed to produce (or import) more, but to consume less. They sought, and still seek, what might be emphatically called the control of energy consump tion, or rather the control of energy demand.
The master thesis of Kevin Berk develops a stochastic model for the electricity demand of small and medium-sized companies that is flexible enough so that it can be used for various business sectors. The model incorporates the grid load as an exogenous factor and seasonalities on a daily, weekly and yearly basis. It is demonstrated how the model can be used e.g. for estimating the risk of retail contracts. The uncertainty of electricity demand is an important risk factor for customers as well as for utilities and retailers. As a consequence, forecasting electricity load and its risk is now an integral component of the risk management for all market participants.
This book offers an in-depth and up-to-date review of different statistical tools that can be used to analyze and forecast the dynamics of two crucial for every energy company processes—electricity prices and loads. It provides coverage of seasonal decomposition, mean reversion, heavy-tailed distributions, exponential smoothing, spike preprocessing, autoregressive time series including models with exogenous variables and heteroskedastic (GARCH) components, regime-switching models, interval forecasts, jump-diffusion models, derivatives pricing and the market price of risk. Modeling and Forecasting Electricity Loads and Prices is packaged with a CD containing both the data and detailed examples of implementation of different techniques in Matlab, with additional examples in SAS. A reader can retrace all the intermediate steps of a practical implementation of a model and test his understanding of the method and correctness of the computer code using the same input data. The book will be of particular interest to the quants employed by the utilities, independent power generators and marketers, energy trading desks of the hedge funds and financial institutions, and the executives attending courses designed to help them to brush up on their technical skills. The text will be also of use to graduate students in electrical engineering, econometrics and finance wanting to get a grip on advanced statistical tools applied in this hot area. In fact, there are sixteen Case Studies in the book making it a self-contained tutorial to electricity load and price modeling and forecasting.
Since 1970 the United States government has spent over half a billion dollars on social experiments intended to assess the effect of potential tax policies, health insurance plans, housing subsidies, and other programs. Was it worth it? Was anything learned from these experiments that could not have been learned by other, and cheaper, means? Could the experiments have been better designed or analyzed? These are some of the questions addressed by the contributors to this volume, the result of a conference on social experimentation sponsored in 1981 by the National Bureau of Economic Research. The first section of the book looks at four types of experiments and what each accomplished. Frank P. Stafford examines the negative income tax experiments, Dennis J. Aigner considers the experiments with electricity pricing based on time of use, Harvey S. Rosen evaluates housing allowance experiments, and Jeffrey E. Harris reports on health experiments. In the second section, addressing experimental design and analysis, Jerry A. Hausman and David A. Wise highlight the absence of random selection of participants in social experiments, Frederick Mosteller and Milton C. Weinstein look specifically at the design of medical experiments, and Ernst W. Stromsdorfer examines the effects of experiments on policy. Each chapter is followed by the commentary of one or more distinguished economists.