The topic of unit commitment has been and continues to be of interest to many researchers and is a primary operation for most utilities. Past research has utilized integer programming, dynamic programming, linear programming, gradient, and heuristic techniques. This research combines both linear programming and dynamic programming for unit commitment decisions within a weekly time frame. The result provides most of the advantages of linear programming and dynamic programming with less stringent requirements on the pre solution information needed for unit transition sequences. Further, the research yields a new tool for the solution of the Transaction Evaluation problem. / Ph. D. / incomplete_metadata

This work applies connectionist neural network learning techniques and symbolic machine learning techniques to the problem of short-term electric load forecasting. The short-term electric load forecasting problem considered here is the prediction of bus loads one day ahead. The forecast quantities of interest are average integrated daily load and daily peak load. The primary objectives of this work are two-fold: to determine the forces driving the load demand and produce a human intelligible model, and use of this model to forecast load for new, unseen scenarios. In the first part of this work, connectionist techniques for modeling bus load is presented. Critical design issues for neural network modeling and implementation such as neural network architecture, training database creation, training dataset selection, training data normalization are presented in context of nonlinear modeling in general and electric load forecasting in particular. Local function approximation and nearest neighbor norms techniques are applied to this task. Simulations are performed for forecast of average bus loads of the town of Blacksburg, Virginia, U.S.A; the connectionist model is able to forecast integrated average daily load with an accuracy of about 2.5%. Connectionist neural network knowledge acquisition algorithms are however, not mature enough, presently, to handle complex real world problems such as knowledge extraction from large databases. Presence of symbolic along with numeric data in input and output poses problems for data pre-processing for neural network training. Only at the time of completion of this thesis are researchers discussing the possibility of using special techniques to present symbolic data for neural networks. Also, multilayer feedforward networks trained by the backpropagation algorithm perform poorly in forecasting chaotic patterns such as those encountered in peak load demand. Symbolic machine learning techniques are powerful concept acquisition techniques that extract underlying knowledge from large databases. They are sufficiently powerful to accept symbolic and numeric data. Inductive learning algorithms employing a statistical 72 test as the splitting criterion are applied to extract load dependency information. The extracted patterns are expressed as graphic decision trees and equivalent human intelligible high level language if-then rules. Implementation details of the statistical decision algorithm are discussed and simulations are performed to construct decision trees. Using this model, new cases are forecast. This algorithm is capable of forecasting holiday and weekend loads too. The proposed algorithm is robust enough to handle raw, unprocessed databases which contain missing data. The peak load forecasting problem is solved using a simple methodology that combines the robustness of decision trees and the numerical accuracy of connectionist models. The two paradigms, connectionist and symbolic learning techniques are compared from a knowledge acquisition and forecasting perspective and directions for further work suggested. / Master of Science

Demand for Data Transfer between computers has increased ever since the introduction of Personal Computers (PC). Data Communicating on the Personal Computer is much more productive as it is an intelligent terminal that can connect to various hosts on the same I/O hardware circuit as well as execute processes on its own as an isolated system. Yet, the PC on its own is useless for data communication. It requires a hardware interface circuit and software for controlling the handshaking signals and setting up communication parameters. Often the data is distorted due to noise in the line. Such transmission errors are imbedded in the data and require careful filtering. The thesis deals with the development of a Data Acquisition system that collects real time load and weather data and stores them as historical database for use in a load forecast algorithm in a load management system. A filtering technique has been developed here that checks for transmission errors in the raw data. The microcomputers used in this development are the IBM PC/XT and the AT&T 3B2 supermicro computer. / Master of Science

Most of the techniques that have been applied to the short-term load forecasting problem fall within the time series approaches. The exception to this has been a new approach based on the application of expert systems. Recently several techniques have been reported which apply the rule-based (or expert systems) approach to the short-term load forecasting problem. However, the maximum lead time used for these forecasts has not gone beyond 48 hours, even though there is a significant difference between these algorithms in terms of their data base requirements (few weeks to 10 years). The work reported in this dissertation deals with two aspects. The first one is the application of rule-based techniques to weekly load forecast. A rule-based technique is presented that is capable of issuing a 168-hour lead-time load forecast. The second aspect is the development of a comprehensive load forecasting system that utilizes both the statistical and rule-based approaches. This integration overcomes the deficiencies that exist in both of these modeling techniques. The load forecasting technique is developed using two parallel approaches. In the first approach expert information is used to identify weather variables, day types and diurnal effects that influence the electrical utility load. These parameters and hourly historical loads are then selectively used for various statistical techniques (e.g., univariate, transfer function and linear regression). A weighted average load forecast is then produced which judiciously combines the forecasts from these three techniques. The second approach, however, is free of any significant statistical computation, and is based totally on rules derived from electric utility experts. The data base requirement for any of these approaches do not extend more than four weeks ol hourly load, dry bulb and dew point temperatures. When the algorithms are applied to generate seven-day ahead load forecasts for summer (August) and winter (February) the average forecast errors for the month come under 3%. / Ph. D.

Demand Side Management (DSM) has begun to emerge as a major component of utility planning, with more utilities than ever before using it to help meet their own needs and those of their customers. DSM encompasses utility and customer activities aimed at modifying load shape, which embodies the timing and level of customer electricity demand. Future load shapes will result from the combined effect of individual DSM programs seeking specific load shape objectives. Load Duration Curve (LDC) is the vehicle through which DSM impacts are incorporated into power system planning and operation. Models of the LDC is one of the most important tools in the analysis of electric power system. The DSM will affect the peak load, the base load and total energy demand of the load duration curve. Those three impacts have to be explicitly modeled into the load duration curve for properly representing the effects of demand side management activities. However, the available models cannot properly represent the impacts of demand side management into load duration curve, because they do not explicitly model those three variables into their load duration curve. A new model that can incorporate the effects of demand side management is needed by utilities to help them with planning and operation. A new way to directly model the inverted load duration curve (ILDC) is presented in this study thus facilitating the representation of DSM impacts. Peak Load, base load and total energy demand are the variables of the new model. Using DSM activities as case studies, the new model produced good results compared to other widely used models, in term of reliability indices (LOLP and ENS) and total energy under the load duration curve. The flexibility, simplicity and the speed of execution of the new model in calculating the reliability indices are demonstrated. The capability of the new model to calculate the capacity credit is also presented. As a result of its ability to represent energy under load duration curve, the new model is inserted into WASP computer program to calculate the production cost. Results obtained from the new model (modified WASP) compared to results from original WASP are very close. Based on these capabilities it can be claimed that the new ILDC model is a better overall model and can be used as an alternative load model in utility planning and operation. / Ph. D.

The problems involved in load forecasting and long-term generation expansion have been discussed and techniques for load forecasting, reliability evaluation and optimal generation expansion analyzed. The results of a sample generation expansion plan using the Capacity Expansion and Reliability System (CERES) are shown to demonstrate typical inputs and outputs of an optimal plan. / M.S.

This thesis is a study of the Weatherford Municipal Light and Power Plant. An attempt has been made to trace the history of electric service in Weatherford, Texas, and to reveal why this present, and previous, service has culminated in a municipally owned system.

To better understand abiotic dynamics in Southern reservoirs receiving heated effluents, water was analyzed before and after impoundment in 330 ha North Lake. Macronutrients, metals, and chlorinated hydrocarbons were measured. Concentrations of nutrients and metals in sediments were quantified in this 2 yr study. River water prior to impoundment contained 16 times more total phosphorus, and supported 23 times more Selenastrum capricornutum cells in an algal assay than reservoir water. The reservoir has essentially no drainage and since evaporation is high, the concentrations of many dissolved solids have increased since the reservoir was filled in 1958. North Lake is now phosphorus limited. Apparently altered chemical equilibria have caused precipitation or adsorption of phosphorus with calcium and iron.

Project Completion Report, OWRT Project No. A-043-ARIZ / Agreement No. 14-31-0001-4003 / Project Dates: July 1973 - June 1974 / Acknowledgment: The work upon which this report is based was supported in large part by funds provided by the United States Department of the Interior, Office of Water Research and Technology, as authorized under the Water Resources Research Act of 1964. / A growing demand for energy production in Arizona has increased the need for assembling and analyzing water resource information relative to energy production, especially electrical power generation. Unit water requirements for cooling of electrical plants, combined with projections of future electrical power demands in Arizona, provide a perspective on future quantities of water needed for cooling. Probabilistic estimates of storage reserves in Arizona groundwater basins indicate that some prospective plant sites can be supplied from groundwater for the 30 -year life of the plant, while others cannot. An estimate of comparative cost for supplying groundwater versus municipal wastewater for cooling electrical plants at selected sites in Arizona showed that use of wastewater would result in considerable savings over use of groundwater, at all sites considered.

Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1978 / Includes bibliographical references. / by Sherman M. Chan. / M.S. / M.S. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science