The thesis presents a method of committing generating units in a hydro-thermal power system within practical computer resources such as computer time and data storage.

The Masters Thesis research focuses on reactive power and voltage control during and following major power system disturbances such as faults and subsequent loss of transmission line(s) or generator(s), voltage recovery phenomena following successful fault clearing, dynamic swings of power systems and local voltage suppression, etc. During these events, load and other system dynamics may cause reactive power deficiencies and system voltage issues such as delayed voltage recovery. These phenomena may lead to secondary events such as tripping of loads and/or circuits. Dynamic VAr sources such as generators, static VAr compensators (SVCs), STATCOMs etc and to a lesser degree static VAr sources such as capacitor or reactor banks, can help the system recover from these contingencies by providing fast modulation of the reactive power. Because of the higher cost of dynamic VAr resources, it is important to optimize the deployment of these devices by minimizing the total installed capacity of dynamic VAR resources while meeting the technical requirement and achieving the necessary performance of the system. We refer to this problem as the optimal allocation of dynamic VAR sources (OAODVARS). OAODVARS has been addressed with traditional analytic methods as well as with Artificial Intelligence methods such as genetic algorithms and Tabu search using mostly power flow type models. Both type of methods, as reported in the literature, have not provided satisfactory solutions because they ignore system dynamics and especially load dynamics, in other words they are based on power flow type models. In addition the AI methods have been proved to be extremely inefficient. We propose a new approach that has the following two advantages: (a) it is based on a realistic model that captures system dynamics and (b) it is based on the efficient successive approximation dynamic programming. The solution is provided as a sequence of planning decisions over the planning horizon. The proposed method will be demonstrated on the IEEE 24-bus reliability test system.

Univ., Copernicus Institute for Sustainable Development and Innovation, Diss.--Utrecht, 2006. / Zsfassung in niederländ. Sprache.

The demand for fossil fuels by Ontario's conventional steam power generation sector is examined. It is hypothesised that the enactment of a carbon fee policy will induce a change in the relative prices of the three fuels used in this sector (coal, natural gas and heavy fuel oil). This would lead to substantial interfuel substitution and greenhouse gas abatement. The demand share equations for the three fuels are derived from the translog functional form and set in a simulation model to estimate the value of a carbon fee necessary, to reduce carbon dioxide emissions in compliance with the Kyoto Protocol. Results suggest that a fuel specific carbon fee policy would be successful in achieving the desired emissions reduction at a negligible net cost to society.

Thesis (Ph. D.)--University of Hong Kong, 2008. / Includes bibliographical references (leaf 127-140) Also available in print.

Thesis (Ph. D.)--University of Hong Kong, 2008. / Includes bibliographical references (leaf 127-140) Also available online.

Published jointly by Ralph M. Parsons Laboratory for Water Resources and Hydrodynamics, Dept. of Civil Engineering, Massachusetts Institute of Technology / Sponsored by Northeast Utilities Service Company, New England Power Service Company under the MIT Energy Laboratory Electric Power Program.

Thesis submitted in fulfilment of the requirements for the degree Master of Technology: Discipline Electrical Engineering in the Faculty of Engineering at the Cape Peninsula University of Technology 2008 / Optimal daily operation of electric power generating plants is very essential for any power utility organization to reduce input costs and possibly the prices of electricity in general. For a fossil fuel – fired power plant for example, the benefits of power generation optimalization (i.e. generate what is reasonably required) extends even to environmental issues such as the subsequent reduction in air pollution. Now to generate “what is reasonably required” one needs forecast the future electricity demands. Because power generation relies heavily on the electricity demand, the consumers are also practically speaking required to wisely manage their loads to consolidate the power utility’s optimal power generation efforts. Thus, for both cases, accurate and reliable electric load forecasting systems are absolutely required. To date, there are numerous forecasting methods developed primarily for electric load forecasting. Some of these forecasting techniques are conventional and often less favoured. To get a broad picture of the problem at hand, a literature survey was first conducted to identify possible drawbacks of the existing forecasting techniques including the conventional one. Artificial neural networks (ANNs) approach for short-term load forecasting (STLF) has been recently proposed by a majority of researchers. But there still is a need to find optimal neural network structures or convenient training approach that would possibly improve the forecasting accuracy. This thesis developed models for STLF using ANNs approach. The evolved models are intended to be a basis for real forecasting application. These models are tested using actual load data of the Cape Peninsula University of Technology (CPUT) Bellville campus reticulation network and weather data to predict the load of the campus for one week in advance. The models were divided into two classes: first, forecasting the load for a whole week at once was evaluated, and then hourly models were studied. In both cases, the inclusion of weather data was considered. The test results showed that the hour-by-hour approach is more suitable and efficient for a forecasting application. The work suggests that incremental training approach of a neural network model should be implemented for on-line testing application to acquire a universal final view on its applicability. Keywords – power system operations, load forecasting, artificial neural networks, training mode, accuracy

D.Ing. / This thesis initially reviews current empirical and probabilistic electrical load models available to distribution design engineers today to calculate voltage regulation levels in low voltage residential, commercial and light industrial consumer networks. Although both empirical and probabilistic techniques have extensively been used for residential consumers in recent years, it has been concluded that commercial and light industrial consumer loads have not been a focus area of probabilistic load study for purposes of low voltage feeder design. However, traditional empirical techniques, which include adjustments for diversity to accommodate non-coincidental electrical loading conditions, have generally been found to be applied using in-house design directives with only a few international publications attempting to address the problem. This work defines the light industrial group of consumers in accordance with its international Standard Industrial Classification (SIC) and presents case studies on a small group of three different types of light industrial sub-classes, It is proposed and proved that the electrical load models can satisfactorily be described as beta-distributed load current models at the instant of group or individual maximum power demand on typical characteristic 24-hour load cycles. Characteristic mean load profiles were obtained by recording repetitive daily loading of different sub-classes, ensuring adequate sample size at all times. Probabilistic modelling of light industrial loads using beta-distributed load current at maximum demand is a new innovation in the modelling of light industrial loads. This work is further -complemented by the development of a new probabilistic summation algorithm in spreadsheet format. This algorithm adds any selected number of characteristic load current profiles, adjusted for scale, power factor, and load current imbalance, and identifies the combined instant of group or system maximum demand. This spreadsheet also calculates the characteristic beta pdf parameters per phase describing the spread and profile of the combined system loading at maximum demand. These parameters are then conveniently used as input values to existing probabilistic voltage regulation algorithms to calculate voltage regulation in single-, bi- and three-phase low voltage distribution networks.

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