This dissertation addresses the crucial problem of how environmental policy uncertainty influences investments in energy technological change. The rising level of carbon emissions due to increasing global energy consumption calls for policy shift. In order to stem the negative consequences on...

Companies use various project evaluation techniques to determine the feasibility and profitability of a project. We surveyed some of the largest “Fortune 500” Industrial and Service corporations in December 1991 about their use of these techniques. The survey was compared to a similar 1978...

High energy efficiency and special low-cost races make cogeneration an attractive energy production method. Large cogeneration units have proven economic for many applications. Here we report a case study on the profitability of a small, gas fired. 4 kW packaged cogenerator in a home or small...

This paper proposes a new approach of generation expansion planning and its risk assessment under the competitive market environment. Different from the traditional probabilistic production costing (PPC) method under monopoly environment which is based on the deterministic generator loading...

The real electricity markets are usually oligopoly, where market suppliers (generators) have some market power and can adopt strategic bidding strategies for maximum profits. Generally, the game-theory based methods are the natural way to analyze the market equilibrium and study the strategic...

Supply interruptions resulting from unpredictable events, such as machine breakdowns, order cancellations, unscheduled maintenance, and labor strikes can produce adverse effects on the production/inventory system. In this article, we consider a periodic-review inventory system subject to random...

We propose to apply the concept of irrelevant variability normalization to the general problem of learning structure from data. Because of the problems of a diversified training data set and/or possible acoustic mismatches between training and testing conditions, the structure learned from the...

Source: Dissertation Abstracts International, Volume: 60-07, Section: A, page: 2569.

Bayesian nonparametric methods are useful for modeling data without having to define the complexity of the entire model a priori, but rather allowing for this complexity to be determined by the data. Two problems considered in this dissertation are the number of components in a mixture model,...