Journal of Energy Engineering

The abuse of market power is a potentially serious problem for market designers. Few indices, if any, exist to measure the potential for market power in real time. In this regard, Murillo-Sanchez et al. derived an expression for a dispatch-to-price sensitivity matrix, $\mathbf{M}$ . The expression requires information about network topology and parameters, as well as the rules used to operate the market. While computing the matrix is conceptually easy for those with all the market and system information, such as an independent system operator, the method is probably impractical for market participants due to the inaccessibility of much of the information. In this paper, a method for estimating the $\mathbf{M}$ matrix by using publically available data are suggested, indicating that any market participant who does the computation will know when conditions permit them to lower or raise prices through decreased/increased bids and/or offers.

Optimization of schemes for black-start is one of the most important factors determining the speed of power system restoration after either a global blackout or a local outage. In this paper a novel method employing the entropy weight-based decision-making theory is proposed for this application. By using the method for the decision making in black start, the subjective weights acquired from knowledge and experience of experts could be used in conjunction with the objective weights (entropy weighs) deduced from some closely related indices. Obviously, the developed method can greatly improve the performance in power system restoration by overcoming the shortcomings of conventional methods that only use either the subjective or the objective weights. Furthermore, the proposed method is applicable in power systems with different levels of black-start experience for assisting the optimal black-start decision making. It is shown that the proposed method is suitable for power systems with different levels of black-start experience.

In order to optimize the hydraulic transportation system efficiency and cost in the surface extraction of Athabasca oil sand deposits in Canada, there is a desire to extend the hydraulic transport system to production faces in oil sands mines using mobile train of Flex-Rite-based flexible pipelines. Hydraulic transportation system based on flexible-pipe arrangement has been shown to be more competitive than the dump-truck transportation system. This flexible arrangement introduces a unique set of hydraulic transport problems which needs rigorous modeling, experimentation, and analysis to understand the system production capacities and efficiency. Part of the work presented here is an attempt to provide multiphase oil sand slurry simulation and modeling by developing a slurry flow simulator, a graphical user interface-based software, for Flex-Rite flexible-pipe train, a form of hydraulic transportation system. Such software provides a tool/platform for rigorous experimentation and analysis of flow and production capacities.

Whenever industrial plants consume power and heat there is a need to consider energy efficient investment on a combined heat and power (CHP) plant. CHP represents a service of proven, reliable, and cost-efficient technologies that are already making an important contribution to meeting global heat and electricity demand. However, while some countries have been able to achieve a higher market share of these technologies, some have shown less or no interest at all, especially the developing countries. The primary purpose of this work is to investigate the viability and financial benefits of incorporating a CHP plant at Botswana Meat Commission. The research suggests that CHP can bring financial benefits to Botswana Meat Commission, by replacing the aging inefficient coal-fired thermal plant and the electricity from the power utility company. Some of the benefits are low emission of pollutants. CHP can also alleviate the problem of acute power shortage in Botswana, while benefiting from the discovery of trillions of coal-bed methane.