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The main objective of generation risk assessment (GRA) is to assess the impact of equipment unavailability and failures on the ability of the plant to produce power over time. The system reliability models employed for this purpose are based on the standard fault tree/event tree approach, which assumes failure rates to be constant. However, this ignores the impact of aging degradation and results in static estimates of expected generation loss. Component and equipment degradation not only increases the probability of failure over time, but also contributes to generation risk through increased unavailability and costs arising from greater requirement for inspection and replacement of degraded components. This paper discusses some of the key challenges associated with integrating the results of component degradation models into GRA. Because many analytical and simulation methods are subject to limitations, the methodology and modeling approach proposed in this work builds on the current GRA practice using the fault tree approach. The modeling of component degradation can be done separately at the fault tree cut set level, assuming the cut sets are independent and the component unavailabilities are relatively small In order to capture the joint contribution of equipment failure and unavailability to generation risk, new risk-based importance measures are also developed using the concept of net present value. [DOI: 10.1115/1.4000892]

The transient behavior of compressor stations, particularly under rapidly changing conditions, is of vital interest to operators. Predicting transient behavior is an important factor in avoiding damage during events such as emergency shutdowns. A limited number of 'accidental' data sets from compressor manufacturers and users are available in the public literature domain. A variety of simulations and modeling approaches have been presented over the last Jew years at industry conferences. The available experimental data is not of sufficient quality and resolution to properly compare predictions with analytical results or simulations available in current software packages. Necessary information about the compressor, driver, valves, and geometry of the system is often missing. Currently utilized software has not been adequately validated with full-scale realistic benchmark data, as this data is not available in the public domain. Modeling procedures and results of surge control system simulations seldom contain validation data achieved through actual testing. This type of transient test data for a dynamic surge condition is often difficult to obtain. The primary objective of this work is to develop experimental transient compressor surge data on a full-scale test facility, which would facilitate the verification and comparison of existing and future transient surge models. Results of the testing and model comparisons will be documented. Relevant, dimensionless parameters will be presented and validated utilizing the test data. Conclusions from the testing and recommendations for the transient analysis software will be provided. [DOI: 10.1115/1.4000093]

In the framework of liberalized electricity markets, distributed generation and controllable demand have the opportunity to participate in the real-time operation of transmission and distribution networks. This may be done by using the virtual power plant (VPP) concept, which consists of aggregating the capacity of many distributed energy resources (DER) in order to make them more accessible and manageable across energy markets. This paper provides an optimization algorithm to manage a VPP composed of a large number of customers with thermostatically controlled appliances. The algorithm, based on a direct load control (DLC), determines the optimal control schedules that an aggregator should apply to the controllable devices of the VPP in order to optimize load reduction over a specified control period. The results define the load reduction bid that the aggregator can present in the electricity market, thus helping to minimize network congestion and deviations between generation and demand. The proposed model, which is valid for both transmission and distribution networks, is tested on a real power system to demonstrate its applicability. Index Terms--Air-conditioning, load management, optimal control.

Fourteen children ages 11 to 12 were interviewed as part of a research conducted to determine their knowledge in operation of power plants and their environmental consequences. Tables on categories of children's ideas about the electricity generation process and energy sources are presented.

The usage of data envelopment analysis to assign common weights to groups of power plants, for the development of a model to measure the efficiency of power plants, is described.

Byline: Rima A. Biswas, Ram A. Pandey, Tapan Chakrabarti, Sukumar Devotta A chemo-biological approach for treating sulphate-rich effluent generated during wet scrubbing of flue gas emissions from fossil fuel fired power plants has been discussed. Microbial sulphate reduction was carried out in an anaerobic up-flow packed bed bioreactor (R.SUB.1) using ethanol as carbon source. More than 90% of Total Equivalent Sulphate (TES) present in the effluent was reduced to sulphide at HRT of 17 h in the tested loading range of 0.58-3.10 kg SO.SUB.4/m[sup.3]/day. Mass balance revealed that more than 88% of sulphate reduced was present as dissolved sulphide (HS.SUP.−) and while only 0.01% was present as H.SUB.2S(g). The paper discusses the feasibility of converting both H.SUB.2S(g) and HS.SUP.− into value-added products such as elemental sulphur and metal sulphide nanoparticles, respectively. Since the amount of H.SUB.2S generated is very low (0.01%), this paper also suggests recovery of dissolved sulphide from the R.SUB.1 effluent, as transition metal sulphide nanoparticles.

Byline: S.R. Hanna, J.C. Chang The Hybrid Plume Dispersion Model (HPDM) has been evaluated with observations from a field experiment at the Kincaid power plant. HPDM is a dispersion model for buoyant plumes that employs parameterisations of boundary-layer wind, temperature, and turbulence profiles and Lagrangian time-scales. The model accounts for the bimodal distribution of turbulent velocities in the convective boundary layer and contains an algorithm for calculating the lofting of a buoyant plume against a capping inversion. The model predictions of maximum plume centreline concentrations show a mean bias of less than l0%, a typical error that is about 50% of the mean, and a correlation of about 0.5.