Journal of Energy Engineering

Transmission expansion planning has become a central challenge in the design of competitive electricity markets. The underlying optimization/decision problem exhibits a high level of complexity, being nonconvex, dynamic, and nonlinear, with multiple objective functions and a wide range of uncertainties. In this paper we propose a methodology for handling the expansion problem that involves a novel adaptation of an iterative optimization method based on response surface models already used successfully in the design and manufacture of integrated circuits. It is implemented in a distributed environment using Web services, and is validated and compared with a genetic algorithm based methodology. An application of the proposed methodology to the Chilean Central Interconnected System is then presented and analyzed. The results demonstrate its potential in the fields of risk analysis and decision support systems in power markets.

Development of new burner technology is common practice nowadays. Standardized test codes are available to conduct tests to determine operating characteristics and thermal performance of different parts of steam generating units. This paper describes the results of testing performed to evaluate thermal efficiency of a $74.5\mathrm{kW}$ fire-tube steam generator fired with a new premixed flame burner in comparison to a typical standard-retrofit diffusion flame burner system. Two premixed flame burners sized at 7.6 and $10.1\mathrm{cm}$ were tested. Testing was performed in accordance with the ASME PTC 4.1 Power Test Code. The evaluation was conducted in the range of small- to mid-sized, natural gas-fired applications, from $1.06\text{to}2.64\times {10}^6\mathrm{kJ}∕\mathrm{h}$ . The environmental nitrogen oxide $\left(\mathrm{N}{\mathrm{O}}_x\right)$ emissions performance was also compared for both burner technologies. The results of this evaluation indicate that firing the test boiler with the $7.6\mathrm{cm}$ new burner slightly improves unit thermal efficiency by 4.0–3.3% for a unit load ranging from approximately $1.06\text{to}1.79\times {10}^6\mathrm{kJ}∕\mathrm{h}$ , respectively. Operation with the $10.1\mathrm{cm}$ new burner improves the thermal efficiency by approximately 1.6% at $2.32\times {10}^6\mathrm{kJ}∕\mathrm{h}$ . The uncertainty in the measurements used in the efficiency calculations should account for approximately $\pm 1.5\%$ uncertainty in the reported gross efficiency. Reductions in $\mathrm{N}{\mathrm{O}}_x$ emissions of the order of 10% resulted from operation with the new premixed flame burners.

Conventional evaluation approaches for complex technologies, such as gas turbine systems, typically use process simulators for modeling, which are usually complicated and time-consuming. In order to facilitate policy analysis, a simplified desktop model for gas turbine systems based upon the air-standard Brayton cycle is developed in Microsoft EXCEL. The simplified model incorporates key process details and includes a comprehensive cost model. The model is calibrated based on a typical “Frame 7F” heavy duty gas turbine fired with natural gas and syngas. The model generally produces accurate and reasonable estimates for performance and cost comparable to reference data. The model responds appropriately to different syngas compositions, such as based on variation in moisture content and $\mathrm{C}{\mathrm{O}}_2$ removal. Changes in syngas composition lead to different syngas heating values and thus affect gas turbine performance and cost. The effects of changes in inputs on key outputs are evaluated. Six key inputs are identified that are critical in order to obtain accurate estimates. The simplified model provides detailed technical information in a format that supports strategic planning and analysis.

The effects of the blockage of a rectangular intake duct and impervious flow boundaries on the critical submergence of a rectangular intake are presented. The potential solution, based on the Rankine stagnation point, is determined to be another approximate method for the prediction of the critical submergence of this kind of intake. It is found that a critical cylindrical sink surface capped with two critical hemispherical sink surfaces at both ends with a radius equal to the radial distance of the stagnation point (which is $2∕\pi$ times the critical submergence of the rectangular intake) can also be used to predict critical submergence. Theoretical results and available experimental data are compared. The theory presented in this study acceptably (by about 1–20%) estimates the critical submergence for the cases where the distance (clearances) of the impervious solid boundaries are larger than 1/2 of the small inner dimension of the intake. On the other hand, the theory overestimates the critical submergence by about 80% for the cases where the distances of the solid boundaries (especially those cutting the free surface such as the dead-end wall) become zero.

A feasible variant of the equation is proposed to relate the efficiency of a turbine plant to the kinetic energy of the gas/steam flow: ${\eta }_i=1∕\left[\right(1∕{\eta }_{\min })-(\mathrm{\Delta }{P}_{\mathrm{use}}+\mathrm{\Delta }{P}_{\mathrm{fr}}{)}_i∕\mathrm{\Delta }{P}_{\mathrm{inert}}]$ , where ${\eta }_i$ =turbine plant efficiency in an $i$ state $(T_i,P_i)$ ; $\mathrm{\Delta }{P}_{\mathrm{use}}$ , $\mathrm{\Delta }{P}_{\mathrm{fr}}$ =kinetic energy of the moving flow used on carrying out effective work and used on overcoming friction between the jets of gas and between the friction parts of the turbine, respectively; ${\eta }_{\min }$ =minimum efficiency attained at a given temperature of the gas flow; $\mathrm{\Delta }{P}_{\mathrm{inert}}$ =kinetic energy of the moving flow used on overcoming the inertia forces of the turbine. Employment of abovementioned equation in processing experimental data makes it possible to estimate ${\eta }_{\min }$ , as well as $\mathrm{\Delta }{P}_{\mathrm{inert}}$ . The equation facilitates functional monitoring of an operating turbine cycle and can be useful in upgrading and optimizing individual units or in designing new turbines.