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Research Papers: Gas Turbines: Cycle Innovations

A Systematic Comparison and Multi-Objective Optimization of Humid Power Cycles—Part II: Economics

[+] Author and Article Information
R. M. Kavanagh

Hopkinson Laboratory, Department of Engineering, Cambridge University, Trumpington Street, Cambridge CB2 1PZ, UKronan.kavanagh@power.alstom.com

G. T. Parks

Department of Engineering, Cambridge University, Trumpington Street, Cambridge CB2 1PZ, UKgtp@eng.cam.ac.uk

J. Eng. Gas Turbines Power 131(4), 041702 (Apr 13, 2009) (10 pages) doi:10.1115/1.3026562 History: Received April 07, 2008; Revised August 11, 2008; Published April 13, 2009

The steam injected gas turbine (STIG), humid air turbine (HAT), and TOP Humid Air Turbine (TOPHAT) cycles lie at the center of the debate on which humid power cycle will deliver optimal performance when applied to an aeroderivative gas turbine and, indeed, when such cycles will be implemented. Of these humid cycles, it has been claimed that the TOPHAT cycle has the highest efficiency and specific work, followed closely by the HAT and then the STIG cycle. In this study, the systems have been simulated using consistent thermodynamic and economic models for the components and working fluid properties, allowing a consistent and nonbiased appraisal of these systems. Part I of these two papers focused on the thermodynamic performance and the impact of the system parameters on the performance, Part II studies the economic performance of these cycles. The three humid power systems and up to ten system parameters are optimized using a multi-objective Tabu Search algorithm, developed in the Cambridge Engineering Design Centre.

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Copyright © 2009 by American Society of Mechanical Engineers
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Figures

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Figure 1

Humid cycle configurations: (a) HAT cycle configuration, (b) STIG cycle configuration, and (c) TOPHAT cycle configuration

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Figure 2

Comparison of humid cycle Pareto-optimal sets

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Figure 3

Comparison of Pareto-optimal sets. (a) Parametric contours:β and TIT. (b) Pareto-optimal sets and parametric contours.

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Figure 4

Results for simple cycle TS optimization. (a) Pareto 1: ηcycle/w Pareto-optimal set; (b) Pareto 2: ηcycle/COE Pareto-optimal set; (c) Pareto 3: COE/w Pareto-optimal set; (d) Pareto 1: β∗ selection; (e) Pareto 2: β∗ selection; (f) Pareto 3: β∗ selection; and (g) P1–3: β∗ selection.

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Figure 5

Results for STIG cycle TS optimization. (a) Paretos 1–3 in ηcycle/COE domain; (b) P2: ηcycle/COE Pareto-optimal set; (c) P3: COE/w Pareto-optimal set; (d) P2: β∗ selection; (e) P2: ΔTsh∗ selection; (f) P2: Π∗ selection; (g) P3: β∗ selection; (h) P3: ΔTsh∗ selection; (i) P3: Π∗ selection; (j) P1–3: β∗ selection; (k) P1–3: ΔTsh∗ selection; and (l) P1–3: Π∗ selection.

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Figure 6

STIG cycle parametric contours: β and ΔTsh

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Figure 7

STIG cycle: three Pareto-optimal sets in COE/w domain

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Figure 8

Results for HAT cycle TS optimization. (a) Paretos 1–3 in ηcycle/COE domain; (b) P2: ηcycle/COE Pareto-optimal set; (c) P3: COE/w Pareto-optimal set; (d) P2: β∗ selection; (e) P2: εrec∗ selection; (f) P2: Π∗ selection; (g) P3: β∗ selection; (h) P3: εrec∗ selection; (i) P3: Π∗ selection; (j) P1–3: β∗ selection; (k) P1–3: εrec∗ selection; and (l) P1–3: Π∗ selection.

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Figure 9

Parametric contours: β and εrec. (a) HAT cycle, and (b) TOPHAT cycle.

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Figure 10

Results for TOPHAT cycle TS optimization. (a) Paretos 1–3 in ηcycle/COE domain; (b) P2: ηcycle/COE Pareto-optimal set; (c) P3: COE/w Pareto-optimal set; (d) P1–3: β∗ selection; (e) P1–3: εrec∗ selection; and (f) P1–3: Π∗ selection.

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Figure 11

Comparison of Pareto-optimal sets: (a) Pareto 2, and (b) Pareto 3

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Figure 12

PEC and specific PEC: (a) purchased equipment costs, and (b) specific PEC

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Figure 13

Comparison of adjusted Pareto-optimal sets: (a) Pareto 2—adjusted TCI, and (b) Pareto 3—adjusted TCI

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Figure 14

IRR for humid cycles: (a) IRR for Pareto 2, and (b) TCI versus IRR for Pareto 2

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