Research Papers: Gas Turbines: Cycle Innovations

Advanced Control for Clusters of SOFC/Gas Turbine Hybrid Systems

[+] Author and Article Information
Iacopo Rossi

Department of Mechanical Engineering,
University of Genoa,
Via Montallegro, 1,
Genova 16145, Italy
e-mail: iacopo.rossi@edu.unige.it

Valentina Zaccaria

Mälardalen University (MDH),
Mälardalens Högskola,
Västerås 721 23, Sweden
e-mail: valentina.zaccaria@mdh.se

Alberto Traverso

Department of Mechanical Engineering,
University of Genoa,
Via Montallegro, 1,
Genova 16145, Italy
e-mail: alberto.traverso@unige.it

Contributed by the Cycle Innovations Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received July 10, 2017; final manuscript received August 29, 2017; published online January 10, 2018. Editor: David Wisler.

J. Eng. Gas Turbines Power 140(5), 051703 (Jan 10, 2018) (8 pages) Paper No: GTP-17-1330; doi: 10.1115/1.4038321 History: Received July 10, 2017; Revised August 29, 2017

The use of model predictive control (MPC) in advanced power systems can be advantageous in controlling highly coupled variables and optimizing system operations. Solid oxide fuel cell/gas turbine (SOFC/GT) hybrids are an example where advanced control techniques can be effectively applied. For example, to manage load distribution among several identical generation units characterized by different temperature distributions due to different degradation paths of the fuel cell stacks. When implementing an MPC, a critical aspect is the trade-off between model accuracy and simplicity, the latter related to a fast computational time. In this work, a hybrid physical and numerical approach was used to reduce the number of states necessary to describe such complex target system. The reduced number of states in the model and the simple framework allow real-time performance and potential extension to a wide range of power plants for industrial application, at the expense of accuracy losses, discussed in the paper.

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Fig. 1

Power comparison between original model and linear state space

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Fig. 2

Degradation rate comparison between original model and linear state space

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Fig. 3

(Top) A scheme of one SG with main valves and components and (bottom) control scheme of the SGs cluster

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Fig. 4

System response to power demand for the test 1

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Fig. 5

Single generators power output in test 1

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Fig. 6

Power tracking performed by the two controllers with the two weight sets in test 2

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Fig. 7

Single generators power outputs in test 2 for (top) case 1 controller weights and (bottom) case 2 controller weights

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Fig. 8

Power tracking with two different weights settings for test 3

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Fig. 9

Power share for (top) constant weights and (bottom) variable weights settings in test 3

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Fig. 10

Summary of voltage evolution and weights throughout the tests

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Fig. 11

Summary of degradation throughout the tests



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