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Research Papers: Gas Turbines: Electric Power

Model Predictive Control of Offshore Power Stations With Waste Heat Recovery

[+] Author and Article Information
Leonardo Pierobon

Department of Mechanical Engineering,
Technical University of Denmark,
Kongens Lyngby 2800, Denmark
e-mail: lpier@mek.dtu.dk

Richard Chan

Industrial Learning Systems, Inc.,
Pittsburgh, PA 15101
e-mail: khchan@ilsystems.net

Xiangan Li

Department of Chemical Engineering,
Carnegie Mellon University,
Pittsburgh, PA 15213
e-mail: lixiangan05572@gmail.com

Krishna Iyengar

Department of Chemical Engineering,
Carnegie Mellon University,
Pittsburgh, PA 15213
e-mail: krishna@alumni.cmu.edu

Fredrik Haglind

Associate Professor
Department of Mechanical Engineering,
Technical University of Denmark,
Kongens Lyngby 2800, Denmark
e-mail: frh@mek.dtu.dk

Erik Ydstie

Professor
Department of Chemical Engineering,
Carnegie Mellon University,
Pittsburgh, PA 15213
e-mail: ydstie@cmu.edu

1Corresponding author.

Contributed by the Electric Power Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received March 15, 2015; final manuscript received October 23, 2015; published online February 9, 2016. Assoc. Editor: Rakesh K. Bhargava.

J. Eng. Gas Turbines Power 138(7), 071801 (Feb 09, 2016) (13 pages) Paper No: GTP-15-1093; doi: 10.1115/1.4032314 History: Received March 15, 2015; Revised October 23, 2015

The implementation of waste heat recovery units on oil and gas offshore platforms demands advances in both design methods and control systems. Model-based control algorithms can play an important role in the operation of offshore power stations. A novel regulator based on a linear model predictive control (MPC) coupled with a steady-state performance optimizer has been developed in the simulink language and is documented in the paper. The test case is the regulation of a power system serving an oil and gas platform in the Norwegian Sea. One of the three gas turbines is combined with an organic Rankine cycle (ORC) turbogenerator to increase the energy conversion efficiency. Results show a potential reduction of frequency drop up to 40% for a step in the load set-point of 4 MW, compared to proportional–integral control systems. Fuel savings in the range of 2–3% are also expected by optimizing on-the-fly the thermal efficiency of the plant.

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References

de Alegría, I. M. , Martín, J. L. , Kortabarria, I. , Andreu, J. , and Ereño, P. I. , 2009, “ Transmission Alternatives for Offshore Electrical Power,” Renewable Sustainable Energy Rev., 13(5), pp. 1027–1038. [CrossRef]
Jones, P. , and Stendius, L. , 2006, “ The Challenges of Offshore Power System Construction. Troll A, Electrical Power Delivered Successfully to an Oil and Gas Platform in the North Sea,” European Wind Energy Conference, pp. 75–78.
Hetland, J. , Kvamsdal, H. M. , Haugen, G. , Major, F. , Kårstad, V. , and Tjellander, G. , 2009, “ Integrating a Full Carbon Capture Scheme Onto a 450MWe NGCC Electric Power Generation Hub for Offshore Operations: Presenting the Sevan GTW Concept,” Appl. Energy, 86(11), pp. 2298–2307. [CrossRef]
Torp, T. A. , and Gale, J. , 2004, “ Demonstrating Storage of CO2 in Geological Reservoirs: The Sleipner and SACS Projects,” Energy, 29(9), pp. 1361–1369. [CrossRef]
He, W. , Jacobsen, G. , Anderson, T. , Olsen, F. , Hanson, T. D. , Korpås, M. , Toftevaag, T. , Eek, J. , Uhlen, K. , and Johansson, E. , 2010, “ The Potential of Integrating Wind Power With Offshore Oil and Gas Platforms,” Wind Eng., 34(2), pp. 125–137. [CrossRef]
Nord, L. O. , and Bolland, O. , 2012, “ Steam Bottoming Cycles Offshore—Challenges and Possibilities,” J. Power Technol., 92(3), pp. 201–207.
Walnum, H. T. , Nekså, P. , Nord, L. O. , and Andresen, T. , 2013, “ Modelling and Simulation of CO2 (Carbon Dioxide) Bottoming Cycles for Offshore Oil and Gas Installations at Design and Off-Design Conditions,” Energy, 59, pp. 513–520. [CrossRef]
Bolland, O. , Førde, M. , and Hånde, B. , 1996, “ Air Bottoming Cycle: Use of Gas Turbine Waste Heat for Power Generation,” ASME J. Gas Eng. Turbines Power, 118(2), pp. 359–368. [CrossRef]
Pierobon, L. , Benato, A. , Scolari, E. , Haglind, F. , and Stoppato, A. , 2014, “ Waste Heat Recovery Technologies for Offshore Platforms,” Appl. Energy, 136, pp. 228–241. [CrossRef]
Pierobon, L. , Nguyen, T.-V. , Larsen, U. , Haglind, F. , and Elmegaard, B. , 2013, “ Multi-Objective Optimization of Organic Rankine Cycles for Waste Heat Recovery: Application in an Offshore Platform,” Energy, 58, pp. 538–549. [CrossRef]
Bhargava, R. , Bianchi, M. , Branchini, L. , De Pascale, A. , Melino, F. , Peretto, A. , and Valentini, E. , 2014, “ Thermo-Economic Evaluation of ORC System in Off-Shore Applications,” ASME Paper No. GT2014-25170.
Qin, S. J. , and Badgwell, T. A. , 1996, “ An Overview of Industrial Model Predictive Control Technology,” 5th International Conference on Chemical Process Control, pp. 232–256.
Sáez, D. , Zúñiga, R. , and Cipriano, A. , 2008, “ Adaptive Hybrid Predictive Control for a Combined Cycle Power Plant Optimization,” Int. J. Adapt. Control Signal Process., 22(2), pp. 198–220. [CrossRef]
Quoilin, S. , Aumann, R. , Grill, A. , Schuster, A. , Lemort, V. , and Spliethoff, H. , 2011, “ Dynamic Modeling and Optimal Control Strategy of Waste Heat Recovery Organic Rankine Cycles,” Appl. Energy, 88(6), pp. 2183–2190. [CrossRef]
Zhang, J. , Zhou, Y. , Wang, R. , Xu, J. , and Fang, F. , 2014, “ Modeling and Constrained Multivariable Predictive Control for ORC (Organic Rankine Cycle) Based Waste Heat Energy Conversion Systems,” Energy, 66, pp. 128–138. [CrossRef]
Peralez, J. , Tona, P. , Nadri, M. , Dufour, P. , and Sciarretta, A. , 2015, “ Optimal Control for an Organic Rankine Cycle on Board a Diesel–Electric Railcar,” J. Process Control, 33, pp. 1–13. [CrossRef]
Luong, D. , 2013, “ Modeling, Estimation, and Control of Waste Heat Recovery Systems,” Ph.D. thesis, University of California, Los Angeles, CA.
Hernandez Naranjo, J. A. , Desideri, A. , Ionescu, C. , Quoilin, S. , Lemort, V. , and De Keyser, R. , 2014, “ Increasing the Efficiency of Organic Rankine Cycle Technology by Means of Multivariable Predictive Control,” 19th World Congress of the International Federation of Automatic Control, Cape Town, South Africa, Aug. 24–29, pp. 2195–2200.
Imsland, L. , Kittilsen, P. , and Schei, T. S. , 2010, “ Model-Based Optimizing Control and Estimation Using Modelica Models,” Model. Identif. Control, 31(3), pp. 107–121. [CrossRef]
Willersrud, A. , Imsland, L. , Hauger, S. O. , and Kittilsen, P. , 2013, “ Short-Term Production Optimization of Offshore Oil and Gas Production Using Nonlinear Model Predictive Control,” J. Process Control, 23(2), pp. 215–223. [CrossRef]
Del Turco, P. , Asti, A. , Del Greco, A. , Bacci, A. , Landi, G. , and Seghi, G. , 2011, “ The ORegen™ Waste Heat Recovery Cycle: Reducing the CO2 Footprint by Means of Overall Cycle Efficiency Improvement,” ASME Paper No. GT2011-45051.
Colonna, P. , Casati, E. , Trapp, C. , Mathijssen, T. , Larjola, J. , Turunen-Saaresti, T. , and Uusitalo, A. , 2015, “ Organic Rankine Cycle Power Systems: From the Concept to Current Technology, Applications, and an Outlook to the Future,” ASME J. Eng. Gas Turbines Power, 137(10), pp. 1–19. [CrossRef]
The MathWorks, Inc., 2014, Getting Started With SIMULINK, The MathWorks, Natick, MA.
Bell, I. H. , Wronski, J. , Quoilin, S. , and Lemort, V. , 2014, “ Pure and Pseudo-Pure Fluid Thermophysical Property Evaluation and the Open-Source Thermophysical Property Library CoolProp,” Ind. Eng. Chem. Res., 53(6), pp. 2498–2508. [CrossRef] [PubMed]
Incropera, F. P. , DeWitt, D. P. , Bergman, T. L. , and Lavine, A. S. , 2007, Fundamentals of Heat and Mass Transfer, 6th ed., Wiley, Hoboken, NJ.
Verein Deutscher Ingenieure, 1953, VDI-Wärmeatlas: Berechnungsblätter für den Wärmeübergang, Springer-Verlag, Berlin.
Schobeiri, M. , 2005, Turbomachinery Flow Physics and Dynamic Performance, Springer, Berlin.
Haglind, F. , and Elmegaard, B. , 2009, “ Methodologies for Predicting the Part-Load Performance of Aero-Derivative Gas Turbines,” Energy, 34(10), pp. 1484–1492. [CrossRef]
Veres, J. P. , 1994, “ Centrifugal and Axial Pump Design and Off-Design Performance Prediction,” NASA, Sunnyvale, United States of America, Technical Memorandum No. 106745.
Pierobon, L. , Casati, E. , Casella, F. , Haglind, F. , and Colonna, P. , 2014, “ Design Methodology for Flexible Energy Conversion Systems Accounting for Dynamic Performance,” Energy, 68, pp. 667–679. [CrossRef]
Iyengar, K. , Rambabu, K. , and Ydstie, E. B. , 2013, “ Dynamic Modeling and Control of Gas Turbines in Combined Cycle Power Plants,” AIChE Annual Meeting.
Casella, F. , Mathijssen, T. , Colonna, P. , and van Buijtenen, J. , 2012, “ Dynamic Modeling of ORC Power Systems,” ASME J. Eng. Gas Turbines Power, 135(4), pp. 1–12.
Quoilin, S. , Broek, M. V. D. , Declaye, S. , Dewallef, P. , and Lemort, V. , 2013, “ Techno-Economic Survey of Organic Rankine Cycle (ORC) Systems,” Renewable Sustainable Energy Rev., 22, pp. 168–186. [CrossRef]
Camacho, E. F. , and Alba, C. B. , 2013, Model Predictive Control, Springer, London.
Chan, K. , Dozal-Mejorada, E. , Cheng, X. , Kephart, R. , and Ydstie, B. , 2014, “ Predictive Control With Adaptive Model Maintenance: Application to Power Plants,” Comput. Chem. Eng., 70, pp. 91–103. [CrossRef]
Bemporad, A. , Morari, M. , and Ricker, N. L. , 2014, Model Predictive Control Toolbox for Use With MATLAB, The MathWorks, Natick, MA.
Schmid, C. , and Biegler, L. T. , 1994, “ Quadratic Programming Methods for Reduced Hessian SQP,” Comput. Chem. Eng., 18(9), pp. 817–832. [CrossRef]
Nelder, J. A. , and Mead, R. , 1965, “ A Simplex Method for Function Minimization,” Comput. J., 7(4), pp. 308–313. [CrossRef]
Ginosar, D. M. , Petkovic, L. M. , and Guillen, D. P. , 2011, “ Thermal Stability of Cyclopentane as an Organic Rankine Cycle Working Fluid,” Energy Fuels, 25(9), pp. 4138–4144. [CrossRef]
Pasetti, M. , Invernizzi, C. M. , and Iora, P. , 2014, “ Thermal Stability of Working Fluids for Organic Rankine Cycles: An Improved Survey Method and Experimental Results for Cyclopentane, Isopentane and n-Butane,” Appl. Therm. Eng., 73(1), pp. 764–774. [CrossRef]
Lazzaretto, A. , Toffolo, A. , Reini, M. , Taccani, R. , Zaleta-Aguilar, A. , Rangel-Hernandez, V. , and Verda, V. , 2006, “ Four Approaches Compared on the TADEUS (Thermoeconomic Approach to the Diagnosis of Energy Utility Systems) Test Case,” Energy, 31( 10–11), pp. 1586–1613. [CrossRef]
Lazzaretto, A. , and Toffolo, A. , 2006, “ A Critical Review of the Thermoeconomic Diagnosis Methodologies for the Location of Causes of Malfunctions in Energy Systems,” ASME J. Energy Resour. Technol., 128(4), pp. 335–342. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Simplified layout of the power system on the Draugen offshore oil and gas platform; the exhaust gases of one engine feed the ORC module. The two remaining gas turbines are not shown.

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Fig. 2

Saturation curve of cyclopentane in a T–s diagram, showing the thermodynamic cycle state points of the ORC system

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Fig. 3

Top-level scheme of the gas turbine connected to the ORC power module on the simulink block-diagram environment

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Fig. 4

Transfer function model of the gas turbine on the simulink block-diagram environment as provided by the engine manufacturer

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Fig. 5

Heat exchanger discretized model

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Fig. 6

Effect of the pump speed on the performance of the combined cycle unit and points of maximum thermal efficiency. The curves are given for four load set-points of the combined cycle plant.

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Fig. 7

(a) Degree of superheating T6−T5 and (b) stack temperature T11 as a function of the combined cycle load. The rotational speed of the pump is set to the optimal value, see Fig. 6.

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Fig. 8

Dynamics of the (a) frequency and (b) valve position as delivered by the controller of the gas turbine manufacturer and by the MPC system for a load step of 4 MW

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Fig. 9

Comparison of the dynamic metrics given by the controller of the gas turbine manufacturer and by the MPC system: (a) frequency undershooting and (b) rise time

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Fig. 10

Dynamic response of the combined cycle unit in normal operation and after a 15% deterioration of the overall heat transfer coefficient induced by fouling in the tubes of the OTB: (a) frequency, (b) valve position, (c) degree of superheating, (d) rotational speed of the pump, (e) shaft power of the gas turbine, and (f) shaft power of the ORC unit

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Fig. 11

Dynamic response of the combined cycle unit for two different control modes, i.e., operation at peak efficiency and constant stack temperature

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Fig. 12

Duration curve of the electric load in 2012 on the Draugen oil and gas platform

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