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2021 | OriginalPaper | Buchkapitel

6. Model Predictive Control for Virtual Inertia Synthesis

verfasst von : Thongchart Kerdphol, Fathin Saifur Rahman, Masayuki Watanabe, Yasunori Mitani

Erschienen in: Virtual Inertia Synthesis and Control

Verlag: Springer International Publishing

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Abstract

Nowadays, a new concept of modern power systems (i.e., smart/micro-grids), which includes various components such as smart meters, smart appliances, renewable energy sources (RESs), distributed generators (DGs), and controllable loads has increased attention worldwide due to its energy efficiency and environmental concerns. Such a modern system requires the employment of real-time application and intelligent control. To properly utilize the virtual inertia control regarding an intelligent ability in future predictions, the model predictive control (MPC) is necessary. The MPC has a fine performance in delivering fast dynamic response with robustness against disturbance and uncertainty, while keeping future control variables in account. Thus, it has been implemented in a wide range of industrial applications, including real-time measurement and control. In this chapter, the design of decentralized MPC-based virtual inertia control is introduced to emulate the suitable virtual inertia power, while predicting the future behavior or event regarding inertia control-based frequency regulation. The MPC controller applies a feedforward control technique to reject the disturbances from RES/DG and load penetration as well as system parameter uncertainty, ensuring rapid dynamic response with the robustness of system operation. The proposed MPC-based virtual inertia control is verified through a nonlinear control area system with high RESs/DGs penetration including the extended communication delay time.

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Vorheriges Kapitel Application of PI/PID Control for Virtual Inertia Synthesis

Nächstes Kapitel Fuzzy Logic Control for Virtual Inertia Synthesis

C. Bordons, F. Garcis-Torres, M.A. Ridao, Model Predictive Control of Microgrids (Springer, Switzerland, 2020)

V. Yaramasu, B. Wu, Model Predictive Control of Wind Energy Conversion Systems (IEEE-Wiley, NJ, USA, 2016)

G. Serale, M. Fiorentini, A. Capozzoli, D. Bernardini, A. Bemporad, Model Predictive Control (MPC) for enhancing building and HVAC system energy efficiency: problem formulation, applications and opportunities. Energies 11(3), 1–35 (2018)CrossRef

T. Dragicevic, Model predictive control of power converters for robust and fast operation of AC microgrids. IEEE Trans. Power Electron. 33(7), 6304–6317 (2018)CrossRef

A.N. Venkat, I.A. Hiskens, J.B. Rawlings, S.J. Wright, Distributed MPC strategies with application to power system automatic generation control. IEEE Trans. Control Syst. Technol. 16(6), 1192–1206 (2008)CrossRef

A.M. Ersdal, L. Imsland, K. Uhlen, Model predictive load-frequency control. IEEE Trans. Power Syst. 31(1), 777–785 (2016)CrossRef

E. Perez, H. Beltran, N. Aparicio, P. Rodriguez, Predictive power control for PV plants with energy storage. IEEE Trans. Sustain. Energy 4(2), 482–490 (2013)CrossRef

Y. Zong, L. Mihet-Popa, D. Kullmann, A. Thavlov, O. Gehrke, H.W. Bindner, Model predictive controller for active demand side management with PV self-consumption in an intelligent building, in Proc. IEEE PES Innovative Smart Grid Technologies Conference Europe, 1–6 (2012)

G. Li, M.R. Belmont, Model predictive control of sea wave energy converters - Part II: the case of an array of devices. Renew. Energy 68(1), 540–549 (2014)CrossRef

10.

T.H. Mohamed, J. Morel, H. Bevrani, T. Hiyama, Model predictive based load frequency control-design concerning wind turbines. Int. J. Electr. Power Energy Syst. 43(1), 859–867 (2012)CrossRef

11.

T. Kerdphol, F.S. Rahman, Y. Mitani, K. Hongesombut, S. Küfeoğlu, Virtual inertia control-based model predictive control for microgrid frequency stabilization considering high renewable energy integration. Sustain 9(5), 1–21 (2017)CrossRef

12.

H. Borhan, A. Vahidi, A.M. Phillips, M.L. Kuang, I.V. Kolmanovsky, S. Di Cairano, MPC-based energy management of a power-split hybrid electric vehicle. IEEE Trans. Control Syst. Technol. 20(3), 593–603 (2012)CrossRef

13.

J. Ma, S.J. Qin, B. Li, T. Salsbury, Economic model predictive control for building energy systems, in Proc. IEEE PES Innovative Smart Grid Technologies Conference Europe, 1–6 (2011)

14.

R. Halvgaard, N.K. Poulsen, H. Madsen, J.B. Jørgensen, Economic model predictive control for building climate control in a smart grid, in Proc. IEEE PES Innovative Smart Grid Technologies, 1-6 (2012)

15.

L. Xie, M.D. Ilić, Model predictive economic/environmental dispatch of power systems with intermittent resources, in Proc. IEEE PES General Meeting (IEEE PES GM), 1–6 (2009)

16.

C. Finck, R. Li, W. Zeiler, Economic model predictive control for demand flexibility of a residential building. Energy 176(1), 365–379 (2019)CrossRef

17.

R. Van Parys, G. Pipeleers, Distributed MPC for multi-vehicle systems moving in formation. Rob. Auton. Syst. 97(1), 144–152 (2017)CrossRef

18.

R.R. Negenborn, B. De Schutter, H. Hellendoorn, Multi-agent model predictive control of transportation networks, in Proc. IEEE International Conference on Networking, Sensing and Control, 296–301 (2006)

19.

S. Roshany-Yamchi, R.R. Negenborn, M. Cychowski, B. De Schutter, J. Connell, K. Delaney, Distributed model predictive control and estimation of large-scale multi-rate systems, in Proc. IFAC Proceedings Volumes, 416–422 (2011)

20.

V. Javalera, B. Morcego, V. Puig, A multi-agent MPC architecture for distributed large scale systems, in Proc. International Conference on Agents and Artificial Intelligence, 1–9 (2010)

21.

L. Jin, R. Kumar, N. Elia, Model predictive control-based real-time power system protection schemes. IEEE Trans. Power Syst. 25(2), 988–998 (2010)CrossRef

22.

J.A. Martin, I.A. Hiskens, Corrective model-predictive control in large electric power systems. IEEE Trans. Power Syst. 32(2), 1651–1662 (2017)CrossRef

23.

L. Jin, R. Kumar, N. Elia, Application of model predictive control in voltage stabilization, in Proc. American Control Conference, 5916–5921 (2007)

24.

A. Bonfiglio et al., Improving power grids transient stability via model predictive control, in Proc. Power Systems Computation Conference (PSCC 2014), 1–7 (2014)

25.

I. Martinez Sanz, B. Chaudhuri, G. Strbac, Corrective control through HVDC links: a case study on GB equivalent system, in Proc. IEEE PES General Meeting (IEEE PES GM), 1–5 (2013)

26.

A. Bonfiglio et al., An MPC-Based approach for emergency control ensuring transient stability in power grids with steam plants. IEEE Trans. Ind. Electron. 66(7), 5412–5422 (2019)CrossRef

27.

P. Xu, D. Song, W. Tang, C. Yang, S. Ma, “Coordinated frequency and voltage correction via model predictive control,” in Proc. International Conference on Power System Technology (POWERCON 2018), 1–6 (2019)

28.

X. Kong, X. Liu, K.Y. Lee, Nonlinear multivariable hierarchical model predictive control for boiler-turbine system. Energy 93(1), 309–322 (2015)CrossRef

29.

X. Liu, D. Jiang, K.Y. Lee, Quasi-min-max fuzzy MPC of UTSG water level based on off-line invariant set. IEEE Trans. Nucl. Sci. 62(5), 2266–2272 (2015)CrossRef

30.

T.H. Mohamed, H. Bevrani, A.A. Hassan, T. Hiyama, Decentralized model predictive based load frequency control in an interconnected power system. Energy Convers. Manag. 52(2), 1208–1214 (2011)CrossRef

31.

H. Bevrani, Robust Power System Frequency Control, 2nd ed. (Springer, New York, USA, 2014)

32.

J. Pahasa, I. Ngamroo, Coordinated control of wind turbine blade pitch angle and PHEVs using MPCs for load frequency control of microgrid. IEEE Syst. J. 10(1), 97–105 (2016)CrossRef

33.

J. Pahasa, I. Ngamroo, PHEVs bidirectional charging/discharging and SoC control for microgrid frequency stabilization using multiple MPC. IEEE Trans. Smart Grid 6(2), 526–533 (2015)CrossRef

34.

L. Wang, Model Predictive Control System Design and Implementation Using MATLAB^®. (Springer-Verlag, London, UK, 2009)

35.

F. Milano, M. Anghel, “Impact of time delays on power system stability. IEEE Trans Circuits Syst. I Regul. Pap. 59(4), 889–900 (2012)MathSciNetCrossRef

Titel: Model Predictive Control for Virtual Inertia Synthesis
verfasst von: Thongchart Kerdphol
Fathin Saifur Rahman
Masayuki Watanabe
Yasunori Mitani
Verlag: Springer International Publishing
Buch: Virtual Inertia Synthesis and Control
Print ISBN: 978-3-030-57960-9

Electronic ISBN: 978-3-030-57961-6

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-57961-6_6