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CSEE Journal of Power and Energy Systems 2022, 8(2): 465-475 https://doi.org/10.17775/CSEEJPES.2021.08470
Open Access | Issue | Published: 14 February 2022

Frequency Response of PV Inverters Toward High Renewable Penetrated Distribution Networks

Show Author's Information Feifei Bai,  Yi Cui,  Ruifeng Yan( ),  Tapan Kumar Saha,  Huajie Gu,  Daniel Eghbal
School of Information Technology and Electrical Engineering, the University of Queensland, Brisbane, QLD 4072, Australia
School of Engineering and Built Environment at Griffith University, Gold Coast, QLD 4222, Australia
Australian Energy Market Operator (AEMO), Brisbane, QLD 4000, Australia
Energy Queensland (Energex), Brisbane, QLD 4006, Australia
Keywords:
Deep learning, dynamic response, distribution networks, distributed PV, load modelling, PV inverter
Cite this article:
Bai F, Cui Y, Yan R, et al. Frequency Response of PV Inverters Toward High Renewable Penetrated Distribution Networks. CSEE Journal of Power and Energy Systems, 2022, 8(2): 465-475. https://doi.org/10.17775/CSEEJPES.2021.08470
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Abstract Full text About this article

Substantial usage of electronic-based renewable energy resources has completely changed the dynamic behaviours and response time of power networks, which are now fundamentally different from traditional power networks dominated by Synchronous Generators (SGs). This paper evaluates the dynamic response of small-scale Photovoltaic (PV) inverters, which dominate the distribution networks and influence the dynamics of the entire power grid. Recently, some critical events which occurred in Australia have shown that the dynamic responses of small-scale inverters do not always follow the inverter standards. Subsequently, these uncertainties make PV inverters’ response unpredictable and have the potential to threaten the security of power networks. The detailed investigation of the dynamic response characteristics of small-scale PV inverters to grid disturbances is lacking in the current literature. This paper presents new findings from experimental testing under extensive network disturbance scenarios. Furthermore, a data-driven method is proposed to accurately describe the dynamics of solar PV subjected to various frequency disturbances. The results provide beneficial insight to the network operators in predicting power system response to extreme disturbances and avoiding potential grid instability issues, which will assist in achieving 100% penetration of power electronics-based renewable energy resources in the future.


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About this article

Frequency Response of PV Inverters Toward High Renewable Penetrated Distribution Networks

Show Author's information Feifei Bai, Yi Cui, Ruifeng Yan( ), Tapan Kumar Saha, Huajie Gu, Daniel Eghbal
School of Information Technology and Electrical Engineering, the University of Queensland, Brisbane, QLD 4072, Australia
School of Engineering and Built Environment at Griffith University, Gold Coast, QLD 4222, Australia
Australian Energy Market Operator (AEMO), Brisbane, QLD 4000, Australia
Energy Queensland (Energex), Brisbane, QLD 4006, Australia

Abstract

Substantial usage of electronic-based renewable energy resources has completely changed the dynamic behaviours and response time of power networks, which are now fundamentally different from traditional power networks dominated by Synchronous Generators (SGs). This paper evaluates the dynamic response of small-scale Photovoltaic (PV) inverters, which dominate the distribution networks and influence the dynamics of the entire power grid. Recently, some critical events which occurred in Australia have shown that the dynamic responses of small-scale inverters do not always follow the inverter standards. Subsequently, these uncertainties make PV inverters’ response unpredictable and have the potential to threaten the security of power networks. The detailed investigation of the dynamic response characteristics of small-scale PV inverters to grid disturbances is lacking in the current literature. This paper presents new findings from experimental testing under extensive network disturbance scenarios. Furthermore, a data-driven method is proposed to accurately describe the dynamics of solar PV subjected to various frequency disturbances. The results provide beneficial insight to the network operators in predicting power system response to extreme disturbances and avoiding potential grid instability issues, which will assist in achieving 100% penetration of power electronics-based renewable energy resources in the future.

Keywords: Deep learning, dynamic response, distribution networks, distributed PV, load modelling, PV inverter

References(32)

[1]
R. F. Yan, N. A. Masood, T. K. Saha, F. F. Bai, and H. J. Gu, “The anatomy of the 2016 South Australia blackout: A catastrophic event in a high renewable network”, IEEE Transactions on Power Systems, Vol. 33, no. 5, pp. 5374–5388, Sep. 2018.
DOI Google Scholar
[2]
Y. J. Peng, Y. Li, K. Y. Lee, Y. Tan, Y. J. Cao, M. Wen, Y. W. Shen, M. M. Zhang, and W. G. Li, “Coordinated control strategy of PMSG and cascaded H-Bridge STATCOM in dispersed wind farm for suppressing unbalanced grid voltage”, IEEE Transactions on Sustainable Energy, vol. 12, no. 1, pp. 349–359, Jan. 2021.
DOI Google Scholar
[3]
H. J. Gu, R. F. Yan, T. K. Saha, “Review of system strength and inertia requirements for the national electricity market of Australia,” CSEE Journal of Power and Energy Systems, vol. 5, no. 3, pp. 295–305, Sep. 2019.
Google Scholar
[4]
Australian PV Institute. [Online]. Available at http://pv-map.apvi.org.au/animation.
[5]
Australian Energy Market Operator (AEMO), Response of existing PV inverters to frequency disturbances, AEMO report, April 2016.
[6]
AEMO, Final report-Queensland and South Australia system separation on 25 August 2018, published on 10 January 2019.
[7]
AEMO, Fault at Torrens Island Switchyard and Loss of Multiple Generating Units on 3 March 2017, published on 10 March 2017.
[8]
Grid Connection of Energy Systems via Inverters – Part 3: Grid Protection Requirements, AS 4777.3-2005, 2005.
[9]
Grid Connection of Energy Systems via Inverters – Part 2: Inverter Requirements, AS/NZS 4777.2:2015, 2015.
[10]
WECC Dynamic Composite Load Model (CMPLDW) specifications. [Online]. http://home.engineering.iastate.edu/~jdm/ee554/WECC%20Composite%20Load%20Model%20Specifications%2001-27-2015.pdf
[11]
B. Mather, O. Aworo, R. Bravo, and P. E. David Piper, “Laboratory testing of a utility-scale PV inverter’s operational response to grid disturbances”, in 2018 IEEE Power & Energy Society General Meeting, Portland, OR, USA, 2018, pp. 1–5.
DOI Google Scholar
[12]
R. J. Bravo, R. Yinger, S. Robles, and W. Tamae, “Solar PV inverter testing for model validation”, in 2011 IEEE Power and Energy Society General Meeting, Detroit, MI, USA, 2011, pp. 1–7.
DOI Google Scholar
[13]
B. Mather and F. Ding, “Distribution-connected PV’s response to voltage sags at transmission-scale,” in 2016 IEEE 43rd Photovoltaic Specialists Conference, Portland, OR, USA, 2016, pp. 2030–2035.
DOI Google Scholar
[14]
Y. T. Tan, D. S. Kirschen, and N. Jenkins, “A model of PV generation suitable for stability analysis,” IEEE Transactions on Energy Conversion, vol. 19, no. 4, pp. 748–755, Dec. 2004.
DOI Google Scholar
[15]
D. Remon, A. M. Cantarellas, and P. Rodriguez, “Equivalent model of large-scale synchronous photovoltaic power plants,” IEEE Transactions on Industry Applications, vol. 52, no. 6, pp. 5029–5040, Nov./Dec. 2016.
DOI Google Scholar
[16]
J. T. Bi, W. Du, and H. F. Wang, “Aggregated dynamic model of grid-connected PV generation farms,” in International Conference on Renewable Power Generation, Beijing, China, 2015.
DOI Google Scholar
[17]
R. T. Guttromson, “Modeling distributed energy resource dynamics on the transmission system,” IEEE Transactions on Power Systems, vol. 17, no. 4, pp. 1148–1153, Nov. 2002.
DOI Google Scholar
[18]
S. N. Shao, M. Pipattanasomporn, and S. Rahman, “Development of physical-based demand response-enabled residential load models,” IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 607–614, May 2013.
DOI Google Scholar
[19]
A. Grandjean, J. Adnot, and G. Binet, “A review and an analysis of the residential electric load curve models,” Renewable and Sustainable Energy Reviews, vol. 16, no. 9, pp. 6539–6565, Dec. 2012.
DOI Google Scholar
[20]
A. Bokhari, A. Alkan, R. Dogan, M. Diaz-Aguiló, F. De León, D. Czarkowski, Z. Zabar, L. Birenbaum, A. Noel, and R. E. Uosef, “Experimental determination of the ZIP coefficients for modern residential, commercial, and industrial loads,” IEEE Transactions on Power Delivery, vol. 29, no. 3, pp. 1372–1381, Jun. 2014.
DOI Google Scholar
[21]
X. Wang, Y. S. Wang, D. Shi, J. H. Wang, and Z. W. Wang, “Two-stage WECC composite load modeling: a double deep Q-learning networks approach”, IEEE Transactions on Smart Grid, vol. 11, no. 5, pp. 4331–4344, Sep. 2020.
DOI Google Scholar
[22]
M. Jin, H. Renmu, and D. J. Hill, “Load modeling by finding support vectors of load data from field measurements,” IEEE Transactions on Power Systems, vol. 21, no. 2, pp. 726–735, May 2006.
DOI Google Scholar
[23]
A. Ellis, M. R. Behnke, and R. T. Elliott, “Generic solar photovoltaic system dynamic simulation model specification”, USDOE National Nuclear Security Administration (NNSA), United States, SAND2013–8876, 2013.
[24]
P. Cicilio and E. Cotilla-Sanchez, “Evaluating measurement-based dynamic load modeling techniques and metrics,” IEEE Transactions on Power Systems, vol. 35, no. 3, pp. 1805–1811, May 2020.
DOI Google Scholar
[25]
I. F. Visconti, D. A. Lima, J. M. C. De Sousa Costa, and N. R. De B. C. Sobrinho, “Measurement-based load modeling using transfer functions for dynamic simulations,” IEEE Transactions on Power Systems, vol. 29, no. 1, pp. 111–120, Jan. 2014.
DOI Google Scholar
[26]
F. F. Bai, X. R. Wang, Y. L. Liu, X. Y. Liu, Y. Xiang, Y. Liu, “Measurement-based power system frequency dynamic response estimation using geometric template matching and recurrent artificial neural network,” CSEE Journal of Power and Energy Systems, vol. 2, no. 3, pp. 10–18, Sep. 2016.
DOI Google Scholar
[27]
E. O. Kontis, T. A. Papadopoulos, A. I. Chrysochos, and G. K. Papagiannis, “Measurement-based dynamic load modeling using the vector fitting technique,” IEEE Transactions on Power Systems, vol. 33, no. 1, pp. 338–351, Jan. 2018.
DOI Google Scholar
[28]
A. Arif, Z. Y. Wang, J. H. Wang, B. Mather, H. Bashualdo, and D. B. Zhao, “Load modeling-a review,” IEEE Transactions on Smart Grid, vol. 9, no. 6, pp. 5986–5999, Nov. 2018.
DOI Google Scholar
[29]
W. W. Price, K. A. Wirgau, A. Murdoch, J. V. Mitsche, E. Vaahedi, and M. El-Kady, “Load modeling for power flow and transient stability computer studies,” IEEE Transactions on Power Systems, vol. 3, no. 1, pp.180–187, Feb. 1988.
DOI Google Scholar
[30]
T. Hiyama, M. Tokieda, W. Hubbi, and H. Andou, “Artificial neural network based dynamic load modeling,” IEEE Transactions on Power Systems, vol. 12, no. 4, pp. 1576–1583, Nov. 1997.
DOI Google Scholar
[31]
Y. T. Ji, E. Buechler, and R. Rajagopal, “Data-Driven load modeling and forecasting of residential appliances”, IEEE Transactions on Smart Grid, vol. 11, no. 3, pp. 2652–2661, May 2020.
DOI Google Scholar
[32]
M. Segal and Y. Y. Xiao, “Multivariate random forests”, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 1, no. 1, pp. 80–87, Jan./Feb. 2011.
DOI Google Scholar
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Received: 14 November 2021
Revised: 31 December 2021
Accepted: 25 January 2022
Published: 14 February 2022
Issue date: March 2022

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© 2021 CSEE

Acknowledgements

This study was performed in part or in full using equipment and infrastructure funded by the Australian Federal Government’s Department of Education AGL Solar PV Education Investment Fund Research Infrastructure Project. The University of Queensland is the Lead Research Organization in partnership with AGL, First Solar and the University of New South Wales.

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