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ZIP load modeling for single and aggregate loads and CVR factor estimation

  • Yiqi Zhang , Yuan Liao ORCID logo EMAIL logo , Evan Jones , Nicholas Jewell and Dan Ionel
Published/Copyright: August 5, 2022

Abstract

ZIP load modeling has been used in various power system applications. The aggregate load modeling is common practice in utility companies. However, little research has been done on the theoretical formulation of the aggregate load and on various factors that affect accurate estimation of the parameters. This paper proposes new methods to systematically formulate the aggregate ZIP load model using the single ZIP load model and comprehensively examines the factors that may affect aggregate ZIP load estimation. Moreover, novel analysis of reactive power ZIP parameter calculation considering different compensating device modeling is presented. ZIP parameter estimation methods including least squares method, optimization method, and neural network method have been used in this paper to estimate ZIP parameters. The proposed new method was illustrated using the IEEE 13-bus and 34-bus systems built in OpenDSS. In addition, the ZIP parameter estimation is also performed using field data, and the conservation through voltage reduction (CVR) factor is further computed based on the estimated ZIP load model. The results provide guidance on calculation and interpretation of CVR estimates. Note that this paper was first presented at PAC world conference 2021 which provides no published conference proceedings.


Corresponding author: Yuan Liao, Department of ECE, University of Kentucky, 453 F Paul Anderson Tower, Lexington, KY 40506, USA, E-mail:

Funding source: LG&E and KU Energy LLC

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This work was funded by LG&E and KU Energy LLC.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

1. Arif, A, Wang, Z, Wang, J, Mather, B, Bashualdo, H, Zhao, D. Load modeling—a review. IEEE Trans Smart Grid 2018;9:5986–99. https://doi.org/10.1109/tsg.2017.2700436.Search in Google Scholar

2. Battapothula, G, Yammani, C, Maheswarapu, S. Multi-objective optimal planning of fast charging stations by considering various load models in distribution system. Int J Emerg Elec Power Syst 2021;22:439–50. https://doi.org/10.1515/ijeeps-2020-0252.Search in Google Scholar

3. Usman, M, Cervi, A, Coppo, M, Bignucolo, F, Turri, R. Centralized OPF in unbalanced multi-phase neutral equipped distribution networks hosting ZIP loads. IEEE Access 2019;7:177890–908. https://doi.org/10.1109/access.2019.2958695.Search in Google Scholar

4. He, D, Habetler, T, Mousavi, MJ, Kang, N. A ZIP model-based feeder load modeling and forecasting method. In: 2013 IEEE power & energy society general meeting; 2013.Search in Google Scholar

5. Dosoglu, MK, Dursun, M. Investigation with ZIP load model of voltage stability analysis in wind turbine integrated power system. Presented at the 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT); 2018.10.1109/ISMSIT.2018.8567310Search in Google Scholar

6. Fan, W, Liao, Y, See, J, Goins, B, Gill, C, Petreshock, J, et al.. Distribution system voltage and var optimization. In: 2012 IEEE power and energy society general meeting; 2012.Search in Google Scholar

7. Fan, W, Hossan, MS, Zheng, H, Cook, A, Zaid, S, Fard, SA, et al.. A CVR on/off status detection algorithm for measurement and verification. Presented at the 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT); 2021.10.1109/ISGT49243.2021.9372257Search in Google Scholar

8. Hossein, ZS, Khodaei, A, Fan, W, Hossan, MS, Zheng, H, Fard, S, et al.. Conservation voltage reduction and volt-VAR optimization: measurement and verification benchmarking. IEEE Access 2020;8:50755–70. https://doi.org/10.1109/access.2020.2979242.Search in Google Scholar

9. Sadeghi, M, Abdollahi Sarvi, G. Determination of ZIP parameters with least squares optimization method. Presented at the Energy Conference (EPEC); 2009.10.1109/EPEC.2009.5420883Search in Google Scholar

10. Bokhari, A, Alkan, A, Dogan, R, Diaz-Aguilo, M, Leon, F, Czarkowski, D, et al.. Experimental determination of the zip coefficients for modern residential, commercial, and industrial loads. IEEE Trans Power Deliv 2014;29:1372–81. https://doi.org/10.1109/tpwrd.2013.2285096.Search in Google Scholar

11. Wang, K, Huang, H, Zang, C. Research on time-sharing ZIP load modeling based on linear BP network. Presented at the 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2013.10.1109/IHMSC.2013.16Search in Google Scholar

12. Shahsavari, A, Farajollahi, M, Mohsenian-Rad, H. Individual load model parameter estimation in distribution systems using load switching events. IEEE Trans Power Syst 2019;34:4652–64. https://doi.org/10.1109/tpwrs.2019.2919901.Search in Google Scholar

13. Bircan, M, Durusu, A, Kekezoglu, B, Elma, O, Selamogullari, US. Experimental determination of ZIP coefficients for residential appliances and ZIP model based appliance identification: the case of YTU Smart Home. Elec Power Syst Res 2020;179:106070. https://doi.org/10.1016/j.epsr.2019.106070.Search in Google Scholar

14. Bai, H, Zhang, P, Ajjarapu, V. A novel parameter identification approach via hybrid learning for aggregate load modeling. IEEE Trans Power Syst 2009;24:1145–54.10.1109/TPWRS.2009.2022984Search in Google Scholar

15. Fan, H, Zhang, T, Yu, H, Geng, G. Identifying ZIP coefficients of aggregated residential load model using AMI data. Presented at the 2019 IEEE 3rd International Electrical and Energy Conference (CIEEC); 2019.10.1109/CIEEC47146.2019.CIEEC-2019582Search in Google Scholar

16. Ji, Y, Zhang, X, Wang, X, Huang, X, Huang, B, Zheng, J, et al.. An equivalent modeling method for multi-port area load based on the extended generalized ZIP load model. Presented at the 2018 International Conference on Power System Technology (POWERCON); 2018.10.1109/POWERCON.2018.8601588Search in Google Scholar

17. Ren, H, Schulz, NN, Krishnan, V, Zhang, Y. Online static load model estimation in distribution systems. Presented at the 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE); 2019.10.1109/ISIE.2019.8781530Search in Google Scholar

18. He, S, Starrett, SK. Modeling power system load using adaptive neural fuzzy logic and artificial neural networks. Presented at the 2009 North American Power Symposium – NAPS; 2009.10.1109/NAPS.2009.5483985Search in Google Scholar

19. IEEE recommended practice for electric power distribution for industrial plants, IEEE Standard 141-1993, 1994:1–768 pp.Search in Google Scholar

20. Rojas-Cubides, HE, Cruz-Bernal, AS, Rojas-Cubides, HD. Analysis of voltage sag compensation in distribution systems using a multilevel DSTATCOM in ATP/EMTP. DYNA 2015;82:26–36. https://doi.org/10.15446/dyna.v82n192.48566.Search in Google Scholar

Received: 2022-02-21
Accepted: 2022-07-15
Published Online: 2022-08-05

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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