Research Article |
Load modeling of electric bus charging station from data obtained through Phasor Measurement Units
Author(s): Ricardo Isaza – Ruget* Cristhian Perilla and Javier Rosero-García
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 12, Issue 4
Publisher : FOREX Publication
Published : 25 October 2024
e-ISSN : 2347-470X
Page(s) : 1188-1195
Abstract
While the increased adoption of electric vehicles (EVs) is a promising alternative to reduce CO2 emissions, it creates new challenges for the power grid due to increased energy demand and power quality (PQ) issues. These impacts vary depending on several factors such as the level of EV adoption, charging technology, network voltage level, charging patterns, charging station location, battery condition, and driving habits. Analyzing these impacts and developing solutions, such as characterizing the demand curve for charging stations and understanding EV charging patterns, is crucial to ensure a sustainable transition to an electrified EV future. A study using the ZIP load model that represents voltage dependence by combining constant impedance (“Z”), constant current (“I”), constant power (“P”) components, and phasor measurement units (PMUs) demonstrates the effectiveness of EV demand characterization. The importance of this aspect for grid stability and charging management is highlighted.
Keywords: load model
, electric vehicle
, minimum mean squared error
.
Ricardo Isaza – Ruget, Universidad Nacional de Colombia; Email: risazar@unal.edu.co
Cristhian Perilla, Universidad Nacional de Colombia; Email: caperillag@unal.edu.co
Javier Rosero-García*, Universidad Nacional de Colombia; Email: jaroserog@unal.edu.co
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[1] G. Brückmann and T. Bernauer, “What drives public support for policies to enhance electric vehicle adoption?,” Environmental Research Letters, vol. 15, no. 9, p. 094002, Sep. 2020, doi: 10.1088/1748-9326/ab90a5.
-
[2] F. Alanazi, “Electric Vehicles: Benefits, Challenges, and Potential Solutions for Widespread Adaptation,” Applied Sciences, vol. 13, no. 10, p. 6016, May 2023, doi: 10.3390/app13106016.
-
[3] G. Santos and H. Davies, “Incentives for quick penetration of electric vehicles in five European countries: Perceptions from experts and stakeholders,” Transp Res Part A Policy Pract, vol. 137, pp. 326–342, Jul. 2020, doi: 10.1016/j.tra.2018.10.034.
-
[4] X. Huang, D. Wu, and B. Boulet, “Ensemble Learning for Charging Load Forecasting of Electric Vehicle Charging Stations,” in 2020 IEEE Electric Power and Energy Conference (EPEC), IEEE, Nov. 2020, pp. 1–5. doi: 10.1109/EPEC48502.2020.9319916.
-
[5] L. T. M. Mota and A. A. Mota, “Load modeling at electric power distribution substations using dynamic load parameters estimation,” International Journal of Electrical Power & Energy Systems, vol. 26, no. 10, pp. 805–811, Dec. 2004, doi: 10.1016/j.ijepes.2004.07.002.
-
[6] H.- Rivera et al., “Citation: An Overview of Electric Vehicle Load Modeling Strategies for Grid Integration Studies,” 2024, doi: 10.3390/electronics13122259.
-
[7] X. Wang, H. J. Kaleybar, M. Brenna, and D. Zaninelli, “Power Quality Indicators of Electric Vehicles in Distribution Grid,” in 2022 20th International Conference on Harmonics & Quality of Power (ICHQP), IEEE, May 2022, pp. 1–6. doi: 10.1109/ICHQP53011.2022.9808823.
-
[8] M. H. Cedillo, H. Sun, J. Jiang, and Y. Cao, “Dynamic pricing and control for EV charging stations with solar generation,” Appl Energy, vol. 326, p. 119920, Nov. 2022, doi: 10.1016/j.apenergy.2022.119920.
-
[9] T. Mannari and H. Hatta, “CIRED workshop on E-mobility and power distribution systems Analysis on the effect of V2G aggregation on distribution network based on traffic simulator,” 2022.
-
[10] U. Bin Irshad and S. Rafique, “Stochastic Modelling of Electric Vehicle’s Charging Behaviour in Parking Lots,” in 2020 IEEE Transportation Electrification Conference & Expo (ITEC), IEEE, Jun. 2020, pp. 748–752. doi: 10.1109/ITEC48692.2020.9161566.
-
[11] A. Huaman-Rivera, R. Calloquispe-Huallpa, A. C. L. Luna Hernandez, and A. Irizarry-Rivera, “An Overview of Electric Vehicle Load Modeling Strategies for Grid Integration Studies,” Electronics 2024, Vol. 13, Page 2259, vol. 13, no. 12, p. 2259, Jun. 2024, doi: 10.3390/ELECTRONICS13122259.
-
[12] K. Fungyai, N. Sangmeg, A. Pichetjamroen, S. Dechanupaprittha, and N. Somakettarin, “Determination of ZIP Load Model Parameters based on Synchrophasor Data by Genetic Algorithm,” 2020 8th International Electrical Engineering Congress, iEECON 2020, Mar. 2020, doi: 10.1109/IEECON48109.2020.229509.
-
[13] S. Han et al., “Measurement-based static load modeling using the PMU data installed on the university load,” Journal of Electrical Engineering and Technology, vol. 7, no. 5, pp. 653–658, 2012, doi: 10.5370/JEET.2012.7.5.653.
-
[14] M. Leinakse, Estimation and Conversion of Static Load Models of Aggregated Transmission System Loads.
-
[15] I. R. Navarro, “Dynamic Load Models for Power Systems Estimation of Time-Varying Parameters During Normal Operation.” [Online]. Available: http://www.iea.lth.se
-
[16] M. Leinakse, Estimation and Conversion of Static Load Models of Aggregated Transmission System Loads.
-
[17] A. Arif, Z. Wang, J. Wang, B. Mather, H. Bashualdo, and D. Zhao, “Load Modeling—A Review,” IEEE Trans Smart Grid, vol. 9, no. 6, pp. 5986–5999, Nov. 2018, doi: 10.1109/TSG.2017.2700436.
-
[18] “Front Matter,” in Variable Generation, Flexible Demand, Elsevier, 2021, p. iii. doi: 10.1016/b978-0-12-823810-3.01001-3.
-
[19] K. M. Ramachandran and C. P. Tsokos, Mathematical statistics with applications. Academic Press, 2009.
-
[20] G. Frigo, A. Derviskadic, Y. Zuo, and M. Paolone, “PMU-Based ROCOF Measurements: Uncertainty Limits and Metrological Significance in Power System Applications,” IEEE Trans Instrum Meas, vol. 68, no. 10, pp. 3810–3822, Oct. 2019, doi: 10.1109/TIM.2019.2907756.
-
[21] A. Derviskadic, Y. Zuo, G. Frigo, and M. Paolone, “Under Frequency Load Shedding based on PMU Estimates of Frequency and ROCOF,” in 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), IEEE, Oct. 2018, pp. 1–6. doi: 10.1109/ISGTEurope.2018.8571481.
-
[22] S. You et al., “Calculate Center-of-Inertia Frequency and System RoCoF Using PMU Data,” in 2021 IEEE Power & Energy Society General Meeting (PESGM), IEEE, Jul. 2021, pp. 1–5. doi: 10.1109/PESGM46819.2021.9638108.
-
[23] D. Tzelepis, E. Tsotsopoulou, Q. Hong, V. Terzija, and C. Booth, “Measuring technologies for future power grids,” Encyclopedia of Electrical and Electronic Power Engineering: Volumes 1-3, vol. 2, pp. 310–319, Jan. 2023, doi: 10.1016/B978-0-12-821204-2.00147-1.
-
[24] J. Reback et al., “pandas-dev/pandas: Pandas 1.0.1,” Feb. 2020, doi: 10.5281/ZENODO.3644238.
-
[25] J. D. Hunter, “Matplotlib: A 2D Graphics Environment,” Comput Sci Eng, vol. 9, no. 3, pp. 90–95, 2007, doi: 10.1109/MCSE.2007.55.
-
[26] F. Pedregosa FABIANPEDREGOSA et al., “Scikit-learn: Machine Learning in Python Gaël Varoquaux Bertrand Thirion Vincent Dubourg Alexandre Passos PEDREGOSA, VAROQUAUX, GRAMFORT ET AL. Matthieu Perrot,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011, Accessed: Oct. 18, 2022. [Online]. Available: http://scikit-learn.sourceforge.net.
-
[27] P. Virtanen et al., “SciPy 1.0: fundamental algorithms for scientific computing in Python,” Nat Methods, vol. 17, no. 3, pp. 261–272, Mar. 2020, doi: 10.1038/s41592-019-0686-2. in Python,” Nat Methods, vol. 17, no. 3, pp. 261–272, Mar. 2020, doi: 10.1038/s41592-019-0686-2.