Review of charging load modeling strategies for electric vehicles: A grid-to-vehicle probabilistic approach comparison
Palabras clave:
electric vehicles, distribution network, EV load modeling (en)Descargas
Different strategies are being considered to combat global warming. One of them is the inclusion of electric vehicles (EVs) within power networks. With the penetration of EVs, new challenges have arisen within the operation, planning and analysis of these networks. The modelling of the penetration of EVs in power network analysis studies has been widely addressed. In this article, we review different approaches to how this penetration can be modeled in power networks. From this review, we identified that these methods can be classified into three groups: deterministic, data driven and uncertainty/variability approaches. Finally, we evaluate and compare experimentally the performance of three uncertainty/variability approaches considering four levels of penetration of EVs. From this comparison, we found that the best model can be represented as a gamma probability distribution.
The full text can be consulted at: https://doi.org/10.14483/22487638.18657
Referencias
A. Alahyari, M. Ehsan, and M. Mousavizadeh, “A hybrid storage-wind virtual power plant (vpp) participation in the electricity markets: A self- scheduling optimization considering price, renewable generation, and electric vehicles uncertainties,” Journal of Energy Storage, vol. 25, p. 100812, 2019.
X. Li, Q. Zhang, Z. Peng, A. Wang, and W. Wang, “A data-driven two- level clustering model for driving pattern analysis of electric vehicles and a case study,” Journal of Cleaner Production, vol. 206, pp. 827–837, 2019.
P. Grahn, J. Rosenlind, P. Hilber, K. Alvehag, and L. Soder, “A method ̈ for evaluating the impact of electric vehicle charging on transformer hotspot temperature,” in 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies, 2011, pp. 1–8.
Y. Kongjeen, K. Bhumkittipich, N. Mithulananthan, I. Amiri, and P. Yu- papin, “A modified backward and forward sweep method for microgrid load flow analysis under different electric vehicle load mathematical models,” Electric Power Systems Research, vol. 168, pp. 46–54, 2019.
Z. Yi and D. Scoffield, “A data-driven framework for residential electric vehicle charging load profile generation,” in 2018 IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 519–524.
G. Li and X. Zhang, “Modeling of plug-in hybrid electric vehicle charg- ing demand in probabilistic power flow calculations,” IEEE Transactions on Smart Grid, vol. 3, no. 1, pp. 492–499, 2012.
S. Shahidinejad, S. Filizadeh, and E. Bibeau, “Profile of charging load on the grid due to plug-in vehicles,” IEEE Transactions on Smart Grid, vol. 3, no. 1, pp. 135–141, 2012.
A. Ahmadian, M. Sedghi, A. Elkamel, M. Aliakbar-Golkar, and M. Fowler, “Optimal WDG planning in active distribution networks based on possibilistic-probabilistic PEVs load modelling,” IET Genera- tion, Transmission and Distribution, vol. 11, pp. 865–875, March 2017.
J. Stiasny, T. Zufferey, G. Pareschi, D. Toffanin, G. Hug, and K. Boulou- chos, “Sensitivity analysis of electric vehicle impact on low-voltage dis- tribution grids,” Electric Power Systems Research, vol. 191, p. 106696, 2021.
O. Frendo, J. Graf, N. Gaertner, and H. Stuckenschmidt, “Data-driven smart charging for heterogeneous electric vehicle fleets,” Energy and AI, vol. 1, p. 100007, 2020.
J. Gil-Aguirre, S. Perez-Londono, and J. Mora-Fl ̃ orez, “A measurement- ́ based load modelling methodology for electric vehicle fast-charging stations,” Electric Power Systems Research, vol. 176, p. 105934, 2019.
E. Xydas, C. Marmaras, L. M. Cipcigan, N. Jenkins, S. Carroll, and M. Barker, “A data-driven approach for characterising the charging demand of electric vehicles: A UK case study,” Applied Energy, vol. 162, pp. 763–771, 2016.
A. Ashtari, E. Bibeau, S. Shahidinejad, and T. Molinski, “PEV charging profile prediction and analysis based on vehicle usage data,” IEEE Transactions on Smart Grid, vol. 3, no. 1, pp. 341–350, 2012.
W. Zhou, K. Xu, Y. Yang, and J. Lu, “Driving cycle development for electric vehicle application using principal component analysis and K- means cluster: With the case of Shenyang, China,” Energy Procedia, vol. 105, pp. 2831–2836, 2017, 8th International Conference on Applied Energy, ICAE2016, 8-11 October 2016, Beijing, China.
C. Crozier, T. Morstyn, and M. McCulloch, “A stochastic model for uncontrolled charging of electric vehicles using cluster analysis,” 2019.
A. Gerossier, R. Girard, and G. Kariniotakis, “Modeling and forecasting electric vehicle consumption profiles,” Energies, vol. 12, no. 7, 2019.
M. Godde, T. Findeisen, T. Sowa, and P. H. Nguyen, “Modelling the charging probability of electric vehicles as a Gaussian mixture model for a convolution based power flow analysis,” in 2015 IEEE Eindhoven PowerTech, 2015, pp. 1–6.
M. B. Arias and S. Bae, “Electric vehicle charging demand forecasting model based on big data technologies,” Applied Energy, vol. 183, pp. 327–339, 2016.
K. Sun, M. R. Sarker, and M. A. Ortega-Vazquez, “Statistical charac- terization of electric vehicle charging in different locations of the grid,” in 2015 IEEE Power Energy Society General Meeting, 2015, pp. 1–5.
Y. B. Khoo, C.-H. Wang, P. Paevere, and A. Higgins, “Statistical modeling of electric vehicle electricity consumption in the victorian EV trial, australia,” Transportation Research Part D: Transport and Environment, vol. 32, pp. 263–277, 2014.
M. G. Flammini, G. Prettico, A. Julea, G. Fulli, A. Mazza, and G. Chicco, “Statistical characterisation of the real transaction data gathered from electric vehicle charging stations,” Electric Power Systems Research, vol. 166, pp. 136–150, 2019.
Y.-W. Chung, B. Khaki, C. Chu, and R. Gadh, “Electric vehicle user behavior prediction using hybrid kernel density estimator,” in 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 2018, pp. 1–6.
L. Chen, X. Huang, and H. Zhang, “Modeling the charging behaviors for electric vehicles based on ternary symmetric kernel density estimation,” Energies, vol. 13, no. 7, 2020.
J. Su, T. Lie, and R. Zamora, “Modelling of large-scale electric vehicles charging demand: A New Zealand case study,” Electric Power Systems Research, vol. 167, pp. 171–182, 2019.
I. G. Tekdemir, B. Alboyaci, D. Gunes, and M. Sengul, “A probabilistic approach for evaluation of electric vehicles’ effects on distribution sys- tems,” in 2017 4th International Conference on Electrical and Electronic Engineering (ICEEE), 2017, pp. 143–147.
A. Ul-Haq, C. Cecati, and E. El-Saadany, “Probabilistic modeling of electric vehicle charging pattern in a residential distribution network,” Electric Power Systems Research, vol. 157, pp. 126–133, 2018.
A. Ahmadian, M. Sedghi, and M. Aliakbar-Golkar, “Stochastic modeling of plug-in electric vehicles load demand in residential grids considering nonlinear battery charge characteristic,” in 2015 20th Conference on Electrical Power Distribution Networks Conference (EPDC), 2015, pp. 22–26.
J. Tan and L. Wang, “Stochastic modeling of load demand of plug- in hybrid electric vehicles using fuzzy logic,” in 2014 IEEE PES T D Conference and Exposition, 2014, pp. 1–5.
S. Hussain, M. A. Ahmed, and Y.-C. Kim, “Efficient power management algorithm based on fuzzy logic inference for electric vehicles parking lot,” IEEE Access, vol. 7, pp. 65 467–65 485, 2019.
A. Ali, S. Mahdi, E. Ali, A.-G. Masoud, and F. Michael, “Optimal WDG planning in active distribution networks based on possibilis- tic–probabilistic pevs load modelling,” IET Generation, Transmission and Distribution, vol. 11, pp. 865–875(10), March 2017.
M. H. Amini, A. Kargarian, and O. Karabasoglu, “ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation,” Electric Power Systems Research, vol. 140, pp. 378–390, 2016.
P. Sokorai, A. Fleischhacker, G. Lettner, and H. Auer, “Stochastic modeling of the charging behavior of electromobility,” World Electric Vehicle Journal, vol. 9, no. 3, 2018.
H. Jiang, H. Ren, C. Sun, and D. Watts, “The temporal-spatial stochastic model of plug-in hybrid electric vehicles,” in 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 2017, pp. 1–6.
R. Garcia-Valle and J. G. Vlachogiannis, “Letter to the editor: Electric vehicle demand model for load flow studies,” Electric Power Compo- nents and Systems, vol. 37, no. 5, pp. 577–582, 2009.
A. Freeman, “Probabilistic modeling as an exploratory decision-making tool,” 2010.
J. Carrillo and G. Toscani, Wasserstein Metric And Large-Time Asymp- totics Of Nonlinear Diffusion Equations, 2005, pp. 234–244.
Cómo citar
APA
ACM
ACS
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Descargar cita
Visitas a la página del resumen del artículo
Descargas
Licencia
Derechos de autor 2023 Simposio Internacional sobre la Calidad de la Energía Eléctrica - SICEL

Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.