A framework for stochastic estimation of electric vehicle charging behavior for risk assessment of distribution networks

Abstract

Power systems are being transformed to enhance the sustainability. This paper contributes to the knowledge regarding the operational process of future power networks by developing a realistic and stochastic charging model of electric vehicles (EVs). Large-scale integration of EVs into residential distribution networks (RDNs) is an evolving issue of paramount significance for utility operators. Unbalanced voltages prevent effective and reliable operation of RDNs. Diversified EV loads require a stochastic approach to predict EVs charging demand, consequently, a probabilistic model is developed to account several realistic aspects comprising charging time, battery capacity, driving mileage, state-of-charge, traveling frequency, charging power, and time-of-use mechanism under peak and off-peak charging strategies. An attempt is made to examine risks associated with RDNs by applying a stochastic model of EVs charging pattern. The output of EV stochastic model obtained from Monte-Carlo simulations is utilized to evaluate the power quality parameters of RDNs. The equipment capability of RDNs must be evaluated to determine the potential overloads. Performance specifications of RDNs including voltage unbalance factor, voltage behavior, domestic transformer limits and feeder losses are assessed in context to EV charging scenarios with various charging power levels at different penetration levels. Moreover, the impact assessment of EVs on RDNs is found to majorly rely on the type and location of a power network.

Key words： electric vehicles (EVs); residential distribution networks (RDNs); voltage unbalance factor (VUF); state-of charge (SOC); time-of-use (TOU)

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11708-019-0648-5 and is accessible for authorized users.

References

1 Tabari M, Yazdani A. An energy management strategy for a DC distribution system for power system integration of plug-in electric vehicles. IEEE Transactions on Smart Grid, 2016, 7(2): 659–668

2 Habib S, Khan M M, Abbas F, Sang L, Shahid M U, Tang H. A comprehensive study of implemented international standards, technical challenges, impacts and prospects for electric vehicles. IEEE Access: Practical Innovations, Open Solutions, 2018, 6: 13866–13890

3 Habib S K, Khan M M, Abbas F, Tang H. Assessment of electric vehicles concerning impacts, charging infrastructure with unidirectional and bidirectional chargers, and power flow comparisons. International Journal of Energy Research, 2018, 42(11): 3416–3441

4 Irle R. EV-volumes—the electric vehicle world sales database. 2018–11–22, available at ev-volumes.com website

5 Wu D, Aliprantis D C, Gkritza K. Electric energy and power consumption by light-duty plug-in electric vehicles. IEEE Transactions on Power Systems, 2011, 26(2): 738–746

6 Lopes J A P, Soares F J, Almeida P M R. Integration of electric vehicles in the electric power system. Proceedings of the IEEE, 2011, 99(1): 168–183

7 Clement-Nyns K, Haesen E, Driesen J. The impact of charging plug-in hybrid electric vehicles on a residential distribution grid. IEEE Transactions on Power Systems, 2010, 25(1): 371–380

8 Steen D, Tuan L A, Carlson O, Bertling L. Assessment of electric vehicle charging scenarios based on demographical data. IEEE Transactions on Smart Grid, 2012, 3(3): 1457–1468

9 Deilami S, Masoum A S, Moses P S, Masoum M A S. Paper presentation—real-time coordination of plug-in electric vehicle charging in smart grids to minimize power losses and improve voltage profile. IEEE Transactions on Smart Grid, 2011, 2(3): 456–467

10 Richardson P, Flynn D, Keane A. Optimal charging of electric vehicles in low-voltage distribution systems. IEEE Transactions on Power Systems, 2012, 27(1): 268–279

11 Wang X, Karki R. Exploiting PHEV to augment power system reliability. IEEE Transactions on Smart Grid, 2017, 8(5): 2100–2108

12 Almutairi A, Bin Humayd A, Salama M M A. Quantifying the impact of PEV charging loads on the reliability performance of generation systems. In: IEEE Power & Energy Society General Meeting, Boston, MA, USA, 2016

13 Hou K, Xu X, Jia H, Yu X, Jiang T, Zhang K, Shu B. A reliability assessment approach for integrated transportation and electrical power systems incorporating electric vehicles. IEEE Transactions on Smart Grid, 2018, 9(1): 88–100

14 Falvo M C, Graditi G, Siano P. Electric vehicles integration in demand response programs. In: International Symposium on Power Electronics Electrical Drives Automation & Motion, 2014, 548–553

15 Di Silvestre M L, Riva Sanseverino E, Zizzo G, Graditi G. An optimization approach for efficient management of EV parking lots with batteries recharging facilities. Journal of Ambient Intelligence and Humanized Computing, 2013, 4(6): 641–649

16 Zhou Y, Li Z, Song Z, The charging and discharging power prediction for electric vehicles. In: IECON 2016–42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy, 2016, 4014–4019

17 Di Somma M, Graditi G, Heydarian-Forushani E, Shafie-khah M, Siano P. Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects. Renewable Energy, 2018, 116: 272–287

18 Liu Z, Wang D, Jia H, Djilali N, Zhang W. Aggregation and bidirectional charging power control of plug-in hybrid electric vehicles: generation system adequacy analysis. IEEE Transaction on Renewable Energy, 2015, 6(2): 325–335

19 Li G, Zhang X P. Modeling of plug-in hybrid electric vehicle charging demand in probabilistic power flow calculations. IEEE Transactions on Smart Grid, 2012, 3(1): 492–499

20 Yang Z, Li K, Foley A. Computational scheduling methods for integrating plug-in electric vehicles with power systems: a review. Renewable & Sustainable Energy Reviews, 2015, 51: 396–416

21 Hu J, You S, Lind M, Ostergaard J. Coordinated charging of electric vehicles for congestion prevention in the distribution grid. IEEE Transactions on Smart Grid, 2014, 5(2): 703–711

22 Hua L, Wang J, Zhou C. Adaptive electric vehicle charging coordination on distribution network. IEEE Transactions on Smart Grid, 2014, 5(6): 2666–2675

23 de Hoog J, Alpcan T, Brazil M, Thomas D A, Mareels I. Optimal charging of electric vehicles taking distribution network constraints into account. IEEE Transactions on Power Systems, 2015, 30(1): 365–375

24 Quirós-Tortós J, Ochoa L F, Alnaser S W, Butler T. Control of EV charging points for thermal and voltage management of LV networks. IEEE Transactions on Power Systems, 2016, 31(4): 3028–3039

25 Navarro-Espinosa A, Ochoa L F. Probabilistic impact assessment of low carbon technologies in LV distribution systems. IEEE Transactions on Power Systems, 2016, 31(3): 2192–2203

26 Humayd A S B, Bhattacharya K. A novel framework for evaluating maximum PEV penetration into distribution systems. IEEE Transactions on Smart Grid, 2018, 9(4): 2741–2751

27 Sachan S, Adnan N. Stochastic charging of electric vehicles in smart power distribution grids. Sustainable Cities and Society, 2018, 40: 91–100

28 Misra R, Paudyal S, Ceylan O, Mandal P. Harmonic distortion minimization in power grids with wind and electric vehicles. Energies, 2017, 10(7): 932

29 Alhazmi Y A, Salama M M A. Economical staging plan for implementing electric vehicle charging stations. Sustainable Energy, Grids Networks, 2017, 10: 12–25

30 Sehar F, Pipattanasomporn M, Rahman S. RahmanS. Demand management to mitigate impacts of plug-in electric vehicle fast charge in buildings with renewables. Energy, 2017, 120: 642–651

31 García-Villalobos J, Zamora I, Knezović K, Marinelli M. Multi-objective optimization control of plug-in electric vehicles in low voltage distribution networks. Applied Energy, 2016, 180: 155–168

32 Muratori M. Impact of uncoordinated plug-in electric vehicle charging on residential power demand. Nature Energy, 2018, 3(3): 193–201

33 National Household Travel Survey, US. NHTS datasets. 2018–06–06, available at nhts.ornl.gov website

34 Godina R, Rodrigues E M G, Matias J C O, Catalão J P S. Smart electric vehicle charging scheduler for overloading prevention of an industry client power distribution transformer. Applied Energy, 2016, 178: 29–42

35 National Transmission and Despatch Company Limited (NTDC), Pakistan. PMS Load Forecast. 2018–04–23, available at ntdc.com website

36 DIgSILENT GmbH Germany. DIgSILENT Power System Software and Engineering. 2014, available at digsilent.de website

37 IEEE Power and Energy Society. IEEE PES AMPS DSAS Test Feeder Working Group. 2018–04–17, available at sites.ieee.org website

38 Penido D R R, de Araujo L R, Carneiro S, Pereira J L R, Garcia P A N. Three-phase power flow based on four-conductor current injection method for unbalanced distribution networks. IEEE Transactions on Power Systems, 2008, 23(2): 494–503