access icon free Modelling probabilistic transmission expansion planning in the presence of plug-in electric vehicles uncertainty by multi-state Markov model

© The Institution of Engineering and Technology
Received 26/08/2016, Accepted 05/02/2017, Revised 22/01/2017, Published 01/03/2017

Increasing penetration of plug-in electric vehicles (PEVs) in the power system makes a need to consider the impact of PEVs on the transmission expansion planning (TEP) studies especially for large-scale PEV parking lots. Modelling of PEVs (as vehicle to grid (V2G)) is highly dependent on the owner's behaviour. In this study, a systematic method based on multi-state Markov model is utilised to represent the uncertainty of V2G's presence. To investigate the impact of PEVs, a probabilistic TEP in the presence of V2Gs (P_TEPV2G) is proposed. In the proposed TEP model, the objective function consists of the total line and risk costs (RCs). Moreover, the optimal place and capacity of PEV parking lots are considered as the decision variables. It is assumed that there is an electric vehicle fleet operator for the management of electric vehicles. Because P_TEPV2G is a complex and non-linear optimisation problem, an improved cuckoo search algorithm (ICS) is utilised to effectively solve the problem. On the IEEE 24-bus system, simulation results show that the availability of the PEVs at the proper bus leads to decreasing RC and deferring construction of new transmission lines.

Inspec keywords: search problems; probability; power transmission planning; nonlinear programming; Markov processes; vehicle-to-grid

Other keywords: multistate Markov model; vehicle-to-grid; plug-in electric vehicles; power system; IEEE 24-bus system; risk costs; improved cuckoo search algorithm; nonlinear optimisation problem; large-scale PEV parking lots; total line costs; probabilistic transmission expansion planning modeling; probabilistic TEP

Subjects: Distributed power generation; Combinatorial mathematics; Power system planning and layout; Markov processes; Transportation; Optimisation techniques

References

    1. 1)
      • 4. Duvall, M., Knipping, E., Alexanser, M.: ‘Environmental assessment of plug-in hybrid electric vehicles’ (EPRI, 2007).
    2. 2)
      • 24. Dong, J., Liu, C., Lin, Z.: ‘Charging infrastructure planning for promoting battery electric vehicles: An activity-based approach using multiday travel data’, Transp. Res. C Emerging Technol., 2014, 38, pp. 44–55.
    3. 3)
      • 7. Hu, J., You, S., Lind, M., et al: ‘Coordinated charging of electric vehicles for congestion prevention in the distribution grid.’, IEEE Trans. Smart Grid, 2014, 5, (2), pp. 703–711.
    4. 4)
      • 3. 2010 Smart Grid System Report. February 2012; Available from: http://energy.gov/sites/prod/files/2010%20Smart%20Grid%20System%20Report.pdf.
    5. 5)
      • 18. Yang, X.-S., Deb, S.: ‘Cuckoo search via Lévy flights. in Nature & Biologically Inspired Computing, 2009’. NaBIC 2009. World Congress on IEEE. 2009.
    6. 6)
      • 23. U.S. Department of Transportation, Federal Highway Administration, 2009 Nationoal Household Travel Survey. 2011, oavailable at http://nhts.ornl.gov.
    7. 7)
      • 21. Choi, J., Tran, T., El-Keib, A.A., Thomas, R., et al: ‘A method for transmission system expansion planning considering probabilistic reliability criteria’, IEEE Trans. Power Syst., 2005, 20, (3), pp. 1606–1615.
    8. 8)
      • 14. Zhao, J.H., Dong, Z.Y., Lindsay, P., et al: ‘Flexible transmission expansion planning with uncertainties in an electricity market’, IEEE Trans. Power Syst., 2009, 24, (1), pp. 479–488.
    9. 9)
      • 25. Abedi, S., Arvani, A., Jamalzadeh, R.: ‘Cyber security of plug-in electric vehicles in smart grids: application of intrusion detection methods’. Plug In Electric Vehicles in Smart Grids., Springer, 2015, pp. 129–147.
    10. 10)
      • 1. Romero, R., Rocha, C., Mantovani, J.R.S., et al: ‘Constructive heuristic algorithm for the DC model in network transmission expansion planning’, IEE Proc.-Gener. Transm. Distri., 2005, 152, (2), pp. 277–282.
    11. 11)
      • 2. The Institution of Engineering and Technology, available from: http://www.theiet.org/policy/key-topics/smart-grid/.
    12. 12)
      • 6. Hu, J., Morais, H., Sousa, T., et al: ‘Electric vehicle fleet management in smart grids: A review of services’, Optimi. Control Aspects Renew. Sustain. Energy Rev., 2016, 56, pp. 1207–1226.
    13. 13)
      • 26. Sierzchula, W., Bakker, S., Maat, K., et al: ‘The influence of financial incentives and other socio-economic factors on electric vehicle adoption’, Energy Policy, 2014, 68, pp. 183–194.
    14. 14)
      • 28. Romero, R., Asada, E.N., Carreno, E., et al: ‘Constructive heuristic algorithm in branch-and-bound structure applied to transmission network expansion planning’, IET Gener. Transm. Distrib., 2007, 1, (2), pp. 318–323.
    15. 15)
      • 15. Billinton, R., Allan, R.N.: ‘Reliability evaluation of engineering systems’ (Springer, 1992).
    16. 16)
      • 8. Rathore, C., Roy, R.: ‘Impact of wind uncertainty, plug-in-electric vehicles and demand response program on transmission network expansion planning’, Int. J. Electr. Power Energy Syst., 2016, 75, pp. 59–73.
    17. 17)
      • 20. Aggarwal, C.C., Reddy, C.K.: ‘Data clustering: algorithms and applications’ (CRC Press, 2013).
    18. 18)
      • 13. Hajebrahimi, A., Abdollahi, A., Rashdinejad, M.: ‘Probabilistic multiobjective transmission expansion planning incorporating demand response resources and large-scale distant wind farms’, IEEE Syst. J., 2015, PP, (99), pp. 1–1.
    19. 19)
      • 16. Munkhammar, J.: ‘Markov-chain modeling of energy users and electric vehicles: Applications to distributed photovoltaics’. 2012.
    20. 20)
      • 22. Yang, X.-S., Deb, S.: ‘Cuckoo search: recent advances and applications’, Neural Comput. Appl., 2014, 24, (1), pp. 169–174.
    21. 21)
      • 11. Sortomme, E., Hindi, M.M., MacPherson, S.D.J., et al: ‘Coordinated charging of plug-in hybrid electric vehicles to minimize distribution system losses’, IEEE Trans. Smart grid, 2011, 2, (1), pp. 198–205.
    22. 22)
      • 12. Galus, M.D., Waraich, R.A., Andersson, G.: ‘Predictive, distributed, hierarchical charging control of PHEVs in the distribution system of a large urban area incorporating a multi agent transportation simulation’. ETH, Eidgenössische Technische Hochschule Zürich, IVT, Institut für Verkehrsplanung und Transportsysteme, 2011.
    23. 23)
      • 19. Zhao, L., Prousch, S., Hubner, M., et al: ‘Simulation methods for assessing electric vehicle impact on distribution grids’. IEEE PES T&D 2010. IEEE, 2010.
    24. 24)
      • 9. Wang, Z., Wang, S.: ‘Grid power peak shaving and valley filling using vehicle-to-grid systems’, IEEE Trans. Power Deliv., 2013, 28, (3), pp. 1822–1829.
    25. 25)
      • 27. Fang, R., Hill, D.J.: ‘A new strategy for transmission expansion in competitive electricity markets’, IEEE Trans. Power Syst., 2003, 18, (1), pp. 374–380.
    26. 26)
      • 17. Soares, F., Lopes, J.A.P., Almeida, P.M.R.: ‘A stochastic model to simulate electric vehicles motion and quantify the energy required from the grid’. Proc. of the Power Systems Computation Conf. (PSCC), Stockholm, Sweden, 2011.
    27. 27)
      • 10. Sandels, C., Franke, U., Ingvar, N., et al: ‘Vehicle to grid—Monte Carlo simulations for optimal aggregator strategies’. 2010 Int. Conf. on IEEE Power System Technology (POWERCON), 2010.
    28. 28)
      • 5. Kiviluoma, J., Meibom, P.: ‘Methodology for modelling plug-in electric vehicles in the power system and cost estimates for a system with either smart or dumb electric vehicles’, Energy, 2011, 36, (3), pp. 1758–1767.
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