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Robust Operation of a Reconfigurable Electrical Distribution System by Optimal Charging Management of Plug-In Electric Vehicles Considering the Technical, Social, and Geographical Aspects

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Planning and Operation of Plug-In Electric Vehicles

Abstract

This chapter proposes a robust approach to study the optimal operation problem of a reconfigurable electrical distribution system while optimally managing the charging/discharging patterns of plug-in electric vehicle (PEV) fleet considering their technical, social, and geographical aspects. Herein, it is assumed that the electrical system is highly penetrated by the renewable energy sources (RESs), and the total daily energy generated by the RESs is adequate for the daily electricity demand of system; however, an effective approach is necessary to reliably and economically operate it. The electrical distribution network includes the electrical loads, RESs, energy storage systems (ESSs), switches installed on the electrical feeders, and PEVs with the capabilities of vehicle-to-grid (V2G) and grid-to-vehicle (G2V). In this study, the drivers are grouped in three different social classes based on their income level, that is, low-income, moderate-income, and high-income. The behavior of each social class of drivers is modelled based on the social and geographical aspects including the drivers’ distance from a charging station (CHS) and the value of incentive to provide the V2G and G2V services at the suggested CHS and recommended period. The proposed approach includes the stochastic model predictive control (MPC) that stochastically, adaptively, and dynamically solves the problem and handles the variability and uncertainties concerned with the probabilistic power of RESs and drivers’ behavior. The simulation results demonstrate that applying the proposed approach can remarkably decrease the minimum operation cost of problem and enhance the system reliability. It is shown that the behavior of drivers can affect the optimal configuration of system, optimal status of ESSs, and even optimal scheme of PEV fleet management (FM). It is proven that the application of proposed approach guarantees the robustness of problem outputs with respect to the prediction errors.

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References

  1. R.E. Brown, Electric Power Distribution Reliability (Marcel Dekker, New York, 2002)

    Book  Google Scholar 

  2. R. Billinton, R.N. Allan, Reliability Evaluation of Power Systems, 2nd edn. (Plenum, New York, 1996)

    Book  Google Scholar 

  3. Monthly Plug-in Sales Scorecard. [Online]. Available: http://insideevs.com/monthly-plug-in-sales-scorecard. Accessed in Jan 2016

  4. Electric Vehicles Initiative (EVI), Clean Energy Ministerial. [Online]. Available: http://cleanenergyministerial.org/Our-Work/Initiatives/Electric-Vehicles. Accessed on Jan 2016

  5. [Online]. Available: https://www.greentechmedia.com/articles/read/everyone-is-revising-electric-vehicle-forecasts-upward#gs.eAwHRWdc. Accessed on Jan 2019

  6. [Online]. Available: https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/the-global-electric-vehicle-market-is-amped-up-and-on-the-rise. Accessed on Jan 2019

  7. F.Y. Melhem, O. Grunder, Z. Hammoudan, N. Moubayed, Energy management in electrical smart grid environment using robust optimization algorithm. IEEE Trans. Ind. Appl. 54(3), 2714–2726 (2018)

    Article  Google Scholar 

  8. P.Y. Kong, A distributed management scheme for energy storage in a smart grid with communication impairments. IEEE Trans. Ind. Inf. 14(4), 1392–1402 (2018)

    Article  MathSciNet  Google Scholar 

  9. M.R. Dorostkar-Ghamsari, M. Fotuhi-Firuzabad, M. Lehtonen, A. Safdarian, Value of distribution network reconfiguration in presence of renewable energy resources. IEEE Trans. Power Syst. 31, 1879–1888 (2016)

    Article  Google Scholar 

  10. N.G.A. Hemdana, B. Deppec, M. Pielked, M. Kurrata, T. Schmedesd, E. Wieben, Optimal reconfiguration of radial MV networks with load profiles in the presence of renewable energy based decentralized generation. Electr. Power Syst. Res. 116, 355–366 (2014)

    Article  Google Scholar 

  11. P. Meneses de Quevedo, J. Contreras, M.J. Rider, J. Allahdadian, Contingency assessment and network reconfiguration in distribution grids including wind power and energy storage. IEEE Trans. Sustain. Energy 6, 1524–1533 (2015)

    Article  Google Scholar 

  12. H. Haghighat, B. Zeng, Distribution system reconfiguration under uncertain load and renewable generation. IEEE Trans. Power Syst. 31(4), 2666–2675 (2016)

    Article  Google Scholar 

  13. J. Liu, H. Chiang, Maximizing available delivery capability of unbalanced distribution networks for high penetration of distributed generators. IEEE Trans. Power Del. 32(3), 1196 (2017)

    Article  Google Scholar 

  14. M.A. Rostami, A. Kavousi-Fard, T. Niknam, Expected cost minimization of smart grids with plug-in hybrid electric vehicles using optimal distribution feeder reconfiguration. IEEE Trans. Ind. Inf. 11(2), 388–397 (2015)

    Article  Google Scholar 

  15. A. Kavousi-Fard, T. Niknam, M. Fotuhi-Firuzabad, Stochastic reconfiguration and optimal coordination of V2G plug-in electric vehicles considering correlated wind power generation. IEEE Trans. Sustain. Energy 6(3), 822–830 (2015)

    Article  Google Scholar 

  16. M. Rahmani-Andebili, M. Fotuhi Firuzabad, An adaptive approach for PEVs charging management and reconfiguration of electrical distribution system penetrated by renewables. IEEE Trans. Ind. Inf. 14(5), 2001–2010 (2018)

    Article  Google Scholar 

  17. M. Rahmani-Andebili, G.K. Venayagamoorthy, SmartPark placement and operation for improving system reliability and market participation. Electr. Pow. Syst. Res. 123(6), 21–30 (2015)

    Article  Google Scholar 

  18. J.B. Rawlings, D.Q. Mayne, Model Predictive Control: Theory and Design (Nob Hill Publishing, LLC, Madison, 2009). [Online]. Available: http://jbrwww.che.wisc.edu/home/jbraw/mpc/electronic-book.pdf

    Google Scholar 

  19. M. Rahmani-Andebili, H. Shen, Energy scheduling for a smart home applying stochastic model predictive control, in Proceedings of 25th Conference on Computer Communication and Networks, Waikoloa, 1–4 Aug 2016

    Google Scholar 

  20. M. Rahmani-Andebili, Chapter 9: Cooperative distributed energy scheduling in microgrids, in Electric Distribution Network Management and Control, (Springer, 2018), pp. 235–254. Chapter DOI: https://doi.org/10.1007/978-981-10-7001-3_9. Book DOI: https://doi.org/10.1007/978-981-10-7001-3

  21. M. Rahmani-Andebili, Chapter 11: Optimal incentive plans for plug-in electric vehicles, in Electric Distribution Network Planning, (Springer, 2018), pp. 299–320. Chapter DOI: https://doi.org/10.1007/978-981-10-7056-3_11. Book DOI: https://doi.org/10.1007/978-981-10-7056-3

  22. A. Papoulis, S.U. Pillai, Probability, Random Variables, and Stochastic Processes, 4th edn. (McGraw-Hill, Boston, 2002). ISBN 0-07-366011-6

    Google Scholar 

  23. IEEE guide for electric power distribution reliability indices. IEEE Std. 1366–2003 (2004)

    Google Scholar 

  24. P. Wright, On minimum spanning trees and determinants. Math. Mag. 73, 21–28 (2000)

    Article  MathSciNet  Google Scholar 

  25. M. Rahmani-Andebili, Dynamic and adaptive reconfiguration of electrical distribution system including renewables applying stochastic model predictive control. IET Gener. Transm. Distrib. 11(16), 3912–3921 (2017)

    Article  Google Scholar 

  26. D. Newbery, The economics of electric vehicles, in EPRG and Imperial College London, E&E Seminar, Cambridge, Jan 2013. [Online]. Available: http://www.eprg.group.cam.ac.uk/wp-content/uploads/2013/01/EEJan13_EconomicsEVs.p

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Rahmani-Andebili, M. (2019). Robust Operation of a Reconfigurable Electrical Distribution System by Optimal Charging Management of Plug-In Electric Vehicles Considering the Technical, Social, and Geographical Aspects. In: Planning and Operation of Plug-In Electric Vehicles. Springer, Cham. https://doi.org/10.1007/978-3-030-18022-5_4

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  • DOI: https://doi.org/10.1007/978-3-030-18022-5_4

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  • Online ISBN: 978-3-030-18022-5

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