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A Multi-layer Physic-based Model for Electric Vehicle Energy Demand Estimation in Interdependent Transportation Networks and Power Systems

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Optimization in Large Scale Problems

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 152))

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Abstract

Future smart cities pave the path toward global electrification. This trend affects various networks, including transportation systems. Transportation electrifications will increase the interdependence between power systems and transportation networks. Electric vehicles (EVs) and charging stations are the main coupling components. Although EVs are introduced as economic and sustainable means of transportation, they can lead to both traffic congestion and power line congestion. This book chapter first provides a big picture of interdependent transportation networks and power systems. It then introduces a comprehensive EV load model, which takes into physical aspects of electric vehicles. Then, it evaluates the required battery power by investigating the required power to overcome different real-world mechanical resistances, such as aerodynamic drag. The goal of this study is to introduce a framework that investigates the physics of the EVs, and analyzes the behavior of electric vehicle drivers based on a given real-world dataset. The proposed EV load model is based on historical driving cycles for 1000 EVs, and hence, reflects the behavior of drivers. Sub-primary level is also investigated, i.e., how can different driving cycles affect the EV's charging demand. We evaluate the required battery power by investigating the required power to overcome the aerodynamic drag, rolling resistance, and the force required for a vehicle climbing a hill.

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Notes

  1. 1.

    Chargecar.org

References

  1. Amini, M.H., Arasteh, H., Siano, P.: Sustainable smart cities through the lens of complex interdependent infrastructures: panorama and state-of-the-art. In: Sustainable Interdependent Networks II, pp. 45–68. Springer, Cham (2019)

    Chapter  Google Scholar 

  2. Amini, M.H.: A panorama of interdependent power systems and electrified transportation networks. In: Sustainable Interdependent Networks II, pp. 23–41. Springer, Cham (2019)

    Chapter  Google Scholar 

  3. Amini, M., Karabasoglu, O.: Optimal operation of interdependent power systems and electrified transportation networks. Energies. 11(1), 196 (2018)

    Article  Google Scholar 

  4. Farhangi, H.: The path of the smart grid. IEEE Power Energ. Mag. 8(1), 18–28 (2010)

    Article  MathSciNet  Google Scholar 

  5. Amini, M.H, Nabi, B., Haghifam, M.R.: Load management using multi-agent systems in smart distribution network. In: 2013 IEEE Power & Energy Society General Meeting. IEEE (2013)

    Google Scholar 

  6. Moslehi, K., Kumar, R.: A reliability perspective of the smart grid. IEEE Trans. Smart Grid. 1(1), 57–64 (2010)

    Article  Google Scholar 

  7. Rahimi, F., Ipakchi, A.: Demand response as a market resource under the smart grid paradigm. IEEE Trans. Smart Grid. 1(1), 82–88 (2010)

    Article  Google Scholar 

  8. Brown, R.E.: Impact of smart grid on distribution system design. In: Proc. IEEE Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century, pp. 1–4 (2008)

    Google Scholar 

  9. Amini, M.H., Frye, J., Ilić, M.D., Karabasoglu, O.: Smart residential energy scheduling utilizing two stage mixed integer linear programming. In: 2015 North American Power Symposium (NAPS), pp. 1–6. IEEE (2015)

    Google Scholar 

  10. Khazraeian, S., Hadi, M.: Intelligent transportation systems in future smart cities. In: Sustainable Interdependent Networks II, pp. 109–120. Springer, Cham (2019)

    Chapter  Google Scholar 

  11. Adnan, N., Nordin, S.M., Althawadi, O.M.: Barriers towards widespread adoption of V2G technology in smart grid environment: from laboratories to commercialization. In: Sustainable Interdependent Networks, pp. 121–134. Springer, Cham (2018)

    Chapter  Google Scholar 

  12. Amini, M.H., Islam, A.: Allocation of electric vehicles’ parking lots in distribution network. In: Innovative Smart Grid Technologies Conference (ISGT), 2014 IEEE PES. IEEE (2014)

    Google Scholar 

  13. Shao, S., Pipattanasomporn, M., Rahman, S.: Grid integration of electric vehicles and demand response with customer choice. IEEE Trans. Smart Grid. 3(1), 543–550 (2012)

    Article  Google Scholar 

  14. Amini, M.H., et al.: Evaluating the effect of demand response programs and fuel cost on PHEV owners behavior, a mathematical approach. In: Smart Grids (ICSG), 2012 2nd Iranian Conference on. IEEE (2012)

    Google Scholar 

  15. Wu, D., Zeng, H., Lu, C., Boulet, B.: Two-stage energy management for office buildings with workplace EV charging and renewable energy. IEEE Trans. Transp. Electrific. 3(1), 225–237 (2017)

    Article  Google Scholar 

  16. Chen, H., et al.: Risk-averse joint capacity evaluation of PV generation and electric vehicle charging stations in distribution networks. In: 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe). IEEE (2017)

    Google Scholar 

  17. Mozafar, M.R., Moradi, M.H., Amini, M.H.: A simultaneous approach for optimal allocation of renewable energy sources and electric vehicle charging stations in smart grids based on improved GA-PSO algorithm. Sustain. Cities Soc. 32, 627–637 (2017)

    Article  Google Scholar 

  18. Humayd, A.S.B., Bhattacharya, K.: Distribution system planning to accommodate distributed energy resources and PEVs. Electr. Power Syst. Res. 145, 1–11 (2017)

    Article  Google Scholar 

  19. Amini, M.H., Moghaddam, M.P., Karabasoglu, O.: Simultaneous allocation of electric vehicles’ parking lots and distributed renewable resources in smart power distribution networks. Sustain. Cities Soc. 28, 332–342 (2017)

    Article  Google Scholar 

  20. Zhu, X., Xia, M., Chiang, H.-D.: Coordinated sectional droop charging control for EV aggregator enhancing frequency stability of microgrid with high penetration of renewable energy sources. Appl. Energy. 210, 936–943 (2018)

    Article  Google Scholar 

  21. Cerna, F.V., et al.: Optimal delivery scheduling and charging of EVs in the navigation of a city map. IEEE Trans. Smart Grid. 9, 4815–4827 (2018)

    Article  Google Scholar 

  22. Xu, S., Yan, Z., Feng, D., Zhao, X.: Decentralized charging control strategy of the electric vehicle aggregator based on augmented Lagrangian method. Int. J. Electr. Power Energ. Syst. 104, 673–679 (2019)

    Article  Google Scholar 

  23. Amini, M.H., McNamara, P., Weng, P., Karabasoglu, O., Xu, Y.: Hierarchical electric vehicle charging aggregator strategy using Dantzig-Wolfe decomposition. IEEE Des. Test. 35(6), 25–36 (2018)

    Article  Google Scholar 

  24. Khaki, B., Chu, C., Gadh, R.: A hierarchical admm based framework for ev charging scheduling. In: 2018 IEEE/PES Transmission and Distribution Conference and Exposition (T&D). IEEE (2018)

    Google Scholar 

  25. Adnan, N., et al.: What make consumer sign up to PHEVs? Predicting Malaysian consumer behavior in adoption of PHEVs. Transp. Res. A Policy Pract. 113, 259–278 (2018)

    Article  Google Scholar 

  26. Amini, M.H., Karabasoglu, O., Ilić, M.D., Boroojeni, K.G., Iyengar, S.S.: Arima-based demand forecasting method considering probabilistic model of electric vehicles’ parking lots. In: 2015 IEEE Power & Energy Society General Meeting, pp. 1–5. IEEE, July (2015)

    Google Scholar 

  27. Xin, J.-b., Wen, Y.-b., Li, R.: Discussion on demand forecast method for electric vehicle charging facilities. Jiangxi Electr. Pow. 34(5), 1–5 (2010)

    Google Scholar 

  28. Amini, M.H., Kargarian, A., Karabasoglu, O.: ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation. Electr. Power Syst. Res. 140, 378–390 (2016)

    Article  Google Scholar 

  29. Amini, M.H., Moghaddam, M.P, Forushani, E.H.: Forecasting the PEV owner reaction to the electricity price based on the customer acceptance index. In: 2013 Smart Grid Conference (SGC). IEEE (2013)

    Google Scholar 

  30. Larminie, J., Lowry, J.: Electric vehicle modelling. In: Electric Vehicle Technology Explained, pp. 183–212. Wiley Online Library, Hoboken (2013)

    Google Scholar 

  31. Chan, C.C., Chau, K.T.: An overview of power electronics in electric vehicles. IEEE Trans. Ind. Electron. 44(1), 3–13 (1997)

    Article  Google Scholar 

  32. Hersey, M.D.: “Rolling Friction” Transactions of the ASME, April 1969, pp. 260–275 and Journal of Lubrication Technology, January 1970, pp. 83–88

    Google Scholar 

  33. http://www.chargecar.org/data

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Authors and Affiliations

  1. School of Computing and Information Sciences, College of Engineering and Computing, Florida International University, Miami, FL, USA

    M. Hadi Amini

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  1. M. Hadi Amini
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Correspondence to M. Hadi Amini .

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Editors and Affiliations

  1. Department of Industrial and Systems Engineering, Mississippi State University, Starkville, MS, USA

    Mahdi Fathi

  2. School of Science and Engineering, Tecnológico de Monterrey, Monterrey, Mexico

    Marzieh Khakifirooz

  3. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

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Amini, M.H. (2019). A Multi-layer Physic-based Model for Electric Vehicle Energy Demand Estimation in Interdependent Transportation Networks and Power Systems. In: Fathi, M., Khakifirooz, M., Pardalos, P.M. (eds) Optimization in Large Scale Problems. Springer Optimization and Its Applications, vol 152. Springer, Cham. https://doi.org/10.1007/978-3-030-28565-4_21

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