Dynamic Pricing and Energy Management Strategy for EV Charging Stations under Uncertainties

Chao Luo, Yih-Fang Huang, Vijay Gupta

Abstract

This paper presents a dynamic pricing and energy management framework for electric vehicle (EV) charging service providers. To set the charging prices, the service providers faces three uncertainties: the volatility of wholesale electricity price, intermittent renewable energy generation, and spatial-temporal EV charging demand. The main objective of our work here is to help charging service providers to improve their total profits while enhancing customer satisfaction and maintaining power grid stability, taking into account those uncertainties. We employ a linear regression model to estimate the EV charging demand at each charging station, and introduce a quantitative measure for customer satisfaction. Both the greedy algorithm and the dynamic programming (DP) algorithm are employed to derive the optimal charging prices and determine how much electricity to be purchased from the wholesale market in each planning horizon. Simulation results show that DP algorithm achieves an increased profit (up to 9%) compared to the greedy algorithm (the benchmark algorithm) under certain scenarios. Additionally, we observe that the integration of a low-cost energy storage into the system can not only improve the profit, but also smooth out the charging price fluctuation, protecting the end customers from the volatile wholesale market.

References

  1. Ban, D., Michailidis, G., and Devetsikiotis, M. (2012). Demand response control for phev charging stations by dynamic price adjustments. 2012 IEEE PES Innovative Smart Grid Technologies, pages 1-8.
  2. Bertsekas, D. (2000). Dynamic Programming and Optimal Control (2nd ed.). Athena Scientific, Belmont, Massachusetts.
  3. Cormen, T., Leiserson, C., Rivest, R., and Stein, C. (2001). Introduction to Algorithm (2nd ed.). MIT Press & McGraw-Hill.
  4. Electric Drive Transportation Association (2015). Electric drive sales dashboard. Available at: http:// electricdrive.org/index.php?ht=d/sp/i/20952/pid/ 20952. [Online].
  5. Fahrioglu, M., Fern, M., and Alvarado, F. (1999). Designing cost effective demand management contracts using game theory. Proc. of IEEE Power Engineering Society 1999 Winter Meeting, 1:427-432.
  6. Faranda, R., Pievatolo, A., and Tironi, E. (2007). Load shedding: A new proposal. IEEE Transactions on Power Systems, 22(4):2086-2093.
  7. Frame, J. (2001). Locational marginal pricing. 2001 IEEE Power Engineering Society Winter Meeting 2001, 1:377-382.
  8. Guo, Y., Liu, X., Yan, Y., Zhang, N., and Su, W. (2014). Economic analysis of plug-in electric vehicle parking deck with dynamic pricing. 2014 IEEE Power and Energy Society General Meeting, pages 1-5.
  9. Guo, Y., Xiong, J., Xu, S., and Su, W. (2016). Two-stage economic operation of microgrid-like electric vehicle parking deck. accepted by IEEE Transactions on Smart Grid (to appear).
  10. Han, Y., Chen, Y., Han, F., and Liu, K. J. R. (2012). An optimal dynamic pricing and schedule approach in v2g. Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, pages 1-8.
  11. Huisman, R., Huurman, C., and Mahieu, R. (2007). Hourly electricity prices in day-ahead markets. SciVerse ScienceDirect Journals, Energy Economics, 29(2):240-248.
  12. IEC (2007). Efficient electrical energy transmission and distribution. Available at: http://www.iec.ch/news centre/onlinepubs/pdf/transmission.pdf. [Online].
  13. Kinter-Meyer, M., Schneider, K., and Pratt, R. (2007). Impacts assessment of plug-in hybrid electric vehicles on electric utilities and regional u.s. power grids: Part i:technical analysis. Online Journal of EUEC, 1.
  14. Lopes, J., Soares, F., and Almeida, P. (2011). Integration of electric vehicles in the electric power system. Proceedings of the IEEE, 99(1):168 - 183.
  15. Martirano, D. L., Devetsikiotis, M., and Pietra, B. (2014). Interactive energy: an approach for the dynamic pricing and dispatching of ev charging service. The 40th Annual Conference of the IEEE - Industrial Electronics Society, IECON 2014, pages 3556-3562.
  16. National Renewable Energy Laboratory (2015). National solar radiation data base. Available at: http://rredc.nrel.gov/solar/old data/nsrdb/. [Online].
  17. Navigant Research (2014). Electric vehicle market forecasts global forecasts for light duty hybrid, plug-in hybrid, and battery electric vehicle sales and vehicles in use: 2014-2023. Available at: http://www.navigantresearch.com/research/electricvehicle-market-forecasts. [Online].
  18. Nemhauser, G. (1996). Introduction to Dynamic Programming. John Wiley and Sons, Inc.
  19. Proakis, J. (2007). Digital Signal Processing (4th ed.). Pearson Prentice Hall, Upper Saddle River, N.J.
  20. Rahbari-Asr, N., Chow, M.-Y., Yang, Z., and Chen, J. (2013). Network cooperative distributed pricing control system for large-scale optimal charging of phevs/pevs. The 39th Annual Conference of the IEEE - Industrial Electronics Society, IECON 2013, pages 6148-6153.
  21. Scott, M., Kintner-Meyer, M., Elliott, D., and Warwick, W. (2007). Economic assessment and impacts assessment of plug-in hybrid vehicles on electric utilities and regional u.s. power grids. part ii. Online Journal of EUEC, 1.
  22. Simpson, A. (2006). Cost-benefit analysis of plugin hybrid electric vehicle technology. The 22nd International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium and Exhibition (EVS-22).
  23. Treinen, R. (2005). Locational marginal pricing (lmp): Basics of nodal price calculation. Available at: http://www.caiso.com/docs/2004/02/13/ 200402131607358643.pdf. [Online].
  24. Yan, Q., Manickam, I., Kezunovic, M., and Xie, L. (2014). A multi-tiered real-time pricing algorithm for electric vehicle charging stations. 2014 IEEE Transportation Electrification Conference and Expo, pages 1-6.
  25. Yang, P., Tang, G., and Nehorai, A. (2013). A gametheoretic approach for optimal time-of-use electricity pricing. IEEE Transactions on Power Systems, 28(2):884-892.
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Paper Citation


in Harvard Style

Luo C., Huang Y. and Gupta V. (2016). Dynamic Pricing and Energy Management Strategy for EV Charging Stations under Uncertainties . In Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-185-4, pages 49-59. DOI: 10.5220/0005797100490059


in Bibtex Style

@conference{vehits16,
author={Chao Luo and Yih-Fang Huang and Vijay Gupta},
title={Dynamic Pricing and Energy Management Strategy for EV Charging Stations under Uncertainties},
booktitle={Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2016},
pages={49-59},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005797100490059},
isbn={978-989-758-185-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Dynamic Pricing and Energy Management Strategy for EV Charging Stations under Uncertainties
SN - 978-989-758-185-4
AU - Luo C.
AU - Huang Y.
AU - Gupta V.
PY - 2016
SP - 49
EP - 59
DO - 10.5220/0005797100490059