# Decision-Making of Determining the Start Time of Charging / Discharging of Electrical Vehicle Based on Prospect Theory

• Liu, Lian ;
• Lyu, Xiang ;
• Jiang, Chuanwen ;
• Xie, Da
• Accepted : 2013.11.29
• Published : 2014.05.01
• 71 27

#### Abstract

The moment when Electrical Vehicle (EV) starts charging or discharging is one of the most important parameters in estimating the impact of EV load on the grid. In this paper, a decision-making problem of determining the start time of charging and discharging during allowed period is proposed and studied under the uncertainty of real-time price. Prospect theory is utilized in the decision-making problem of this paper for it describes a kind of decision making behaviors under uncertainty. The case study uses the parameters of Springo SGM7001EV and adopts the historical realtime locational marginal pricing (LMP) data of PJM market for scenario reduction. Prospect values are calculated for every possible start time in the allowed charging or discharging period. By comparing the calculated prospect values, the optimal decisions are obtained accordingly and the results are compared with those based on Expected Utility Theory. Results show that with different initial State-of-Charge ($SoC_0$) and different reference points, the optimal start time of charging can be the one between 12 a.m. to 3 a.m. and optimal discharging starts at 2 p.m. or 3p.m. Moreover, the decision results of Prospect Theory may differ from that of the Expected Utility Theory with the reference points changing.

#### Keywords

Electrical vehicle (EV);Prospect theory;Expected utility theory;Reference price;Decision-making

#### References

1. Electric Vehicle Market Forecasts [EB/OL]. http://www.navigantresearch.com/research/electricvehicle-market-forecasts
2. Electric Vehicle Charging Equipment [EB/OL]. http://www.navigantresearch.com/research/electricvehicle-charging-equipment
3. Guille C, Gross G. A conceptual framework for the vehicle-to-grid(v2g) implementation [J]. Energy Policy, 2009, 37(11): 4379-4390. https://doi.org/10.1016/j.enpol.2009.05.053
4. Minghong Peng, Lian Liu, Chuanwen Jiang, A review on the economic dispatch and risk management of the large-scale plug-in electric vehicles (PHEV)-penetrated power systems, Renewable and Sustainable Energy Reviews, Volume 16, Issue 3, April 2012, Pages 1508-1515 https://doi.org/10.1016/j.rser.2011.12.009
5. Elgowainy A, Han J, Poch L, et al. Well-to-wheels analysis of energy use and greenhouse gas emissions of plug-in hybrid electric vehicles[R]. Argonne National Laboratory (ANL), 2010.
6. Won J R, Yoon Y B, Lee K J. Prediction of electricity demand due to PHEVs (Plug-In Hybrid Electric Vehicles) distribution in Korea by using diffusion model[C]//Transmission & Distribution Conference & Exposition: Asia and Pacific, 2009. IEEE, 2009: 1-4.
7. Ahmadi L, Croiset E, Elkamel A, et al. Impact of PHEVs Penetration on Ontario's Electricity Grid and Environmental Considerations [J]. Energies, 2012, 5(12): 5019-5037. https://doi.org/10.3390/en5125019
8. Tian L, Shi S, Jia Z. A statistical model for charging power demand of electric vehicles [J]. Power System Technology, 2010, 34(11): 126-130(in Chinese).
9. YANG H, XIONG L, LIU B. Probabilistic analysis of charging and discharging for plug-in hybrid electric vehicles[J]. Journal of Electric Power Science and Technology, 2010, 25(3): 8-12(in Chinese).
10. Hadley S W, Tsvetkova A A. Potential impacts of plug-in hybrid electric vehicles on regional power generation[J]. The Electricity Journal, 2009, 22(10): 56-68. https://doi.org/10.1016/j.tej.2009.10.011
11. Wu D, Aliprantis D C, Ying L. Load scheduling and dispatch for aggregators of plug-in electric vehicles [J]. Smart Grid, IEEE Transactions on, 2012, 3(1): 368-376. https://doi.org/10.1109/TSG.2011.2163174
12. Sortomme E, Cheung K W. Intelligent dispatch of Electric Vehicles performing vehicle-to-grid regulation [C]//Electric Vehicle Conference (IEVC), 2012 IEEE International. IEEE, 2012: 1-6.
13. Parks K, Denholm P, Markel AJ. Costs and emissions associated with plug-in hybrid electric vehicle charging in the Xcel Energy Colorado service territory[M]. Golden, CO: National Renewable Energy Laboratory, 2007.
14. Letendre S, Watts R A. Effects of plug-in hybrid electric vehicles on the Vermont electric transmission system[C]//Transportation Research Board Annual Meeting, Washington DC. 2009: 11-15.
15. Wu D, Aliprantis D C, Gkritza K. Electric energy and power consumption by light-duty plug-in electric vehicles[J]. Power Systems, IEEE Transactions on, 2011, 26(2): 738-746. https://doi.org/10.1109/TPWRS.2010.2052375
16. Kahneman D, Tversky A. Prospect theory: An analysis of decision under risk[J]. Econometrica: Journal of the Econometric Society, 1979: 263-291.
17. Tversky A, Kahneman D. Advances in prospect theory: Cumulative representation of uncertainty[J]. Journal of Risk and uncertainty, 1992, 5(4): 297-323. https://doi.org/10.1007/BF00122574
18. YANG J, WANG Y, QIAN D, et al. Research on Prospect Theory-based Decision Model [J]. Journal of System Simulation, 2009, 9: 003. (in Chinese)
19. Santos A, McGuckin N, Nakamoto H Y, et al. Summary of travel trends: 2009 national household travel survey[R]. 2011.
20. Daily Real-Time LMP Files [EB/OL]. http://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx
21. Mazumdar T, Raj S P, Sinha I. Reference price research: review and propositions[J]. Journal of marketing, 2005: 84-102.

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