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A Decision Function Based Smart Charging and Discharging Strategy for Electric Vehicle in Smart Grid

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Abstract

As the number of Electric Vehicle (EV) increases, the uncontrolled EV charging behaviors may cause the grid load fluctuations and other passive effects. In order to balance the EV charging load in the smart grid, an Electric Vehicle Charging and Discharging Strategy (EVCDS) which is based on a Charging Decision Function (CDF) as well as a Discharging Decision Function (DDF) is proposed. In the CDF and DDF, there are three sub-functions related to the residual energy of battery, EV’s charging habits, and the charging efficiency of charging station respectively. The residual energy of battery is used to calculate the excepted probability that satisfies the user’s mileage requirement, which is an important factor. The charging habits are used to calculate the second sub-function value, which stands for the user’s comfort. The charging efficiency and distance are combined together to calculate the third sub-function. All the sub-functions are weighted and combined into the CDF and DDF to decide whether to charge, discharge or do nothing. In the numerical results, we set up a scenario for the commercial vehicles and private vehicles. After compared with other strategies, EVCDS performs well in terms of reducing the charging demand fluctuations and improving the charging demand balance among charging stations.

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References

  1. Qian K, Zhou C, Allan M, Yuan Y (2011) Modeling of load demand due to EV battery charging in distribution systems. IEEE Trans Power Syst 26(2):802–810

    Article  Google Scholar 

  2. Yang H, Yang S, Xu Y, Cao E, Lai M, Dong Z (2015) Electric vehicle route optimization considering time-of-use electricity price by learnable partheno-genetic algorithm. IEEE Trans Smart Grid 6(2):657–666

    Article  Google Scholar 

  3. Rigas ES, Ramchurn SD, Bassiliades N (2015) Managing electric vehicles in the smart grid using artificial intelligence: a survey. IEEE Trans Intell Transp Syst 1(4):1619–1635

    Article  Google Scholar 

  4. Vastardis N, Yang K (2013) Mobile social networks: architectures, social properties, and key research challenges. IEEE Commun Surv Tutorials 15(3):1355–1371

    Article  Google Scholar 

  5. Tang Q, Xie M-Z, Wang L, Luo Y-S, Yang K, Load Balanced A (2017) Charging strategy for electric vehicle in smart grid. In: Proceedings of the first international conference on smart grid inspired future technologies, pp 155–164

    Chapter  Google Scholar 

  6. Cao Y, Tang S, Li C, Zhang P, Tan Y, Zhang Z, Li J (2012) An optimized EV charging model considering TOU price and SOC curve. IEEE Trans Smart Grid 3(1):388–393

    Article  Google Scholar 

  7. Tang Q, Yang K, Zhou D, Luo Y, Yu F (2016) A real-time dynamic pricing algorithm for smart grid with unstable energy providers and malicious users. IEEE Internet Things J 3(4):554–562

    Article  Google Scholar 

  8. Rivera J, Wolfrum P, Hirche S, Goebel C, Jacobsen H-A (2013) Alternating direction method of multipliers for decentralized electric vehicle charging control. In: Proceedings of the 52nd IEEE Annual Conference on Decision and Control (CDC), p 6960–6965

  9. Zhou K, Cai L (2014) Randomized PHEV charging under distribution grid constraints. IEEE Trans Smart Grid 5(2):879–887

    Article  Google Scholar 

  10. Gan L, Topcu U, Low SH (2012) Optimal decentralized protocol for electric vehicle charging. IEEE Trans Power Syst 28(2):940–951

    Article  Google Scholar 

  11. Ma T, Mohammed OA (2014) Optimal charging of plug-in electric vehicles for a Car-Park infrastructure. IEEE Trans Ind Appl 50(4):2323–2330

    Article  Google Scholar 

  12. Xie S, Zhong W, Xie K, Yu R, Zhang Y (2016) Fair energy scheduling for vehicle-to-grid networks using adaptive dynamic programming. IEEE Trans Neural Netw Learn Syst 27(8):1697–1707

    Article  MathSciNet  Google Scholar 

  13. Yu R, Zhong W, Xie S, Yuen C, Gjessing S, Zhang Y (2016) Balancing power demand through EV mobility in vehicle-to-grid mobile energy networks. IEEE Trans Ind Inf 12(1):79–90

    Article  Google Scholar 

  14. Mwasilu F, Justo JJ, Kim E-K, Do TD, Jung J-W (2014) Electric vehicles and smart grid interaction: a review on vehicle to grid and renewable energy sources integration. Renew Sust Energ Rev 34:501–516

    Article  Google Scholar 

  15. Sbordone D, Bertini I, Pietra BD, Falvo MC, Genovese A, Martirano L (2015) EV fast charging stations and energy storage technologies: a real implementation in the smart microgrid paradigm. Electr Power Syst Res 120:96–108

    Article  Google Scholar 

  16. Yang K, Yu Q, Leng S, Fan B, Wu F (2016) Data and energy integrated communication networks for wireless big data. IEEE Access 4:713–723

    Article  Google Scholar 

  17. Frieske B, Kloetzke M, Mauser F (2013) Trends in vehicle concept and key technology development for hybrid and battery electric vehicles. In: Proceedings of the 2013 world electric vehicle symposium and exhibition (EVS27), p 1–12

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Acknowledgments

The work presented in this paper is in part funded by a project supported by the Scientific Research Fund of Hunan Provincial Education Department (No.13C1023), and a project supported by the Natural Science Foundation of Hunan Province, China (Grant No. 13JJ4052). It is also partly funded by a project supported by the National Natural Science Foundation of China (Grant No. 61303043, 61772087).

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Authors and Affiliations

  1. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, Hunan, China

    Qiang Tang, Mingzhong Xie, Yuansheng Luo & Yun Song

  2. School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, CO4 3SQ, UK

    Kun Yang

  3. School of Information and Software, Northeast Normal University, Changchun, 130024, Jilin, China

    Dongdai Zhou

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  1. Qiang Tang
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  2. Mingzhong Xie
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  3. Kun Yang
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  4. Yuansheng Luo
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  5. Dongdai Zhou
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  6. Yun Song
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Correspondence to Qiang Tang.

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Tang, Q., Xie, M., Yang, K. et al. A Decision Function Based Smart Charging and Discharging Strategy for Electric Vehicle in Smart Grid. Mobile Netw Appl 24, 1722–1731 (2019). https://doi.org/10.1007/s11036-018-1049-4

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  • DOI: https://doi.org/10.1007/s11036-018-1049-4

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