Skip to main content

Advertisement

SpringerLink
Log in

Many-objective optimization for large-scale EVs charging and discharging schedules considering travel convenience

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

The uncontrolled charging behaviors of large-scale electric vehicles (EVs) increase the security risk of the power grid and bring a new challenge for the computing ability of the power system. Using vehicle to grid (V2G) technology, most control systems coordinate the power interaction between EVs and power grid by minimizing the load fluctuation and user cost, but their optimization results are often achieved at the expense of reducing personal travel time. EVs should first meet basic travel needs and then obey the scheduling arrangement. Based on this idea, a four-objective optimal control method for EV charging and discharging schedules considering travel convenience is proposed, including minimization of the load fluctuation and user cost and maximization of the flexible travel time and state of charge (SOC). To solve this large-scale many-objective problem, a resource allocation-based preference-inspired coevolutionary algorithm (PICEAg-EV) is presented. Taking the IEEE 33-node system as an example, the simulation and analysis verify the effectiveness of the proposed control strategy and optimization algorithm. The experimental results show that PICEAg-EV outperforms seven popular intelligence algorithms under EV participation rate setting of 10%, 25%, 50%, 100%. Compared with 2- and 3-objective optimization models, the 4-objective optimization model can provide sufficient flexible travel time and a higher SOC for traveling, which is a better match for the user needs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Toquica D, Jesus PMDOD, Cadena AI (2020) Power market equilibrium considering an ev storage aggregator exposed to marginal prices - a bilevel optimization approach. J Energy Storage 28:101267. https://doi.org/10.1016/j.est.2020.101267

    Article  Google Scholar 

  2. Mehrjerdi H, Rakhshani E (2019) Vehicle-to-grid technology for cost reduction and uncertainty management integrated with solar power. J Clean Prod 229:463–469. https://doi.org/10.1016/j.jclepro.2019.05.023

    Article  Google Scholar 

  3. Liu M, Crisostomi E, Gu Y, Shorten R (2015) Optimal distributed consensus algorithm for fair v2g power dispatch in a microgrid. In: 2014 IEEE international electric vehicle conference, IEVC 2014, pp 1–7. https://doi.org/10.1109/IEVC.2014.7056085

  4. Wang B, Hu Y, Zeng F (2017) A user cost and convenience oriented ev charging and discharging scheduling algorithm in v2g based microgrid. In: International conference on circuits devices and systems, pp 156–162. https://doi.org/10.1109/ICCDS.2017.8120470

  5. Liu J, Li P, Zhong W, Wang L, An Y, Li H (2018) Optimal charging/discharging strategy of electric vehicles in residential area considering user comprehensive satisfaction. E3S Web Conf 53:02012. https://doi.org/10.1051/e3sconf/20185302012

    Article  Google Scholar 

  6. Khasa P, Ravi JD (2016) Simultaneous charging and discharging integrating ev for v2g and g2v. https://doi.org/10.1109/IICPE.2016.8079452

  7. Hou K, Xu X, Yu X, Jiang T, Shu B, Zhang K (2016) A reliability assessment approach for integrated transportation and electrical power systems incorporating electric vehicles. IEEE Trans Smart Grid 9(1):88–100. https://doi.org/10.1109/TSG.2016.2545113

    Article  Google Scholar 

  8. Yi W, Xiu MA, Yi W, Xingzhe H, Ke Z, Wenli C (2019) Sequential charge-discharge guidance strategy for electric vehicles based on time-sharing charging-discharging margin. Power Syst Technol 12(43):4353–4361

    Google Scholar 

  9. Aluisio B, Conserva A, Dicorato M, Forte G, Trovato M (2017) Optimal operation planning of v2g-equipped microgrid in the presence of ev aggregator. Electr Power Syst Res 152:295–305. https://doi.org/10.1016/j.epsr.2017.07.015

    Article  Google Scholar 

  10. Korolko N, Sahinoglu Z (2017) Robust optimization of ev charging schedules in unregulated electricity markets. IEEE Trans Smart Grid 1(8):149–157. https://doi.org/10.1109/TSG.2015.2472597

    Article  Google Scholar 

  11. Dai S, Gao F, Guan X, Yan C, Liu K, Dong J, Yang L (2020) Robust energy management for a corporate energy system with shift-working v2g. IEEE Trans Autom Sci Eng, pp 1–18. https://doi.org/10.1109/TASE.2020.2980356

  12. Huang Z, Xie Z, Zhang C, Chan SH, Milewski J, Xie Y, Yang Y, Hu X (2019) Modeling and multi-objective optimization of a stand-alone pv-hydrogen-retired ev battery hybrid energy system. Energy Convers Manag 181:80–92. https://doi.org/10.1016/j.enconman.2018.11.079

    Article  Google Scholar 

  13. Habib HUR, Subramaniam U, Waqar A, Farhan BS, Kotb KM, Wang S (2020) Energy cost optimization of hybrid renewables based v2g microgrid considering multi objective function by using artificial bee colony optimization. IEEE Access 8:62076–62093. https://doi.org/10.1109/ACCESS.2020.2984537

    Article  Google Scholar 

  14. Wang S, Dong ZY, Luo F, Meng K, Zhang Y (2018) Stochastic collaborative planning of electric vehicle charging stations and power distribution system. IEEE Trans Ind Inform 14(1):321–331. https://doi.org/10.1109/TII.2017.2662711

    Article  Google Scholar 

  15. Luca F, Calderaro V, Galdi V (2020) A fuzzy logic-based control algorithm for the recharge/v2g of a nine-phase integrated on-board battery charger. Electronics 9:946. https://doi.org/10.3390/electronics9060946

    Article  Google Scholar 

  16. Faddel S, Aldeek A, Al-Awami AT, Sortomme E, Al-Hamouz Z (2018) Ancillary services bidding for uncertain bidirectional v2g using fuzzy linear programming. Energy 160:986–995. https://doi.org/10.1016/j.energy.2018.07.091

    Article  Google Scholar 

  17. Kaur K, Dua A, Jindal A, Kumar N, Singh M, Vinel AV (2015) A novel resource reservation scheme for mobile phevs in V2G environment using game theoretical approach. IEEE Trans Veh Technol 64 (12):5653–5666. https://doi.org/10.1109/TVT.2015.2482462

    Article  Google Scholar 

  18. Rostami N, Shams H, Sadeghfam A, Tohidi S (2019) An exact approach for charging of pevs with v2g capability to improve microgrid reliability. vol. 13 pp 3690–3695. https://doi.org/10.1049/iet-gtd.2018.6752

  19. Rahbari-Asr N, Chow MY, Chen J, Deng R (2016) Distributed real-time pricing control for large scale unidirectional v2g with multiple energy suppliers. IEEE Trans Ind Inform 12(5):1953–1962. https://doi.org/10.1109/TII.2016.2569584

    Article  Google Scholar 

  20. Qian F, Gao W, Yang Y, Dan Y (2020) Economic optimization and potential analysis of fuel cell vehicle-to-grid (fcv2g) system with large-scale buildings. Energy Convers Manag 205(1):112463. https://doi.org/10.1016/j.enconman.2019.112463

    Article  Google Scholar 

  21. Guannan W, Jingfei Y, Shuo W, Jia Z, Jiyun H, Yatong W (2019) Distributed dispatching optimization considering discharging of electric vehicles and security constraints. pp 612–618. https://doi.org/10.1109/ICIT.2019.8754913

  22. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 6(2):182–197. https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

  23. Deb K, Jain H (2014) An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: Solving problems with box constraints. IEEE Trans Evol Comput 18(4):577–601. https://doi.org/10.1109/TEVC.2013.2281535

    Article  Google Scholar 

  24. Zhang Q, Li H (2007) Moea/d: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731. https://doi.org/10.1109/TEVC.2007.892759

    Article  Google Scholar 

  25. Li K, Deb K, Zhang Q, Kwong S (2015) An evolutionary many-objective optimization algorithm based on dominance and decomposition. IEEE Trans Evol Comput 19 (5):694–716. https://doi.org/10.1109/TEVC.2014.2373386

    Article  Google Scholar 

  26. Ma X, Liu F, Qi Y, Wang X, Li L, Jiao L, Yin M, Gong M (2016) A multiobjective evolutionary algorithm based on decision variable analyses for multiobjective optimization problems with large-scale variables. IEEE Trans Evol Comput 20(2):275–298. https://doi.org/10.1109/TEVC.2015.2455812

    Article  Google Scholar 

  27. Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279. https://doi.org/10.1109/TEVC.2004.826067

    Article  Google Scholar 

  28. Wang R, Purshouse R, Fleming P (2013) Preference-inspired coevolutionary algorithms for many-objective optimization. IEEE Trans Evol Comput 17(4):474–494. https://doi.org/10.1109/TEVC.2012.2204264

    Article  Google Scholar 

  29. Rawat T, Niazi K (2019) Impact of ev charging/discharging strategies on the optimal operation of islanded microgrid. J Eng 2019(18):4819–4823. https://doi.org/10.1049/joe.2018.9335

    Article  Google Scholar 

  30. Zhou Y, Xu G, Chang M (2014) Demand side management for ev charging/discharging behaviours with particle swarm optimization. vol 2015 pp 3660–3664. https://doi.org/10.1109/WCICA.2014.7053325

  31. Huang Y, Liu J, Chen J, Fan K, Zhao J (2012) Load frequency control considering vehicle to grid. Autom Electric Power Syst 36(9):24–28. https://doi.org/10.3969/j.issn.1000-1026.2012.09.005

    Google Scholar 

  32. Khan S, Khawaja K, Haider Z, Bukhari SBA, Lee SJ, Rafique M, Kim CH (2018) Energy management scheme for an ev smart charger v2g/g2v application with an ev power allocation technique and voltage regulation. Appl Sci 8:648–671. https://doi.org/10.3390/app8040648

    Article  Google Scholar 

  33. Aki H, Murata A, Han S (2015) survey and analyses on private vehicle use for the development of v2g/v2h management. In: 2015 IEEE vehicle power and propulsion conference (VPPC), pp 1–5. https://doi.org/10.1109/VPPC.2015.7352884

  34. Bitencourt LDA, Borba BSMC, Maciel RS, Fortes MZ, Ferreira VH (2017) Optimal ev charging and discharging control considering dynamic pricing. In: 2017 IEEE Manchester PowerTech, pp 1–6. https://doi.org/10.1109/PTC.2017.7981231

  35. Huber M, Trippe A, Kuhn P, Hamacher T (2012) Effects of large scale ev and pv integration on power supply systems in the context of singapore. In: 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), pp 1–8. https://doi.org/10.1109/ISGTEurope.2012.6465831

  36. Liu Z, Wang Y, Bingchuan W (2019) Indicator-based constrained multiobjective evolutionary algorithms. IEEE Trans Syst Man Cybern Syst PP(99):1–13. https://doi.org/10.1109/TSMC.2019.2954491

    Google Scholar 

  37. Li K, Wang R, Zhang T, Ishibuchi H (2018) Evolutionary many-objective optimization: A comparative study of the state-of-the-art. IEEE Access 6:26194–26214. https://doi.org/10.1109/ACCESS.2018.2832181

    Article  Google Scholar 

  38. Jian JR, Zhan ZH, Zhang J (2020) Large-scale evolutionary optimization: a survey and experimental comparative study. Int J Mach Learn Cybern 11(3):729–745. https://doi.org/10.1007/s13042-019-01030-4

    Article  Google Scholar 

  39. Li M, Yao X (2019) Quality evaluation of solution sets in multiobjective optimisation: A survey. ACM Comput Surv 52(2):26:1–26:38. https://doi.org/10.1145/3300148

    Google Scholar 

  40. Wei D, Zhang C, Sun B, Cui N (2014) A time-of-use price based multi-objective optimal dispatching for charging and discharging of electric vehicles. Power Syst Technol 38(11):2972–2977. https://doi.org/10.13335/j.1000-3673.pst.2014.11.005

    Google Scholar 

  41. Thakur A, Prabakaran R, Elkadeem M, Sharshir SW, Arıcı M, Wang C, Zhao W, Hwang JY, Saidur R (2020) A state of art review and future viewpoint on advance cooling techniques for lithium–ion battery system of electric vehicles. J Energy Storage 32:101771. https://doi.org/10.1016/j.est.2020.101771

    Article  Google Scholar 

  42. Ma X, Li X, Zhang Q, Tang K, Liang Z, Xie W, Zhu Z (2019) A survey on cooperative co-evolutionary algorithms. IEEE Trans Evol Comput 23(3):421–441. https://doi.org/10.1109/TEVC.2018.2868770

    Article  Google Scholar 

  43. Purshouse R, Jalba C, Fleming P (2011) Proceedings of the evolutionary multi-criterion optimization. https://doi.org/10.1007/978-3-642-19893-9_10

  44. Wang L, Yu W, Qiu F, Ren Y, Lu J, Fu P (2021) Preference-inspired coevolutionary algorithm based on differentiated space for many-objective problems. Soft Comput 25:1–15. https://doi.org/10.1007/s00500-020-05369-7

    Article  Google Scholar 

  45. Na YU, Fei YU, Dawei H, Houhe C, Pengyu Z (2019) Multi-agent system based charging and discharging of electric vehicles distributed coordination dispatch strategy. Power Syst Prot Control 47 (5):1–9

    Google Scholar 

  46. Shang K, Ishibuchi H, He L, Pang LM (2021) A survey on the hypervolume indicator in evolutionary multiobjective optimization. IEEE Trans Evol Comput 25(1):1–20. https://doi.org/10.1109/TEVC.2020.3013290

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by Natural Science Foundation of Zhejiang Province (LQ20F020014), in part by the National Natural Science Foundation of China (61472366, 61379077), in part by the Natural Science Foundation of Zhejiang Province (LY17F020022), in part by Key Projects of Science and Technology Development Plan of Zhejiang Province (2018C01080).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, College of software, Zhejiang University of Technology, 310023, Hangzhou, China

    Xiaotian Pan, Liping Wang & Guodao Zhang

  2. Zhejiang Lab, 310023, Hangzhou, China

    Qicang Qiu

  3. College of Education Science and Technology, Zhejiang University of Technology, 310023, Hangzhou, China

    Feiyue Qiu

Authors
  1. Xiaotian Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liping Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qicang Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Feiyue Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guodao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Liping Wang or Feiyue Qiu.

Ethics declarations

Conflict of interests

The authors declare that they have no conflict of interest.

Additional information

Human participants

This study does not contain any studies with human participants or animals performed by any of the authors.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pan, X., Wang, L., Qiu, Q. et al. Many-objective optimization for large-scale EVs charging and discharging schedules considering travel convenience. Appl Intell 52, 2599–2620 (2022). https://doi.org/10.1007/s10489-021-02494-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02494-0

Keywords

Search

Navigation