Skip to main content

Advertisement

Log in

Coordinated dispatch of the wind-thermal power system by optimizing electric vehicle charging

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The rapid development of renewable energy poses new challenges to grid operation. Owing to deficiencies in peak regulation capability, wind power cannot be fully connected to the grid. As a controllable load, electric vehicle (EV) charging enables the coordinated operation of EV charging, and the wind/thermal power generation system. In order to determine the coordinated operation mechanism of a power system with large-scale EV charging, a new dynamic multi-objective dispatch (DMOD) model of the power system that includes the economy, pollutant discharge, and abandonment volume was established to optimize the output of thermal power units. Here, we report the two-phase optimization strategy we developed to solve this model. In the first phase of the strategy, EV charging load is determined by optimizing user charging, combined with the time-sharing price of electricity, and the charging protocol. In its second phase, an improved multi-objective evolutionary algorithm (IMOEA) based on a modified particle swarm optimization (PSO) method was proposed to determine the coordinated operation of EV charging, and wind/thermal power generation system. A power system with 10 conventional units, and a grid-connected wind farm was simulated, and the analysis verifies the feasibility of the dispatching model and the effectiveness of the proposed optimization algorithm as a solution.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Association, Chinese Wind Energy: 2016 Annual review and outlook on China wind power. CWEA Head Office, Beijing (2016)

    Google Scholar 

  2. Xu, Y., Yin, M., Dong, Z.Y., Zhang, R., et al.: Robust dispatch of high wind power-penetrated power systems against transient instability. IEEE Trans. Power Syst. 33(1), 174–186 (2018)

    Article  Google Scholar 

  3. Hoang, D.T., Wang, P., Niyato, D., Hossain, E.: Charging and discharging of plug-in electric vehicles (PEVs) in vehicle-to-grid (V2G) systems: a cyber insurance-based model. IEEE Access 5, 732–754 (2017)

    Article  Google Scholar 

  4. Andervazh, M.R., Javadi, S.: Emission-economic dispatch of thermal power generation units in the presence of hybrid electric vehicles and correlated wind power plants. IET Gener. Transm. Distrib. 11(9), 2232–2243 (2017)

    Article  Google Scholar 

  5. Kavousi-Fard, A., Niknam, T., Fotuhi-Firuzabad, M.: Stochastic reconfiguration and optimal coordination of V2G plug-in electric vehicles considering correlated wind power generation. IEEE Trans. Sustain. Energy 6(3), 822–830 (2017)

    Article  Google Scholar 

  6. Huang, Q., Jia, Q.S., Guan, X.: Coordinating EV charging demand with wind supply in a bi-level energy dispatch framework. American Control Conference (ACC). Boston, MA 2016, 6233–6238 (2016)

    Google Scholar 

  7. Jebaraj, L., Venkatesan, C., Soubache, I., et al.: Application of differential evolution algorithm in static and dynamic economic or emission dispatch problem: a review. Renew. Sustain. Energy Rev. 77(2017), 1206–1220 (2017)

    Article  Google Scholar 

  8. Mahdi, Fahad Parvez, Vasant, Pandian, Kallimani, Vish, et al.: A holistic review on optimization strategies for combined economic emission dispatch problem. Renew. Sustain. Energy Rev. 81(2), 3006–3020 (2018)

    Article  Google Scholar 

  9. Hongbin, Wu, Liu, Xingyue, Ding, Ming: Dynamic economic dispatch of a microgrid: mathematical models and solution algorithm. Int. J. Electr. Power Energy Syst. 63, 336–346 (2014)

    Article  Google Scholar 

  10. Li, M.S., Wu, Q.H., Ji, T.Y., Rao, H.: Stochastic multi-objective optimization for economic-emission dispatch with uncertain wind power and distributed loads. Electr. Power Syst. Res. 116, 367–373 (2014)

    Article  Google Scholar 

  11. Alham, M.H., Elshahed, M., Ibrahim, Doaa Khalil, et al.: A dynamic economic emission dispatch considering wind power uncertainty incorporating energy storage system and demand side management. Renew. Energy 96, 800–811 (2016)

    Article  Google Scholar 

  12. Yang, Z., Li, K., Niu, Q., et al.: A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads. J. Mod. Power Syst. Clean Energy 2, 298–307 (2014)

    Article  Google Scholar 

  13. Saber, A.Y., Venayagamoorthy, G.K.: Plug-in vehicles and renewable energy sources for cost and emission reductions. IEEE Trans. Ind. Electron. 58(4), 1229–1238 (2011)

    Article  Google Scholar 

  14. Liu, H., Zeng, P., Guo, J., et al.: An optimization strategy of controlled electric vehicle charging considering demand side response and regional wind and photovoltaic. J. Mod. Power Syst. Clean Energy 3, 232–239 (2015)

    Article  Google Scholar 

  15. Peng, C., Sun, H., Guo, J.: Dynamic economic dispatch for wind-thermal power system using a novel bi-population chaotic differential evolution algorithm. Int. J. Electr. Power Energy Syst. 42(1), 119–126 (2012)

    Article  Google Scholar 

  16. Basu, M.: Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II. Int. J. Electr. Power Energy Syst. 30(2), 140–149 (2008)

    Article  Google Scholar 

  17. Nwulu, N.I., Xia, X.: Multi-objective dynamic economic emission dispatch of electric power generation integrated with game theory based demand response programs. Energy Conversat. Manage 89, 963–974 (2015)

    Article  Google Scholar 

  18. Pandit, N., Tripathi, A., Tapaswi, S.: An improved bacterial foraging algorithm for combined static/dynamic environmental economic dispatch. Appl Soft Comput 12(11), 3500–3513 (2012)

    Article  Google Scholar 

  19. Abido, M.A.: Multi-objective particle swarm optimization for environmental/economic dispatch problem. Electr. Power Syst. Res. 79(7), 1105–1113 (2009)

    Article  Google Scholar 

  20. Peng, Minghong, Liu, Lian, Jiang, Chuanwen: A review on the economic dispatch and risk management of the large-scale plug-in electric vehicles (PHEVs)-penetrated power systems. Renew. Sustain. Energy Rev. 16(3), 1508–1515 (2012)

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. Kennedy, J., Eberhart. R.: Particle swarm optimization. In: Proceedings of IEEE Conference on Neural Networks, IEEE, pp 1942–1948. (1995)

  23. Li, Z., Ouyang, M.G.: The pricing of charging for electric vehicles in China—Dilemma and solution. Energy 36, 5765–5778 (2011)

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Grant: 61673164), the key research project of Education Department of Hunan Province (Grant: 14A032).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xizheng Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, X., Zheng, L. Coordinated dispatch of the wind-thermal power system by optimizing electric vehicle charging. Cluster Comput 22 (Suppl 4), 8835–8845 (2019). https://doi.org/10.1007/s10586-018-1974-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-1974-9

Keywords

Navigation