Cluster Computing

, Volume 22, Supplement 4, pp 8835–8845 | Cite as

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

  • Xizheng ZhangEmail author
  • Liang Zheng


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.


Electric vehicle charging Dynamic economic environmental dispatch Modified particle swarm optimization Wind power 



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


  1. 1.
    Association, Chinese Wind Energy: 2016 Annual review and outlook on China wind power. CWEA Head Office, Beijing (2016)Google Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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)CrossRefGoogle Scholar
  6. 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. 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)CrossRefGoogle Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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)CrossRefGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 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)CrossRefGoogle Scholar
  13. 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)CrossRefGoogle Scholar
  14. 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)CrossRefGoogle Scholar
  15. 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)CrossRefGoogle Scholar
  16. 16.
    Basu, M.: Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II. Int. J. Electr. Power Energy Syst. 30(2), 140–149 (2008)CrossRefGoogle Scholar
  17. 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)CrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 19.
    Abido, M.A.: Multi-objective particle swarm optimization for environmental/economic dispatch problem. Electr. Power Syst. Res. 79(7), 1105–1113 (2009)CrossRefGoogle Scholar
  20. 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)CrossRefGoogle Scholar
  21. 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)CrossRefGoogle Scholar
  22. 22.
    Kennedy, J., Eberhart. R.: Particle swarm optimization. In: Proceedings of IEEE Conference on Neural Networks, IEEE, pp 1942–1948. (1995)Google Scholar
  23. 23.
    Li, Z., Ouyang, M.G.: The pricing of charging for electric vehicles in China—Dilemma and solution. Energy 36, 5765–5778 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Collaborative Innovation Center of Wind Power and Energy ConversionHunan Institute of EngineeringXiangtanChina

Personalised recommendations