SpringerLink
Log in

Multi-objective Optimization Approach for Coordinated Scheduling of Electric Vehicles-Wind Integrated Power Systems

  • Chapter
  • First Online:
Artificial Intelligence Enabled Computational Methods for Smart Grid Forecast and Dispatch

Part of the book series: Engineering Applications of Computational Methods ((EACM,volume 14))

  • 208 Accesses

Abstract

It is well recognized that renewable energy and electric vehicles are widely deployed for adapting to our society in an environmental way [1,2,3,4].

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

Access this chapter

Log in via an institution
Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now
Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 128.39
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 171.19
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
EUR 171.19
Price includes VAT (Germany)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. L. Zhang, W. Lee, Z. Ding, Y. Lu, D. Chen, A stochastic resource-planning scheme for phev charging station considering energy portfolio optimization and price-responsive demand. 54, 5590–5598, IEEE (2018)

    Google Scholar 

  2. A. Kulvanitchaiyanunt, V.C. Chen, J. Rosenberger, P. Sarikprueck, W.J. Lee, Novel hybrid market price forecasting method with data clustering techniques for ev charging station application. IEEE Trans. Indus. Appl. 51(3), 1987–1996 (2015)

    Article  Google Scholar 

  3. A.A. Stoorvogel, D. Wu, T. Yang, J. Stoustrup, Distributed optimal coordination for distributed energy resources in power systems. IEEE Trans. Autom. Sci. Eng. 14(2), 414–424 (2017)

    Article  Google Scholar 

  4. T. Zhao, M. Yu, Y. Liu, Y. Li, Z. Ni, L. Wu, Coordinated stochastic scheduling for improving wind power adsorption in electric vehicles-wind integrated power systems by multi-objective optimization approach. 2019 IEEE Industry Applications Society Annual Meeting, pp. 1–10 (2019)

    Google Scholar 

  5. X. Zhao, D. Luo, Driving force of rising renewable energy in China: environment, regulation and employment. In Renew. Sustain. Energy Rev. 68, 48–56 (2017)

    Article  Google Scholar 

  6. S. Zhang, K. Luo, X. Zhao, Q. Cai, The substitution of wind power for coal-fired power to realize China’s CO\(_2\) emissions reduction targets in 2020 and 2030. Energy 120, 164–178 (2017)

    Article  Google Scholar 

  7. V.J. Karplus, T. Qi, X. Zhang, The energy and CO\(_2\) emissions impact of renewable energy development in China. Energy Policy 68, 60–69 (2014)

    Article  Google Scholar 

  8. S.T. Revankar, M.S. Kumar, Development scheme and key technology of an electric vehicle: an overview. Renew. Sustain. Energy Rev. 70, 1266–1285 (2017)

    Article  Google Scholar 

  9. K. Ahn, C. Fiori, H.A. Rakha, Power-based electric vehicle energy consumption model: model development and validation. Appl. Energy 168, 257–268 (2016)

    Article  Google Scholar 

  10. D. Ouyang, J. Du, Progress, of Chinese electric vehicles industrialization in 2015: a review. Appl. Energy 188, 529–546 (2017)

    Article  Google Scholar 

  11. Z. Lukszo, M. Weijnen, Y. Li, C. Davis, Electric vehicle charging in China’s power system: energy, economic and environmental trade-offs and policy implications. Appl. Energy 173, 535–554 (2016)

    Article  Google Scholar 

  12. V. Modi, M. Waite, Modeling wind power curtailment with increased capacity in a regional electricity grid supplying a dense urban demand. Appl. Energy 183, 299–317 (2016)

    Article  Google Scholar 

  13. Y. Tan, J. Yan, H. Li, P.E. Campana, Feasibility study about using a stand-alone wind power driven heat pump for space heating. Appl. Energy 228, 1486–1498 (2018)

    Article  Google Scholar 

  14. Y. Mu, C. Xu, Y. Jiang, H. Jia, X. Li, Coordinated control for ev aggregators and power plants in frequency regulation considering time-varying delays. Appl. Energy 210, 1363–1367 (2018)

    Article  Google Scholar 

  15. B. Zhang, Z. Li, W. Wu, B. Wang, Adjustable robust real-time power dispatch with large-scale wind power integration. IEEE Trans. Sustain. Energy 6, 357–368 (2015)

    Article  Google Scholar 

  16. X. Zhang, Z. Wu, P. Zeng, Q. Zhou, A solution to the chance-constrained two-stage stochastic program for unit commitment with wind energy integration. IEEE Trans. Power Syst. 31(6), 4185–4196 (2016)

    Article  Google Scholar 

  17. M. He, S. Abedi, D. Obadina, Congestion risk-aware unit commitment with significant wind power generation. IEEE Trans. Power Syst. 33, 6861–6869 (2018)

    Article  Google Scholar 

  18. H. Saboori, R. Hemmati, Stochastic risk-averse coordinated scheduling of grid integrated energy storage units in transmission constrained wind-thermal systems within a conditional value-at-risk framework. Energy 113, 762–775 (2016)

    Article  Google Scholar 

  19. J. Teh, I. Cotton, Reliability impact of dynamic thermal rating system in wind power integrated network. IEEE Trans. Reliabil. 65(12), 1081–1089 (2016)

    Article  Google Scholar 

  20. M. Humayun, A. Safdarian, M. Ali, M.Z. Degefa, M. Lehtonen, Increased utilization of wind generation by coordinating the demand response and real-time thermal rating. IEEE Trans. Power Syst. 31(5), 3737–3746 (2016)

    Article  Google Scholar 

  21. B. Shen, S. Dang, J. Zhang, Y. Zhou, N. Zhang, Z. Hu, A source-grid-load coordinated power planning model considering the integration of wind power generation. Appl. Energy 168, 13–24 (2016)

    Article  Google Scholar 

  22. Y. Sun, Y. Jiang, J. Xu, Day-ahead stochastic economic dispatch of wind integrated power system considering demand response of residential hybrid energy system. Appl. Energy 190, 1126–1137 (2017)

    Article  Google Scholar 

  23. E. Heydarian-Forushani, M.D. Somma, G. Graditi, Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects. Renew. Energy 116, 272–287 (2018)

    Article  Google Scholar 

  24. S. Lu, Z. Luo, C. Wu, W. Gu, J. Wang, Optimal operation for integrated energy system considering thermal inertia of district heating network and buildings. Appl. Energy 199, 234–246 (2017)

    Article  Google Scholar 

  25. Y. Sun, Y. Bao, S. Liao, J. Xu, B. Tang, Control of energy-intensive load for power smoothing in wind power plants. IEEE Trans. Power Syst. 33(6), 6142–6154 (2018)

    Article  Google Scholar 

  26. G. Graditi, M.C. Falvo, P. Siano, Electric vehicles integration in demand response programs. 2014 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, pp. 548–553 (2014)

    Google Scholar 

  27. G. Deconinck, X. Guan, Z. Qiu, Y. Yang, Q. Jia, Z. Hu, Distributed coordination of ev charging with renewable energy in a microgrid of buildings. IEEE Trans. Smart Grid 9, 6253–6264 (2018)

    Article  Google Scholar 

  28. M. Aliakbar-Golkar, M. Fowler, A. Elkamel, A. Ahmadian, M. Sedghi, Two-layer optimization methodology for wind distributed generation planning considering plug-in electric vehicles uncertainty: a flexible active-reactive power approach. Energy Convers. Manage. 124, 231–246 (2016)

    Article  Google Scholar 

  29. Q. Jia, Q. Huang, X. Guan, Robust scheduling of ev charging load with uncertain wind power integration. IEEE Trans. Smart Grid 9(2), 1043–1054 (2018)

    Article  Google Scholar 

  30. P. Meibom, J. Kiviluoma, Influence of wind power, plug-in electric vehicles, and heat storages on power system investments. Energy 35(3), 1244–1255 (2010)

    Article  Google Scholar 

  31. Z. Li, W. Tian, Y. Sun, Z. Chen, M. Shahidehpour, Ev charging schedule in coupled constrained networks of transportation and power system. IEEE Trans. Smart Grid 10(5), 4706–4716 (2019)

    Article  Google Scholar 

  32. G. Krajacic, N. Duic, P. Prebeg, G. Gasparovic, Long-term energy planning of croatian power system using multi-objective optimization with focus on renewable energy and integration of electric vehicles. Appl. Energy 184, 1493–1507 (2016)

    Article  Google Scholar 

  33. M. Wietschel, D. Dallinger, Grid integration of intermittent renewable energy sources using price-responsive plug-in electric vehicles. Renew. Sustain. Energy Rev. 16(5), 3370–3382 (2012)

    Article  Google Scholar 

  34. F. Bouffard, F.D. Galiana, Stochastic security for operations planning with significant wind power generation. IEEE Trans. Power Syst. 23(2), 306–316 (2008)

    Article  Google Scholar 

  35. M. Lange, On the uncertainty of wind power predictions-analysis of the forecast accuracy and statistical distribution of errors. J. Solar Energy Eng. 127(2), 177–284 (2005)

    Article  Google Scholar 

  36. M.R. Narimani, T. Niknam, R. Azizipanah-Abarghooee, An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation. Appl. Energy 99, 455–470 (2012)

    Article  Google Scholar 

  37. M.S. Li, J.P. Zhan, Y.Z. Li, Q.H. Wu, Mean-variance model for power system economic dispatch with wind power integrated. Energy 72, 510–520 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Yuanzheng Li, Yong Zhao & Zhigang Zeng

  2. Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA

    Lei Wu

Authors
  1. Yuanzheng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yong Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lei Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhigang Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuanzheng Li .

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Li, Y., Zhao, Y., Wu, L., Zeng, Z. (2023). Multi-objective Optimization Approach for Coordinated Scheduling of Electric Vehicles-Wind Integrated Power Systems. In: Artificial Intelligence Enabled Computational Methods for Smart Grid Forecast and Dispatch. Engineering Applications of Computational Methods, vol 14. Springer, Singapore. https://doi.org/10.1007/978-981-99-0799-1_9

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-0799-1_9

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-0798-4

  • Online ISBN: 978-981-99-0799-1

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation