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Risk management and participation planning of electric vehicles in smart grids for demand response

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  • Nezamoddini, Nasim
  • Wang, Yong

Abstract

Demand response (DR) can serve as an effective tool to better balance the electricity demand and supply in the smart grid. It is defined as "the changes in electricity usage by end-use customers from their normal consumption patterns" in response to pricing and incentive payments. This paper focuses on new opportunities for DR with electric vehicles (EVs). EVs are potential distributed energy resources that support both the grid-to-vehicle and vehicle-to-grid modes. Their participation in the time-based (e.g., time-of-use) and incentive-based (e.g., regulation services) DR programs helps improve the stability and reduce the potential risks to the grid. Smart scheduling of EV charging and discharging activities also supports high penetration of renewables with volatile energy generation. This paper proposes a novel stochastic model from the Independent System Operator's perspective for risk management and participation planning of EVs in the smart grid for DR. The risk factors considered in this paper involve those caused by uncertainties in renewables (wind and solar), load patterns, parking patterns, and transmission lines' reliability. The effectiveness of the model in response to various settings such as the area type (residential, commercial, and industrial), the EV penetration level, and the risk level has been investigated.

Suggested Citation

  • Nezamoddini, Nasim & Wang, Yong, 2016. "Risk management and participation planning of electric vehicles in smart grids for demand response," Energy, Elsevier, vol. 116(P1), pages 836-850.
    • Handle: RePEc:eee:energy:v:116:y:2016:i:p1:p:836-850
    DOI: 10.1016/j.energy.2016.10.002
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    2. Sharma, S. & Jain, Prerna, 2023. "Risk-averse integrated DR and dynamic V2G scheduling of parking lot operator for enhanced market efficiency," Energy, Elsevier, vol. 275(C).
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    10. Einolander, Johannes & Lahdelma, Risto, 2022. "Explicit demand response potential in electric vehicle charging networks: Event-based simulation based on the multivariate copula procedure," Energy, Elsevier, vol. 256(C).
    11. Figueiredo, Raquel & Nunes, Pedro & Brito, Miguel C., 2017. "The feasibility of solar parking lots for electric vehicles," Energy, Elsevier, vol. 140(P1), pages 1182-1197.
    12. Subramanian, Vignesh & Das, Tapas K., 2019. "A two-layer model for dynamic pricing of electricity and optimal charging of electric vehicles under price spikes," Energy, Elsevier, vol. 167(C), pages 1266-1277.
    13. Afaq Ahmad & Muhammad Khalid & Zahid Ullah & Naveed Ahmad & Mohammad Aljaidi & Faheem Ahmed Malik & Umar Manzoor, 2022. "Electric Vehicle Charging Modes, Technologies and Applications of Smart Charging," Energies, MDPI, vol. 15(24), pages 1-32, December.
    14. Connor, P.M. & Axon, C.J. & Xenias, D. & Balta-Ozkan, N., 2018. "Sources of risk and uncertainty in UK smart grid deployment: An expert stakeholder analysis," Energy, Elsevier, vol. 161(C), pages 1-9.
    15. Azadeh Ahkamiraad & Yong Wang, 2018. "An Agent-Based Model for Zip-Code Level Diffusion of Electric Vehicles and Electricity Consumption in New York City," Energies, MDPI, vol. 11(3), pages 1-17, March.
    16. Qiwei Xu & Jianshu Huang & Yue Han & Yun Yang & Lingyan Luo, 2020. "A Study on Electric Vehicles Participating in the Load Regulation of Urban Complexes," Energies, MDPI, vol. 13(11), pages 1-23, June.
    17. Aghajani, Saemeh & Kalantar, Mohsen, 2017. "Operational scheduling of electric vehicles parking lot integrated with renewable generation based on bilevel programming approach," Energy, Elsevier, vol. 139(C), pages 422-432.
    18. Haghnegahdar, Lida & Chen, Yu & Wang, Yong, 2022. "Enhancing dynamic energy network management using a multiagent cloud-fog structure," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    19. Ioakimidis, Christos S. & Thomas, Dimitrios & Rycerski, Pawel & Genikomsakis, Konstantinos N., 2018. "Peak shaving and valley filling of power consumption profile in non-residential buildings using an electric vehicle parking lot," Energy, Elsevier, vol. 148(C), pages 148-158.
    20. Anna Auza & Ehsan Asadi & Behrang Chenari & Manuel Gameiro da Silva, 2023. "A Systematic Review of Uncertainty Handling Approaches for Electric Grids Considering Electrical Vehicles," Energies, MDPI, vol. 16(13), pages 1-25, June.
    21. Nunes, Pedro & Brito, M.C., 2017. "Displacing natural gas with electric vehicles for grid stabilization," Energy, Elsevier, vol. 141(C), pages 87-96.
    22. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    23. He, Yongxiu & Zhang, Qi & Pang, Yuexia, 2017. "The development pattern design of Chinese electric vehicles based on the analysis of the critical price of the life cycle cost," Energy Policy, Elsevier, vol. 109(C), pages 382-388.
    24. Dusan Medved & Lubomir Bena & Maksym Oliinyk & Jaroslav Dzmura & Damian Mazur & David Martinko, 2023. "Assessing the Effects of Smart Parking Infrastructure on the Electrical Power System," Energies, MDPI, vol. 16(14), pages 1-16, July.
    25. Gough, Rebecca & Dickerson, Charles & Rowley, Paul & Walsh, Chris, 2017. "Vehicle-to-grid feasibility: A techno-economic analysis of EV-based energy storage," Applied Energy, Elsevier, vol. 192(C), pages 12-23.

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