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Demand response to improve the shared electric vehicle planning: Managerial insights, sustainable benefits

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  • Ran, Cuiling
  • Zhang, Yanzi
  • Yin, Ying

Abstract

Massive adoption of shared electric mobility benefits people’s daily commute and environment but creates overload issues into the power grid, then further cause challenges to charging service operations and power management. Previous research always focuses on single optimization process on shared vehicle planning, rather than the combination of demand management into day-ahead planning operations. To this end, we attempt to propose a mixed integer programming model integrating demand response operations to further explore the impacts of demand response on shared electric vehicle planning operations. We first model a two-stages model integrating charging facility location in the first stage and vehicle relocation in the second stage. Moreover, both supply-side and demand-side uncertainties are considered and approximated into tractable form by applying sample average approximation and distributional robust set featuring the entropy knowledge and electric vehicle’s multi-level charging duration. The demand response policy is also proposed to reshape the original charging demand into an economical and reliable way to improve operational efficiency and mitigate the power overload issues caused by massive electric vehicle adoption. Further, we conduct a real-world case study in Amsterdam, the Netherlands, to explore the social-operational impacts of vehicle planning optimization model integrating the demand response, robust charging facility planning in three areas: (1) The demand response integration promote electric vehicle planning operations on cost-saving for about 3%. (2) Data richness of serviceability towards charging piles influence all decisions through the shared electric vehicle charging station planning. (3) A trade-off exists between technical progress on charging rate and charging technology stability.

Suggested Citation

  • Ran, Cuiling & Zhang, Yanzi & Yin, Ying, 2021. "Demand response to improve the shared electric vehicle planning: Managerial insights, sustainable benefits," Applied Energy, Elsevier, vol. 292(C).
    • Handle: RePEc:eee:appene:v:292:y:2021:i:c:s0306261921003238
    DOI: 10.1016/j.apenergy.2021.116823
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    References listed on IDEAS

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    Cited by:

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    2. Zeynali, Saeed & Nasiri, Nima & Marzband, Mousa & Ravadanegh, Sajad Najafi, 2021. "A hybrid robust-stochastic framework for strategic scheduling of integrated wind farm and plug-in hybrid electric vehicle fleets," Applied Energy, Elsevier, vol. 300(C).
    3. Wang, Yitong & Fan, Ruguo & Du, Kang & Bao, Xuguang, 2023. "Exploring incentives to promote electric vehicles diffusion under subsidy abolition: An evolutionary analysis on multiplex consumer social networks," Energy, Elsevier, vol. 276(C).
    4. Zhang, Dongdong & Li, Chunjiao & Goh, Hui Hwang & Ahmad, Tanveer & Zhu, Hongyu & Liu, Hui & Wu, Thomas, 2022. "A comprehensive overview of modeling approaches and optimal control strategies for cyber-physical resilience in power systems," Renewable Energy, Elsevier, vol. 189(C), pages 1383-1406.
    5. Oikonomou, Konstantinos & Tarroja, Brian & Kern, Jordan & Voisin, Nathalie, 2022. "Core process representation in power system operational models: Gaps, challenges, and opportunities for multisector dynamics research," Energy, Elsevier, vol. 238(PC).

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