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Crowdsourcing Electrified Mobility for Omni-Sharing Distributed Energy Resources

In: AI and Analytics for Smart Cities and Service Systems

Author

Listed:
  • Wenqing Ai

    (Tsinghua University, Beijing)

  • Tianhu Deng

    (Tsinghua University, Beijing)

  • Wei Qi

    (McGill University)

Abstract

Ever-increasing coupling of energy and mobility sectors is underway in our cities, but whether and how to utilize such coupling to optimize the portfolio of urban energy assets has rarely been studied. A large amount of energy storage, an important energy asset of distributed energy resources (DERs), is not only costly but harmful to the environment. Improving efficiency and reducing redundancy of storage is imperative to the development of smart energy. We fill this gap by studying an omni energy sharing paradigm, which is a novel business model of crowdsourcing energy from the ride-sharing market into a local DER community. We formulate the omni energy sharing problem as a two-stage model including external and internal sharing. The external sharing model proposes the optimal energy payment scheme to obtain energy supply from crowdsourcing shared electric vehicles (CSEVs). Then, the internal sharing model designs the cost allocation rules to ensure the stability of the grand coalition between prosumers holding storage in the same DER community. As the existing analytical allocation rules are not suitable for the omni sharing problem and other methods are computationally intensive for a large-scale problem, we build scalable allocation rules and identify conditions to enable energy demand distributions within a community. Our major finding is that the omni-sharing strategy dominates the peer-to-peer energy sharing model by reducing both daily energy operating cost and total storage. We also uncover that omni energy sharing generates more synergy effects than competition effects for the ride-sharing market. From a wider perspective, this paper deepens our knowledge about the mobility-energy orchestration in future smart cities.

Suggested Citation

  • Wenqing Ai & Tianhu Deng & Wei Qi, 2021. "Crowdsourcing Electrified Mobility for Omni-Sharing Distributed Energy Resources," Lecture Notes in Operations Research, in: Robin Qiu & Kelly Lyons & Weiwei Chen (ed.), AI and Analytics for Smart Cities and Service Systems, pages 365-382, Springer.
  • Handle: RePEc:spr:lnopch:978-3-030-90275-9_29
    DOI: 10.1007/978-3-030-90275-9_29
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