Crowdsourcing Electric Vehicles for Omni-Sharing Distributed Energy Storage

Problem definition: Ever-increasing coupling of energy and mobility sectors is underway in our cities. However, whether and how to use such coupling to optimize the portfolio and operations of urban energy assets have rarely been studied. We fill this gap by studying “omni-sharing,” which is a novel business model (beyond “peer-to-peer” energy sharing) that allows a community of energy consumers with storage devices to also crowdsource electricity from electric vehicles (EVs). Methodology/results: We analytically model two salient features of omni-sharing operations: the optimal payment to crowdsourced EV drivers and the cost allocation among energy consumers. In doing so, we enrich the newsvendor cooperative game theory by generalizing the newsvendor model with the underage cost being endogenous to the storage investment decision. We prove (and quantify with data on residential energy consumption and ride-sharing markets) that omni-sharing can reduce the total storage capacity needed and the total energy cost for the community. Managerial implications: Our analysis reveals that omni-sharing can bring storage and cost savings for all consumers (whereas peer-to-peer sharing cannot) by efficiently matching local energy supply and demand. Moreover, omni-sharing remains operationally stable and financially robust against the variations in ride-sharing driver incomes. These findings strengthen our understanding of urban energy-mobility orchestration. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72301027, 72242106, 72521001, 72072010, 72525012, 72025101, 72188101] and the China Postdoctoral Science Foundation [Grants 2023M730215 and 2025T180213]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2021.0610 .

• 저널Manufacturing & Service Operations Management
• 토픽Transportation and Mobility Innovations · Automotive Engineering
• DOI10.1287/msom.2021.0610
• 저자Wenqing Ai, Tianhu Deng, Wei Qi, W Gu