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CONSENT: A Negotiation Framework for Leveraging User Flexibility in Vehicle-to-Building Charging under Uncertainty

arXiv 2601.01581 ↗First posted 4 Jan 2026cs.MA cs.AI cs.GTcited by 0

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Abstract (arXiv)

The growth of Electric Vehicles (EVs) creates a conflict in vehicle-to-building (V2B) settings between building operators, who face high energy costs from uncoordinated charging, and drivers, who prioritize convenience and a full charge. To resolve this, we propose a negotiation-based framework that, by design, guarantees voluntary participation, strategy-proofness, and budget feasibility. It transforms EV charging into a strategic resource by offering drivers a range of incentive-backed options for modest flexibility in their departure time or requested state of charge (SoC). Our framework is calibrated with user survey data and validated using real operational data from a commercial building and an EV manufacturer. Simulations show that our negotiation protocol creates a mutually beneficial outcome: lowering the building operator's costs by over 3.5% compared to an optimized, non-negotiating smart charging policy, while simultaneously reducing user charging expenses by 22% below the utility's retail energy rate. By aligning operator and EV user objectives, our framework provides a strategic bridge between energy and mobility systems, transforming EV charging from a source of operational friction into a platform for collaboration and shared savings.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 34 references that are in the map. First posted on arXiv 4 Jan 2026.

Builds on

4 of 34 references.

References · 4

  1. 2021Smoothed Least-Laxity-First Algorithm for EV Chargingcited by 5
  2. 2024Reserve Provision from Electric Vehicles: Aggregate Boundaries and Stochastic Model Predictive Controlcited by 2
  3. 2016Control of Charging of Electric Vehicles through Menu-Based Pricingcited by 1
  4. 2025Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewardscited by 1

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