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

TL;DR

CONSENT tackles the challenge of coordinating vehicle-to-building charging under uncertainty by coupling a stochastic MPC-based charging controller with a negotiated, incentive-aware menu for users. The framework uses Monte Carlo MPC (MC-MPC) within a semi-Markov decision process (SMDP) to generate and evaluate personalized deviation options $(\Delta e^{\max}_l, \Delta t^{\max}_{\mathrm{dep},l})$, ensuring strategy-proofness, budget feasibility, and voluntary participation. Calibrated with survey data and validated on real building/EV data, CONSENT achieves mutual gains: operator costs are reduced relative to non-negotiated baselines, and user expenses drop below utility rates while maintaining high acceptance and low rejection. The approach provides a scalable, data-driven bridge between energy management and mobility needs, transforming EV charging into a collaborative platform for shared savings and grid stability. Future work may explore multi-day dynamics and richer game-theoretic analyses to model repeated interactions among rational users.

Abstract

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.

Figures (13)

• Figure 1: Negotiation workflow: Upon arrival, users receive forecast-based offers. Accepted offers are then optimally scheduled to meet the negotiated SoC.
• Figure 2: Comparison of computational runtime per decision across varying EV arrival intensities.
• Figure 3: Silhouette method shows $k=6$ as the optimal choice (highest score). However, $k=3$ is numerically very close, and is most suitable as users might get overwhelmed with more options.
• Figure 4: Elbow method shows a sharp decrease until $k=3$, indicating the elbow point.
• Figure 5: Clustered user deviation limits data provides Negotiation Options and flexibility limits

Theorems & Definitions (8)

• Theorem 1: Strategy-Proofness for Departure Time and Requested SoC
• Theorem 2: Budget Feasibility
• Theorem 3: Voluntary Participation
• Theorem 4
• proof
• proof : Proof
• proof
• proof