arXiv subjects

Saurabh Vaishampayan

Publications and source records attributed to Saurabh Vaishampayan.

3 recordsLinked to original sources

arXiv2026

Eliciting Truthful Feedback for Preference-Based Learning via the VCG Mechanism

We study resource allocation problems in which a central planner allocates resources among strategic agents with private cost functions in order to minimize a social cost, defined as an aggregate of the agents' costs. This setting poses two main challenges: (i) the agents' cost functions may be unknown to them or difficult to specify explicitly, and (ii) agents may misreport their costs strategically. To address these challenges, we propose an algorithm that combines preference-based learning with Vickrey-Clarke-Groves (VCG) payments to incentivize truthful reporting. Our algorithm selects informative preference queries via D-optimal design, estimates cost parameters through maximum likelihood, and computes VCG allocations and payments based on these estimates. In a one-shot setting, we prove that the mechanism is approximately truthful, individually rational, and efficient up to an error of $\tilde{\mathcal O}(K^{-1/2})$ for $K$ preference queries per agent. In an online setting, these guarantees hold asymptotically with sublinear regret at a rate of $\tilde{\mathcal O}(T^{2/3})$ after $T$ rounds. Finally, we validate our approach through a numerical case study on demand response in local electricity markets.

cs.GT↗

arXiv2026

Uncertainty and Autarky: Cooperative Game Theory for Stable Local Energy Market Partitioning

Local energy markets empower prosumers in distribution grids to form coalitions for collective self-consumption. An open question is to analyze the scale and composition of local energy market coalitions formed by strategic prosumers in distribution grids. This analysis must account for grid constraints, stochasticity of load and generation, as well as the interaction between possibly multiple local energy markets in the distribution grid. In this work, we present a cooperative game theoretic framework to study distribution grid partitioning into local energy markets under uncertain prosumption, grid constraints, and coalitional externalities. We formulate the optimal stable partitioning problem to balance the interests of the grid operator with that of strategic prosumers. Under deterministic load and generation, we show that the largest market coalition is the optimal stable partition. Under high levels of grid congestion, we show that individual self-consumption corresponds to the optimal stable partition. For the general case of stochastic prosumption and moderate grid congestion levels, we provide an algorithm to evaluate the optimal stable partition. We validate our algorithm and theory using numerical experiments on benchmark and real world distribution grids. Our results help in understanding the impact of prosumption uncertainty and grid constraints on coalition formation.

eess.SY↗

arXiv2025

Scheduling the Charge of Temporally Flexible Electric Vehicles: a Market-based Approach

The increasing electrification of human activities and the rapid integration of variable renewable energy sources strain the power grid. A solution to address the need for more grid storage is to use the battery of electric vehicles as a back-up capacity. However, drivers tend to disconnect their electric vehicle when its battery is needed the most. We propose a charge scheduler that incentivizes drivers to delay their disconnection to improve vehicle-to-grid services. We also leverage drivers' temporal flexibility to alleviate congestion in oversubscribed charging stations. We formulate the computation of an optimal flexible schedule as a mixed-integer quadratic problem. We tractably approximate its solution using the Alternating Direction Method of Multipliers. Considering the possibility that strategic drivers misreport their charging preferences to the station coordinator, we then propose a Vickrey-Clarke-Groves mechanism that incentivizes truthful reporting. We conclude with a simulated case study using real-world data to quantitatively assess the added value of drivers' temporal flexibility for enhancing vehicle-to-grid services and reducing station congestion.

eess.SY↗