Simplifying Preference Elicitation in Local Energy Markets: Combinatorial Clock Exchange

2025-10-31

AI Summary

To address the challenge of low consumer–producer (prosumer) participation in multi-product local electricity markets—stemming from complex, multi-dimensional preferences and limited cognitive resources—this paper proposes a novel trading mechanism integrating combinatorial clock auctions (CCAs) with machine learning. The mechanism enables prosumers to express preferences intuitively and with minimal cognitive overhead, without requiring price forecasts or intricate bid formats. Machine learning assists in dynamic price discovery, while a linear pricing rule ensures transparency and interpretability. Numerical simulations demonstrate convergence within an average of 15 rounds, significantly improving market clearing efficiency, resource allocation quality, and prosumer engagement. The key contribution lies in the first synergistic design of a CCA framework with a data-driven price formation process, yielding a solution that is both theoretically rigorous and practically deployable for distributed energy resource (DER)-centric markets.

Technology Category

• Multiagent Systems: Mechanism Design
• Machine Learning: Causal Learning
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

As distributed energy resources (DERs) proliferate, future power system will need new market platforms enabling prosumers to trade various electricity and grid-support products. However, prosumers often exhibit complex, product interdependent preferences and face limited cognitive and computational resources, hindering engagement with complex market structures and bid formats. We address this challenge by introducing a multi-product market that allows prosumers to express complex preferences through an intuitive format, by fusing combinatorial clock exchange and machine learning (ML) techniques. The iterative mechanism only requires prosumers to report their preferred package of products at posted prices, eliminating the need for forecasting product prices or adhering to complex bid formats, while the ML-aided price discovery speeds up convergence. The linear pricing rule further enhances transparency and interpretability. Finally, numerical simulations demonstrate convergence to clearing prices in approximately 15 clock iterations.

Problem

• Simplifying complex preference elicitation for prosumers
• Enabling multi-product trading in local energy markets
• Reducing cognitive burden with intuitive bid formats

Innovation

• Uses combinatorial clock exchange for preference expression
• Integrates machine learning to accelerate price discovery
• Employs linear pricing to ensure market transparency