Simplifying Preference Elicitation in Local Energy Markets: Combinatorial Clock Exchange

TL;DR

This paper introduces a simplified, ML-assisted multi-product market platform for prosumers in local energy markets, enabling complex preference expression with fewer cognitive demands and faster convergence.

Contribution

It presents a novel combinatorial clock exchange mechanism combined with machine learning for efficient preference elicitation in energy markets.

Findings

Converges to clearing prices in about 15 iterations

Eliminates need for complex bid formats and price forecasting

Enhances transparency with linear pricing rule

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.