Combinatorial Auctions and Graph Neural Networks for Local Energy Flexibility Markets

TL;DR

This paper introduces a novel combinatorial auction framework utilizing graph neural networks to efficiently allocate energy flexibility in local markets, significantly reducing computation time while maintaining near-optimal solutions.

Contribution

It presents a new graph neural network-based approach for winner determination in energy flexibility auctions, addressing NP-completeness with high efficiency and accuracy.

Findings

Achieves less than 5% deviation from optimal solutions.

Linear inference time complexity.

Effective in solving complex energy market optimization problems.

Abstract

This paper proposes a new combinatorial auction framework for local energy flexibility markets, which addresses the issue of prosumers' inability to bundle multiple flexibility time intervals. To solve the underlying NP-complete winner determination problems, we present a simple yet powerful heterogeneous tri-partite graph representation and design graph neural network-based models. Our models achieve an average optimal value deviation of less than 5\% from an off-the-shelf optimization tool and show linear inference time complexity compared to the exponential complexity of the commercial solver. Contributions and results demonstrate the potential of using machine learning to efficiently allocate energy flexibility resources in local markets and solving optimization problems in general.