Combinatorial Auctions and Graph Neural Networks for Local Energy Flexibility Markets
This addresses efficiency in local energy flexibility markets, but it is incremental as it applies existing graph neural network methods to a specific optimization problem.
The paper tackles the problem of prosumers being unable to bundle multiple flexibility time intervals in local energy markets by proposing a combinatorial auction framework, achieving an average optimal value deviation of less than 5% from an optimization tool with linear inference time complexity.
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.