Combinatorial Auctions and Graph Neural Networks for Local Energy
  Flexibility Markets
Awadelrahman M. A. Ahmed Frank Eliassen Yan Zhang
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.