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.

翻译：本文提出了一种面向本地能源灵活性市场的组合拍卖新框架，解决了产消者无法将多个灵活时隙捆绑的问题。为求解底层NP完全的胜者确定问题，我们提出了一种简单而强大的异构三分图表示方法，并设计了基于图神经网络的模型。与现成的优化工具相比，我们的模型实现了平均最优值偏差低于5%，且推理时间复杂度呈线性，而商业求解器为指数复杂度。本研究的贡献与结果表明，利用机器学习能在本地市场中高效分配能源灵活性资源，并可为一般优化问题的求解提供新思路。