Finding optimal bidding strategies for generation units in electricity
markets would result in higher profit. However, it is a challenging problem due
to the system uncertainty which is due to the unknown other generation units'
strategies. Distributed optimization, where each entity or agent decides on its
bid individually, has become state of the art. However, it cannot overcome the
challenges of system uncertainties. Deep reinforcement learning is a promising
approach to learn the optimal strategy in uncertain environments. Nevertheless,
it is not able to integrate the information on the spatial system topology in
the learning process. This paper proposes a distributed learning algorithm
based on deep reinforcement learning (DRL) combined with a graph convolutional
neural network (GCN). In fact, the proposed framework helps the agents to
update their decisions by getting feedback from the environment so that it can
overcome the challenges of the uncertainties. In this proposed algorithm, the
state and connection between nodes are the inputs of the GCN, which can make
agents aware of the structure of the system. This information on the system
topology helps the agents to improve their bidding strategies and increase the
profit. We evaluate the proposed algorithm on the IEEE 30-bus system under
different scenarios. Also, to investigate the generalization ability of the
proposed approach, we test the trained model on IEEE 39-bus system. The results
show that the proposed algorithm has more generalization abilities compare to
the DRL and can result in higher profit when changing the topology of the
system.

本研究提出了一种基于 DRL 和 GCN 的分布式学习算法，使代理人能够通过从环境中获取反馈来改进投标策略并增加利润，同时将空间系统拓扑的信息输入到 GCN 中，帮助代理人了解系统结构并提高决策的准确性。在 IEEE 30-bus 系统和 IEEE 39-bus 系统的测试中表明该算法具有较好的泛化能力和更高的利润。