Optimizing bidding strategy in electricity market based on graph convolutional neural network and deep reinforcement learning

Formulating optimal bidding strategies is pivotal for market participants to enhance electricity market profits. The main challenge for finding optimal bidding strategies is how to deal with system uncertainty, which stems from the inherent unpredictability and fluctuation within the electricity market. In the previous works, deep reinforcement learning (DRL) is proved a promising approach in multi-agent system with uncertainty. But few works model the relevance between agents for processing system uncertainty, especially the dynamic correlation in the operation of market. For this purpose, this paper proposes to model the correlation between agents to cope with the system uncertainty in a representative centralized double-sided auction market by combining graph convolutional neural network (GCN) with deep deterministic policy gradient (DDPG) algorithm, which is not only able to deal with the system uncertainty by aggregating correlative information of neighboring agents, but also helps obtain superior bidding strategies for the market participants. The proposed algorithm is evaluated on 4-bus, 30-bus and 57-bus congested network, where both supply side and demand side with elastic demand are modeled as RL agents. The results demonstrate that the proposed algorithm achieves higher system profits than the DRL based algorithms without GCN.