Layered Temporal Spatial Graph Attention Reinforcement Learning for Multiplex Networked Industrial Chains Energy Management

Demand response has recently become an essential means for businesses to reduce production costs in industrial chains. Meanwhile, the current industrial chain structure has also become increasingly complex, forming new characteristics of multiplex networked industrial chains. Fluctuations in real-time electricity prices in demand response propagate through the coupling and cascading relationships within and between these network layers, resulting in negative impacts on the overall energy management cost. However, existing demand response methods based on reinforcement learning typically focus only on individual agents without considering the influence of dynamic factors on intra and inter-network relationships. This paper proposes a layered temporal spatial graph attention (LTSGA) reinforcement learning algorithm suitable for demand response in multiplex networked industrial chains to address this issue. The algorithm first uses long short-term memory to learn the dynamic temporal characteristics of electricity prices for decision-making. Then, LTSGA incorporates a layered spatial graph attention model to evaluate the impact of dynamic factors on the complex multiplex networked industrial chain structure. Experiments demonstrate that the proposed LTSGA approach effectively characterizes the influence of dynamic factors on intra- and inter-network relationships within the multiplex industrial chain, enhancing convergence speed and algorithm performance compared with existing state-of-the-art algorithms.