Multi-agent optimal scheduling for integrated energy system considering the global carbon emission constraint

In a multi-regional integrated energy system (MIES), optimal scheduling under random renewable supply and user demand is crucial to promote the process of carbon neutrality. Further, the total carbon emission of multiple regions is expected to strictly restricted under a threshold, while intensifying the complex coupling of multiple agents. To address the optimal dispatching problem, we establish a typical MIES model with the global carbon emission constraint, which is formulated as a partially observable Markov decision-making process (POMDP). Then we propose an improved multi-agent deep deterministic policy gradient (MADDPG) method, which utilizes a centralized training and decentralized execution (CTDE) framework to effectively improve the multi-agent stationarity. Moreover, an attention mechanism is employed to enhance the efficiency of communication and coordination among agents. Experiments are carried out on multi-regional datasets, and the results certify that the proposed algorithm can decrease system operation costs, reduce carbon emissions, and speed up the convergence of the multi-agent system.