Carbon emission and system flexibility in integrated energy systems: Optimal scheduling via a Q-learning-based particle swarm optimization

With the improvement of energy system flexibility demand and the promotion of carbon neutrality strategy, the multi-energy synergy characteristics of the integrated energy system (IES) make it an essential direction of energy optimization scheduling. Aiming at the problem that the traditional integrated energy system does not fully consider the system flexibility and carbon emission constraints simultaneously, an optimal scheduling method is proposed to integrate flexible supply and demand balance constraints and carbon emission constraints. Firstly, considering the flexibility of the system and carbon emission constraints, an integrated energy system scheduling model integrating flexibility, supply and demand balance constraints, and carbon emission constraints is constructed to minimise economic costs. Secondly, a particle swarm optimization algorithm based on Q-learning (QPSO) is designed to address the proposed scheduling model. Finally, the simulation results of different scenarios show that the case considering both flexibility and carbon emission constraints significantly reduces the system’s operating cost and realizes low-carbon operation. The comparative experiments of the algorithm verify that the algorithm has better solution properties for solving this problem.