Synergy-Driven Optimal Scheduling of Integrated Energy Systems Under Carbon Trading Mechanism

Facing the trend of global low-carbon transformation, this study focuses on the collaborative optimization of the carbon trading mechanism and integrated energy systems (IES). An optimization scheduling model considering the coupling of electricity-heat-gas multi-energy flows and equipment operation constraints is constructed, integrating carbon market price signals with demand response strategies. An improved particle swarm optimization algorithm is proposed to enhance multi-objective optimization efficiency. Case studies show that the carbon trading mechanism reduces system operation costs by 3.95% without increasing carbon emissions, and combining demand response further reduces costs by 12.86%. The research confirms that the synergy between the carbon market and the demand side can balance the economic efficiency and low-carbon performance of the system, providing a new path for energy system optimization.