Integrated energy system (IES) enables the supply of multiple forms of energy,but the large amount of carbon dioxide it emitted affects the surrounding environment.Here,an optimal scheduling approach based on Twin Delayed Deep Deterministic Policy Gradient (TD3) is proposed for low-carbon economic scheduling of IES.First,taking the minimum operation cost as the objective function,an IES model with multiple complementary energies of electricity,heat and cold is established considering carbon capture technology and power-to-gas technology.Second,a carbon trading mechanism is introduced to stimulate the enthusiasm of energy conservation and emission reduction under optimal scheduling.Then,according to the reinforcement learning framework,the state space,action space and reward function of the optimization model are designed,and the agents in the TD3 algorithm are used to interact with the environment to explore strategies and learn the IES operation strategies.Finally,the historical data are used to train the agents of TD3 algorithm,and the linear programming and particle swarm optimization are compared under different scenarios.The results show that the proposed approach can reduce the IES carbon emission and operating cost,thus realizing the low-carbon economic dispatch of the integrated energy system.