An integrated energy system includes multiple sub-systems of electricity

heating

and gas. It is difficult to achieve dynamic dispatch of multi-energy flows due to challenges in establishing detailed mathematical models and the impacts of uncertain factors. Reinforcement learning has the ability to fit non-convex and nonlinear problems. It avoids simplifying detailed mathematical models and complex

uncertain factors in the solution process

like traditional methods

which provides a new idea for achieving dynamic energy dispatch. However

existing research generally relies on soft constraints

such as reward function penalties

to obtain the dispatch strategy

which may violate the system's operational safety. This paper ensures the security of dynamic energy dispatch strategies by adding two constraint mechanisms on reinforcement learning. First

boundary conditions of the action security domain are established through continuous interaction and feedback between the agent and the environment. It can effectively restrict infeasible actions. Second

the strategy is constrained by the truncation function in the trust domain

and strategy update is always kept within a controllable range to improve convergence and stability of the IES dynamic energy dispatch model. Finally

simulation results indicate that the proposed method can effectively constrain agent action selections and strategy updates during the dynamic energy dispatch process. This is of great significance to ensure the safe and stable operation of the system.