To address challenges in the operation of multi-energy microgrids

such as the single hydrogen energy utilization mode and uncertainties in supply and demand

this paper proposes a deep reinforcement learning-based low-carbon economic dispatch method for multi-energy microgrids considering dynamic hydrogen blending in gas. First

a diversified hydrogen utilization model with dynamically adjustable thermoelectric ratio and hydrogen blending ratio is constructed to enhance system operational flexibility. Next

a low-carbon economic dispatch model of the multi-energy microgrid incorporating a tiered carbon trading mechanism is formulated and transformed into a reinforcement learning framework under a stochastic environment. An action space with safety guarantees based on limit truncation is designed to ensure that agent actions remain within safe operational ranges. Finally

the dispatch problem is solved using the twin delayed deep deterministic policy gradient algorithm

realizing adaptive response of the agent to uncertainties under multi-energy coupling constraints. Case study results demonstrate that the proposed method can effectively address uncertainties in supply and demand

significantly reducing both operational costs and carbon emissions.