Low-carbon economic dispatch of multi-energy microgrids considering dynamic hydrogen blending in gas based on reinforcement learning
DOI：10.19783/j.cnki.pspc.250374
Key Words:multi-energy microgrid  dynamic hydrogen blending in gas  diversified hydrogen utilization  deep reinforcement learning  low-carbon economy dispatch
Author NameAffiliation
ZHAO Liyuan State Key Laboratory of Intelligent Power Distribution Equipment and System, Hebei University of Technology, Tianjin 300401, China 
ZHANG Pengju State Key Laboratory of Intelligent Power Distribution Equipment and System, Hebei University of Technology, Tianjin 300401, China 
ZHAO Zhigang State Key Laboratory of Intelligent Power Distribution Equipment and System, Hebei University of Technology, Tianjin 300401, China 
LI Musong State Key Laboratory of Intelligent Power Distribution Equipment and System, Hebei University of Technology, Tianjin 300401, China 
YANG Junbo State Key Laboratory of Intelligent Power Distribution Equipment and System, Hebei University of Technology, Tianjin 300401, China 
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Abstract: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.
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