Two-stage stochastic optimal scheduling for multi-microgrid networks with natural gas blending with hydrogen and low carbon incentive under uncertain envinronments

Multi-energy multi-microgrid (MMG) networks have the advantage of integrating various energy resources and promoting energy utilization efficiency. To further improve the economy and low-carbon operation of networks, this paper decarbonizes the multi-energy MMG network, introduces the hydrogen-natural gas blending tech-nology, and establishes a stochastic planning model of a multi-energy MMG network considering natural gas blending with hydrogen and low-carbon incentive. Firstly, the hydrogen energy storage (HES) model is devel-oped by blending hydrogen into natural gas and supplying it to the gas unit. Considering the incentive mech-anism in the case of surplus carbon allowances, a stepped carbon trading mechanism with a variable incentive coefficient is designed. Secondly, to effectively and simply measure the sources and load uncertainties in the MMG network, Conditional Value-at-Risk (CVaR) is calculated by the interaction cost between the MMG and the distribution network. A two-stage stochastic planning model for the MMG network is established based on the Nash negotiation theory to minimize the total cost of the MMG network in the first stage, allocate the benefits, and maximize the benefits of each microgrid in the second stage. Finally, by dynamically correcting the penalty factor through the quantitative relationship between the original residual and the pairwise residual, an improved alternating direction multiplier method (ADMM) based on the dynamic adaptive penalty factor is constructed to realize the distributed solution of the scheduling model. Through the comparative analysis of different cases of the arithmetic system test, it is verified that the proposed model can maximize each microgrid's benefits while ensuring the MMG network's lowest cost, promoting renewable energy consumption, and reducing carbon emissions.