Two-Stage Robust Optimal Configuration of Multi-Energy Microgrid Considering Tiered Carbon Trading and Demand Response

To further explore the potential of CO2 emission reduction and optimize the cost of microgrids, a two-stage robust optimization configuration method for multi-energy microgrids is proposed, considering uncertainty, tiered carbon trading, and demand response. The model incorporates power-to-gas (P2G) and carbon capture and storage (CCS) technologies to enhance renewable energy utilization and reduce carbon emissions. A tiered carbon trading mechanism is introduced to penalize high emissions, while incentive-based demand response is employed to adjust load profiles and improve economic performance. The optimization model is formulated as a two-stage robust problem: the outer stage minimizes annual investment and maintenance costs, while the inner stage identifies the worst-case scenario under uncertainties. The model is solved using the Column-and-Constraint Generation (C&CG) algorithm and implemented in MATLAB R2022b with the Gourbi solver. Simulation results demonstrate that the proposed approach reduces carbon emissions by up to 31.9% and total costs by 3.28% compared to conventional configurations, while increasing the penetration of renewable energy. This study provides practical reference for the low-carbon and economic planning of microgrids with P2G and CCS integration.