Optimal Low-Carbon Scheduling for Smart Microgrids With Dynamic Thermal Capacity Constraints

This study aims to integrate electric vehicles, photovoltaic and battery energy storage systems, and distribution network information in a microgrid to achieve decarbonized optimal operation. Under the different operating states of distribution networks, the paper proposes a decarbonized two-stage deeply integrated operational mode for a photovoltaic, battery energy storage system, and electric vehicles integrated microgrid, incorporating the electricity market to optimize overall revenue. In the optimization process, this paper introduces statistical node carbon emission factors and dynamic thermal capacity constraints to explore microgrids’ decarbonized potential and maximize system equipment’s utilization efficiency. This study proposes a two-stage day-ahead robust optimization framework for the microgrids, integrating tri-level min-max-min modeling to address multidimensional uncertainties through parameterized uncertainty sets to balance conservatism and optimize costs under worst-case scenarios. This work develops an adaptive threshold tightening mechanism for Column-and-Constraint Generation algorithms to balance computational efficiency and solution precision. Additionally, this paper proposes an adaptive dynamic pricing framework that integrates electric vehicle users’ cost-adaptive behavior with supply-demand fluctuations, using incentive-driven temporal optimization to maintain real-time grid equilibrium and enhance microgrid flexibility. Simulation results show that the proposed approach improves system profitability by up to 55.2% while enhancing transformer safety and operational flexibility.