Dynamic carbon emission flow tracking with a new optimal storage schedule for power grids

With the global drive toward carbon neutrality, the deep integration of variable renewable energy sources (VRES) and energy storage systems (ESS) has rendered traditional static carbon accounting methods insufficient to capture the spatiotemporal dynamics of carbon flows in power grids, highlighting the critical need for accurate tracking and equitable allocation of carbon responsibility. This paper proposes a dynamic carbon emission flow tracking framework tailored to the Jibei power grid in China, integrating a dynamic generator carbon intensity model and power transfer distribution factor (PTDF) enhanced network tracking. The framework also includes an optimal carbon allocation matrix and a predictive ESS scheduling model that links the carbon intensity during charging periods to emissions during discharging. Validated using a modified IEEE 30-bus system representing five cities in the Jibei region, results show that the dynamic model achieves a 15.2% higher accuracy than static methods, optimal ESS scheduling reduces system-wide emissions by 8.7%, and the framework maintains over 93% tracking accuracy under extreme uncertainties. Moreover, the framework quantifies inter-city carbon transfers and allocates responsibilities among grid participants, thus enabling real-time monitoring. It provides a robust foundation for carbon-aware dispatch and nodal carbon pricing, supporting the transition toward carbon-neutral power systems.