Low-carbon scheduling of mobile energy storage in distribution networks based on an equivalent reconfiguration method

Abstract

Under the context of low-carbon power systems, the integration of high-penetration renewable energy and mobile energy storage systems (MESS) presents new challenges for distribution network scheduling, primarily in the coupling of power and transportation networks and the complexity of allocating users' carbon emission responsibilities. To address these challenges, this study proposes a bi-level optimization model that combines demand response mechanisms and carbon flow theory for the low-carbon scheduling of MESS. The upper-level model optimizes the global scheduling strategy of distribution network operators, while the lower-level model captures the dynamic demand response behavior of users, achieving collaborative optimization among multiple stakeholders. Using an equivalent reconfiguration method, the coupling issue between the power grid and transportation network is transformed into a pure distribution network problem. Furthermore, carbon flow theory and the Shapley value method are employed to analyze carbon emission distribution and allocate carbon responsibility on the load side. Simulation results based on the IEEE-33 node distribution network and a simple transportation network show that the proposed model can reduce system carbon emissions by 21.14 %, lower user costs by 5.2 %, and increase operator revenue by 10.21 %. These findings validate the model’s ability to balance economic benefits and low-carbon operational goals, providing a practical and effective solution for the optimal scheduling of distribution networks with high renewable energy penetration.