Dynamic electricity-carbon factors driven fast day-ahead coordination and optimization of source-load-storage in distribution networks with a white box accelerator

Achieving carbon neutrality in power systems necessitates advanced coordination mechanisms between grid operators and demand-side aggregators. However, existing bi-layer optimization models face dual challenges in aligning low-carbon incentives with computational efficiency. Two key limitations hinder current models: (i) the absence of efficient low-carbon guidance mechanisms for aggregators, and (ii) the sluggish solving speed due to frequent information interactions between upper and lower layers. To address these challenges, we propose a dynamic electricity-carbon factor-driven coordination framework integrating a dendritic network-based white box accelerator. The upper layer minimizes power fluctuation of distribution networks via gas turbines and hydrogen storage, while the lower layer optimizes costs (electricity and carbon) of electric vehicle aggregators through charge-discharge scheduling. Analogous to the time-of-use electricity price for economic operation, a dynamic carbon emission factor is constructively introduced as a pivotal driven factor for low-carbon energy consumption. Furthermore, to mitigate frequent interactions and boost solving speed, a novel white box accelerator based on a dendritic net is developed. Finally, two extended test systems (i.e., IEEE 33-bus system and IEEE 69-bus system) are executed to demonstrate the feasibility and effectiveness of the proposed strategy. Leveraging the white box accelerator yields an average reduction in computation time by 43.6 %. Moreover, the power variance and the total cost of electric vehicle aggregators can be reduced by up to 90.9 % and 88.7 %, respectively.