Data for review of submission TII-24-4657

Existing AC/DC power flow computations necessi- tate sequential convergence-oriented trial-and-error under vari- ous DC control modes, rising computational burden. This paper thus proposes a physics-guided multi-agent graph learning (PG- MAGL) method towards real-time power flow analysis with DC control mode adaptation. The tailored graph structure with built- in DC control modes and state variables is firstly advanced to ensure topology adaptability. Then, MAGL is proposed to enable adaptive jump over DC control modes. The trick is to organize multi-agent to parameterize power flow solutions under various DC control modes, and set aside trigger signals according to the operational violations of converters for the agent switching to the follow-up agent. To clarify the trigger signals, an augmented Lagrangian method-based PG-MAGL method is finally designed. It relaxes the control boundaries into the violation minimizers and enforces other constraints, such that DC control switching can be identified by the only violation signal. Utilizing inductive biases to rectify experiential biases in pure data-driven models, PG-MAGL enables precise inference of DC control mode feasibility. Case study shows that, relative to the other 7 data-driven rivals, only the proposed method matches the performance of the model-based baseline, also beats it in efficiency beyond 10 times.

This is the data asked by reviewer during the round-2 review.