Power Load Data

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.