PACR: Parameter-Optimized AC Power Flow Restoration for AC Feasible DCOPF Dispatch
For power system operators, this method provides a computationally efficient way to ensure DC dispatches satisfy AC feasibility, addressing a key bottleneck in real-time operations.
The paper proposes a differentiable AC power-flow restoration method that maps DC optimal power flow dispatches to AC-consistent operating points, using trained parameters for slack distribution and reactive-power regulation. On a 9,241-bus system, it improves cost difference by 80% over conventional recovery and reduces solving time by 75% compared to ACOPF.
The DC optimal power flow is widely used in power system operations because of its computational efficiency and scalability. However, DC dispatches are not guaranteed to satisfy the nonlinear AC power-flow equations or associated operational limits. This paper develops a parameterized, differentiable AC power-flow restoration method for mapping DC dispatches to AC-consistent operating points. The method incorporates distributed slack for active-power balancing and PV/PQ switching for reactive-power regulation, both implemented using smooth differentiable surrogates with tunable parameters, including slack participation factors, voltage setpoints, and regulation steepness. These parameters are trained offline by differentiating through the AC restoration equations using the implicit function theorem. Once trained, the optimized parameters are fixed and used directly during AC power-flow recovery from DC dispatches. The approach is evaluated on IEEE, ACTIVSg, and PEGASE test systems using setpoints computed by standard DC optimal power flow. Results show that the optimized restoration method improves AC feasibility recovery across various systems relative to conventional single-slack AC power-flow recovery. On the 9,241-bus case, the optimized method improves cost difference by 80% relative to the conventional recovery baseline and improves solving time relative to ACOPF by 75%.