Accelerated Probabilistic Power Flow in Electrical Distribution Networks via Model Order Reduction and Neumann Series Expansion

This paper develops a computationally efficient algorithm which speeds up the probabilistic power flow (PPF) problem by exploiting inherently low-rank nature of voltage profile in electrical distribution networks. The is accordingly termed Accelerated-PPF (APPF), since it can accelerate “any” sampling-based PPF solver. As APPF runs, concurrently generates low-dimensional subspace orthonormalized solution vectors. used to construct and update reduced order model (ROM) full nonlinear system, resulting highly simulation for future profiles. When constructing updating subspace, must still be solved on system. In computation these solutions, Neumann expansion modified Jacobian implemented. Applicable when load bus injections are small, this allows considerable speed system solves during standard Newton iterations. test results, from experiments run IEEE 8500-node feeder, finally presented.