Multi-area state estimation for active distribution networks under multiple uncertainties: An affine approach

Abstract

An enormous challenge for the state estimation (SE) process in large-scale distribution networks lies in how to deal with multiple uncertainties and the increasing computational tasks simultaneously. This paper aims to address the issue and propose a multi-area architecture for perceiving system status with uncertain inputs. The first step involves the introduction of a novel Louvain-flow-based partition structure, which effectively divides large-scale unbalanced distribution networks into several sub-areas. Subsequently, an affine state estimation (ASE) model, employing the Neumann series method, is formulated for each sub-area, taking into account uncertainties in system measurements and line parameters. Finally, the interior-point filter line-search algorithm combined with consensus theory is utilized to solve all local ASE models, and the interaction of boundary state between adjacent sub-areas is focused and completed, to output the global state results of the distribution network precisely. Case studies are presented to verify that the proposed distributed ASE model yields obvious advantages in tracking system uncertainties by contrast with the existing SE model.