Three-Phase Infeasibility Analysis for Distribution Grid Studies

TL;DR

This paper introduces a three-phase infeasibility analysis method for distribution grids that identifies weak points by optimizing slack current sources, improving planning and fault detection in complex unbalanced systems.

Contribution

It develops a novel optimization-based approach that detects infeasible power flow cases and localizes deficiencies using sparsity-promoting objectives, applicable to large unbalanced networks.

Findings

Effective in identifying weak locations in large unbalanced networks

Handles infeasible power flow scenarios by introducing slack currents

Localizes power deficiencies using sparse optimization techniques

Abstract

With the increase of distributed energy resources in the distribution grid, planning to ensure sufficient infrastructure and resources becomes critical. Planning at the distribution level is limited by the complexities of optimizing unbalanced systems. In this paper we develop a three-phase infeasibility analysis that identifies weak locations in a distribution network. This optimization is formulated by adding slack current sources at nodes in the system and minimizing their norm subject to distribution power flow constraints. Through this analysis we solve instances of power flow that would otherwise be infeasible and diverge. Under conditions when power flow is feasible, our approach is equivalent to standard three-phase power flow; however, for cases where power flow fails, the nonzero slack injection currents compensate for missing power to make the grid feasible. Since an uncountable number of injected currents can provide feasibility, we further explore the optimization formulation that best fits the solution objective through use of both a least squares and an L1 norm objective. Our L1 norm formulation localizes power deficient locations through its inherent sparsity. We show the efficacy of this approach on realistic unbalanced testcases up to 8500 nodes and for a scenario with a high penetration of electric vehicles.