Three-phase infeasibility analysis for distribution grid studies

• Published in: Electric power systems research Vol. 212; p. 108486
• Main Authors: Foster, Elizabeth, Pandey, Amritanshu, Pileggi, Larry
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2022
• Subjects: Distribution system; Equivalent circuit approach; L1-norm sparse solution; Nonlinear optimization; Three-phase unbalanced networks; Distribution system; L1-norm sparse solution; Three-phase unbalanced networks; Equivalent circuit approach; Nonlinear optimization
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2022.108486

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. •A novel three-phase system optimization that is applicable to unbalanced systems.•Robust and scalable approach that is demonstrated up to 8500 nodes.•Application of slack variables with a physical meaning that provides grid insight.•An L1 norm formulation localizes solutions for grid improvement recommendations.