Impact of Phase Selection on Accuracy and Scalability in Calculating Distributed Energy Resources Hosting Capacity

TL;DR

This paper investigates how the choice of DER connection phase affects accuracy and scalability in computing three-phase hosting capacity ($HC$) and dynamic operating envelopes ($DOE$) using OPF formulations. It contrasts nonlinear current–voltage OPF, its linearised LinDist3Flow variant, and multiple phase-selection strategies (including binary-augmented MINLP and binary-free heuristics) within the ppOPF tool applied to two LV networks. Key findings show that optimal phase selection, when solved with binary variables, yields higher DER hosting capacities and DOE limits (up to 14 MW in some cases), but at substantial computational cost, especially for DOE on larger networks; linearised formulations offer scalability but may overestimate limits and reduce reliability near network constraints. The work provides an open-source, reproducible framework and demonstrates that accurate phase-aware modelling is crucial for planning and day-ahead operation, guiding tool choice and solver selection for DSOs.

Abstract

Hosting capacity (HC) and dynamic operating envelopes (DOEs), defined as dynamic, time-varying HC, are calculated using three-phase optimal power flow (OPF) formulations. Due to the computational complexity of such optimisation problems, HC and DOE are often calculated by introducing certain assumptions and approximations, including the linearised OPF formulation, which we implement in the Python-based tool ppOPF. Furthermore, we investigate how assumptions of the distributed energy resource (DER) connection phase impact the objective function value and computational time in calculating HC and DOE in distribution networks of different sizes. The results are not unambiguous and show that it is not possible to determine the optimal connection phase without introducing binary variables since, no matter the case study, the highest objective function values are calculated with mixed integer OPF formulations. The difference is especially visible in a real-world low-voltage network in which the difference between different scenarios is up to 14 MW in a single day. However, binary variables make the problem computationally complex and increase computational time to several hours in the DOE calculation, even when the optimality gap different from zero is set.

Figures (8)

• Figure 1: Residential CIGRE LV feeder
• Figure 2: Residential real-world LV feeder
• Figure 3: Voltage magnitude in HC calculation, CS 1-1, S1 - Real-world feeder
• Figure 4: Voltage magnitude in HC calculation, CS 1-1, S2-S3 - Real-world feeder
• Figure 5: Voltage magnitude in HC calculation, CS 1-1, S4-S5 - Real-world feeder