Impact of Full-Load and Daily Load-Curve Representations on PSO-Based EV–DER Distribution Feeder Assessment

Abstract

Electric-vehicle (EV) charging can intensify voltage drops, feeder currents, and technical losses in radial distribution networks, particularly when charging coincides with periods of high residential demand. This paper investigates how the adopted load representation affects reported EV–DER feeder performance when rule-based vehicle-to-grid (V2G) support and renewable distributed energy resources are considered. Particle swarm optimization (PSO) is used for DG siting, sizing, and power-factor selection at the full-load planning condition, after which the selected DG plans are evaluated using a 24 h, 15 min backward/forward sweep (BFS) time-series simulation. Two load representations are compared using the same EV, V2G, and DER models: a constant full-load representation in which the nominal base demand is maintained over all 96 intervals, and a daily load-curve (DLC) representation in which the base demand varies over time. Across the three examined scenarios, the DLC representation reduces daily loss energy by approximately 29.2–36.0% relative to the constant full-load case, while changes in peak real-power loss and peak source current remain comparatively smaller. The results demonstrate that load representation has a substantial influence on energy-based conclusions even when the feeder, DG plan, and operational models are unchanged. The study therefore supports time-series DLC assessment for energy-based reporting and transparent comparison of EV–DER distribution-feeder performance.