Study of hosting capacity for harmonic distortion and resonance in transmission networks under large-scale wind and PV integration: a Python-based Monte Carlo simulation analysis

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The increasing penetration of converter-interfaced renewable energy sources (RES) significantly alters the harmonic behavior and frequency-dependent impedance characteristics of transmission networks, posing challenges to power quality and system reliability. This paper proposes a probabilistic harmonic hosting capacity approach for transmission-level systems by integrating Monte Carlo–based uncertainty propagation with frequency-domain modeling in DIgSILENT PowerFactory. Unlike previous studies, the proposed method explicitly incorporates stochastic variations in operating conditions and resonance-sensitive impedance characteristics at the transmission level. The methodology is applied to a 110 kV transmission network, considering multiple points of common coupling with wind and photovoltaic plants under different penetration levels. The results indicate that smaller wind installations maintain total harmonic distortion (THD) within acceptable limits (1.51%), allowing additional integration capacity, whereas larger wind plants approach and exceed the 2% limit (2.56%), indicating reduced hosting capacity. In contrast, PV plants exhibit higher distortion levels (3.19%) and lower hosting margins, reflecting stronger sensitivity to impedance variations and resonance effects. Overall, the study demonstrates that harmonic hosting capacity is strongly governed by converter characteristics, installed capacity, and resonance-sensitive impedance behavior, and enables a risk-based probabilistic assessment of harmonic compliance for planning transmission systems with high shares of inverter-based generation.

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