Methodology for Aggregated Distribution System Representation in Dynamic Performance Assessment of Transmission Networks
J. Edo Puig (TU Delft - Electrical Engineering, Mathematics and Computer Science)
J.L. Rueda Torres – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
J.A. Aviles Cedeño – Mentor
Jorrit Bos – Mentor (TenneT TSO B.V.)
Zian Qin – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
F.A. Muñoz Muñoz – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Transmission system-level studies face a growing challenge: the distribution networks they interface with are no longer passive. The rapid expansion of rooftop solar, heat pumps, electric vehicles, and other power-electronic-interfaced devices has fundamentally changed how distribution systems behave during disturbances, making the simplified load representations that have long served as industry standard increasingly unreliable.
This thesis develops a methodology for constructing location-specific and time-dependent aggregated load models suitable for transmission-level dynamic stability studies. Working within the Composite Load Model (CLM) framework, residential, industrial, and commercial load profiles are built from the ground up using end-use decomposition — combining publicly available Dutch and proxy datasets to derive time-varying CLM parameters across different sectors. The resulting models reflect genuine differences in load composition driven by time of day, season, and sector-specific process characteristics.
A sensitivity analysis of the CLM reveals that induction motor dynamics dominate voltage recovery behaviour under fault conditions. Static load fractions improve post-fault recovery, while power-electronic components are shown to influence system behaviour mainly through their protection and reconnection logic rather than intrinsic electrical properties. Multivariate analysis confirms that these effects are strongly nonlinear and cannot be adequately captured by first-order regression models.
To manage the dimensionality of temporal variability, K-means clustering is applied to compress thousands of hourly operating states into a small number of representative CLM parameter sets. Finally, a node importance assessment based on the Dutch transmission network admittance and impedance matrices is developed, showing that electrical distance to generating units outperforms purely topological centrality measures as a predictor of dynamic voltage rate of change. Together, these contributions provide a scalable and reproducible framework for parameterising distribution system equivalents in large-scale transmission simulations.