Computationally Efficient Spline-Based Modeling of DER Dynamics for Voltage Stability in Active Distribution Networks

TL;DR

This paper introduces a spline-based data-driven modeling method for DER dynamics that significantly reduces computational effort, enabling real-time voltage stability analysis in active distribution networks.

Contribution

It presents a novel approach using B-splines to efficiently estimate low-order differential equations for DER dynamics, improving speed while maintaining high accuracy.

Findings

Achieved 98.74% goodness-of-fit, comparable to existing methods.

Reduced computational time by a factor of 4.8, enabling real-time application.

Validated model suitable for integration into power dispatch systems.

Abstract

The increasing integration of Distributed Energy Resources (DERs) into power systems necessitates the accurate representation of their dynamic behavior at the transmission level. Traditional electromagnetic transient models (EMT), while effective, face scalability challenges due to their reliance on detailed system information. Data-driven approaches, such as System Identification (SysID), offer a promising alternative by modeling system dynamics without detailed system knowledge. However, SysID and similar methods are computationally intensive, requiring the computation of complex ordinary differential equations (ODEs) or transfer functions estimation. This makes them less effective for real-time operation. We therefore propose a novel data-driven approach that simplifies the modeling of DERs dynamics by leveraging B-splines to transform discrete system data into continuous differentiable functions. This enables the estimation of lower order linear ordinary differential equations with simple linear regression to represent the underlying dynamics at a very low computational cost. Furthermore, the extracted dynamic equations are discretized by the backward Euler method for potential integration into discrete-time power dispatch models. Validation results indicate a goodness-of-fit (GoF) of 98.74%, comparable to the 99.03% GoF of the SysID method, yet, 4.8 times faster. Our proposed model's execution time of less than one minute makes it more suitable for real-time applications in power system operations.