Dynamic Ancillary Services: From Grid Codes to Transfer Function-Based Converter Control

TL;DR

The paper tackles the challenge of implementing dynamic ancillary services specified by time-domain grid-code curves in converter-based generators. It develops a systematic method to map piece-wise linear time-domain curves into a parametric rational transfer function matrix $T_ ext{des}(s,\boldsymbol{\alpha})$ with decoupled blocks $T_ ext{des}^ ext{fp}$ and $T_ ext{des}^ ext{vq}$, enabling frequency- and voltage-domain control. A four-step translation procedure—segment decomposition, Laplace transform, Padé approximation, and summation—yields a stable, implementable $T_ ext{des}(s,\boldsymbol{\alpha})$ that can be realized with PI-based matching control. Case studies demonstrate that TF-based control satisfies grid-code and device-level constraints and outperforms conventional virtual inertia and droop schemes in compliance tests, offering a scalable path for future grid-code formulations.

Abstract

Conventional grid-code specifications for dynamic ancillary services provision such as fast frequency and voltage regulation are typically defined by means of piece-wise linear step-response capability curves in the time domain. However, although the specification of such time-domain curves is straightforward, their practical implementation in a converter-based generation system is not immediate, and no customary methods have been developed yet. In this paper, we thus propose a systematic approach for the practical implementation of piece-wise linear time-domain curves to provide dynamic ancillary services by converter-based generation systems, while ensuring grid-code and device-level requirements to be reliably satisfied. Namely, we translate the piece-wise linear time-domain curves for active and reactive power provision in response to a frequency and voltage step change into a desired rational parametric transfer function in the frequency domain, which defines a dynamic response behavior to be realized by the converter. The obtained transfer function can be easily implemented e.g. via a PI-based matching control in the power loop of standard converter control architectures. We demonstrate the performance of our method in numerical grid-code compliance tests, and reveal its superiority over classical droop and virtual inertia schemes which may not satisfy the grid codes due to their structural limitations.

Figures (11)

• Figure 1: Examples of piece-wise linear time-domain capability curves to provide dynamic ancillary services in different grid codes (simplified).
• Figure 2: Normalized unit step response time-domain capability curve of a general grid-code specification with linear curve segments.
• Figure 3: Unit step response of the rational transfer functions (a) $T_\mathrm{des}^\mathrm{fp}(s,\alpha)$ and (b) $T_\mathrm{des}^\mathrm{vq}(s,\alpha)$ for different orders $n$ of the Padé-approximation.
• Figure 4: Unit step responses of (a) $T_\mathrm{des}^\mathrm{fp}(s,\alpha)$ and (b) $T_\mathrm{des}^\mathrm{vq}(s,\alpha)$ for the two boundary scenarios of the parameter choice $\alpha$: satisfying minimum grid-code requirements vs. exploiting maximum device-level limitations.
• Figure 5: One-line diagram of three-phase converter interface with matching control implementation. The converter model is in per unit, where $\mathcal{Z}_\mathrm{f}=L_\mathrm{f}\mathcal{J}_2+R_\mathrm{f}\mathcal{I}_2$ with $\mathcal{J}_2=[0\,\,\text{-}1;1\,\,0]$ and $\mathcal{I}_2=[1\,\,0;0\,\,1]$, and the associated parameters are provided in \ref{['tab:converter_parameters']}.

Theorems & Definitions (2)

• Remark 1
• Remark 2