Optimal Dynamic Ancillary Services Provision Based on Local Power Grid Perception

TL;DR

This work presents a systematic closed-loop framework, called perceive-and-optimize (P&O), for delivering optimal dynamic ancillary services from converter-interfaced generators. It first identifies a local grid dynamic equivalent $G(s)$ at the PCC using online black-box identification, then encodes desired frequency and voltage responses as a parametric transfer function matrix $T_ ext{des}(s,\alpha)$ and optimizes $\alpha$ to minimize a weighted $\mathcal{H}_2$ performance criterion for the closed-loop interconnection. The approach enforces grid-code and device-level constraints, handles time-varying grid conditions through repeated perception/optimization cycles, and is demonstrated with numerical EMT case studies on a modified Kundur two-area system showing substantial improvements in RoCoF, frequency nadir, and voltage overshoot over baseline open-loop prescriptions. The results support the potential of P&O to enable high-performing, grid-aware dynamic ancillary services with converter-based assets and motivate future market design and multi-converter coordination research.

Abstract

In this paper, we propose a systematic closed-loop approach to provide optimal dynamic ancillary services with converter-interfaced generation systems based on local power grid perception. In particular, we structurally encode dynamic ancillary services such as fast frequency and voltage regulation in the form of a parametric transfer function matrix, which includes several parameters to define a set of different feasible response behaviors, among which we aim to find the optimal one to be realized by the converter system. Our approach is based on a so-called "perceive-and-optimize" (P&O) strategy: First, we identify a grid dynamic equivalent at the interconnection terminals of the converter system. Second, we consider the closed-loop interconnection of the identified grid equivalent and the parametric transfer function matrix, which we optimize for the set of transfer function parameters, resulting in a stable and optimal closed-loop performance for ancillary services provision. In the process, we ensure that grid-code and device-level requirements are satisfied. Finally, we demonstrate the effectiveness of our approach in different numerical case studies based on a modified Kundur two-area test system.

Figures (20)

• Figure 1: Exemplary active power time-domain capability curve for FCR provision after a frequency step change european2016commission. The minimum curve requirement (i.e., the lower bound of the open-loop response curve) is indicated in red.
• Figure 2: Sketch of a grid-connected reserve unit to provide closed-loop optimal dynamic ancillary services in the form of a desired rational transfer function matrix $T_\mathrm{des}(s,\alpha^\star)$. The identified grid dynamic equivalent is captured by $G(s)$.
• Figure 3: Examples of piece-wise linear time-domain grid-code curves (simplified) and their approximation as rational parametric transfer functions.
• Figure 4: Examples of dynamic ancillary services products (simplified) encoded as rational parametric transfer functions.
• Figure 5: Closed-loop interconnection of the identified grid dynamic equivalent $G(s)$ with the rational parametric transfer function matrix $T_\mathrm{des}(s,\alpha)$ which is optimized for dynamic ancillary services provision.

Theorems & Definitions (3)

• Remark 1
• Remark 2
• Remark 3