Optimal Dynamic Ancillary Services Provision for Converter-Interfaced Generation Based on Local Power Grid Perception

Core Concepts

The core message of this article is to propose a systematic closed-loop approach to provide optimal dynamic ancillary services, such as fast frequency and voltage regulation, with converter-interfaced generation systems based on local power grid perception.

Abstract

The article proposes a systematic closed-loop approach to provide optimal dynamic ancillary services with converter-interfaced generation systems based on local power grid perception. The key aspects are:

Encoding dynamic ancillary services as a parametric transfer function matrix Tdes(s,α) in the frequency domain, which includes several parameters to define a set of different feasible response behaviors.

Adopting a "perceive-and-optimize" (P&O) strategy:

First, identifying a grid dynamic equivalent G(s) at the interconnection terminals of the converter system.
Second, optimizing the parameters α of Tdes(s,α) to achieve an optimal and stable closed-loop performance of the entire power grid response, while ensuring grid-code and device-level requirements are satisfied.

Demonstrating the effectiveness of the approach in numerical case studies based on a modified Kundur two-area test system. The results show that the proposed P&O strategy can significantly improve the overall closed-loop power grid performance compared to just satisfying the minimum open-loop grid-code requirements.

Stats

The active power provision for frequency containment reserve (FCR) has to satisfy the following grid-code requirements:
0 ≤ tfcr_i ≤ tfcr_i,max
tfcr_i ≤ tfcr_a ≤ tfcr_a,max
|Δpfcr| ≤ (tfcr_a - tfcr_i) / Rp_max
The reactive power provision for voltage regulation has to satisfy the following grid-code requirements:
tvq_90 ≤ tvq_90,max
tvq_100 ≤ tvq_100,max

Quotes

"Today's grid-code specifications for dynamic ancillary services provision (e.g., fast frequency and voltage regulation) with converter-based generation units are typically defined by a prescribed time-domain step-response characteristic."
"Future grid-code specifications for dynamic ancillary services provision may have to respect distinguishably the local grid dynamic characteristics in a closed-loop manner."

Key Insights Distilled From

Optimal Dynamic Ancillary Services Provision Based on Local Power Grid Perception

by Vere... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2401.17793.pdf

Deeper Inquiries

How can the proposed P&O strategy be extended to handle more complex multi-input multi-output specifications for dynamic ancillary services

The proposed "perceive-and-optimize" (P&O) strategy can be extended to handle more complex multi-input multi-output specifications for dynamic ancillary services by adapting the parametric transfer function matrix to accommodate a wider range of control behaviors. This extension involves structurally encoding a set of diverse dynamic ancillary service products, each with its own parametric structure, to define a comprehensive desired response behavior. By superimposing these transfer functions, the overall transfer function matrix can capture the complex interactions between different control objectives, enabling the system to provide a more sophisticated and versatile response to grid dynamics. Additionally, the optimization process can be enhanced to consider the interdependencies between multiple input and output variables, ensuring that the closed-loop performance optimally addresses the system's dynamic requirements.

What are the potential drawbacks or limitations of the closed-loop optimization approach compared to open-loop grid-code satisfaction

One potential drawback of the closed-loop optimization approach compared to open-loop grid-code satisfaction is the increased computational complexity and real-time implementation challenges. While closed-loop optimization offers the advantage of achieving optimal and stable system performance by considering the dynamic interactions between the converter system and the grid, it requires continuous monitoring and adjustment of the control parameters to adapt to changing grid conditions. This real-time optimization may introduce additional latency and complexity to the control system, potentially impacting the system's responsiveness and robustness. Furthermore, the closed-loop approach may be more resource-intensive in terms of computational requirements and system modeling, making it less straightforward to implement and maintain compared to open-loop grid-code compliance, which provides a simpler and more deterministic control strategy.

How can the P&O strategy be integrated with other power system control and optimization frameworks to achieve a more holistic grid management

The P&O strategy can be integrated with other power system control and optimization frameworks to achieve a more holistic grid management approach. By combining the P&O strategy with advanced control techniques such as model predictive control (MPC) or reinforcement learning, the system can adaptively optimize the converter control parameters based on real-time grid conditions and performance objectives. Additionally, integrating the P&O strategy with grid-level optimization algorithms, such as optimal power flow (OPF) or energy management systems (EMS), enables a coordinated control of multiple converter-based generation units to maximize grid stability, efficiency, and reliability. By leveraging synergies between different control and optimization frameworks, the P&O strategy can contribute to a comprehensive and intelligent grid management system that optimizes dynamic ancillary services provision while ensuring grid stability and resilience.