Data-driven Communication and Control Design for Distributed Frequency Regulation with Black-box Inverters

TL;DR

The paper tackles secondary frequency regulation in grids with heavy inverter-based resources and black-box models by proposing a two-stage data-driven framework that first designs a communication topology under a stability constraint and then synthesizes a distributed controller aligned with that topology using persistently exciting data. Controller gains are computed via data-driven LMIs with a recovery rule $K=YG^{-1}$, and performance is quantified through an ${\mathcal{H}}_2$ objective, while a cost on communication links enforces a practical trade-off. Case studies on the IEEE 39-bus system demonstrate fast regulation on the order of seconds and reveal the benefits and limits of different communication topologies in achieving stable, evenly shared secondary reserves. The approach offers a scalable, decentralised alternative to centralized AGC for modern grids, providing stability guarantees without requiring white-box models and enabling explicit control over communication resource expenditure.

Abstract

The increasing penetration of inverter-based resources into the power grid, with often only black-box models available, challenges long-standing frequency control methods. Most recent works take a decentralized approach without online device coordination via communication. This paper considers both dynamic behavior and communication within secondary frequency control on an intermediate timescale. We develop a distributed data-driven approach that utilizes peer-to-peer communication between inverters to avoid the need for a central control center. To enable a trade off between communication network requirements and control performance, we present a framework to guide communication topology design for secondary frequency regulation. Following design of the inter-agent information exchange scheme, we design a controller that is structured according to the communication topology with a closed-loop stability guarantee. Case studies on the IEEE 39-bus system validate the framework and illustrate the trade-off between communication requirements and control performance that is enabled by our approach.

Figures (12)

• Figure 1: Block diagram of our SG model, showing the primary droop, secondary input, re-heater process and inertial response.
• Figure 2: Block diagram of our IBR model with VSG control, showing the secondary input and inertial response.
• Figure 3: Block diagram of our IBR model with droop control, showing the secondary input response, low-pass filter and droop gain.
• Figure 4: The IEEE 39-bus test system used for our case studies, showing the location of the SG, solar PV units, wind farms and battery energy storage systems (BESS).
• Figure 5: System frequency trajectory evolution is shown against time, with a stable response observed.