Dissipativity-Based Synthesis of Distributed Control and Communication Topology Co-Design for AC Microgrids

TL;DR

This work advances AC microgrid control by embedding dissipativity theory into a joint design of distributed controllers and communication topology. It models the MG as a networked system of DGs, lines, and loads, and proposes a three-layer DG control structure (steady-state, local voltage feedback, and distributed consensus-based coordination) to achieve voltage regulation, frequency synchronization, and proportional power sharing in islanded operation. The main contributions are a convex LMIs-based co-design framework that selects controller gains and a sparse communication topology while ensuring Y-dissipativity of the network, along with a detailed equilibrium-point analysis and local controller synthesis under nonlinearity via quadratic constraints. The framework provides robust, scalable guarantees for AC MG performance and offers a path toward practical deployment with future work on communication delays and cybersecurity enhancements.

Abstract

This paper introduces a dissipativity-based framework for the joint design of distributed controllers and communication topologies in AC microgrids (MGs), providing robust performance guarantees for voltage regulation, frequency synchronization, and proportional power sharing across distributed generators (DGs). The closed-loop AC MG is represented as a networked system in which DGs, distribution lines, and loads function as interconnected subsystems linked through cyber-physical networks. Each DG utilizes a three-layer hierarchical control structure: a steady-state controller for operating point configuration, a local feedback controller for voltage tracking, and a distributed droop-free controller implementing normalized power consensus for frequency coordination and proportional power distribution. The operating point design is formulated as an optimization problem. Leveraging dissipativity theory, we derive necessary and sufficient subsystem dissipativity conditions. The global co-design is then cast as a convex linear matrix inequality (LMI) optimization that jointly determines distributed controller parameters and sparse communication architecture while managing the highly nonlinear, coupled dq-frame dynamics characteristic of AC systems.

Figures (6)

• Figure 1: A representative diagram of the AC MG network. The sets $\mathcal{D}$, $\mathcal{L}$, and $\mathcal{P}$ are represented as DGs, distribution lines and loads.
• Figure 2: Electrical schematic of a DG $\Sigma_i^{DG}$ with a local load and a line $\Sigma_l^{Line}$.
• Figure 3: Electrical scheme of ZIP load in AC MG.
• Figure 4: Hierarchical control architecture combining local and distributed controllers through physical and communication interconnections.
• Figure 5: Networked system representation showing interconnections between DGs, lines, and loads AC MG.

Theorems & Definitions (23)

• Definition 1
• Definition 2
• Remark 1
• Proposition 1
• Remark 2
• Remark 3
• Proposition 2
• Lemma 1
• proof
• Remark 4