A Cyber-Physical Perspective to Pinning-Decision for Distributed Multi-Agent Control in Microgrid against Stochastic Communication Disruptions

TL;DR

This paper introduces a cyber-physical decision-making strategy for distributed control in microgrids, optimizing generator pinning amidst communication disruptions using network theory, genetic algorithms, and deep learning.

Contribution

It presents a novel integrated approach combining complex network theory, genetic algorithms, and deep learning for rapid, optimal pinning decisions in microgrid control under stochastic communication failures.

Findings

Effective pinning decision strategy reduces control complexity.

Deep learning enables near-instantaneous decision-making after disruptions.

Simulation confirms improved stability and synchronization.

Abstract

In this study, we propose a decision-making strategy for pinning-based distributed multi-agent (PDMA) automatic generation control (AGC) in islanded microgrids against stochastic communication disruptions. The target microgrid is construed as a cyber-physical system, wherein the physical microgrid is modeled as an inverter-interfaced autonomous grid with detailed system dynamic formulation, and the communication network topology is regarded as a cyber-system independent of its physical connection. The primal goal of the proposed method is to decide the minimum number of generators to be pinned and their identities amongst all distributed generators (DGs). The pinningdecisions are made based on complex network theories using the genetic algorithm (GA), for the purpose of synchronizing and regulating the frequencies and voltages of all generator busbars in a PDMA control structure, i.e., without resorting to a central AGC agent. Thereafter, the mapping of cyber-system topology and the pinning decision is constructed using deeplearning (DL) technique, so that the pinning-decision can be made nearly instantly upon detecting a new cyber-system topology after stochastic communication disruptions. The proposed decision-making approach is verified using a 10-generator, 38-bus microgrid through time-domain simulation for transient stability analysis.