Recent Progress in Optimising Sustainable Energy Smart Grids Using Swarm Robotics: A Systematic Narrative Review

This systematic narrative review examines recent advances in the application of swarm robotics and swarm intelligence to smart grid systems in the context of renewable energy sources. Smart grids represent a transformative paradigm in power systems, integrating advanced communication, monitoring, and control technologies to enhance the reliability, performance, and sustainability of power distribution. Swarm robotics, drawing inspiration from the collective behaviour of social insects, enables the coordination of numerous autonomous agents to carry out complex tasks in a decentralised, scalable, and fault-tolerant manner. Over the past decade, swarm intelligence approaches—including Particle Swarm Optimisation, Ant Colony Optimisation, and consensus-based distributed control—have emerged as promising methods for addressing smart grid challenges such as decentralised energy management, fault detection, infrastructure monitoring, and maintenance. This review was conducted through a two-stage systematic search of three databases (Scopus, IEEE Xplore, and ACM Digital Library; Web of Science was not accessible during the search period and was therefore excluded), which identified 54 primary studies meeting the full-text inclusion criteria, supplemented by 11 additional records located through hand-search, for a final corpus of 65 references. For each application domain (monitoring and inspection, energy distribution optimisation, fault detection and resilience, and cybersecurity and communication), we summarise the methodologies employed, the reported performance outcomes, and the evidence level of the available studies. We also analyse the scalability limits of current swarm approaches, communication constraints relevant to grid deployment, and the integration of swarm systems with existing SCADA and EMS infrastructure. The review identifies a significant gap between laboratory demonstrations and utility-scale deployment, and outlines priority directions for future research.