Optimization-Based Approaches for Boosting Microgrid Resilience to Fault Events

Abstract

In this research endeavour, we delve into the realm of microgrid management, focusing specifically on enhancing its performance in the aftermath of a fault event. Microgrids, characterized by their incorporation of diverse replenishable energy sources like the sun and wind, alongside storage options like batteries and conventional methods of backup, like diesel generators, face significant challenges when confronted with sudden spikes in demand due to faults or disruptions. To address these challenges, we explore the application of three distinct optimization methodologies: Genetic Algorithm (GA), Simulated Annealing (SA), and Particle Swarm Optimization (PSO).These techniques are employed to dynamically adjust load demand within the microgrid, aiming to mitigate the impacts of the fault and restore stability and efficiency to the system. Through a comprehensive comparative analysis, we assess the efficacy of every optimization approach regarding its capacity for optimize load demand effectively, maintain system reliability, and maximize resource utilization. By examining key performance metrics such as cost reduction, load balancing, and energy efficiency improvement, Our goal is to provide insightful information about the strengths additionally limitations of each optimization technique. Ultimately, our study contributes to the body of knowledge surrounding microgrid management strategies, offering practical guidance for decision-makers and engineers tasked with optimizing microgrid performance in real-world scenarios.