Review of Component Specific Modelling, Optimization, and Grid Integration Strategies for Hybrid Renewable Microgrids

Abstract

Hybrid renewable microgrids (HRMs) integrating solar photovoltaic (PV), wind turbines, diesel generators, and battery storage systems are critical for resilient, low-carbon energy transitions. However, their inherent complexity driven by intermittent generation, multi-objective optimization, and dynamic grid interactions necessitates advanced modelling and control strategies to balance techno-economic and operational demands. This review synthesizes advancements in component-specific modelling, including non-linear fuel consumption curves for diesel generators, Weibull-distributed wind resource analysis, and Kalman filter-enhanced state-of-charge estimation for batteries. It highlighted the transformative role of artificial intelligence (AI)-driven energy management systems and digital twin frameworks in optimizing dispatch, fault recovery, and predictive maintenance. Critical gaps in real-time adaptability, bidirectional grid synchronization, and standardized regulatory frameworks for peer-to-peer energy trading are examined. By bridging global innovations in adaptive control, blockchain-enabled transactive energy, and next-generation storage (e.g., solid-state batteries) with socio-technical insights, this review provides a roadmap for deploying HRMs that align with decarbonization targets and energy equity goals, particularly in underserved regions with volatile demand and resource constraints.