Review of Component Specific Modelling, Optimization, and Grid Integration Strategies for Hybrid Renewable Microgrids
Etiebet Asuquo Udo *
Department of Electrical/Electronic Engineering, University of Uyo, Uyo, Nigeria.
Nseobong Ibanga Okpura
Department of Electrical/Electronic Engineering, University of Uyo, Uyo, Nigeria.
Kingsley Monday Udofia
Department of Electrical/Electronic Engineering, University of Uyo, Uyo, Nigeria.
*Author to whom correspondence should be addressed.
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.
Keywords: Hybrid renewable microgrids, artificial intelligence, grid integration, adaptive control, decarbonization, kalman filter