Abstract
While standalone microgrids are an essential means of electrifying remote communities, high renewable penetration poses significant problems with power sharing, voltage/frequency stability, and optimal dispatch in low-inertia, communication-constrained scenarios. Using structured analysis across control methodologies, optimization techniques, and validation platforms, this paper synthesizes emerging paradigms in hierarchical control and energy management systems (EMS) through a systematic review of studies conducted in 2025. The following key findings show clear shifts: (i) adaptive droop and event-triggered consensus reduce communication overhead by 80% while maintaining voltage accuracy within ±2%; (ii) super-twisting sliding mode control shows chattering-free operation with 98% cyber-attack detection capability; (iii) hybrid model predictive control frameworks enable real-time execution on embedded hardware with 25%-40% cost reduction; and (iv) deep reinforcement learning-based EMS shows 12% cost improvement and 97.8% reduction in computational load. There are still significant gaps: 68% of studies do not have hardware validation, 78% do not integrate cyber-security, power-sharing errors surpass 5% when there is an impedance mismatch, and there are no standardized benchmarking protocols. The review offers practical suggestions covering lifecycle-aware battery management, distributionally robust optimization (DRO) for renewable uncertainty, edge-computing architectures for communication-light operation, and cooperative cyber-physical testbeds for field validation. This synthesis provides a well-organized road map for developing technically demanding, financially feasible, and operationally robust microgrids that can provide sustainable access to electricity in underserved areas.