Control and energy management of standalone microgrids in remote areas: A review of recent advances, challenges, and opportunities for future research
Shehu M.A. Talapiden K. Chau T.T. Haruna A. Aly M. Gali V. Do T.D. Alhassan A.B.
March 2026Elsevier B.V.
Engineering Science and Technology, an International Journal
2026#75
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.
Energy management systems (EMS) , Hybrid AC–DC microgrids , Model predictive control (MPC) , Reinforcement learning (DRL) , Sliding-mode control (SMC) , Standalone microgrids
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Department of Robotics, School of Engineering and Digital Sciences, Nazarbayev University, 53 Kabanbay Batyr Ave, Astana, 010000, Kazakhstan
School of Aeronautics, Northwestern Polytechnical University, Shaanxi Province, Xi’an, 710072, China
Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Bellavista 7, Santiago, 8420524, Chile
Department of Electrical and Electronics Engineering, School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, AL109AB, United Kingdom
Department of Robotics
School of Aeronautics
Facultad de Ingeniería
Department of Electrical and Electronics Engineering
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026