Dr Mohammed Mahmoud Youssif

Citation:

Elnady, S. M., Guerrero J. M., Louassaa K., Azeem A., Zhang X., Alham M. H., et al. (2026).  Fractional-order techniques in renewable energy microgrids: A comprehensive survey of modeling, control, and forecasting methods. Renewable and Sustainable Energy Reviews. 239,

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Abstract:

Renewable energy microgrids exhibit memory-dependent, nonlinear, and multi-timescale dynamics that classical integer-order methods cannot adequately represent. Fractional calculus extends differentiation and integration to non-integer orders, providing a rigorous framework for capturing hereditary effects, anomalous diffusion, and long-range dependence across microgrid subsystems. Despite growing research activity, existing reviews address isolated topics without a unified treatment spanning all major application domains. This survey organizes fractional-order methodologies within a two-domain framework — fractional-order control and integrated fractional calculus-based methods — spanning five technical categories: component modeling, hierarchical control, optimization, forecasting, and validation. Fractional-order equivalent circuit models with constant phase elements are reviewed for state-of-charge and state-of-health estimation in electrochemical storage and photovoltaic systems. Fractional-order control schemes, encompassing single-loop and cascade topologies, optimized by metaheuristic methods, exhibit reliable improvements in transient response and robustness compared to integer-order designs. Fractional grey models, FARIMA formulations, and hybrid fractional machine-learning architectures are assessed for renewable generation and load forecasting. Three standardized case studies provide quantitative cross-domain comparisons under consistent conditions. Identified deployment barriers include fractional operator computational cost, absent tuning protocols, and limited hardware validation, motivating future research in physics-informed fractional learning, digital twin integration, and edge computing. This survey provides researchers and practitioners with a unified reproducible framework for applying fractional-order techniques in next-generation renewable energy microgrids.

Notes:

• This paper is a comprehensive review article on fractional-order techniques in renewable energy microgrids.
• It surveys the use of fractional calculus for modeling, control, optimization, forecasting, and validation of renewable-energy-based microgrid systems.
• The review highlights how fractional-order methods can better represent memory effects, long-range dependence, and multi-timescale dynamics compared with classical integer-order approaches.
• The paper organizes the literature into two main domains: fractional-order control and integrated fractional-calculus-based methods.
• It discusses applications in electrochemical storage systems, photovoltaic systems, renewable generation forecasting, load forecasting, and hierarchical microgrid control.
• The review also identifies key deployment challenges, including computational cost, lack of standard tuning protocols, and limited hardware validation.
• Future research directions include physics-informed fractional learning, digital twin integration, edge computing, and more reproducible benchmarking frameworks.

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