Abstract—The rapid integration of variable Renewable Energy Sources (RES) into power systems demands control strategies that are scalable, privacy-preserving, and cyber-resilient. This review synthesizes recent advances in forecasting, optimization, and coordination frameworks relevant to Distributed Energy Resources (DERs), emphasizing three core findings: (i) distributed multi-agent approaches combined with model-predictive and learning-based controllers provide the most promise for scalable real-time coordination, (ii) federated and edge-cloud learning paradigms substantially reduce data-sharing requirements but require stronger adversarial defenses and standardized interfaces, and (iii) a persistent gap exists between simulation results and field-level validation, particularly for forecast–control co-design and cybersecurity-by-design. We present a novel taxonomy that organizes methods by control architecture and computational foundation, a comparative evaluation framework to assess scalability and resilience, and a conceptual, modular architecture to guide future experimental deployments. Finally, we identify prioritized research directions—explainable Artificial Intelligence (AI), federated Multi-Agent Systems (MAS), regulatory co-design, and large-scale testbeds—aimed at closing the research-to-deployment gap.

Keywords—smart grids, Model Predictive Control (MPC), Artificial Intelligence (AI), Multi-Agent Systems (MAS), Federated Learning (FL), blockchain, cyber security, forecasting, Distributed Energy Resources (DERs), edge computing

Cite: Md Rayhan Tanvir, "Intelligent, Secure, Sustainable, and Scalable Control of Renewable Energy in Smart Grid: A Review on Forecasting, Optimization, and Coordination Frameworks," International Journal of Smart Grid and Clean Energy, Vol. 14, No. 2, pp. 40-62, 2025. doi: 10.12720/sgce.14.2.40-62

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).