Intelligent, Secure, Sustainable, and Scalable Control of Renewable Energy in Smart Grid: A Review on Forecasting, Optimization, and Coordination Frameworks

Abstract

The large-scale penetration of renewable energy sources (RES) in electric power generation systems raises new challenges in terms of variability, control, and cyber-physical coordination. The article provides a comprehensive review of state-of-the-art control schemes and future trends that enable intelligent, secure, and scalable smart grid management. It categorizes the control schemes (centralized, decentralized, distributed), control approaches (model predictive control, reinforcement learning, metaheuristics), and forecasting techniques (statistical, machine learning, deep learning). Furthermore, the review surveys enabling technologies, including multi-agent systems (MAS), federated learning (FL), and blockchain, for resilient coordination and secure transactive energy systems (TES). Relevant challenges such as real-time scaling, cyber-resilience, data privacy, and regulatory interoperability are presented. A comparison of the review literature reveals performance trade-offs and identifies a number of enduring gaps yet to be sufficiently addressed, including the absence of forecast–control integration, sparse real-world validation, and immature cybersecurity-by-design frameworks. Moreover, the paper provides research directions for the future, such as explainable AI, federated MAS architectures, and regulatory co-design for decentralized energy markets. This survey will serve as a reference for advancing the research and deployment of the smart grid in high-penetration renewable energy.