Hybrid AI-Driven Energy Management System for Smart Grid Optimization

Energy Management Systems (EMS) are essential for the modernization of smart grids via the integration of Renewable Energy Resources (RER) and the optimization of energy distribution. The intrinsic intermittency of renewable energy resources (RERs) and the intricacies of distributed energy resources (DER) need sophisticated control systems. This paper offers a Hybrid AI-Driven Energy Management System that integrates robust and stochastic optimization with artificial intelligence (AI) and blockchain to improve efficiency, flexibility, and security. The system employs metaheuristic optimization methods, including Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), for energy scheduling, while machine learning models, such as Artificial Neural Networks (ANN) and Support Vector Regression (SVR), enhance forecasting precision. Adaptive demand response systems using reinforcement learning (RL) help to provide real-time optimisation. Blockchain technology also helps transactional security in transitive energy markets to be reinforced. A 15–20% gain in energy efficiency and a 10–12% decrease in operating expenses follow from improved performance resulting from a comparison of current approaches. The results show how effectively a multi-layered artificial intelligence-driven Energy Management System can greatly improve grid stability, resilience, and the integration of large renewable energy sources.