Advances in Optimization Algorithms for Renewable Integration and Production-Side Management in Smart Grids

EEIS-Lab, ENSET Mohammedia, Hassan II University, Casablanca

Abstract

The integration of renewable energy sources into modern power grids has led to challenges in balancing generation and demand, maintaining grid stability, and ensuring operational efficiency. The smart grid paradigm, bolstered by AI, IoT, and advanced optimization algorithms, offers a promising platform to address these issues. Energy Management Systems (EMS) play a crucial role in coordinating distributed energy resources, enhancing forecasting, and optimizing generation scheduling while minimizing costs and emissions. A systematic review of recent algorithmic developments in production-side energy management for smart grids reveals four dominant categories: deterministic algorithms, metaheuristics, artificial intelligence-based methods, and hybrid approaches. The findings highlight the shift from conventional optimization to intelligent, adaptive, and hybrid solutions that combine forecasting, optimization, and control, underscoring the importance of advanced algorithms in enabling reliable, efficient, and sustainable operation of future smart grids.