The energy management system (EMS) has significant value for attaining automatic control, reducing operating costs, and achieving optimal energy storage capacity. The EMS requires an optimized design to satisfy the energy demands of society. The utilization of the EMS can support the balance of supply and demand of electricity. The EMS exchanges energy between energy resources and supplies and loads reliably, safely, and efficiently under a variety of circumstances essential for the operation of the power grid. The use of machine learning (ML) techniques, effective planning, and modeling are critical for energy forecasting and the optimized performance of the EMS in the smart grid.

Although EMS technologies are being developed, some challenges persist within this field. The application of renewable energy resources and smart grids is a sustainable solution for the mitigation and efficient management of rising energy demands. ML could be used to create an optimized Energy Management Model (EMM) that combines renewable energy sources with smart grids. Innovative machine learning algorithms can provide specific and optimized solutions for energy production, power grid balance, and energy consumption analysis, and EMM complexity can be predicted and characterized. ML methods are evolving and providing optimized algorithms for developing energy management strategies, such as renewable energy sources management, battery, PEV charge or discharge management, etc., Thus, ML models offer a promising future for renewable energy sources (RES) and the smart grid.

This Special Issue outlines the significance of enhancing the EMS with ML for automated design and operation management in smart grids and renewable energy to attain optimization and for energy control systems through in-depth analysis. We welcome researchers, scientists, engineers to demonstrate novel ideas on the use of ML in this field. Both original research and review articles are welcome.

Potential topics include but are not limited to the following:

• ML algorithms in energy hub management and applications for smart grids
• ML models in EMS for predictive modeling of demand analysis, production, and consumption with accuracy
• Novel metaheuristics and ML for selective operations in energy storage and management systems
• ML-driven design optimization EMS solutions and control strategies for the next generation of smart grids
• Challenges and applications of integrating RES to the smart grid for EMS with ML
• Advanced machine learning and reinforcement learning in the hybrid renewable energy system (HRES) for smart microgrids
• ML-based optimization in sizing, maximum power point tracking control, and EMS to smart grids
• ML for analyzing, designing, modeling, and simulation in smart grids and renewable energy
• ML-based optimization in planning and operation of energy management for smart grids
• Testbed implementation of ML-based energy management system for smart grids, power converters, and renewable energy