The field of energy management and control is witnessing a significant shift towards the integration of renewable energy sources and the development of smart grid technologies. Recent research has focused on the application of reinforcement learning (RL) and other advanced optimization techniques to improve the efficiency and resilience of microgrids and energy storage systems. These approaches have shown great promise in reducing operational costs, CO2 emissions, and dependence on non-renewable energy sources. Furthermore, the use of digital twins and simulation environments has enabled the development of more realistic and effective control strategies. Noteworthy papers in this area include:

• A study that proposed a real-time energy management framework for hybrid community microgrids, which demonstrated the potential of DRL-based approaches to enable cost-effective and resilient microgrid operations.
• A paper that presented a novel RL-based methodology for optimizing microgrid energy management, which outperformed rule-based methods and existing RL benchmarks.
• A research work that applied RL to synchronise resin flow fronts in a dual-gate resin infusion system, highlighting its potential towards improving process control and product quality in composites manufacturing.
• A paper that presented a unified framework for the optimal scheduling of battery dispatch and internal power allocation in Battery energy storage systems, which compared model-based Linear Programming and model-free Reinforcement Learning approaches for optimization.