Review and Evaluation of Reinforcement Learning Frameworks on Smart Grid Applications

With the rise in electricity, gas and oil prices persistently high levels of carbon emissions, there is an increasing demand for effective energy management systems, including electrical grids. Recent literature exhibits large potential optimizing behavior such systems towards performance, reducing peak loads exploiting environmentally friendly ways production. However, primary challenge relies on optimization which introduces significant complexities since they present quite dynamic behavior. Such cyberphysical frameworks usually integrate multiple interconnected components as power plants, transmission lines, distribution networks various types energy-storage while these affected by external factors user individual requirements, weather conditions, market prices. Consequently, traditional optimal control approaches—such Rule-Based Control (RBC)—prove inadequate to deal with diverse dynamics define complicated frameworks. Moreover, even sophisticated techniques—such Model Predictive (MPC)—showcase model-related limitations that hinder applicability scheme. To this end, AI model-free techniques Reinforcement Learning (RL) offer a fruitful embedding efficient cases systems. studies promising results fields engineering, indicating RL may prove key element delivering smart buildings, electric vehicle charging grid applications. The current paper provides comprehensive review implementations frameworks—such Renewable Energy Sources (RESs), Building Energy-Management Systems (BEMSs) Electric Vehicle Charging Stations (EVCSs)—illustrating benefits opportunities approaches. work examines more than 80 highly cited papers focusing recent research applications—between 2015 2023—and analyzes regards systems’ future.