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

The efficient control of HVAC devices in building structures is mandatory for achieving energy savings and comfort. To balance these objectives efficiently, it is essential to incorporate adequate advanced control strategies to adapt to varying environmental conditions and occupant preferences. Model-free control approaches for building HVAC systems have gained significant interest due to their flexibility and ability to adapt to complex, dynamic systems without relying on explicit mathematical models. The current review presents the recent advancements in HVAC control, with an emphasis on reinforcement learning, artificial neural networks, fuzzy logic control, and their hybrid integration with other model-free algorithms. The main focus of this study is a literature review of the most notable research from 2015 to 2023, highlighting the most highly cited applications and their contributions to the field. After analyzing the concept of each work according to its control strategy, a detailed evaluation across different thematic areas is conducted. To this end, the prevalence of methodologies, utilization of different HVAC equipment, and diverse testbed features, such as building zoning and utilization, are further discussed considering the entire body of work to identify different patterns and trends in the field of model-free HVAC control. Last but not least, based on a detailed evaluation of the research in the field, the current work provides future directions for model-free HVAC control considering different aspects and thematic areas.

The rapid evolution of Generative Artificial Intelligence (GenAI) is reshaping the energy sector, enabling new levels of adaptability, efficiency, and user-centric interaction. This review systematically maps and critically evaluates the chosen literature across buildings, grids, and urban systems. Through major scientific databases and for the span of five years, from 2021 to 2025, the review aims to identify key application domains, synergies, and research gaps. The analysis on recent advancements illustrates how GenAI enhances energy forecasting, demand–response strategies, anomaly detection, and cyber-resilience in power networks, while also supporting predictive modeling and optimal control in distributed renewable integration. Within smart buildings, GenAI empowers autonomous agents and AI copilots to balance comfort with energy efficiency through adaptive environmental control and user preference modeling. At the grid level, generative models improve renewable generation forecasting, grid stability, and decision support for operators. A further emerging application lies in the generation of synthetic energy data, which supports model training, scenario simulation, and robust decision-making in data-scarce environments. In the broader context of smart cities, GenAI-driven digital twins, multi-agent systems, and conversational interfaces facilitate sustainable planning and energy-aware citizen engagement. A central theme across these applications is the alignment of technological solutions with human needs and sustainability objectives. Key challenges remain in uncertainty quantification, trustworthy deployment, and data governance, underscoring the need for secure, adaptive, and human-centered GenAI systems to drive the next generation of intelligent energy management. This review provides a comprehensive analysis to promote a better understanding of generative models as they are being applied in a variety of scenarios in the energy domain.

This paper presents a bilevel Reinforcement Learning (RL) framework for optimizing Electric Vehicle (EV) charging through price-mediated coordination between grid operators and charging stations. Unlike prior work relying on direct control or manual subgoal engineering, the proposed approach uses dynamic pricing as an implicit coordination signal to address a complex multi-objective optimization problem involving grid stability, user satisfaction, and economic efficiency. To manage this complexity, the problem is decomposed into two levels comprised of an upper-level Distribution System Operator (DSO) that determines dynamic pricing strategies, and multiple lower-level Load Aggregators (LAs) responsible for EV charging decisions at individual stations in response to these prices. This bilevel structure captures the leader–follower interaction between DSOs and LAs, with each level operating at different temporal scales. Deep Deterministic Policy Gradient (DDPG) agents are deployed at both levels, enabling adaptive decision-making under operational constraints. Extensive simulations compare the framework against multiple Rule-Based Control (RBC) baselines. Results demonstrate that the DDPG-based DSO achieves a 42.4% higher mean reward and 19.1% higher profit compared to the best-performing RBC baseline, while preserving grid stability and user satisfaction. These results validate the effectiveness of bilevel RL for complex energy optimization problems, highlighting its potential as a scalable control paradigm for smart management systems.