This paper presents a methodology for the analysis of frequency dynamics in large-scale power systems with high level of wind energy penetration by means of a simplified model for DFIG-based wind turbines. In addition, a virtual inertia controller version of the optimized power point tracking (OPPT) method is implemented for this kind of wind turbines, where the maximum power point tracking curve is shifted to drive variations in the active power injection as a function of both the grid frequency deviation and its time derivative. The proposed methodology integrates the model in a primary frequency control scheme to analyze the interaction with the rest of the plants in the power system. It is also proven that, under real wind conditions, the proposed version of the OPPT method allows us to smooth the wind power injected into the grid, thereby reducing frequency fluctuations.

This systematic review paper examines the current integration of artificial intelligence into energy management systems for electric vehicles. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, 46 highly relevant articles were systematically identified from extensive literature research. Recent advancements in artificial intelligence, including machine learning, deep learning, and genetic algorithms, have been analyzed for their impact on improving electric vehicle performance, energy efficiency, and range. This study highlights significant advancements in energy management optimization, route planning, energy demand forecasting, and real-time adaptation to driving conditions through advanced control algorithms. Additionally, this paper explores artificial intelligence’s role in diagnosing faults, predictive maintenance of electric propulsion systems and batteries, and personalized driving experiences based on driver preferences and environmental factors. Furthermore, the integration of artificial intelligence into addressing security and cybersecurity threats in electric vehicles’ energy management systems is discussed. The findings underscore artificial intelligence’s potential to foster innovation and efficiency in sustainable mobility, emphasizing the need for further research to overcome current challenges and optimize practical applications.

This review highlights the latest advancements in thermal energy storage systems for renewable energy, examining key technological breakthroughs in phase change materials (PCMs), sensible thermal storage, and hybrid storage systems. Practical applications in managing solar and wind energy in residential and industrial settings are analyzed. Current challenges and research opportunities are discussed, providing an overview of the field’s current and future state. Following the PRISMA 2020 guidelines, 1040 articles were initially screened, resulting in 49 high-quality studies included in the final synthesis. These studies were grouped into innovations in TES systems, advancements in PCMs, thermal management and efficiency, and renewable energy integration with TES. The review underscores significant progress and identifies future research directions to enhance TES’s efficiency, reliability, and sustainability in renewable energy applications.

The increasing adoption of electric vehicles has introduced challenges in maintaining grid stability, energy efficiency, and economic optimization. Advanced control strategies are required to ensure seamless integration while enhancing system reliability. This study systematically reviews predictive control applications in energy systems, particularly in electric vehicle integration and bidirectional energy exchange. Using the PRISMA 2020 methodology, 101 high-quality studies were selected from an initial dataset of 5150 records from Scopus and Web of Science. The findings demonstrate that predictive control strategies can significantly enhance energy system performance, achieving up to 35% reduction in frequency deviations, 20–30% mitigation of harmonic distortion, and a 15–20% extension of battery lifespan. Additionally, hybrid approaches combining predictive control with adaptive learning techniques improve system responsiveness by 25% under uncertain conditions, making them more suitable for dynamic and decentralized networks. Despite these advantages, major barriers remain, including high computational demands, limited scalability for large-scale electric vehicle integration, and the absence of standardized communication frameworks. Future research should focus on integrating digital modeling, real-time optimization, and machine learning techniques to improve predictive accuracy and operational resilience. Additionally, the development of collaborative platforms and regulatory frameworks is crucial for large-scale implementation.

Microgrids have emerged as a key element in the transition towards sustainable and resilient energy systems by integrating renewable sources and enabling decentralized energy management. This systematic review, conducted using the PRISMA methodology, analyzed 74 peer-reviewed articles from a total of 4205 studies published between 2014 and 2024. This review examines critical areas such as reinforcement learning, multi-agent systems, predictive modeling, energy storage, and optimization algorithms—essential for improving microgrid efficiency and reliability. Emerging technologies like artificial intelligence (AI), the Internet of Things, and flexible power electronics are highlighted for enhancing energy management and operational performance. However, challenges persist in integrating AI into complex, real-time control systems and managing distributed energy resources. This review also identifies key research opportunities to enhance microgrid scalability, resilience, and efficiency, reaffirming their vital role in sustainable energy solutions.