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This review paper thoroughly explores the impact of artificial intelligence on the planning and operation of distributed energy systems in smart grids. With the rapid advancement of artificial intelligence techniques such as machine learning, optimization, and cognitive computing, new opportunities are emerging to enhance the efficiency and reliability of electrical grids. From demand and generation prediction to energy flow optimization and load management, artificial intelligence is playing a pivotal role in the transformation of energy infrastructure. This paper delves deeply into the latest advancements in specific artificial intelligence applications within the context of distributed energy systems, including the coordination of distributed energy resources, the integration of intermittent renewable energies, and the enhancement of demand response. Furthermore, it discusses the technical, economic, and regulatory challenges associated with the implementation of artificial intelligence-based solutions, as well as the ethical considerations related to automation and autonomous decision-making in the energy sector. This comprehensive analysis provides a detailed insight into how artificial intelligence is reshaping the planning and operation of smart grids and highlights future research and development areas that are crucial for achieving a more efficient, sustainable, and resilient electrical system.

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.

Looking toward the future, governments around the world have started to change their energy mix due to climate change. The new energy mix will consist mainly of Inverter-Based Resources (IBRs), such as wind and solar power. This transition from a synchronous to a non-synchronous grid introduces new challenges in stability, resilience, and synchronization, necessitating advanced control strategies. Among these, Grid-Forming (GFM) control techniques have emerged as an effective solution for ensuring stable operations in microgrids and large-scale power systems with high IBRs integration. This paper presents a systematic review of GFM control techniques, focusing on their principles and applications. Using the PRISMA 2020 methodology, 75 studies published between 2015 and 2025 were synthesized to evaluate the characteristics of GFM control strategies. The review organizes GFM strategies, evaluates their performance under varying operational scenarios, and emphasizes persistent challenges like grid stability, inertia emulation, and fault ride-through capabilities. Furthermore, this study examines real-world implementations of GFM technology in modern power grids. Notable projects include the UK’s National Grid Pathfinder Program, which integrates GFM inverters to enhance stability, and Australia’s Hornsdale Power Reserve, where battery energy storage with GFM capabilities supports grid frequency regulation.

Effective thermal management is essential for ensuring the safety, performance, and longevity of lithium-ion batteries across diverse applications, from electric vehicles to energy storage systems. This paper presents a thorough review of thermal management strategies, emphasizing recent advancements and future prospects. The analysis begins with an evaluation of industry-standard practices and their limitations, followed by a detailed examination of single-phase and multi-phase cooling approaches. Successful implementations and challenges are discussed through relevant examples. The exploration extends to innovative materials and structures that augment thermal efficiency, along with advanced sensors and thermal control systems for real-time monitoring. The paper addresses strategies for mitigating the risks of overheating and propagation. Furthermore, it highlights the significance of advanced models and numerical simulations in comprehending long-term thermal degradation. The integration of machine learning algorithms is explored to enhance precision in detecting and predicting thermal issues. The review concludes with an analysis of challenges and solutions in thermal management under extreme conditions, including ultra-fast charging and low temperatures. In summary, this comprehensive review offers insights into current and future strategies for lithium-ion battery thermal management, with a dedicated focus on improving the safety, performance, and durability of these vital energy sources.

Electric vehicles are increasingly seen as a viable alternative to conventional combustion-engine vehicles, offering advantages such as lower emissions and enhanced energy efficiency. The critical role of batteries in EVs drives the need for high-performance, cost-effective, and safe solutions, where thermal management is key to ensuring optimal performance and longevity. This study is motivated by the need to address the limitations of current battery thermal management systems (BTMS), particularly the effectiveness of cooling methods in maintaining safe operating temperatures. The hypothesis is that immersion cooling offers superior thermal regulation compared to the widely used indirect liquid cooling approach. Using MATLAB Simulink, this research investigates the dynamic thermal behaviour of three cooling systems, including air cooling, indirect liquid cooling, and immersion cooling, by comparing their performance with an uncooled battery. The results show that immersion cooling outperforms indirect liquid cooling in terms of temperature control and safety, providing a more efficient solution. These findings challenge the existing literature, positioning immersion cooling as the optimal BTMS. The main contribution of this paper lies in its comprehensive evaluation of cooling technologies and its validation of immersion cooling as a superior method for enhancing EV battery performance.

The efficiency and dynamics of hybrid electric vehicles are inherently linked to effective energy management strategies. However, complexity is heightened due to uncertainty and variations in real driving conditions. This article introduces an innovative strategy for extended-range electric vehicles, grounded in the optimization of driving cycles, prediction of driving conditions, and predictive control through neural networks. First, the challenges of the energy management system are addressed by merging deep reinforcement learning with strongly convex objective optimization, giving rise to a pioneering method called DQL-AMSGrad. Subsequently, the DQL algorithm has been implemented, allowing temporal difference-based updates to adjust Q values to maximize the expected cumulative reward. The loss function is calculated as the mean squared error between the current estimate and the calculated target. The AMSGrad optimization method has been applied to efficiently adjust the weights of the artificial neural network. Hyperparameters such as the learning rate and discount factor have been tuned using data collected during real-world driving tests. This strategy tackles the “curse of dimensionality” and demonstrates a 30% improvement in adaptability to changing environmental conditions. With a 20%-faster convergence speed and a 15%-superior effectiveness in updating neural network weights compared to conventional approaches, it also highlights an 18% reduction in fuel consumption in a case study with the Nissan Xtrail e-POWER system, validating its practical applicability.

Air–water heat pumps are gaining interest in modern architectures, and they are a suitable option as a replacement for fossil fuel-based heating systems. These systems consume less electricity by combining solar panels, a heat pump, thermal storage, and a smart control system. This study was applied to a completely ecological rural health sub-center built on the basis of recycled bottles, and that, for its regular operation, requires an energy system according to the needs of the patients in the rural community. Detailed analyses were performed for heating and hot water preparation in two scenarios with different conditions (standard and fully integrated). From a technical perspective, different strategies were analyzed to ensure its functionality. If the photovoltaic system is sized to achieve advanced control, the system can even operate autonomously. However, due to the need to guarantee the energy efficiency of the center, the analyses were performed with a grid connection, and it was determined that the photovoltaic system guarantees at least two-thirds of the energy required for its autonomous operation. The results show that the system can operate normally thanks to the optimal size of the photovoltaic system, which positively influences the rural population in the case under analysis.

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.

The increasing adoption of renewable energy resources and the growing need for efficient and adaptable energy management have emphasized the importance of innovative solutions for energy sharing and storage. This study aims to analyze the application of advanced optimization techniques in decentralized energy systems, focusing on strategies that improve energy distribution, adaptability, and reliability. This research employs a comprehensive review methodology, examining reinforcement learning approaches, demand response mechanisms, and the integration of battery energy storage systems to enhance the flexibility and scalability of P2P energy markets. The main findings highlight significant advancements in robust decision-making frameworks, the management of energy storage systems, and real-time optimization for decentralized trading. Additionally, this study identifies key technical and regulatory challenges, such as computational complexity, market uncertainty, and the lack of standardized legal frameworks, while proposing pathways to address them through intelligent energy management and collaborative solutions. The originality of this work lies in its structured analysis of emerging energy trading models, providing valuable insights into the future design of decentralized energy systems that are efficient, sustainable, and resilient.

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.