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Transitioning to low-carbon energy systems is crucial for sustainable development, particularly in oil-rich developing countries (ORDCs) that face intertwined economic and environmental challenges. This review uses the PRISMA methodology to systematically assess the current state and prospects of low-carbon thermal electricity generation and utilization technologies in ORDCs. The study emphasizes clean thermal technologies such as biogas, biofuels, biomass, hydrogen, and geothermal energy, focusing on solutions that are technically feasible, economically viable, and efficient in combustion processes. These nations face significant challenges, including heavy reliance on fossil fuels, transmission losses, and financial constraints, making energy diversification urgent. The global shift towards renewable energy and the need to mitigate climate change presents an opportunity to adopt low-carbon solutions that align with Sustainable Development Goals related to energy access, economic growth, and climate action. This review aims to (1) evaluate the current state of low-carbon thermal electricity technologies, (2) analyze the technical and economic challenges related to combustion processes and energy efficiency, and (3) provide recommendations for research and policy initiatives to advance the transition toward sustainable thermal energy systems in ORDCs. The review highlights practical approaches for diversifying energy sources in these nations, focusing on overcoming existing barriers and supporting the implementation of clean thermal technologies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The increasing demand for reliable and sustainable electricity has driven the development of microgrids (MGs) as a solution for decentralized energy distribution. This study reviews advancements in MG planning and optimization for renewable energy integration, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology to analyze peer-reviewed articles from 2013 to 2024. The key findings highlight the integration of emerging technologies, like artificial intelligence, the Internet of Things, and advanced energy storage systems, which enhance MG efficiency, reliability, and resilience. Advanced modeling and simulation techniques, such as stochastic optimization and genetic algorithms, are crucial for managing renewable energy variability. Lithium-ion and redox flow battery innovations improve energy density, safety, and recyclability. Real-time simulations, hardware-in-the-loop testing, and dynamic power electronic converters boost operational efficiency and stability. AI and machine learning optimize real-time MG operations, enhancing predictive analysis and fault tolerance. Despite these advancements, challenges remain, including integrating new technologies, improving simulation accuracy, enhancing energy storage sustainability, ensuring system resilience, and conducting comprehensive economic assessments. Further research and innovation are needed to realize MGs' potential in global energy sustainability fully. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources into isolated microgrids introduces significant power fluctuations due to their intermittent nature. This study addresses the need for advanced power smoothing methods to enhance the stability of isolated networks. An innovative adaptive strategy is presented, combining photovoltaic solar generation with vehicle-to-grid technology, utilizing an enhanced adaptive moving average filter with fuzzy logic control. The primary objective is to dynamically optimize the time frame of the Li-ion battery energy storage system for immediate power stabilization, leveraging the high energy density and rapid response capabilities inherent in electric vehicle batteries. The methodology encompasses data acquisition from photovoltaic panels, definition of fuzzy logic control rules, and implementation of the proposed method within a computer-controlled system connected to a bidirectional three-phase inverter. Experimental results highlight the proposed method's superiority over conventional moving averages and ramp-rate filters. [ABSTRACT FROM AUTHOR]

This review article focuses on applying operation state detection and performance optimization techniques in industrial electrical systems. A comprehensive literature review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology to ensure a rigorous and transparent selection of high-quality studies. The review examines in detail how soft sensing technologies, such as state estimation and Kalman filtering, along with hybrid intelligent modeling techniques, are being used to enhance efficiency and reliability in the electrical industry. Specific case studies are analyzed in areas such as electrical network monitoring, fault detection in high-voltage equipment, and energy consumption optimization in industrial plants. The PRISMA methodology facilitated the identification and synthesis of the most relevant studies, providing a robust foundation for this review. Additionally, the article explores the challenges and research opportunities in applying these techniques in specific industrial contexts, such as steel metallurgy and chemical engineering. By incorporating findings from meticulously selected studies, this work offers a detailed, engineering-oriented insight into how advanced technologies are transforming industrial processes to achieve greater efficiency and operational safety. [ABSTRACT FROM AUTHOR]

This paper provides a thorough exploration of the evolution and contemporary trends in electrical-distribution networks, with a focus on smart grids in the context of Industry 4.0. Beginning with the traditional components of electrical grids, the study highlights the transition towards sustainable energy sources and the integration of renewables. Key trends include economic operation, the application of distributed energy resources, and the significance of photovoltaic solar energy. The paper unfolds in seven sections, examining smart-electrical-network architecture, sustainable technology progression, energy efficiency, carbon-emission-reduction challenges, future perspectives, and concluding insights. Each section delves into specific layers and aspects, such as data management, electrical infrastructure, automation, and consumer interaction. The intricate role of smart meters and their impact on energy management is explored, providing a comprehensive overview of the current state and future directions of electrical-distribution networks. [ABSTRACT FROM AUTHOR]

The internet of things is undergoing rapid expansion, transforming diverse industries by facilitating device connectivity and supporting advanced applications. In the domain of energy production, internet of things holds substantial promise for streamlining processes and enhancing efficiency. This research introduces a comprehensive monitoring and energy management model tailored for the University of Cuenca's microgrid system, employing internet of things and ThingSpeak as pivotal technologies. The proposed approach capitalizes on intelligent environments and employs ThingSpeak as a robust platform for presenting and analyzing data. Through the integration of internet of things devices and sensors, the photovoltaic system's parameters, including solar radiation and temperature, are monitored in real time. The collected data undergo analysis using sophisticated models and are presented visually through ThingSpeak, facilitating effective energy management and decision making. The developed monitoring system underwent rigorous testing in a laboratory microgrid setup, where the photovoltaic system is interconnected with other generation and storage systems, as well as the electrical grid. This seamless integration enhances visibility and control over the microgrid's energy production. The results attest to the successful implementation of the monitoring system, highlighting its efficacy in improving the supervision, automation, and analysis of daily energy production. By leveraging internet of things technologies and ThingSpeak, stakeholders gain access to real-time data, enabling them to analyze performance trends and optimize energy resources. This research underscores the practical application of internet of things in enhancing the monitoring and management of energy systems with tangible benefits for stakeholders involved. [ABSTRACT FROM AUTHOR]

The reliance on imported fuels for electricity generation and internal transportation in insular electrical systems has historically posed a significant challenge due to their geographic isolation. The vulnerability of insular ecosystems to pollution has driven the need to transition toward renewable energy sources. Despite their inherent variability, wind and solar energy have gained acceptance. Integrating these renewable technologies into insular grids presents technical challenges that impact the quality of the power supply, particularly with the increasing presence of electric vehicles. Nevertheless, the batteries of these vehicles provide an opportunity to enhance network performance. This article introduces an innovative power smoothing technique that utilizes electric vehicle batteries to optimize self-consumption and reduce power fluctuations. The proposed method is an enhanced version of the ramp-rate energy smoothing method, incorporating adaptability through real-time control of the ramp-rate using fuzzy logic. It employs an aggregated model of lithium-ion batteries with a bidirectional power electronic converter. Experimental validation is carried out in the Micro-Grid Laboratory of the University of Cuenca, Ecuador. Experimental results demonstrate a significant 14% reduction in energy generation variability, resulting in a more stable electrical supply profile. Additionally, there is a marginal improvement in energy delivery, with an additional injection of 0.23 kWh compared to scenarios without the participation of electric vehicle batteries in power smoothing tasks. These findings support the effectiveness of the proposed approach in optimizing the integration of intermittent renewable generators and electric vehicle charging in insular energy systems. [ABSTRACT FROM AUTHOR]

The integration of renewable energy is transcendental for sustainable development. This article analyses a hybrid grid-connected system composed of renewable energy technologies (photovoltaichydrokinetic), where several scenarios for energy management are proposed. They include a battery system as energy storage and a system without storage but with resale fee to grid, with the aim of determining the best economic and environmental balance. The results show that, by having a (PV-HKT-GRID) system with energy storage and no resale fee to the grid, the Net Present Cost (NPC) is increased by USD $ 132, 760 and the Cost of Energy (COE) decreases $ 0.013/kWh when compared to the grid. In addition, the same hybrid system without energy storage and no resale fee to grid, presents an energy cost savings of $ 0.043/kWh, and an additional cost of USD $ 43, 630. Finally, if a grid resale rate is included in the renewable hybrid system, then the difference is noticeable, the savings in the Cost of Energy is $ 0.073/kWh and presents a saving in the NPC of USD $ 39, 930. In all cases, CO2 emissions have been avoided.