With growing concerns over environmental degradation and climate change, the shipping industry is under increasing pressure to reduce its environmental impact. This has led to the development of green port initiatives in the field of maritime transport and logistics, which aim to promote sustainable practices and reduce the environmental footprint of ports and their operations. One of the key strategies for achieving this goal is the use of renewable energy technologies (RETs). This paper summarizes the potentials, challenges, and economic analysis of RETs applications in green ports, emphasizing those that require aquatic environments for operation, including floating photovoltaic systems, offshore wind turbines, and ocean energy. The paper investigates the concept of green ports and explores the feasibility of integrating RETs into these facilities. Also, the potential of the various RETs is presented in terms of technical and economic aspects and installed capacity. Additionally, due to high flexibility in electrical systems and compatibility with maritime transportation, the use of fuel cells in green ports has been discussed as a feasible solution for supplying power to ports (either as the primary or backup source). The findings of this study show that RETs can significantly contribute to achieving sustainable goals in the maritime industry and pave the way for the creation of more efficient and environmentally friendly ports.

This study investigates the integration of electric vehicle (EV) charging stations into buildings as a sustainable strategy to reduce greenhouse gas emissions and promote clean energy adoption. While this initiative offers significant environmental advantages, it also introduces challenges such as high installation costs, the need for electrical infrastructure upgrades, and potential power quality concerns. To address these issues, the research utilizes Pareto frontier analysis and multi-objective optimization (MOO) techniques to identify optimal trade-offs among key objectives, including cost, energy efficiency, and system reliability. Three strategies (A, B, and C) are assessed, with Strategy C emerging as the most balanced and cost-effective option. The findings underscore the importance of prioritizing both economic viability and operational efficiency in decision-making processes. Overall, integrating EV charging stations into buildings demonstrates considerable potential as a step toward sustainability. However, a thorough evaluation of all influencing factors is critical to ensuring long-term success and alignment with global environmental goals.

While efficiency and real-time monitoring have significantly improved with the integration of Internet of Things (IoT) devices into smart grids (SGs), new cybersecurity vulnerabilities have also been brought about by this process. This study examines the security risks associated with IoT-enabled SGs, emphasizing the possibility of cyberattacks that could interfere with grid operations or compromise sensitive data, as well as the use of IoT in certain industries, IoT standards, and the significance of individual protocols. These days, SG cybersecurity is essential to ensuring the global security of our energy networks. Presenting a thorough overview of the security issues, risks, and potential solutions for Internet of Things-based SGs is the aim of this paper. Our research focuses on network vulnerabilities, security needs, and cyber-attacks in SG applications to assess their effects on the network and provide guidance for future paths in cyber-security development.

Presently, transmission lines play a major role for transferring electricity from generating stations to consumer premises. As the components are exposed to environmental conditions, damage to these components is more severe. Among those components insulators area accountable for 81.3% of line interruptions. In this paper, a drone-based transmission line inspection using mobile net single shot detection (SSD) is proposed that aims to detect the potential defects of insulators. The proposed system employs a regression-based algorithm using mobile net uses an upgraded compact mobile net SSD to identify targets network, allowing for precise categorization and placement of electrical equipment. Simulation is performed by loading the datasets that are collected. There are totally 800 images of ceramic insulators these are sub divided into 3 classes train class has images with 590 test class with 130 and validation (Val) class with 80. In this methodology, mobile net SSD can identify and detect the ceramic insulators with pollution flashover, corroded and broken. Precision, recall, F1 Score are calculated. Analysis is carried out on the variation of batch parameters, size etc to ascertain the effectiveness of the proposed method.

Carbon trading is a market-based technique to decrease greenhouse gas (GHG) emissions through the sale and purchase of carbon offsets. Incorporating artificial intelligence (AI) into carbon trading can alter the industry by improving information processing, statistical modeling, and trade automation. This paper presents an extensive structure for AI-driven carbon trading that considers critical aspects such as carbon trading volume and pricing to maximize productivity and sustainability. The study assesses numerous AI and machine learning (ML) theories, including their use in cost prediction, real-time market forecasting, and financial risk assessment. The main results show that AI integration increases market transparency, lowers fraud, and promotes informed decision-making, all of which helps to establish an environmentally friendly, effective, and adaptable carbon market. Furthermore, this work underscores the role of AI in advancing carbon-neutral economies by fostering innovation in emissions monitoring and reporting. These advancements highlight AI's critical contribution to achieving global climate objectives and addressing the urgent challenges posed by climate change.

The threats posed by cyber-attacks on energy networks, especially microgrids, have become a significant concern as the global energy landscape becomes more interconnected and reliant on technological advancements. This study investigates the fundamental vulnerabilities in energy networks and the potential impact of cyber-attacks on power generation, consumption, and backup systems. The importance of robust identification systems cannot be overstated; real-time identification, mitigation, and response to cyber-attacks rely on advanced detection technology. This research underscores the critical role of active security protocols in safeguarding energy networks from cyber-attacks, focusing on coding in MATLAB and the development of best practices. The paper explores two scenarios - normal operations and attack situations - for three key units: power generation, power consumption, and power storage. The methodology involves: primary detection through statistical analysis and secondary detection through SPC. These findings emphasize the essential role of strong cybersecurity measures in ensuring the stability and dependability of microgrids, particularly in the face of escalating cyber threats.