The economic growth and demographic progression in Saudi Arabia increased spending on the development of conventional power plants to meet the national energy demand. The conventional generation and continued use of fossil fuels as the main source of electricity will raise the operational environmental impact of electricity generation. Therefore, using different renewable energy sources might be a solution to this issue. In this study, a grid-connected solar PV-wind hybrid energy system has been designed considering an average community load demand of 15,000 kWh/day and a peak load of 2395 kW. HOMER software is used to assess the potential of renewable energy resources and perform the technical and economic analyses of the grid-connected hybrid system. The meteorological data was collected from the Renewable Resources Atlas developed by the King Abdullah City of Atomic and Renewable Energy (KACARE). Four different cities in the Kingdom of Saudi Arabia, namely, the cities of Riyadh, Hafar Albatin, Sharurah, and Yanbu were selected to do the analyses. The simulation results show that the proposed system is economically and environmentally feasible at Yanbu city. The system at this city has the lowest net present cost (NPC) and levelized the cost of energy (LCOE), highest total energy that can be sold to the grid, as well as the lowest CO₂ emissions due to a highly renewable energy penetration. This grid-connected hybrid system with the proposed configuration is applicable for similar meteorological and environmental conditions in the region, and around the world. Reduction of some greenhouse gasses as well as the reduction of energy costs are main contributors of this research.

Microgrids (MG), considered the future of power systems, has gained significant attention in the present time. MG’s inherent capability of combining both AC and DC operations in a single frame. Its function is crucial in achieving a sustainable electricity supply and advances a decentralized power system approach. MG not only combines the benefits of both AC and DC but also incorporates distributed generations (DGs) and distributed renewable energy sources (DREs). However, the advancement of DGs and DREs with power electronics interfaces (PEIs), and smart loads (SLs) poses the dynamic nature of DGs and DREs such as bi-directional power flow and varying output, which has introduced certain protection challenges in MG maneuver. Therefore, this paper reviews the protection challenges in MG and critically addresses the assessment of existing protection schemes developed so far. It also categorizes the MG protection issues including its components and faults separately for both AC and DC MGs and analyzes the performance of measurement and protection techniques for MG protection strategy on the basis of operational modes AC and DC MGs.

Intelligent transportation systems (ITSs) derive significant advantages from advanced models like YOLOv8, which excel in predicting traffic incidents in dynamic urban environments. Roboflow plays a crucial role in organizing and preparing image data essential for computer vision models. Initially, a dataset of 1000 images is utilized for training, with an additional 500 images reserved for validation purposes. Subsequently, the Deep Simple Online and Real-time Tracking (Deep-SORT) algorithm enhances scene analyses over time, offering continuous monitoring of vehicle behavior. Following this, the YOLOv8 model is deployed to detect specific traffic incidents effectively. By combining YOLOv8 with Deep SORT, urban traffic patterns are accurately detected and analyzed with high precision. The findings demonstrate that YOLOv8 achieves an accuracy of 98.4%, significantly surpassing alternative methodologies. Moreover, the proposed approach exhibits outstanding performance in the recall (97.2%), precision (98.5%), and F1 score (95.7%), underscoring its superior capability in accurate prediction and analyses of traffic incidents with high precision and efficiency.

Utilizing renewable energy sources to produce hydrogen is essential for promoting cleaner production and improving power utilization, especially considering the growing use of fossil fuels and their impact on the environment. Selecting the most efficient method for distributing power and capacity is a critical issue when developing hybrid systems from scratch. The main objective of this study is to determine how a backup system affects the performance of a microgrid system. The study focuses on power and hydrogen production using renewable energy resources, particularly solar and wind. Based on photovoltaics (PVs), wind turbines (WTs), and their combinations, including battery storage systems (BSSs) and hydrogen technologies, two renewable energy systems were examined. The proposed location for this study is the northwestern coast of Saudi Arabia (KSA). To simulate the optimal size of system components and determine their cost-effective configuration, the study utilized the Hybrid Optimization Model for Multiple Energy Resources (HOMER) software (Version 3.16.2). The results showed that, when considering the minimum cost of energy (COE), the integration of WTs, PVs, a battery bank, an electrolyzer, and a hydrogen tank brought the cost of energy to almost 0.60 USD/kWh in the system A. However, without a battery bank, the COE increased to 0.72 USD/kWh in the same location because of the capital cost of system components. In addition, the results showed that the operational life of the fuel cell decreased significantly in system B due to the high hours of operation, which will add additional costs. These results imply that long-term energy storage in off-grid energy systems can be economically benefited by using hydrogen with a backup system.