Promoting the development of green technologies and replacing fossil fuel vehicles with electric ones can abate the environmental anxieties and issues associated with energy supply security. The increasing demand for electric vehicles requires an upgrade and expansion of the available charging infrastructure to accommodate the fast public adoption of this type of transportation. Ethiopia set a pro-electric cars policy and made them excise-free even before the first electric vehicle charging stations were launched by Marathon Motors Engineering in 2021. This paper presents the first ever technical, economic and environmental evaluation of electric vehicle charging stations powered by hybrid intermittent generation systems in three cities in Ethiopia. This paper tests this model using three different battery types: Lead-acid (LA), Flow-Zince-Bromine (ZnBr) and Lithium-ion (LI), used individually. Using these three battery technologies, the proposed hybrid systems are then compared in terms of system sizing, economy, technical performance and environmental stability. The results show that the feasible configuration of Solar Photovoltaic (PV)/Diesel Generator (DG)/ZnBr battery systems provide the lowest net present cost (NPC), with values of $2.97M, $2.72M and $2.85M, and cost of energy (COE), with values $0.196, $0.18 and $0.188, in Addis Ababa, Jijiga and Bahir Dar, respectively. Of all feasible systems, the Wind Turbine (WT)/PV/LI, PV/LI and WT/PV/LI configurations have the highest values of NPC and COE in Addis Ababa, Jijiga and Bahir Dar. Using this configuration, the results demonstrate that ZnBr battery is the most favorable choice because the economic parameters, including total NPC and COE, are found to be lowest.

Grid-Scale Battery Energy Storage Systems (GS-BESS) play a crucial role in modern power grids, addressing challenges related to integrating renewable energy sources (RESs), load balancing, peak shaving, voltage support, load shifting, frequency regulation, emergency response, and enhancing system stability. However, harnessing their full potential and lifetime requires intelligent operational strategies that balance technological performance, economic viability, and environmental sustainability. This systematic review examines how artificial intelligence (AI)-based intelligent optimization enhances GS-BESS performance, focusing on its techno-economic, environmental impacts, and policy and regulatory implications. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we review the evolution of GS-BESS, analyze its advancements, and assess state-of-the-art applications and emerging AI techniques for GS-BESS optimization. AI techniques, including machine learning (ML), predictive modeling, optimization algorithms, deep learning (DL), and reinforcement learning (RL), are examined for their ability to improve operational efficiency and control precision in GS-BESSs. Furthermore, the review discusses the benefits of advanced dispatch strategies, including economic efficiency, emissions reduction, and improved grid resilience. Despite significant progress, challenges persist in data availability, model generalization, high computational requirements, scalability, and regulatory gaps. We conclude by identifying emerging opportunities to guide the next generation of intelligent energy storage systems. This work serves as a foundational resource for researchers, engineers, and policymakers seeking to advance the deployment of AI-enhanced GS-BESS for sustainable, resilient power systems. By analyzing the latest developments in AI applications and BESS technologies, this review provides a comprehensive perspective on their synergistic potential to drive sustainability, cost-effectiveness, and energy systems reliability.

This paper studies the risk data and protection measurements of lightning based on the IEC62305 standard. In addition, Visual Basic (VB) is used to build a lightning risk calculation program with a Graphical User Interface (GUI). The data structure, including environment data, line data, zone data, economic data, and protection measures is designed to simulate risk of loss of human life (R1) and risk of loss of economic value (R4). To achieve the most economical protection structure design, additional protection measurements and annual savings are considered. In the practical application, the main purpose is to discuss effective protection distances of surge protective devices (SPDs) for low-voltage power distribution. This paper takes advantage of Electromagnetic Transients Program (EMTP) to simulate the voltage of equipment with different types of loads and length of the cable. After using the protection measures, the value of risk of human life reduces form 21.299 × 10⁻⁵ to 0.439 × 10⁻⁵ and the value of risk of economic value reduces from 2696.754 × 10⁻⁵ to 98.062 × 10⁻⁵. The results mean that the protection measures let the values of the risk below the tolerance. By considering the annual cost saving. Assume the cost of protection measures, the interest rate, the depreciation rate, and the maintenance rate are 150,000 $, 4%, 5%, and 1%, respectively. The annual cost before using protective measures, the annual cost after using protective measures, the annual cost of protective measures, and the annual cost saving are $925,000, $33,635, $15,000 and $876,365, respectively. Consequently, it is feasible that the simulation result can provide users with great suggestions to choose the best installation location and achieve the most effective protection design.