The accumulation of dust on any given photovoltaic (PV) module surface depends on the type of dust, environment, surroundings, weather, module properties, and its installation design. In this research, equations were developed for the preliminary evaluation and comparison of the reduction in power from PV modules because of dust soiling. Use of these equations shows that dust accumulation decreases solar irradiance and thus the power output of modules, and that there is a linear relationship between the power output degradation and density or amount of accumulated dust. The equations can be used to conduct analysis of average photon energy and the PV module power output reduction from accumulated dust. The study also showed that type of PV module can also affect the degree of power output reduction. The amorphous silicon PV modules are more affected compared to poly crystalline silicon PV modules as the latter has a spectrum response which still has the range that can produce full energy and therefore, dust soiling has lesser impact on them. PV power plants should regularly clean the modules, if not, production of electricity decreases, and so too the revenue from selling electricity, making the payback of the power plant longer.

Effects of high humid weather conditions on photovoltaic (PV) modules were examined in this study, particularly insulation resistance. Three types of tests were conducted which include leakage voltage test, leakage current test, and wet leakage current test. Due to the usual field constraints in the study of insulation resistance, which limited measurements of leakage current, assurance was made that representative sampling were conducted. The study found a high number of modules with low insulation resistance and high leakage voltage values, which can interrupt the PV plant operation. High leakage voltage creates safety hazards issues. About two third of the samples, which showed deep moisture ingress in the modules, could not pass the minimum criterion of IEC 61646 standard for wet insulation resistance testing. The leakage current results showed the same trend as of leakage voltage, proving that leakage voltage test, which is quite easy and economical, can be used to detect such type of problems in field tests. Prolonged humidity conditions of the PV power plant particularly from natural disaster, should be avoided. Efficient drainage system should be supported in and around installations and all other moisture sources should be regularly removed from the plant area to keep proper operation and minimize losses in energy production.

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.