Electric vehicles (EVs) improve the power grid by increasing intermittent renewable energy consumption and providing financial support to EV users via vehicle-to-grid (V2G) integration. While estimating these advantages, a number of studies have neglected to consider the effect of driving and charging behavior patterns on their results. This article provides a framework that systematically evaluates EV driving and charging behaviors to improve charge management in the light of recent standards and advancements. In addition, the collected data on driving habits are analyzed in order to provide a consistent and usable dataset. By evaluating the individual and simultaneous charging demand characteristics, the V2G potential is further explored. Moreover, managerial recommendations for EV charging management are offered by improving the time step using the Bootstrap approach for more precise results than lower resolution. It is also addressed that the simultaneous use of a limited number of EVs required minimum time. According to the findings of this study, daily travel habits have a crucial influence in defining seasonal and individual charging demands. In order to continue with EV charging-related assessments with a confidence interval of more than 95%, the findings suggest that time steps of lower than 10 minutes must be used. In addition, the purpose of this study is to assist researchers from academia and business with further information as they build initiatives linked to EV charging infrastructure and real-time charging management standards that account environmental aspects.

Considering photovoltaic systems’ sustainability and environmental friendliness, they have been widely used due to ease of installation as their cost reduces and their efficiency is improved. Analytical maximum power point tracking methods for photovoltaic system work effectively under uniform weather conditions. However, they may fall into local maximum power points due to partial shading conditions. Although numerous meta-heuristic methods can overcome these challenges, they can still be improved regarding the convergence time to the global maximum power point. This paper suggests an improved grey wolf optimization method to track global maximum power points, enhancing the convergence process and efficiency under various weather conditions. The proposed method has been verified experimentally under dynamic and real weather conditions, consisting of uniform and non-uniform weather conditions. The method provides better dynamic tracking speed and efficiency up to 82% and 1.4% compared to the basic grey wolf optimization. According to the daily performance evaluation, the IGWO reduces the runtime by up to 76% and improves energy harvesting up to 2.3% compared to basic grey wolf optimization. The obtained results validate the superiority of the method compared under partial shading conditions in terms of tracking time and accuracy.