The market for electric vehicles (EVs) has grown with each year, and EVs are considered to be a proper solution for the mitigation of urban pollution. So far, not much attention has been devoted to the use of EVs for public transportation, such as taxis and buses. However, a massive introduction of electric taxis (ETs) and electric buses (EBs) could generate issues in the grid. The challenges are different from those of private EVs, as their required load is much higher and the related time constraints must be considered with much more attention. These issues have begun to be studied within the last few years. This paper presents a review of the different approaches that have been proposed by various authors, to mitigate the impact of EBs and ETs on the future smart grid. Furthermore, some projects with regard to the integration of ETs and EBs around the world are presented. Some guidelines for future works are also proposed.

The penetration of solar energy within power systems is imperative to ensure the enduring sustainability of power systems, especially in cases of isolated systems such as microgrids. However, the variability in solar energy generation, which can impact grid stability and supply‐demand balance, highlights the necessity for reliable forecasting methods and advanced technological tools in addressing power system challenges. This paper presents an in‐depth exploration of solar irradiance forecasting, utilising a combination of advanced techniques, including deep learning and ensemble models. In particular, the utilisation of long short‐term memory (LSTM), random forest and extra trees in conjunction with multivariate meteorological data is investigated to enhance the accuracy and reliability of solar irradiance predictions. A comprehensive technique for modelling multivariate time series is employed to predict solar irradiance by incorporating various meteorological factors, such as temperature, relative humidity, and barometric pressure, among other relevant variables. A case study in Cuenca, Ecuador, was chosen based on real data obtained from a meteorological station, ensuring the accuracy and reliability of the data. The proposed method significantly enhances performance when compared to a baseline model, with the LSTM model notably excelling in predicting maximum and minimum solar irradiance, offering valuable insights for extended forecasting applications.

Algorithms for the maximum power point tracking MPP (Maximum Power Point), seek to maximize the delivered power of a photovoltaic panel. The Perturb and Observe (P&O) algorithm is the more used due to simplicity, however it is not the most efficient due to the perturbation step is fixed, at higher the size step fastest the MPP will be reached, but there would be a high steady state error; if the size step is low there would be less steady state error, but the MPP tracking became slow. Other well know method is the algorithm of Conductance Incremental (IC), in this method the step depends of the power variation versus the voltage. The variable step for P&O is more efficient for short atmospheric variations with less oscillations in the tracking and the variable step for CI is more efficient for big atmospheric variations with a fast MPP tracking. In this paper the 2 methods are combine to reduce oscillations and for a fast tracking for big and short irradiance variations.