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.
Many efforts have been made to define patterns, predict, and forecast energy use. However, changes in energy consumption may be studied in detail using various methodologies. This work presents a statistical methodology to assess changes in a facility consumption profile. Consumption patterns are obtained from a historical database of a predefined time interval, according to the type of day (day of the week, working or non-working), and an index that assesses change in the electrical consumption profile is proposed. Assessing these changes enables associating these values with possible events in a facility, which can serve to generate alarms in an energy management system, and reduce costs and maintenance periods. Additionally, a multi-criteria interpretation of the applied test table is presented that offers explanations and identifies possible causes of anomalous consumption.
The electricity sector presents new challenges in the operation and planning of power systems, such as the forecast of power demand. This paper proposes a comprehensive approach for evaluating statistical methods and techniques of electric demand forecast. The proposed approach is based on smoothing methods, simple and multiple regressions, and ARIMA models, applied to two real university buildings from Ecuador and Spain. The results are analyzed by statistical metrics to assess their predictive capacity, and they indicate that the Holt-Winter and ARIMA methods have the best performance to forecast the electricity demand (ED).
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