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As previously discussed with the editorial team of Energies, it is necessary to modify two references due to errors [...]

The theoretical impact of the electric vehicle (EV) market share growth has been widely discussed with regards to technical and socioeconomic aspects in recent years. However, the prospection of EV scenarios is a challenge, and the difficulty increases with the granularity of the study and the set of variables affected by user behavior and regional aspects. Moreover, the lack of a robust database to estimate fast-charging stations’ load curves, for example, affects the quality of planning, allocation, or grid impact studies. When this problem is evaluated on highways, the challenge increases due to the reduced number of trips related to the reduced number of charger units installed and the limited EVs range, which influence user anxiety. This paper presents a methodology to estimate the highway fast-charging station operation condition, considering regional and EV user aspects. The process is based in a block of traffic simulation, considering the traffic information and highway patterns composing the matrix solution model. Also, the output block estimates charging stations’ operational conditions, considering infrastructure scenarios and simulated traffic. A Monte Carlo simulation is presented to model entrance rates and charging times, considering the PDF of stochastic inputs. The results are shown for the aspects of load curve and queue length for one case study, and a sensibility study was conducted to evaluate the impact of model inputs.

The article presents the development of a Rules-Based Energy Management System for a nanogrid that serves an electric vehicle charging station. This nanogrid is composed of photovoltaic generation, a wind turbine, a battery energy storage system, and a fast electric vehicle charger. The objective is to prioritize the use of renewable energy sources, reducing costs and promoting energy efficiency. The methodology includes forecasting models based on an Artificial Neural Network for photovoltaic generation, a parametric estimation for wind generation, and a Monte Carlo simulation to predict the energy consumption of electric vehicles. The developed algorithm makes decisions every 15 min, considering variables such as energy tariff, battery state of charge, renewable generation forecast, and energy consumption forecast. The results showed that the system adequately balances energy generation, consumption, and storage, even under forecasting uncertainties. The use of the Monte Carlo simulation was crucial for evaluating the financial impacts of forecast errors, enabling robust decision-making. This energy management system proved to be effective and sustainable for nanogrids dedicated to electric vehicle charging, with the potential to reduce operational costs and increase energy reliability and the use of renewable energy sources.

The fast charging of electric vehicles (EVs) has stood out prominently as an alternative for long-distance travel. These charging events typically occur at public fast charging stations (FCSs) within brief timeframes, which requires a substantial demand for power and energy in a short period. To adequately prepare the system for the widespread adoption of EVs, it is imperative to comprehend and establish standards for user behavior. This study employs agglomerative clustering, kernel density estimation, beta distribution, and data mining techniques to model and identify patterns in these charging events. They utilize telemetry data from charging events on highways, which are public and cost-free. Critical parameters such as stage of charge (SoC), energy, power, time, and location are examined to understand user dynamics during charging events. The findings of this research provide a clear insight into user behavior by separating charging events into five groups, which significantly clarifies user behavior and allows for mathematical modeling. Also, the results show that the FCSs have varying patterns according to the location. They serve as a basis for future research, including topics for further investigations, such as integrating charging events with renewable energy sources, establishing load management policies, and generating accurate load forecasting models.

Current environmental challenges demand immediate action, especially in the transport sector, which is one of the largest CO2 emitters. Vehicle electrification is considered an essential strategy for emission mitigation and combating global warming. This study presents methodologies for the modeling and energy management of microgrids (MGs) designed as charging stations for electric vehicles (EVs). Algorithms were developed to estimate daily energy generation and charging events in the MG. These data feed an energy management algorithm aimed at minimizing the costs associated with energy trading operations, as well as the charging and discharging cycles of the battery energy storage system (BESS). The problem constraints ensure the safe operation of the system, availability of backup energy for off-grid conditions, preference for reduced tariffs, and optimized management of the BESS charge and discharge rates, considering battery wear. The grid-connected MG used in our case study consists of a wind turbine (WT), photovoltaic system (PVS), BESS, and an electric vehicle fast charging station (EVFCS). Located on a highway, the MG was designed to provide fast charging, extending the range of EVs and reducing drivers’ range anxiety. The results of this study demonstrated the effectiveness of the proposed energy management approach, with the optimization algorithm efficiently managing energy flows within the MG while prioritizing lower operational costs. The inclusion of the battery wear model makes the optimizer more selective in terms of battery usage, operating it in cycles that minimize BESS wear and effectively prolong its lifespan.

Distributed Energy Resources (DERs) aggregation increases the sustainability of the Electric Vehicles (EVs) market. For example, Fast Charging Stations (FCSs) associated with distributed generation and storage systems in a microgrid infrastructure may be beneficial in increasing self-consumption and peak-shaving strategies and mitigating impacts on the grid. However, microgrid sizing planning is a complex challenge, mainly due to numerous factors related to EV market growth and user behavior. This work defines a methodology focusing on sizing planning and analysis of microgrids for FCSs based on quantitative indices formulated according to the Net Zero Energy Building (NZEB) concept, optimizing self-sufficiency and limiting impacts on the primary electrical grid. The methodology is applied to a real case study considering the growth of EVs in southern Brazil. The developed analyses demonstrate that the proposed microgrid meets the energy needs of the FCS and presents the best NZEB indexes within the considered study horizon. Additionally, representative profiles were characterized for different load and generation conditions, complementing the analyses. It was shown that the storage promotes a delay and reduction in the reverse peak power flow, further enhancing the NZEB indexes.