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.

This paper presents the development of a methodology to evaluate candidates places for installation an EV fast charging microgrid (EVFCM), composed by charging station, electrical grid connection, photovoltaic carport and a small wind generation. The methodology proposed focus on evaluating pre-defined points over eleven different criteria, such as: vehicle flux, proximity of medium voltage grid, availability of wind and solar generation, and socioeconomical data from place and population. The overall goal is to classify hierarchically this points to find the best options places to install the EVFCM. The case study is applied in the east of the state of Rio Grande do Sul, and the predefined points are sitting along important highways that connect Brazil to Uruguay.

O avanço acelerado do consumo de combustíveis fósseis, impulsionado pelo crescimento exponencial da produção e do consumo de mercadorias – sobretudo nos países altamente industrializados –, tem provocado impactos ambientais irreversíveis, comprometendo o equilíbrio ecológico e agravando a crise climática global. Diante desse cenário, torna-se imprescindível uma transição energética estruturada, baseada na integração de fontes renováveis, garantindo a redução das emissões de carbono e a mitigação dos efeitos das mudanças climáticas. Além disso, essa transformação deve estar alinhada à preservação da biodiversidade, assegurando que a adoção de energias limpas não resulte na degradação de ecossistemas sensíveis. Paralelamente, é fundamental promover uma economia verde, capaz de conciliar desenvolvimento sustentável e inclusão socioeconômica, fomentando políticas que incentivem investimentos nessa área. Dito isso, indagamos: De que maneira a transição energética pode ser implementada de forma eficaz, garantindo a integração de fontes renováveis, a preservação da biodiversidade e o fortalecimento da economia verde em um contexto de crise climática? Desse modo, nos debruçamos, teoricamente, sobre os trabalhos de Warming (1908), Sachs (2000), Bautista Vidal e Vasconcellos (2001), Vasconcellos (2002), Galindo-Leal e Câmara (2005), Vasconcellos, Bautista Vidal e Guimarães (2005), Alvim e Badiru (2014), Bursztyn (2012), Wilson (2012), Bursztyn e Bursztyn (2013), Holmgren (2013), Peixoto, Luz e Brito (2016), Mauad, Ferreira e Trindade (2017), Moreira (2019), entre outros. Metodologicamente, fizemos uso da pesquisa qualitativa conforme Minayo (2006), descritiva e bibliográfica de acordo com Gil (2006) e com o viés compreensivo a partir de Weber (1969). Os achados evidenciam que a transição energética eficaz requer um planejamento integrado, considerando aspectos tecnológicos, ambientais e socioeconômicos. A adoção de fontes renováveis deve ser acompanhada de políticas públicas que incentivem pesquisa e inovação, minimizando impactos sobre ecossistemas sensíveis. Além disso, aeconomia verde deve ser fomentada por meio de incentivos fiscais e investimentos sustentáveis, promovendo inclusão social. Observou-se que países com estratégias articuladas entre Estado e setor privado alcançam melhores resultados na descarbonização. Por fim, destaca-se a necessidade de uma governança global para garantir a efetividade das ações climáticas.

Global Electric Vehicle (EV) stock is expanding rapidly. As EV penetration grows, the power demand by EV for charging will increase, indicating a potential reshaping of load profile on the distribution network and likely impacts on the distribution transformers. This study forecasts EV load in a large residential network using specific probabilistic modelling approach. The EV load is estimated using light duty vehicles usage pattern based on a trip travel survey analysis. The trip survey is categorized into trip frequency, trip purpose, distance driven, trip time and duration of travel, which are all treated as distribution of probabilities. In addition to the trip survey analysis, the probabilistic model considers the EV type, model, battery capacity, state of charge, on-board charger level and fuel consumption to forecast energy demand by EV. The performance of the distribution transformers in response to the EV behavior are further investigated from the comparison of the proposed EV demand forecasting model with a generic Poisson model. The results show that, depending on EV penetration level, the distribution transformer can be overload, especially for EV participation higher than 50%.

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.