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.

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.

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

The current environmental challenges demand immediate actions, especially in the transport sector, 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 modeling and energy management of microgrids (MG), 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 BESS charge and discharge rates, considering battery wear. The grid-connected MG used in the case study consists of a wind turbine (WT), photovoltaic system (PVS), battery energy storage system (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 study results 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 battery usage, operating it in cycles that minimize BESS wear and effectively prolong its lifespan.

Energy is indispensable for the global functioning of the economy. However, when using fossil fuels, the effects on the environment have been shown to be quite harmful, according to data from the IPCC, the UN body responsible for controlling carbon dioxide emissions and causing the atmosphere to heat up or the greenhouse effect. Thus generating energy from renewable sources is important to create a future scenario of technological and economic development without damage to our habitat. In this work, we analyze data from intensive swine farming in the Northwest Mesoregion of the State of Rio Grande do Sul, Brazil, to present the wasted energy potential by not transforming the biogas from the activity into electricity and how much this volume of gas generates from dioxide carbon equivalent directly into the atmosphere. By analyzing this region, with data from the Secretariat of Agriculture and Livestock, it was possible to determine an energy potential above 13 thousand cubic meters per hour (13,000 m3/h) of biogas, which means an energy potential for transformation of 37 MWh and an emission of CO2 greater than 6.5 tCO2e/h, in a conversion between the potential of Methane (21 times more polluting than CO2) contained in the biogas in a concentration around 60% and that brings a considerable calorific power to transform this biogas into electricity. For this purpose, the rules of Aneel Resolution 482/2012 and revised by 687/2015 will be used, which allows energy compensation for renewable energy thrown directly into the electricity distribution network from the application of biodigesters. Through the analysis carried out it was possible to determine that there is a high potential to generate electricity and reduce the emission of gases that cause the greenhouse effect in this region, as it concentrates, a large volume of swines in intensive livestock.

Energy planning is a crucial tool in the search for planned solutions, to evaluate the behavior and performance of electrical systems. This article presents an energy planning strategy to compare the economic feasibility of electricity storage in the White Tariff versus the Conventional Tariff. In this analysis, the Homer Pro software was used to carry out simulations of a photovoltaic generation with electrical energy storage to supply a residential load of 11.25 kWh/d. The analysis considers the White and Conventional tariffs of the Rio Grande Energia (RGE) and Equatorial-PA distributors. To determine the solar radiation data, it is considered the load location in the city of Santa Maria-RS. For this research, there was progressive information about inverters, photovoltaic panels and commercial batteries in order to define the appropriate data and costs for the standard arrangement. At the end of the simulations, a comparison was made between the settings chosen by the software for Conventional and White tariffs, as well as for the different company’s prices of the tariff. The simulation results for the used example indicated that the most advantageous option for the city of Santa Maria-RS served by RGE is a photovoltaic generation connected to the grid, which presenting the lowest energy cost and the lowest Net Present Cost (NPC) over the course of a 25-year project, both for the White Tariff and for the Conventional one. However, in a scenario where the tariff value increases for Equatorial-PA, it was observed that combination of storage accompanied by the photovoltaic system and network participation would become ideal for the White tariff.