Electric vehicle (EV) charging may impose a substantial power demand on existing low voltage (LV) and medium voltage (MV) networks, which are usually not prepared for high power demands in short time intervals. The influx of E-mobility may require an increase in grid reinforcements, but these can be reduced and optimized by a combination of new technologies, tools, and strategies, such as the deployment of solar PV generation integrated with aggregated energy storage systems. One of the challenges in the implementation of charging infrastructures in public stations is coupling the projected sizes of energy demand and power requirements in each location for each charger. This paper describes a method to estimate projected values for energy consumption and power demand in EV fast charging stations (CS). The proposed ideas were applied in a concept facility located in Campinas, Brazil, in a structure equipped with two 50 kW DC Fast Chargers, local 12.5 kW/13.2 kWp PV generation (to reduce energy impacts to the grid), and a 100 kW/200 kWh storage system, using electrochemical batteries (to minimize peak power requirements).

This work discusses the use of a battery energy storage system applied to the smoothing of power generated at the output of wind turbines based on a fuzzy logic power control. The fuzzy control logic proposed can perform the aforementioned activity while the state of charge of the energy storage system is maintained within operational limits. In order to assess the fuzzy logic power control’s effectiveness at maintaining the state of charge levels within the allowed range, two operating situations are explored: one in which the state of charge is above the upper limit allowed, and another in which the state of charge is in the minimum value allowed. The numerical results show that, when using the battery energy storage system in conjunction with the control logic proposed, the active power provided as the point of common coupling by the wind turbines can be smoothed, thus contributing to the Electric Power System reliability and stability. The main results of this paper are based on measurements of wind and active power associated with a wind generation plant installed in the northeast region of Brazil and equipped with 2.1 MW wind turbines.

Accelerated environmental impacts are a growing concern in the modern world. Electric mobility and the transition to a cleaner energy matrix have become increasingly discussed topics. In this context, this work presents a framework for controlling an electric vehicle (EV)-charging station integrated into a microgrid application as a basis for creating the infrastructure integrated into a smart grid concept. Considering the electrification of the transportation sector future perspectives, a brief review is conducted on the impacts of EV fleet growth in different countries and how smart-charging technologies are identified as solutions for mitigating the negative effects of energy and power consumption associated with EV-charging stations. An analysis of the technical characteristics and the tools that enable the deployment of a fleet-charging operator are examined, specifically focusing on the communication protocol for EVs, such as the OCPP (Open Charge Point Protocol) parameterization/configuration. A new EV-charging station control method is proposed to manage the impacts of distributed solar photovoltaic generation and mitigate the effects of the duck curve. Finally, an integration architecture via IEC 61850 for these elements is proposed, in a practical implementation for variable power control, considering different strategies to deal with distributed generation impact using EV-fleet-charging power demand dynamic management.

The application of automation techniques to water pump systems, combined with modern control techniques, has been increasing the hydraulic and energy efficiency of such systems. In this context, the objective of this work is to present an intelligent method of flow control based on Brain Emotional Learning Basic Intelligent Control (BELBIC), which will be applied to an experimental workbench of a pumping system, located in the Energy Efficiency and Energy Quality Laboratory (LEEQE) at Federal University of Pernambuco (UFPE). The parameters of this controller are optimized with a particle swarm optimization (PSO) technique with minimization of Integral Absolute Error (IAE). Initial tests were performed in a computational environment so that the system’s performance could be pre-tested, thereby the dynamics of the system was modeled from real data generated in the process. The experimental results were obtained through the implementation of this control system in a programmable logic controller (PLC), which was the device responsible for all the automation of the workbench previously mentioned. The data of this workbench were collected using a supervisory system exclusively developed for this work. These data were then used to analyze the performance of the proposed control system, which demonstrated that its behavior was efficient.

Accelerated environmental impacts are a growing concern in the modern world. As a result, electric mobility and the transition to a cleaner energy matrix have become increasingly discussed topics. Several countries and entities have already made medium and long-term commitments related to electric mobility. In this context, this work presents a framework for controlling an electric vehicle (EV) charging station, integrated into a microgrid application, as a basis for creating the infrastructure integrated into a smart grid concept. Considering the future perspectives associated with the electrification of the transportation sector and expected impacts, a brief review is conducted on the prospects for the growth of electric vehicle fleets in different countries and how smart charging technologies are identified as solutions for mitigating the negative effects of energy and power consumption associated with EV charging stations. An analysis of the technical characteristics of EV charging is carried out, and the tools that enable the deployment of a fleet charging operator are examined specifically focusing on the communication protocol for EVs, such as the OCPP (Open Charge Point Protocol) parameterization/configuration. Additionally, a new charging station control method is proposed, aiming to manage the impacts of integrating distributed solar photovoltaic generation and mitigate the effects of the duck curve. Finally, this work also proposes an integration architecture for these elements, resulting in a practical implementation for variable power control throughout the day, considering different strategies to deal with distributed generation impact using EV fleet charging power demand dynamic management. Finally, a proposal for integration via IEC 61850 using low-cost devices for the implementation of a digital communication system is presented.