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Electric car-sharing (ECS) is an increasingly popular service in many European cities. The management of an ECS fleet is more complex than its thermal engine counterpart due to the longer ”refueling“ time and the limited autonomy of the vehicles. To ensure adequate autonomy, the ECS provider needs high-capacity charging hubs located in urban areas where available peak power is often limited by the system power rating. Lastly, electric vehicle (EV) charging is typically entrusted to operators who retrieve discharged EVs in the city and connect them to the charging hub. The timing of the whole charging process may strongly differ among the vehicles due to their different states of charge on arrival at the hub. This makes it difficult to plan the charging events and leads to non-optimal exploitation of charging points. This paper provides a smart charging (SC) method that aims to support the ECS operators’ activity by optimizing the charging points’ utilization. The proposed SC promotes charging duration management by differently allocating powers among vehicles as a function of their state of charge and the desired end-of-charge time. The proposed method has been evaluated by considering a real case study. The results showed the ability to decrease charging points downtime by 71.5% on average with better exploitation of the available contracted power and an increase of 18.8% in the average number of EVs processed per day.

In large-scale photovoltaic (PV) power plants, the integration of a battery energy storage system (BESS) permits a more flexible operation, allowing the plant to support grid stability. In hybrid PV+BESS plants, the storage system can be integrated by using different power conversion system (PCS) layouts and different charge–discharge strategies. In the AC-coupling layout, the BESS is connected to the ac-side of the system through an additional inverter. In the DC-coupling layout, the BESS is connected to the dc-side, with or without a dedicated dc–dc converter, and no additional inverter is needed. Referring to a 288 MWp PV plant with a 275 MWh BESS, this paper compares the PCS efficiency between AC- and DC-coupling solutions. The power injected into the grid is obtained considering providing primary power-frequency regulation services. A charging and discharging strategy of the BESS is proposed to ensure cyclic battery energy shifting. The power flows in the different components of the system that are obtained under realistic operating conditions, and total energy losses and annual average efficiency are calculated accordingly. Finally, results show a higher efficiency of DC-coupling compared to the AC-coupling layout.

Electrical power grids are rapidly evolving into smart grids, with smart homes also making an important contribution to this. In fact, the well-known and emerging technologies of renewables, energy storage systems and electric mobility are each time more distributed throughout the power grid and included in smart homes. In such circumstances, since these technologies are natively operating in DC, it is predictable for a revolution in the electrical grid craving a convergence to DC grids. Nevertheless, traditional loads natively operating in AC will continue to be used, highlighting the importance of hybrid AC/DC grids. Considering this new paradigm, this paper has as main innovation points the proposed control algorithms regarding the role of front-end AC/DC converters in hybrid AC/DC smart homes, demonstrating their importance for providing unipolar or bipolar DC grids for interfacing native DC technologies, such as renewables and electric mobility, including concerns regarding the power quality from a smart grid point of view. Furthermore, the paper presents a clear description of the proposed control algorithms, aligned with distinct possibilities of complementary operation of front-end AC/DC converters in the perspective of smart homes framed within smart grids, e.g., enabling the control of smart homes in a coordinated way. The analysis and experimental results confirmed the suitability of the proposed innovative operation modes for hybrid AC/DC smart homes, based on two different AC/DC converters in the experimental validation., This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. This work has been supported by the FCT Project newERA4GRIDs PTDC/EEI-EEE/30283/2017, and by the FCT Project DAIPESEV PTDC/EEIEEE/30382/2017.

Lithium-Ion batteries are playing an essential role in electric vehicles and renewable sources development. In order to reduce the charging time, high power chargers are necessary. However, lithium-ion chemistry limits the maximum current and charging speed. The diffusion rate of lithium ions into the electrodes determines the rate of charging. The slow lithium diffusion, especially experienced after high current rates, inevitably results in concentration polarization. The increase of the concentration polarization, in addition to the growth of the charging time, may lead to a faster battery deterioration. To deal with this obstacle, the Pulse Charging (PC) protocol has been proposed. There is no common opinion about the benefits given by the PC to the battery charging process in comparison with the conventional constant-current, constant-voltage (CCCV) protocol. Nevertheless, the purpose of this work is to provide an overview of possible methods that can be used to generate current pulses, without focusing on its advantages. Different techniques with the corresponding control algorithms have been implemented and analyzed through simulations in MATLAB/Simulink environment.

In the context of electric vehicle (EV) development and positive energy districts with the growing penetration of non-programmable sources, this paper provides a method to predict and manage the aggregate power flows of charging stations to optimize the self-consumption and load profiles. The prediction method analyzes each charging event belonging to the EV population, and it considers the main factors that influence a charging process, namely the EV’s characteristics, charging ratings, and driver behavior. EV’s characteristics and charging ratings are obtained from the EV model’s and charging stations’ specifications, respectively. The statistical analysis of driver behavior is performed to calculate the daily consumptions and the charging energy request. Then, a model to estimate the parking time of each vehicle is extrapolated from the real collected data of the arrival and departure times in parking lots. A case study was carried out to evaluate the proposed method. This consisted of an industrial area with renewable sources and electrical loads. The obtained results show how EV charging can negatively impact system power flows, causing load peaks and high energy demand. Therefore, a charging management system (CMS) able to operate in the smart charging mode was introduced. Finally, it was demonstrated that the proposed method provides better EV integration and improved performance.