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Demand response is expected to play a fundamental role in renewable energy communities to alleviate the electricity demand–supply mismatch, especially in the presence of stochastic load and generation. In this paper, we consider an electric vehicle charging station that participates in incentive-based demand response programs. A real-time charging scheme is devised to optimize the charging station operation by coordinating the charging process of the electric vehicles, and complying with the incoming demand response requests. In this context, vehicle demand is assumed uncertain, while demand response requests ask for a change in the charging profile over certain time intervals, in exchange for a monetary reward. By exploiting the probability distributions describing the vehicle charging process, a stochastic formulation is employed to devise a novel charging algorithm aimed at reducing the charging station operational cost. Such a procedure can (i) handle the uncertainty affecting the charging process in different settings and scenarios, and (ii) exploit the information collected in real-time to refine forecasts and hence ensure a higher demand flexibility. Numerical results show that the proposed approach ensures considerable cost reduction compared to the benchmarks, and features highly scalable runtimes. • A stochastic receding horizon scheme is introduced for real-time EV charging. • Acquired online information is employed to refine load forecasts. • EV probability distributions are used to manage the uncertain demand of EVs. • The algorithm performance is validated by comparison with oracle-based optimization. • Computation times are feasible for problem instances involving high EV penetrations. [ABSTRACT FROM AUTHOR]

This paper is aimed to present an experimental criterion that allows researchers and designers to evaluate the performance of DC micro-grids dedicated to charging operations of full EV. The laboratory methodology is explained through tests on a demonstrator of power architecture, specifically designed as simplified case study of a DC charging station for fully-electrified low-power two-wheeler, such as: electric scooters and bikes. This experimental prototype is composed by a 20 kW AC/DC bidirectional grid-tied converter, which realizes the DC conversion stage, and two DC/DC power converters, interconnected with the micro-grid through a DC bus. The power architecture provides on one hand the charging operations of electric two-wheeler battery packs and on the other hand the integration of the micro-grid with an ES buffer, which has the main function of supporting the main grid while an electric vehicle is on charge. The performance of the considered architecture is characterized and analyzed in different operative conditions, through a specific management of the energy fluxes. The laboratory tests evaluate efficiency, charging times and impact on the main grid, with specific reference to the DC charging operations of electric scooters. The obtained experimental results show the advantages of adopting the DC buffer architecture, compared with an AC commercial battery charger, considered as reference in this work. Finally, the numerical data of this paper, related to the single power components and to the experimental results evaluated for the whole demonstrator, effectively support the lack of knowledge in the literature about charging stations for EVs. In fact, these pieces of information, based on experimental tests, are expected to support the building of simulation models and the identification of the best energy management and control strategies to be adopted in a smart-grid scenario, characterized by distributed generation systems including renewable energy sources. [ABSTRACT FROM AUTHOR]

This paper is aimed to analyze design criteria, setting up, control strategies and experimental tests related to a power configuration of DC micro-grid for fast charging of full electric and plug in hybrid vehicles. The proposed DC fast charging architecture is derived by an analysis comparing the main characteristics of well known architectures, mainly based on AC and DC bus, taking also into account the integration of renewable energy sources (RESs) with stationary energy storage systems and fleets of road electric/hybrid vehicles. On the base of the proposed architecture a laboratory prototype of charging station has been realized by means of a 20 kW AC/DC bidirectional grid tie converter interconnected with two different power DC/DC converters of similar rated power. In this micro-grid architecture the AC/DC converter realizes a conversion stage at 790 V DC, whereas other two converters allow either the electric vehicle battery packs to be charged or an energy storage buffer to save electric energy and support the main grid during the fast charging operations. The laboratory tests described in this paper are mainly devoted to characterize the laboratory demonstrator, in different operative conditions, such as vehicle-to-grid (V2G), charging/discharging operations of different types of storage systems and fast charging operations of road electric vehicles. Then the study of the proposed power conversion architecture is focused on the evaluation of charging/discharging power, efficiency, energy flux management and its impact on the main grid. In addition proper control strategies are evaluated and implemented, allowing the proposed architecture to follow the required operations. The obtained experimental results demonstrate real advantages in terms of charging times and power requirements from the main grid, when adopting DC buffer architecture for fast charging operations. Finally, these results support the identification of a knowledge base, useful to evaluate energy management and control strategies to be adopted for DC charging stations and each one of their power converters in a smart grid scenario with distributed generation systems. [ABSTRACT FROM AUTHOR]

• A novel system is presented that predicts day-ahead demand profile and optimizes EV charging/discharging to minimize peak demand. • The system is designed to comply with preplanned EV trip schedules and minimum state of charge (SOC) requirements. • Four machine learning methods are tested to predict day ahead demand profile of a case study building at 15-minute intervals. • Five peak demand reduction scenarios are analyzed using combination of EVs, a stationary battery, and a PV system. • The results show up to 36 % reduction in peak demand using two EVs, one stationary battery, and PV system of 40 kW capacity. Reducing electricity peak demand is essential to maintain the balance between supply and demand side in electricity power markets, as well as reduce utility costs and environmental impacts. With growth in adoption of Electric Vehicles (EVs), there is an emerging opportunity to balance electrical power demand of buildings by storing electricity in EVs during low demand periods and discharging electricity into buildings during peak demand periods. Due to uncertainty in time and magnitude of peak demand, decision makers are always faced with a challenging task to identify optimal schedules for charging and discharging EVs to minimize peak electricity demand. This paper presents the development of a novel system that is capable of predicting day-ahead building electricity demand profile and identifying optimum schedule of charging and discharging EVs to minimize electricity peak demand. The system is designed to comply with planned EV trip schedules and minimum state of charge (SOC). The system consists of (1) machine Learning (ML) model to predict electrical power demand, and (2) demand management optimization model to identify optimal schedule for charging and discharging EVs. Four methods are explored to develop the ML model, including histogram-based gradient boosting, random forest, deep artificial neural network (DNN), and long short-term memory (LSTM). A case study of multi-tenant commercial building is analyzed to evaluate the performance of the system and demonstrate its new capabilities. The results of the case study shows that LSTM has the best performance in terms of mean absolute error, root mean square error, and mean absolute percentage error with average values of 7.44, 17.78, and 20.08 %, respectively. Five scenarios for shaving peak electricity demand, including combinations of two electric vehicles, a stationary battery, and a PV system are investigated. Scenarios including the stationary battery and the PV system are considered to evaluate the full potential of peak demand reduction in the case study building. The results of the demand management optimization model show up to 36 % reduction in peak demand using two EVs, one stationary battery, and PV system of 40 kW capacity. The key contributions that this study adds to existing knowledge are: (1) developing machine learning models to predict day-ahead electricity demand in 15-minute intervals, (2) integrating machine learning and optimization algorithms in identifying EV charging and discharging schedules to minimize utility cost by shaving peak energy demand, and (3) considering planned EV trips and minimum SOC requirements in identifying optimal charging and discharging of EVS to shave peak energy demand in buildings. The implementation of this system provides practical solutions for managing electricity demand in commercial buildings using EVs. By reducing energy consumption and promoting the innovative use of EVs, this system offers a sustainable approach to managing electricity demand in commercial buildings. [ABSTRACT FROM AUTHOR]

• Control authority in smart e-mobility is usually on the side of charging stations. • Electric vehicle based concept is an alternative where vehicles control charging. • In a proposed concept charging stations are merely an enabling infrastructure. • Electric vehicle based system yields higher revenues for the vehicle owners. The existing models designed to reap the benefits of electric vehicles' flexibility in the literature almost exclusively identify charging stations as active players exploiting this flexibility. Such stations are seen as static loads able to provide flexibility only when electric vehicles are connected to them. This standpoint, however, suffers from two major issues. First, the charging stations need to anticipate important parameters of the incoming vehicles, e.g. time of arrival/departure, state-of-energy at arrival/departure. Second, it interacts with vehicles only when connected to a specific charging station, thus overlooking the arbitrage opportunities when they are connected to other stations. This conventional way of addressing the electric vehicles is referred to as charging station-based e-mobility system. A new viewpoint is presented in this paper, where electric vehicles are observed as dynamic movable storage that can provide flexibility at any charging station. The paper defines both the existing system, where the flexibility is viewed from the standpoint of charging stations, and the proposed one, where the flexibility is viewed from the vehicles' standpoint. The both concepts are mathematically formulated as linear optimization programs and run over a simple case study to numerically evaluate the differences. Each of the four issues identified are individually examined and omission of corresponding constraints is analysed and quantified. The main result is that the proposed system yields better results for the vehicle owners. [ABSTRACT FROM AUTHOR]

• The capacity optimization model of the integrated photovoltaic- energy storage-charging station was built. • The case study bases on the data of 21 charging stations in Beijing. • The construction of the integrated charging station shows the maximum economic and environment benefit in hospital and minimum in residential. • A decline in energy storage costs increases the benefits of all-scale investments, an increase in electric vehicles promotes the benefits of small-scale investments, expansion of the peak-to-valley price distance increases the benefits of large-scale investments. The coupled photovoltaic-energy storage-charging station (PV-ES-CS) is an important approach of promoting the transition from fossil energy consumption to low-carbon energy use. However, the integrated charging station is underdeveloped. One of the key reasons for this is that there lacks the evaluation of its economic and environmental benefits. Based on the electricity load of different types of buildings and the data of electric vehicle charging stations in Beijing, this paper analyzes the economic and environmental benefits of integrated charging station developed over different scales by the capacity optimization model. Said benefits are shown to differ considerably with different locations (building types): the construction of the integrated charging station in places where the electricity load is high at night, and where the curve of daytime electricity consumption is consistent with the PV generation curve (e.g., hospitals), shows maximal economic benefits and carbon dioxide emissions reduction. In other (e.g., residential) areas, where power consumption is lower across both day and nighttime hours, the economic benefits and carbon dioxide emissions reduction are lowest. The economic and environmental benefits of the integrated charging station also markedly differ on different scales: with scale expansion, the rate of return on investment and the carbon dioxide emissions reduction first increase and then decrease. A decline in energy storage costs increases the economic benefits of all integrated charging station scales, an increase in EVs increases the economic benefits of small-scale investments, and expansion of the peak-to-valley price difference increases the economic benefits of large-scale investments. [ABSTRACT FROM AUTHOR]

• Real-world functional implementation of smart charging technology. • Development of scalable electric vehicle-to-building integration platform. • Proven demand charge mitigation using real-time electric vehicle charging. • Extensive economic analysis on installation and operation cost of smart charging. Higher electric vehicle (EV) adoption will stress the importance of demand flexibility to achieve more economic, efficient, and reliable grid operation. Charging technologies will be paramount in shifting temporally to better fit the variable generation of wind and solar. Therefore, analysis is warranted on the benefits of EV charge scheduling with respect to installation cost, operation cost, difficulty of implementation, and grid flexibility. We tackle this by analyzing the cost savings of implementing an EV charge scheduling infrastructure to reduce demand charges and installation costs. In this paper, we analyze a case study for operation of 16 level 2 chargers and 1 fast charger for two different building types. We then evaluate various test phases for controlling building and charging loads using an adaptive charging network (ACN) algorithm to characterize the ACN's potential to reduce overall project cost. [ABSTRACT FROM AUTHOR]

• A method to size charging stations and schedule charging times of EV is presented. • 200.000 observed real parking events at the University of Salerno are analyzed. • A packetized energy approach allows for solving the charging scheduling problem. • The solution ensures a high QoS level and minimize the cost for the CS manager. • Method and results are useful to study the potential of V2G applications. The expected deployment of battery electric vehicles (BEVs) strongly depends on the development of an adequate charging station (CS) infrastructure that guarantees a certain level of quality of service (QoS) to the BEV users. This paper proposes a two-stage method to select the number and type of CSs in parking areas (PAs) and schedule the charging sessions of the incoming BEVs ensuring a predetermined QoS level while minimizing the cost for the CS manager. In particular, stage one solves the CS sizing problem while stage two involves a probabilistic simulation procedure able to solve the charging scheduling problem by using a packetized energy approach. We also take into account the typical charging current and voltage characteristic of a BEV battery pack, and the real statistical distribution of BEV arriving times and expected parking times. A case study based on the PA at the University of Salerno Campus is used to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]