• Energy Research

A novel self-heating technique is proposed to clear snow from photovoltaic panels as a solution to the issue of winter snow accumulation in photovoltaic (PV) power plants. This approach aims to address the shortcomings of existing methods. It reduces PV cell wear, resource loss, and safety risks, without the need for additional devices. A self-heating current is applied to the solar panel to melt the snow covering its surface, which is then allowed to slide off the panel due to gravity. The proposed system consists of a bidirectional DC-DC converter, which removes the snow cover by heating the solar PV modules using electricity from the grid or electric vehicle (EV) batteries. It also charges the EV battery pack and/or supplies the DC bus when no EV is plugged into the charging station. For each mode of operation, a current-controlled system was implemented using a PI controller and a model predictive controller (MPC). The MPC approach achieved a faster rise time, shorter settling time, very low current ripples, and high stability for the proposed system. Specifically, the settling time decreased from 9 ms and 155 ms when using the PI controller at 20 µs and 35 µs with the MPC controller for both the buck and boost modes, respectively.

Stochastic fast-charging of electric vehicles (EVs) affect the security and economic operation of the distribution power network. Aggregator awareness in the electric power industry is fast growing in tandem with the growing number of EVs. This paper proposes a novel smart techno-economic operation of the electric vehicle charging station (EVCS) in Egypt controlled by the aggregator based on a hierarchal model. The deterministic charging scheduling of the EVs is the upper stage of the model to balance the generated and consumed power of the station and flat the surplus power supplied to the utility grid. Mixed-integer linear programming (MILP) is used to solve the first stage where the peak demand value is reduced to 48.17% (4.5 kW) without using any extra battery storage systems. The second challenging stage is to maximize the charging station profit whilst minimizing the EV charging tariff, which needs a trade-off. In this stage, MILP and Markov Decision Process Reinforcement Learning (MDP-RL) resulted an increase in EVCS revenue by 28.88% and 20.10%, respectively. However, the EVs charging tariff is increased by 21.19%, and 15.03%, respectively. Hence, MDP-RL is an adequate algorithm for such a complex model. The outcomes reveal a sufficient techno-economic hierarchal model concerning the normal operation stated in the literature.

Despite fast technological advances, the worldwide adoption of electric vehicles (EVs) is still hampered mainly by charging time, efficiency, and lifespan. Lithium-ion batteries have become the primary source for EVs because of their high energy density and long lifetime. Currently, several methods intend to determine the health of lithium-ion batteries fast-charging protocols. Filling a gap in the literature, a clear classification of charging protocols is presented and investigated here. This paper categorizes fast-charging protocols into the power management protocol, which depends on a controllable current, voltage, and cell temperature, and the material aspects charging protocol, which is based on material physical modification and chemical structures of the lithium-ion battery. In addition, each of the charging protocols is further subdivided into more detailed methodologies and aspects. A full evaluation and comparison of the latest studies is proposed according to the underlying parameterization effort, the battery cell used, efficiency, cycle life, charging time, and increase in surface temperature of the battery. The pros and cons of each protocol are scrutinized to reveal possible research tracks concerning EV fast-charging protocols.

Micro-Grid (MG) with hybrid power resources can supply electric loads independently. In case of surplus power, the neighborhood micro-grids can be integrated together in order to supply the overloaded micro-grid. The challenge is to select the most suitable, optimal and preferable micro-grid within a distributed network, which consists of islanded MGs, to form that integration. This paper presents an intelligent decision-making criteria based on the Weighted Arithmetic Mean (WAM) of different technical indices, for optimal selection of micro-grids integration in case of overloaded event due to either unusual increase in consumed power or any deficiency in power generation. In addition, overloading is expected due to excess increase or decrease in weather temperature. This may lead to extreme increase of load due to increase of air conditioning or heating loads respectively. The proposed arithmetic mean determination based on six multi-objective indices, which are voltage deviation, frequency deviation, reliability, power loss in transmission lines, electricity price and CO2 emission is applied. This work is developed through three main scenarios. The first scenario studies the effect of each index on the integrated micro-grid formation. The second scenario is the biased optimization analysis. In this stage, the optimal micro-grids integration is based on intentionally chosen multi-objective index weights to fulfil certain requirements. The third scenario targets the optimal selection of the multi-objective indices’ effectiveness weights for power system optimum redistribution. The sharing weights of each index will be optimally selected by Water Cycle Optimization Technique (WCOT) and Genetic Algorithm (GA) addressing the system optimal power sharing through optimum micro-grids re-formation (integration). WCOT and GA are simulated using MATLAB (R2017a, The MathWorks Ltd, Natick, MA, USA). The developed work is applied to a distributed network which consists of a five micro-grid tested system, with one overloaded micro-grid. The three modules are utilized for multi-objective analysis of different alternative micro-grids. Both WCOT and GA results are compared. In addition, it is investigated to find and validate the optimum solution. Final decision-making for optimal combination is determined, aiming to reach a perfect technical, economic and environmental solution. The results indicate that the optimal decision may be modified after each individual index weight exceeds a specific limit.

This paper presses a smart charging decision-making criterion that significantly contributes in enhancing the scheduling of the electric vehicles (EVs) during the charging process. The proposed criterion aims to optimize the charging time, select the charging methodology either DC constant current constant voltage (DC-CCCV) or DC multi-stage constant currents (DC-MSCC), maximize the charging capacity as well as minimize the queuing delay per EV, especially during peak hours. The decision-making algorithms have been developed by utilizing metaheuristic algorithms including the Genetic Algorithm (GA) and Water Cycle Optimization Algorithm (WCOA). The utility of the proposed models has been investigated while considering the Mixed Integer Linear Programming (MILP) as a benchmark. Furthermore, the proposed models are seeded using the Monte Carlo simulation technique by estimating the EVs arriving density to the EVS across the day. WCOA has shown an overall reduction of 13% and 8.5% in the total charging time while referring to MILP and GA respectively.

AbstractThe objective of this paper is to develop a generic electric vehicle battery charging framework using wind energy as the direct energy source. A robust model for a small vertical axis wind turbine based on an artificial neural network algorithm is used for predicting its performance over a wide range of operating conditions. The proposed framework can be implemented at any location worldwide where full prediction of the wind signature is perfectly obtained. In this paper, a small vertical axis wind turbine has been experimentally characterized at different operating conditions, where measured data, output power, and torque have been used to build the model. Once the model has been developed, the model is inserted into the MATLAB/Simulink software tool to predict the charging performance of a battery for an electric vehicle. An rpm controller has been used to achieve the maximum generated power from the wind turbine across the day with various wind speeds. Hence, the generated power is fed to the EV battery charger to implement the constant current constant voltage charging protocol. The charging current reached the desired value in a settling time of 4.5 s, whatever the intermittency of the wind energy. The proposed application of wind energy to EV provides sufficient constant power supported by the utility grid. Graphical Abstract

Toward automobile electrification and automation, a smart scenario of DC-charging plug-in electric vehicles (PEVs) at any parking lot equipped with chargers is proposed. In this paper, this scenario is composed of four main stages; In the first stage, an investigation of the temperature or/and relative humidity impact on the charging process of the PEVs is implemented using the constant current-constant voltage (CC-CV) protocol. This was followed by a novel PEV classification model under the impacts of various ambient circumstances. Then an estimation of the charging characteristic parameters at the corresponding conditions is obtained. Finally, the model identification of the battery dynamic behaviour is sufficiently proposed. The feedforward backpropagation neural network (FFBP-NN) as a supervised classification algorithm supported by the statistical analysis of an instant charging current sample is used, which achieves an accuracy of 83.2%. In addition, the FFBP-NN perfectly estimated the charging current, terminal voltage, and charging interval time with a maximum error of 1%. Eventually, a sufficient identification model of the battery dynamic behaviour based on the Hammerstein-Wiener (HW) model is introduced with the best fit of 89.62% and an error of 1.1876%. The experimental and simulated results are within 1%error with the preceding research literature.

Due to the exponential expansion of the global fleet of electric vehicles (EVs) in the utility grid, the vehicle-to-grid paradigm is gaining more attention to alleviate the pressure on the grid. Therefore, an EV aggregator acts as a resilient load to enhance the power deficiency in the electrical grid. This paper proposes the vital development of a central aggregator to optimize the hierarchical bi-directional technique throughout the vehicle-to-grid (V2G) and grid-to-vehicle (G2V) technologies. This study was implemented using three different types of EVs that are assumed to penetrate the utility grid throughout the day in an organized pattern. The aggregator determines the number of EVs that would participate in the electric power trade during the day and sets the charging/discharging capacity level for each EV. In addition, the proposed model minimized the battery degradation cost while maximizing the revenue of the EV owner using the V2G technology and ensuring a sufficient grid peak load demand shaving based on the genetic algorithm (GA). Three case studies were investigated based on the parking interval time where the battery degradation cost was minimized to reach approx. 82.04%. However, the revenue of the EV owner increased when the battery degradation cost was ignored. In addition, the load demand decreased by 26.5%. The implemented methodology ensured an effective grid stabilization service by shaving the load demand to identify the average required power throughout the day. The efficiency of the proposed methodology is ensured since our output findings were in good agreement with the literature survey.

In recent years, the operation of the electric power grid has become more efficient and resilient due to the integration of renewable energy sources (RESs). Solar and wind energy are being incorporated aggressively into the main grid, while other RESs like biomass and geothermal energy are also on the rise. However, the intermittent nature of these RESs necessitates the use of energy storage devices (ESDs) as a backup for electricity generation such as batteries, supercapacitors, and flywheel energy storage systems (FESS). This paper provides a thorough review of the standardization, market applications, and grid integration of FESS. It examines the components of FESS, including the electric motor/generator set, power converters, bearings, and control techniques. The paper also highlights the application of modern artificial intelligence (AI) methodologies in optimizing FESS operations, referencing over 240 recent publications in reputable journals. Metaheuristic optimizers, machine learning techniques, and well-matures software's are the main AI aspects discussed in this paper. Additionally, it explores the use of FESS in commercial sectors such as marine, space, and transportation, and its integration with RESs for participating in green energy. Finally, the paper emphasizes the role of AI in enhancing the synergy between FESS and RESs to contribute to a more sustainable and secure energy future.