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Öğe Analysis of electric vehicle charging demand forecasting model based on Monte Carlo simulation and EMD-BO-LSTM(Institute of Electrical and Electronics Engineers Inc., 2022) Akıl, Murat; Dokur, Emrah; Bayındır, RamazanThe stochastic charging behaviors of Electric Vehicle (EV) users illustrate the negative effects of bulk charging during peak hours on the grid. To overcome this problem, the bulk EV charging demand forecasting approach is investigated using historical EV charge demand dataset and EV driver mobility statictics in this paper. In this model, a Monte Carlo Simulation (MCS) is perfomed that considers the charging behavior of EV users for the generation of EV charging times. Moreover, the EV charging times are combined with the bulk EV demand hybrid forecasting model using decomposition and deep learning time series method. In first stage, the EV demand time series dataset are divided to improve the model performance by empirical mode decomposition (EMD). Then, all decomposed signals are forecasted separately using the Bayesian optimized Long Short-Term Memory LSTM network (BO-LSTM). Finally, to evaluate the model perfomance, the power system analysis using IEEE 33 busbar test system is performed in terms of distribution network power losses, busbar voltage drops and transformer loading conditions.Öğe Optimal scheduling of aggregated electric vehicle charging with a smart coordination approach(Institute of Electrical and Electronics Engineers Inc., 2022) Akıl, Murat; Dokur, Emrah; Bayındır, RamazanConventional internal combustion engine vehicles are one of the main reasons for the increase in carbon emissions. The Electric Vehicles (EVs) in the transportation sector to significantly reduce these emissions, can be expanded collectively instead of these vehicles. While EVs are still hindered from adoption due to their battery life, cost and few other challenges, the global fuel crisis around the world, sanctions and incentives in government policies are helping large-scale EVs adoption. The increase in EVs penetration adds an indefinite amount of electricity to the grid and is likely to pose a very complex operating problem for distribution grid operators. Since EV users want to leave with maximum battery energy capacity, uncoordinated charging can damage grid equipment in the distribution system. Accurate charge scheduling of EVs is essential for seamless integration of EVs into the grid. However, in this charging scheduling, it is necessary to consider the battery energy capacities of the EVs as well as the charging costs. In this paper, the optimal charging scheduling of EVs under the proposed smart coordination was performed according to the battery capacity. In this way, uncoordinated charging was prevented, which led to an increase in the peak power of the distribution system. Data for EV charging time, waiting time and battery energy-capacity were obtained by Monte Carlo Simulations (MCSs) based on statistical data. The Mixed Integer Linear programming (MILP) technique was used for charging scheduling of EVs. The results show that the proposed approach is a systematic reference, as it both reduces the charging cost of the users when charging the EVssand efficiently uses the load smoothing and load-shifting strategies in the distribution network.Öğe Optimal scheduling of on-Street EV charging stations(Institute of Electrical and Electronics Engineers Inc., 2022) Akıl, Murat; Dokur, Emrah; Bayındır, RamazanThe uncoordinated charging of Electric Vehicles (EVs) into the grid increases the stochastic rebound peak on the grid. These charging demands can strain grid equipment at the street charging points in an area. In this study, a smart coordination approach is proposed for charging process management by considering the parking times of EVs. EV types with different characteristics are used in the smart coordination approach. This approach limits the charging powers to the minimum value between the charging point and the EV maximum power ratio. Also, the approach using quadratic programming (QP) for charge scheduling of 20 EV minimizes the cost of daily charging via the Generic Algebraic Modeling System (GAMS). The results show that EV charges occur within the maximum allowable grid limits, reducing the cost of charging. Additionally, the proposed smart coordination prevented the occurrence of daily on-grid rebound peaks at street charging points in the area.Öğe Uncoordinated charging profile of EVs based on an actual charging session data(Institute of Electrical and Electronics Engineers Inc., 2021) Akıl, Murat; Kılıç, Ensar; Bayındır, RamazanThe energy profile of gathered EVs is difficult to determine, as the actual charging data of Electric Vehicles (EVs) cannot be shared among distribution service providers. In this study, a dataset containing weekday and weekend information over a one-day period based on actual EV charging sessions in the Perth and Kinross region is used. This dataset contains the start and end times of EVs charging. Based on these data, a total of 5000 vehicle session data were derived for Monte Carlo Simulation (MCS) at 15-minute intervals. According to the data obtained, uncoordinated (without power reduction, restriction and timing) AC charge load profiles of bulk EVs with 50kWh battery capacity and maximum power up to 22kW in accordance with IEC 61851-1 standards were generated. Accordingly, peak loading and peak loading times of 5000 EVs in the distribution line in case of uncoordinated charging for Perth and Kinross regions were evaluated for weekdays and weekends. The results found provided information on EV load peak times for distribution service providers in the city of Perth, both weekdays and weekends.Öğe Electric vehicles charging management with Monte Carlo simulation(Institute of Electrical and Electronics Engineers Inc., 2021) Kılıç, Ensar; Akıl, Murat; Bayındır, RamazanThe effects of climate change are being felt more and more day by day. Reducing emissions from fossil fuels is among the priority measures to deal with the climate crisis. With the increase in the sales of zero-emission Electric Vehicles (EV), the number of charging stations for the charging needs of EV will also increase. In order to meet the EV charging need, grid must be operated in a stable, efficient and uninterrupted manner. In this paper, the charging power required for EVs at home and in public areas was modeled with Monte Carlo Simulation (MCS) over real data, peak load and total load values were calculated. The results showed that the charging need can be met with lower peak load values in public areas instead of charging at home traditionally.Öğe A systematic data-driven analysis of electric vehicle electricity consumption with wind power integration(Institute of Electrical and Electronics Engineers Inc., 2021) Akıl, Murat; Dokur, Emrah; Bayındır, RamazanReal-time charging data of Electric Vehicles (EVs) cannot be easily shared between service providers, making analysis of the energy profile is difficult of collective EVs. This paper uses a real-time dataset that analyzes real-world charging load profiles of EVs to the nearest 15 minutes for one day period. This dataset includes charging data from 21 EVs at different session times and different locations in a region. The data was systematically expanded to take advantage of the Wind Turbine (WT) generation power which is one of the Renewable Energy Sources (RES) in the charge energy consumption of collective EVs in modified bus-2 network of the Roy Billington Test System (RBTS). Instead of assuming that EVs were constantly charging at maximum power in creating a charge-load profile, collective charge-load profiles were simulated based on the actual charging at varying power. Simulation results show that EV charging peak loads can decrease with an onsite WT generation power. Thus, the load balancing was performed due to the wind energy conversion system instead of load shifting in the modeled power system.Öğe Impact of electric vehicle charging profiles in data-driven framework on distribution network(Institute of Electrical and Electronics Engineers Inc., 2021) Akil, Murat; Dokur, Emrah; Bayındır, RamazanIn the field of transportation and energy production, Electric Vehicles (EVs) with rechargeable property is encouraged to using in many countries against carbon emissions. EVs are produced with different charging rate and energy capacity in last years. The uncertainties of EVs and EV users show the negative effects of charging at times of bulk charging on the grid. A successful distribution network operator has the option to charge EVs, which are increasing day by day with new investments in infrastructure and other equipment. However, new investments do not please both EV users and charging service providers in terms of cost and time. In this paper, the power management with the SOC-based coordinated charging method, which enable dynamic charging of EVs using real data-driven charging profiles, was proposed in the existed grid infrastructure. Firstly, 30 different EV types in 50 EV charging units connected to added between Bus 35 and Bus 36 in the Roy Billinton Bus-2 Test System. The coordinated charging method was compared with the uncoordinated charging method in terms of grid drawn active power at peak time and line loading. Secondly, peak load conditions of the grid were reduced with the integration of photovoltaic (PV) generation and battery energy storage (BES) system to the relevant bus on the test system. In addition, energy efficiency in terms of line loading has been demonstrated according to the uncoordinated charging method of the proposed coordinated charging approach.
