Analysis of electric vehicle charging demand forecasting model based on Monte Carlo simulation and EMD-BO-LSTM

The 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

Optimal scheduling of aggregated electric vehicle charging with a smart coordination approach

Conventional 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

Optimal scheduling of on-Street EV charging stations

The 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

A systematic data-driven analysis of electric vehicle electricity consumption with wind power integration

Real-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.Honda R and D Co., Ltd.Power Electronics in Everything (PEiE)TMEi

Impact of electric vehicle charging profiles in data-driven framework on distribution network

In 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.Power Electronics in Everything / (PEIE)TMEi

Smart coordination of predictive load balancing for residential electric vehicles based on EMD‐Bayesian optimised LSTM

Abstract The charging load forecasting of residential Electric Vehicles help grid operators make informed decisions in terms of scheduling and managing demand response. The residence can include integrated residential appliances with multi‐state and high‐frequency features. For this reason, it is difficult to estimate the total load of residence accurately. To overcome this problem, this paper proposes a hybrid forecasting model using the empirical mode decomposition and Bayesian optimised Long Short‐Term Memory for load balancing based on residential electricity meter data. The residential electricity meter data includes three datasets as Electric Vehicle, heat pump and photovoltaic system. To decompose of the data characteristics, the empirical mode decomposition method performs to the original data. Then, the Bayesian optimised Long Short‐Term Memory is applied to forecast for each sub‐component of the data sequentially. The main features of the proposed model include a significant improvement in prediction accuracy and capture the local maximums. The advantage of the proposed method over existing methods are also verified over with experiments of data‐driven on the IEEE 33 busbar test system. The result of simulation forecasting model indicates that predict closely the busbar outflow power, voltage drop, transformer loading states and power losses to compare with actual load model

Modeling and evaluation of SOC-based coordinated EV charging for power management in a distribution system

The importance of using clean energy in electrical energy generation and transportation network planning has recently increased due to carbon footprint rising. In this direction, the use of electric vehicles (EV), known as ultra-low carbon emission vehicles, has become widespread in addition to renewable energy sources (RES) such as wind and photovoltaic (PV) power generations. The trend of EVs to be preferred the primary means of transport has revealed the effects of charging an additional load on the grid. There is a need to create coordinated charging methods by considering the approaches for real-time charging models of EVs. In this paper, SOC-based EV coordinated charging was proposed for power management to prevent adverse effects including transformer overload, instantaneous peak loading and line overload in the existing distribution network. The proposed coordinated EV charging method was tested on the modified Roy Billinton test system (RBTS) Bus 2 network. AC 11 kW uncoordinated charging units have been respectively 123.76% distribution transformer and 115.16% distribution line overloading for 500 EVs on the grid with 13,9% diversity factor. However, these values that are 72.05% of distribution transformer and 67.01% of distribution grid overloading according to permittable level were decreased by the proposed coordinated charging method. Also, the state of charge (SOC) based coordinated method can increase 3.5% rate the diversity factor of charging capacity at the charging station with PV and battery energy system (BES) while ensured grid stability and energy efficiency

Smart coordination of predictive load balancing for residential electric vehicles based on EMD-Bayesian optimised LSTM

The charging load forecasting of residential Electric Vehicles help grid operators make informed decisions in terms of scheduling and managing demand response. The residence can include integrated residential appliances with multi-state and high-frequency features. For this reason, it is difficult to estimate the total load of residence accurately. To overcome this problem, this paper proposes a hybrid forecasting model using the empirical mode decomposition and Bayesian optimised Long Short-Term Memory for load balancing based on residential electricity meter data. The residential electricity meter data includes three datasets as Electric Vehicle, heat pump and photovoltaic system. To decompose of the data characteristics, the empirical mode decomposition method performs to the original data. Then, the Bayesian optimised Long Short-Term Memory is applied to forecast for each sub-component of the data sequentially. The main features of the proposed model include a significant improvement in prediction accuracy and capture the local maximums. The advantage of the proposed method over existing methods are also verified over with experiments of data-driven on the IEEE 33 busbar test system. The result of simulation forecasting model indicates that predict closely the busbar outflow power, voltage drop, transformer loading states and power losses to compare with actual load model

The soc based dynamic charging coordination of evs in the pv-penetrated distribution network using real-world data

A successful distribution network can continue to operate despite the uncertainties at the charging station, with appropriate equipment retrofits and upgrades. However, these new investments in the grid can become complex in terms of time and space. In this paper, we propose a dynamic charge coordination (DCC) method based on the battery state of charge (SOC) of electric vehicles (EVs) in line with this purpose. The collective uncoordinated charging profiles of EVs charged at maximum power were investigated based on statistical data for distances of EVs and a real dataset for charging characteristics in the existing grid infrastructure. The proposed strategy was investigated using the modified Roy Billinton Test System (RBTS) performed by DIgSILENT Powerfactory simulation software for a total 50 EVs in 30 different models. Then, the load balancing situations were analyzed with the integration of the photovoltaic (PV) generation and battery energy storage system (BESS) into the bus bars where the EVs were fed into the grid. According to the simulation results, the proposed method dramatically reduces the effects on the grid compared to the uncoordinated charging method. Furthermore, the integration of PV and BESS system, load balancing for EVs was successfully achieved with the proposed approach

Adoption of Electric Vehicles: Purchase Intentions and Consumer Behaviors Research in Turkey

Electric vehicles (EVs) hold promise for attaining sustainable development objectives and mitigating the effects of global climate change due to their substantial benefits, such as high energy efficiency and low carbon emissions. Research on purchase intentions and behaviors may accelerate the adoption of EVs. Considering that the number of studies on EVs increases in tandem with the size of the market and that mutual interaction supports this two-way growth, conducting studies on consumer behavior in this area in countries where the electric vehicle market is developing, such as Turkey, will provide valuable insights for both the industry and the government. In this study, published articles on the consumer behavior of current and potential purchasers of electric vehicles were analyzed on the axis of Turkey, and the trend of academic studies in the literature was systematized from a holistic standpoint. Co-citation, co-keyword, geographical, and thematic analysis were applied to articles about EV consumer behaviors published between 2004 and 2022 in journals indexed by WoS, Scopus, TR Index, and DergiPark. The results of this study can inform numerous inter-disciplinary studies and researchers on the consumer behavior of electric vehicles. The bibliometric analysis of academic studies on electric vehicle consumers not only closes the market’s knowledge gap and accelerates the adoption process by increasing consumer awareness, but also provides industry representatives and policymakers with insights for the expansion of the EV market