Electric Vehicle Charging Station Load Research Articles

Intelligent Solar PV Grid Connected and Standalone UPQC for EV Charging Station Load

The insertion of renewable energy sources into the grid, as well as the development of power electronics technology to regulate loads that are not linear, had an impact on power quality (PQ). This study focuses on the PQ enhancement of grid-connected and standalone solar PV systems (SPVS) with battery energy storage device (BESD) for the Electric vehicle (EV) charging station (EVCS) load in addition to the local load. Here, a hybrid control strategy that uses both the superior qualities of the sliding mode controller (SMC) and the fuzzy logic controller (FLC) is suggested for the unified power quality conditioner (UPQC's) shunt filter. It's major goal is to achieve steady DC capacitance voltage (SVDC) during load (like EVCS, non linear balanced/unbalanced etc.) and irradiation variations, diminish of total harmonic distortion (THD) in source current and load voltage, and mitigate sag, swell disturbances, and source voltage unbalances. The created model's performance is assessed using two scenarios (grid and island) under four case studies with varying combinations of loads and grid voltage circumstances. However, to establish the superiority of the proposed technology, comparative research with standard technologies such as proportional integral controller (PIC) and SMC controllers is required. THD is reduced by the proposed method to 2.25 %, 2.36 %, and 1.71 %, It is inferior to the current approaches found in the survey.

Using Bayesian Deep Learning for Electric Vehicle Charging Station Load Forecasting

In recent years, replacing internal combustion engine vehicles with electric vehicles has been a significant option for supporting reducing carbon emissions because of fossil fuel shortage and environmental contamination. However, the rapid growth of electric vehicles (EVs) can bring new and uncertain load conditions to the electric network. Precise load forecasting for EV charging stations becomes vital to reduce the negative influence on the grid. To this end, a novel day-ahead load forecasting method is proposed to forecast loads of EV charging stations with Bayesian deep learning techniques. The proposed methodological framework applies long short-term memory (LSTM) network combined with Bayesian probability theory to capture uncertainty in forecasting. Based on the actual operational data of the EV charging station collected on the Caltech campus, the experiment results show the superior performance of the proposed method compared with other methods, indicating significant potential for practical applications.

Electric Vehicle Charging Station Load Prediction Based on IWOA-LSSVM Combined Model with Improved Variational Mode Decomposition

Accurately predicting the electric vehicle charging station load is the basis for the optimal design of power supply planning. Aiming at the problem that the load data of electric vehicle charging station has strong randomness and volatility due to the interaction of various factors. The electric vehicle charging load prediction model is constructed, which considers the highest temperature, day type and weather type factors to select similar days. Firstly, an algorithm for optimizing variational modal decomposition parameters with relative entropy is proposed to decompose the extracted similar daily charging load data into multiple sub-sequences. Then, the decomposed data are respectively input into the combined prediction model of the least squares support vector machine optimized by the whale algorithm improved by the adaptive probability threshold and the random difference mutation strategy, and finally the predicted value of each subsequence is superimposed and summed to obtain the final predicted value. Based on the measured charging load data and weather forecast data of an electric vehicle charging station in Shanghai, simulation experiments are carried out to compare and verify the accuracy and effectiveness of the model in this paper.

A new method to generate daily load scenario of electric vehicle charging station considering time correlation

Abstract Due to the influence of random factors such as travel behavior of car owners and traffic condition, the electric vehicle (EV) charging station load has strong randomness. Establishing an appropriate probability model to describe the stochastic of EV charging station load is of great significance to the safe operation analysis of distribution network. Therefore, this paper proposes the probability modeling and the scenario generation method for EV charging station load based on historical data. Firstly, the load of each period is regarded as a random variable, and the probability distribution model of each random variable is obtained by fitting historical data. Secondly, according to the analysis of load correlation, 96 periods of a day are divided into several sets of adjacent periods. Considering the correlation between different period loads in each set of adjacent periods, the Pair-copula method and the D-Vine structure is used to obtain the joint distribution model in each set of adjacent period loads is obtained. Thirdly, according to the joint distributionmodel, all sets of adjacent periods of a day and the corresponding load scenarios are obtained, and the daily load curve considering time correlation are generated. Finally, referring to the actual historical load curve data, the effectiveness of the proposed method in this paper is verified by comparing with the daily load curve, which is generated based on the independent distribution model of each period load without considering time correlation.

Impact of Electric Vehicle Charging Station Load on Distribution Network

Recent concerns about environmental pollution and escalating energy consumption accompanied by the advancements in battery technology have initiated the electrification of the transportation sector. With the universal resurgence of Electric Vehicles (EVs) the adverse impact of the EV charging loads on the operating parameters of the power system has been noticed. The detrimental impact of EV charging station loads on the electricity distribution network cannot be neglected. The high charging loads of the fast charging stations results in increased peak load demand, reduced reserve margins, voltage instability, and reliability problems. Further, the penalty paid by the utility for the degrading performance of the power system cannot be neglected. This work aims to investigate the impact of the EV charging station loads on the voltage stability, power losses, reliability indices, as well as economic losses of the distribution network. The entire analysis is performed on the IEEE 33 bus test system representing a standard radial distribution network for six different cases of EV charging station placement. It is observed that the system can withstand placement of fast charging stations at the strong buses up to a certain level, but the placement of fast charging stations at the weak buses of the system hampers the smooth operation of the power system. Further, a strategy for the placement of the EV charging stations on the distribution network is proposed based on a novel Voltage stability, Reliability, and Power loss (VRP) index. The results obtained indicate the efficacy of the VRP index.

Time-Series Modeling of Aggregated Electric Vehicle Charging Station Load

The widespread proliferation of Electric Vehicles (EVs) can have a transformative effect on the electric power system. The power and energy consumed by EVs when charging is substantial, which has consequences on power system operation and planning. This paper identifies, evaluates, and proposes time-series seasonal autoregressive integrated moving average (ARIMA) models of aggregated EV charging station load. The modeling is based on 2 years of time-stamped aggregate power consumption from over 2400 charging stations in Washington State and San Diego, California. The different data sets allow the influence of time-of-use pricing on the time-series models to be explored. Weekday, weekend, and near-term and long-term models are developed and analyzed. The best performing near-term weekday models are (2, 0, 0) × (0, 1, 1)24 × (1, 0, 0)120 for Washington State and (2, 0, 0) × (1, 1, 0)24 × (0, 0, 1)48 for San Diego. Applications of the seasonal ARIMA models to aggregate EV charging station load forecasting and creation of synthetic time-series at different penetration levels are discussed.

Probabilistic Modeling and Statistical Analysis of Aggregated Electric Vehicle Charging Station Load

The number of electric vehicles is increasing worldwide. The charging of a single electric vehicle can draw several kilowatts of power, and the aggregate effects of charging thousands of electric vehicles on electric power system infrastructure, operation, and planning must be considered. An important tool in studying the integration of electric vehicles and developing associated technologies and controls within the framework of the smart grid are probabilistic models of charging station load. This article identifies, evaluates, and proposes probabilistic models and analyzes the statistical characteristics of aggregated electric vehicle charging station load. A data-driven approach is taken, using measured time-stamped power consumption from charging stations to formulate and evaluate suitable parametric probabilistic models. The influence of time-of-use pricing on electric vehicle charging station load characteristics is also examined. Two data sets—one from Washington State, the other from San Diego, CA—each covering over 2 years, are used to create the models.

Electric Vehicle Charging Station Load Forecasting and Impact of the Load Curve

With the popularity of electric vehicles, a large number of charging stations connected to the grid, will bring about tremendous influence on the power, voltage and current of grid. This paper briefly introduces several common types of charging mode, and analyzes the characteristics of them. According to statistics, a resistive model of charging stations, simulating the regional power grid with a IEEE34 node model, has been established to forecast the daily load curve, using Monte Carlo simulation. An analysis is performed for a power grid to demonstrate the impacts of the daily load curve considering different power of charging stations, which are under coordinated charging conditions, to indicate the harm of uncoordinated charging and put forward solutions.