Constraint-Aware Electricity Consumption Estimation for Prevention of Overload by Electric Vehicle Charging Station

Abstract

An increase in the number of electrical vehicles has resulted in an increase in the number of electrical vehicle charging stations. As a result, the electricity load consumed by charging stations has become large enough to de-stabilize the electricity supply system. Therefore, real-time monitoring of how much electricity each charging station is consuming has become very much important. However, only limited information such as charging time is available from the operators of electric vehicle charging stations. The actual electricity consumption data is not provided in real time. Conventional methods estimate the accumulated electricity consumption of charging stations using a linear regression curve. However, an estimate of the electricity consumption for each charge is needed. In this paper, we propose an advanced electricity estimation system which predicts the energy consumption for each charge. The proposed method uses a constraint-aware non-linear regression curve, and performs additional data selection processes. The experimental results show that the proposed system achieves about 73% regression accuracy. In addition, the proposed system can display the energy consumption per hour and visualize this information on a map. This makes it possible to monitor the electricity consumption of the charging stations in real-time and by location, which helps to select appropriate locations where new vehicle charging stations need to be installed.

Prediction of electricity energy consumption including COVID‐19 precautions using the hybrid MLR‐FFANN optimized with the stochastic fractal search with fitness distance balance algorithm

“…14 Estimation of electricity consumption was also carried out for different areas such as polymer material production 15 and electric vehicle charging stations. 16 Wang et al proposed a model called ESN-DE, which uses an improved echo state network (ESN) to perform the estimation of electrical energy consumption. In the proposed model, the differential evolution (DE) algorithm is used to find the optimal values of three important parameters of the ESN.…”

“…For fractals entering the second update process, the position of the fractal is changed according to whether a randomly generated number between 0 and 1 is smaller or larger than 0.5. The second update process is performed with the expression given in Equation (16).…”

“…It has also been determined in similar studies that in order to provide acceptable accuracy, an ANN should have a sufficient number of samples 14 . Estimation of electricity consumption was also carried out for different areas such as polymer material production 15 and electric vehicle charging stations 16 . Wang et al proposed a model called ESN‐DE, which uses an improved echo state network (ESN) to perform the estimation of electrical energy consumption.…”

Aggregated Electric Vehicle Fast-Charging Power Demand Analysis and Forecast Based on LSTM Neural Network

“…Although big data technologies [20], a temporal-spatial approach using a stochastic model [21], and a queueing network [22] have been proposed as EV charging demand forecasting models, these works only presented methodologies without any comparison with real data. In addition, even though a linear regression model [23], constrained-aware polynomials [24], and SVR [25] were proposed to forecast EV charging power demand, compared with real-world datasets, these models were only validated at station-level EV load demand forecasting.…”

The ElectricalVehicle Simulator for Charging Station in Mode 3 of IEC 61851-1 Standard

“…Romania, according to European Commission (EC) Directives 2016/30 November 2016 [11][12][13], has as strategic objective the clean energy [14,15]. Recently, the interest in producing electrical energy by using renewable sources is growing up.…”