Forecasting Charging Demand of Electric Vehicles Using Time-Series Models

Abstract

This study compared the methods used to forecast increases in power consumption caused by the rising popularity of electric vehicles (EVs). An excellent model for each region was proposed using multiple scaled geographical datasets over two years. EV charging volumes are influenced by various factors, including the condition of a vehicle, the battery’s state-of-charge (SOC), and the distance to the destination. However, power suppliers cannot easily access this information due to privacy issues. Despite a lack of individual information, this study compared various modeling techniques, including trigonometric exponential smoothing state space (i.e., Trigonometric, Box–Cox, Auto-Regressive-Moving-Average (ARMA), Trend, and Seasonality (TBATS)), autoregressive integrated moving average (ARIMA), artificial neural networks (ANN), and long short-term memory (LSTM) modeling, based on past values and exogenous variables. The effect of exogenous variables was evaluated in macro- and micro-scale geographical areas, and the importance of historic data was verified. The basic statistics regarding the number of charging stations and the volume of charging in each region are expected to aid the formulation of a method that can be used by power suppliers.

Multi-Objective Optimization of PV and Energy Storage Systems for Ultra-Fast Charging Stations

“…where 𝑥 indicate the set of decision variable, which in this case are the number of PV panels (𝑁 𝑃𝑉 ) and batteries (𝑁 𝐵𝐸𝑆𝑆 ) to be installed; 𝑓 1 is the NPC of the station expressed in (15); and 𝑓 2 expresses the pollutant emissions 𝐸 𝑝𝑜𝑙𝑙 computed in (20).…”

“…Therefore, given the importance of a priori knowledge of the daily load profiles, in literature can be found many studies that try to assess the effects of EVs charging on the grid. Most of them focus on residential charging [15] [16] [17]. For instance, in [15], the authors compare time-series techniques and machine learning methods used to forecast the growth in building power consumption caused by the rising of EVs chargers.…”

“…Most of them focus on residential charging [15] [16] [17]. For instance, in [15], the authors compare time-series techniques and machine learning methods used to forecast the growth in building power consumption caused by the rising of EVs chargers. According to the results, the machine learning techniques have the advantage of requiring few initial assumptions, but they do not always show good predictive results.…”

“…In particular, the k components are the PV sources and the BESS, since the cost of the charging infrastructure is not considered. Therefore, the formula to compute NPC is reported in (15):…”

A Simplified Approach to Estimate EV Charging Demand in Urban Area: An Italian Case Study

“…Usually, energy demand can be estimated when data on the use of the charging infrastructure are available, as presented in [12], where aggregated charging profiles were obtained by a statistical combination of specific charging power dataset to evaluate the impact of EV charging on the distribution grid. Other similar approaches are proposed in [13,14] where the estimation of EV charging profile is based on data from public charging stations and big data, data driven and machine learning techniques are considered. A more simplified statistical approach is instead proposed in [15], where the charging demand in public stations is estimated through data from national surveys where potential charging location and mobility behaviours are investigated.…”