Electric Vehicle Aggregate Power Flow Prediction and Smart Charging System for Distributed Renewable Energy Self-Consumption Optimization

Abstract

In the context of electric vehicle (EV) development and positive energy districts with the growing penetration of non-programmable sources, this paper provides a method to predict and manage the aggregate power flows of charging stations to optimize the self-consumption and load profiles. The prediction method analyzes each charging event belonging to the EV population, and it considers the main factors that influence a charging process, namely the EV’s characteristics, charging ratings, and driver behavior. EV’s characteristics and charging ratings are obtained from the EV model’s and charging stations’ specifications, respectively. The statistical analysis of driver behavior is performed to calculate the daily consumptions and the charging energy request. Then, a model to estimate the parking time of each vehicle is extrapolated from the real collected data of the arrival and departure times in parking lots. A case study was carried out to evaluate the proposed method. This consisted of an industrial area with renewable sources and electrical loads. The obtained results show how EV charging can negatively impact system power flows, causing load peaks and high energy demand. Therefore, a charging management system (CMS) able to operate in the smart charging mode was introduced. Finally, it was demonstrated that the proposed method provides better EV integration and improved performance.

Analysis of Energy Transition Impact on the Low-Voltage Network Using Stochastic Load and Generation Models

“…The statistical modeling of measurement data related to electricity networks and energy technologies such as PV, EV, and HP has been discussed in, for example, [12][13][14][15][16][17], using various distribution functions. However, all these models show applicability only to one specific set and no mention is made of generalizability to other data.…”

A Tool for Evaluating the Performance of SiC-Based Bidirectional Battery Chargers for Automotive Applications

“…In V2G applications, the goal is to suitably manage the flow of active and reactive powers, according to Equation (4). By estimating the phase angle of the AC system through the PLL and imposing V sq = 0, it follows that it is possible to rewrite the power relationships given in Equation ( 4) according to Equation (5), where the coupling terms have been cancelled.…”

“…This limitation in green energy utilization can be overcome with EVs, since they involve an increment in the local load. Moreover, they can be used as energy storage systems which are able to mitigate fluctuations in primary energy resources and, more generally, are useful when coping with optimal power flow [4].…”

Electric Vehicles Charging Management System for Optimal Exploitation of Photovoltaic Energy Sources Considering Vehicle-to-Vehicle Mode

“…For this reason, multiple authors proposed CMSs capable of providing the above benefits at a local level without necessarily involving the active interaction with the grid and all the associated technological challenges (Bons et al, 2020;Lo Franco et al, 2020). These SC systems, mainly if employed over an aggregate EV fleet, blend particularly well with the definitions of V2B and V2C (Thompson, 2018;Noel et al, 2019).…”

“…Multiple SC methods have been presented in the literature (Wang et al, 2016;Fachrizal and Munkhammar, 2020;Fouladi et al, 2020;Lo Franco et al, 2020). The list below compares the achieved results of some main reference papers and points out the literature gap that this work tries to fill:…”