Quantitive analysis of electric vehicle flexibility : a data-driven approach

The electric vehicle (EV) flexibility, indicates to what extent the charging load can be coordinated (i.e., to flatten the load curve or to utilize renewable energy resources). However, such flexibility is neither well analyzed nor effectively quantified in literature. In this paper we fill this gap and offer an extensive analysis of the flexibility characteristics of 390k EV charging sessions and propose measures to quantize their flexibility exploitation. Our contributions include: (1) characterization of the EV charging behavior by clustering the arrival and departure time combinations that leads to the identification of type of EV charging behavior, (2) in-depth analysis of the characteristics of the charging sessions in each behavioral cluster and investigation of the influence of weekdays and seasonal changes on those characteristics including arrival, sojourn and idle times, and (3) proposing measures and an algorithm to quantitatively analyze how much flexibility (in terms of duration and amount) is used at various times of a day, for two representative scenarios. Understanding the characteristics of that flexibility (e.g., amount, time and duration of availability) and when it is used (in terms of both duration and amount) helps to develop more realistic price and incentive schemes in DR algorithms to efficiently exploit the offered flexibility or to estimate when to stimulate additional flexibility. (C) 2017 Elsevier Ltd. All rights reserved

Quantifying flexibility in EV charging as DR potential : analysis of two real-world data sets

The increasing adoption of electric vehicles (EVs) presents both challenges and opportunities for the power grid, especially for distribution system operators (DSOs). The demand represented by EVs can be significant, but on the other hand, sojourn times of EVs could be longer than the time required to charge their batteries to the desired level (e.g., to cover the next trip). The latter observation means that the electrical load from EVs is characterized by a certain level of flexibility, which could be exploited for example in demand response (DR) approaches (e.g., to balance generation from renewable energy sources). This paper analyzes two data sets, one from a charging-at-home field trial in Flanders (about 8.5k charging sessions) and another from a large-scale EV public charging pole deployment in The Netherlands (more than 1M sessions). We rigorously analyze the collected data and quantify aforementioned flexibility: (1) we characterize the EV charging behavior by clustering the arrival and departure time combinations, identifying three behaviors (charging near home, charging near work, and park to charge), (2) we fit statistical models for the sojourn time, and flexibility (i.e., non-charging idle time) for each type of observed behavior, and (3) quantify the the potential of DR exploitation as the maximal load that could be achieved by coordinating EV charging for a given time of day t, continuously until t vertical bar Delt

Modelling earthquakes : characterizing magnitudes and inter-arrival times

peer reviewedStatistical modelling of earthquakes is a challenging and delicate topic: research activity is vivid in this respect, and tailored to an improved understanding of the seismic phenomena and of their dynamics over time and space in all its shades. By surfing on some of the available literature, a critical investigation of the probability distributions best fitting earthquake sizes and inter-arrival times is performed, by using data on the Pacific Ring of Fire as illustrative example. As a by-product of our analysis, new ideas about adequate modelling of earthquake sizes and inter-event times together with the location of the earthquakes are advanced, which in turn could pave the way to further developments in a directional perspective