Statistical characterisation of the real transaction data gathered from electric vehicle charging stations

Flammini, Marco Giacomo; Prettico, Giuseppe; Julea, Andreea; Fulli, Gianluca; Mazza, Andrea; Chicco, Gianfranco

doi:10.1016/j.epsr.2018.09.022

Cited by 117 publications

(74 citation statements)

References 35 publications

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“…After obtaining the similarity of frequency matrix, the estimation model is defined to fit the evaluation indexes concerning the advantages and disadvantages of original data. It is composed of Equations (19) and 20.…”

Section: Estimation Evaluation Modelmentioning

confidence: 99%

“…In that case, studies on the modeling of charging behaviors with the actual EVs operating data have been rising gradually. In terms of modeling data, these research data are derived from charging data [3,4] of electric taxis in Shenzhen, EVs charging data [17] in Kanagawa, EV parking and charging data [18,19] of CSs in Netherlands, EV charging data [20] of CSs in UK, and EVs charging data [21] of CSs in Nanjing, etc. In terms of modeling objects, these objects primarily consist of the arrival time (or initial charging time), the staying time (that contains waiting time and charging time), and the charging capacity.…”

Section: Introductionmentioning

confidence: 99%

“…When EVs charging behaviors are modelled with PE, the variable to be estimated conforming to a certain known probability distribution should be presupposed before estimating parameters of the assumed probability distribution with EVs charging behaviors data for testing. Common PE methods are composed of Gaussian distribution [6], mixed Gaussian distribution [4], mixed Beta distribution [19], and Weibull distribution [22] etc. The premise of using the PE approach is to presuppose that the probability distribution is approaching the actual situation.…”

Section: Introductionmentioning

confidence: 99%

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Modeling the Charging Behaviors for Electric Vehicles Based on Ternary Symmetric Kernel Density Estimation

2020

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The accurate modeling of the charging behaviors for electric vehicles (EVs) is the basis for the charging load modeling, the charging impact on the power grid, orderly charging strategy, and planning of charging facilities. Therefore, an accurate joint modeling approach of the arrival time, the staying time, and the charging capacity for the EVs charging behaviors in the work area based on ternary symmetric kernel density estimation (KDE) is proposed in accordance with the actual data. First and foremost, a data transformation model is established by considering the boundary bias of the symmetric KDE in order to carry out normal transformation on distribution to be estimated from all kinds of dimensions to the utmost extent. Then, a ternary symmetric KDE model and an optimum bandwidth model are established to estimate the transformed data. Moreover, an estimation evaluation model is also built to transform simulated data that are generated on a certain scale with the Monte Carlo method by means of inverse transformation, so that the fitting level of the ternary symmetric KDE model can be estimated. According to simulation results, a higher fitting level can be achieved by the ternary symmetric KDE method proposed in this paper, in comparison to the joint estimation method based on the edge KDE and the ternary t-Copula function. Moreover, data transformation can effectively eliminate the boundary effect of symmetric KDE.

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Section: Estimation Evaluation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling the Charging Behaviors for Electric Vehicles Based on Ternary Symmetric Kernel Density Estimation

2020

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“…The same process is applied to EV charging stations, where the demand profile is taken from [33], and randomly associated to a different location in the grid. The same ratio utilization of the charging columns, presented in [33], has been applied in this analysis. As suggested in [34], one or more slow charging points will be required for every 15 electric vehicles.…”

Section: Matpower Input Data Descriptionmentioning

confidence: 99%

Distribution Network Model Platform: A First Case Study

2019

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Decarbonisation policies have recently seen an uncontrolled increase in local electricity production from renewable energy sources (RES) at distribution level. As a consequence, bidirectional power flows might cause high voltage/ medium voltage (HV/MV) transformers to overload. Additionally, not-well-planned installation of electric vehicle (EV) charging stations could provoke voltage deviations and cables overloading during peak times. To ensure secure and reliable distribution network operations, technology integration requires careful analysis which is based on realistic distribution grid models (DGM). Currently, however, only not geo-referenced synthetic grids are available inliterature. This fact unfortunately represents a big limitation. In order to overcome this knowledge gap, we developed a distribution network model (DiNeMo) web-platform aiming at reproducing the DGM of a given area of interest. DiNeMo is based on metrics and indicators collected from 99 unbundled distribution system operators (DSOs) in Europe. In this work we firstly perform a validation exercise on two DGMs of the city of Varaždin in Croatia. To this aim, a set of indicators from the DGMs and from the real networks are compared. The DGMs are later used for a power flow analysis which focuses on voltage fluctuations, line losses, and lines loading considering different levels of EV charging stations penetration.In literature several works have focused on creating realistic reference networks and validation methodology. A non-exhaustive list follows. A synthetic MV test grid in [2] was modeled to demonstrate the impact of distributed power generation on the grid using the tool called Smart Grid Metric. Urban and rural networks were modeled differently because of insufficient data for rural grids. Two types of rural area were built using Google Earth and statistics report for load density: large area with low population and small area with high population with a remark that no load is located outside the residential area. To investigate the impact of distributed energy resources (DER) penetration on MV network in [3], urban, rural, and industrial area are modeled based on combining a variable number of the typical representative feeders. Additionally, an economic analysis is performed through providing ancillary services at several market models. Three market frameworks were presented: flexibility bids offered directly from DER, or coupled and represented by distribution system operator (DSO) or aggregator. A statistical tool has been developed in [4] for generating representative distribution networks. Firstly, technical and geographical grid data were collected and different metrics have been investigated. Secondly, the purpose of the grid analysis needed to be identified in order to successfully select the best method for network generation. Finally, the validation was performed through comparing the performance of real grids and generated networks. The authors in [5] used metric-based validation process to demonstrate that public test...

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“…Also, flexibility and willingness to postpone charging need is dependent on plug-in time, mileage, charging power rate, incentive/charging price structure, etc. 44 In this work, the authors have considered weekdays of summer season and EVs leave for morning trip with SOC 0.9 and return in the evening for recharge with an initial SOC. Early/late departure/arrival due to some unexpected hurdles in its running behavior are considered by standard deviation of 2 hours.…”

Section: Mobility Behavior Considerations Of An Evmentioning

confidence: 99%

Integrated TOU price‐based demand response and dynamic grid‐to‐vehicle charge scheduling of electric vehicle aggregator to support grid stability

Sharma

Jain

2019

Int Trans Electr Energ Syst

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Summary Unregulated and simultaneous charge scheduling of large‐scale electric vehicles (EVs) may lead to grid instability and peak demand rise. EV aggregator (EVA) can adopt coordinated and distributed charging rates for EVs over the connection hours providing stability and regulation to system operator (SO). However, against flat rate, price‐based demand response (PBDR) integrated scheduling motivates EVs to charge in off peak hours and utilize their flexibility for load levelling and peak reduction. PBDR reduces EV charging cost as well. Real‐time price (RTP) demand response (DR), mostly adopted in literature, has low acceptance rate by EVs for being too dynamic to response and being infeasible for few charge cycles in a day. Time of use (TOU), being less dynamic, is natural price signal for EVs under PBDR but may reduce EVA's profit. Considering this, an integrated TOU‐PBDR model for EVA's grid‐to‐vehicle (G2V) charge scheduling is proposed for energy and regulation market. EV owner perspective for charging cost minimization is considered along with EVA profit maximization. TOU is designed from RTP using agglomerative hierarchical clustering (AHC) method. EVs mobility uncertainty characterization makes scheduling dynamic. Sensitivity analysis based on charging rate upper limit and number of EVs validates the method. Results show interesting trends regarding EV charging cost, EVA profit, and regulation provision to SO.

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Statistical characterisation of the real transaction data gathered from electric vehicle charging stations

Cited by 117 publications

References 35 publications

Modeling the Charging Behaviors for Electric Vehicles Based on Ternary Symmetric Kernel Density Estimation

Modeling the Charging Behaviors for Electric Vehicles Based on Ternary Symmetric Kernel Density Estimation

Distribution Network Model Platform: A First Case Study

Integrated TOU price‐based demand response and dynamic grid‐to‐vehicle charge scheduling of electric vehicle aggregator to support grid stability

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