Electric vehicles’ impacts on residential electric local profiles – A stochastic modelling approach considering socio-economic, behavioural and spatial factors

Applied Energy 233–234 (2019) 644–658

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Applied Energy journal homepage: www.elsevier.com/locate/apenergy

Electric vehicles’ impacts on residential electric local profiles – A stochastic modelling approach considering socio-economic, behavioural and spatial factors

T

David Fischera, , Alexander Harbrechta,b, Arne Surmanna, Russell McKennab ⁎

a b

Fraunhofer Institute for Solar Energy Systems, Freiburg, Germany Chair for Energy Economics, Karlsruhe Institute for Technology (KIT) Karlsruhe, Germany

HIGHLIGHTS

of electric cars on the electric load profile. • Impact bottom-up modelling. • Stochastic Markov-chain. • Inhomogeneous of socioeconomic, technical and spatial factors. • Consideration • Analyses of uncontrolled charging of EV. ARTICLE INFO

ABSTRACT

Keywords: Electric vehicles Load profiles Stochastic bottom-up modelling Behavioural modelling Markov-chain

This paper presents a stochastic bottom-up model to assess electric vehicles’ (EV) impact on load profiles at different parking locations as well as their potential for load management strategies. The central innovation lies in the consideration of socio-economic, technical and spatial factors, all of which influence charging electricity demand and behaviour at different locations. Based on a detailed statistical analysis of a large dataset on German mobility, the most statistically significant influencing factors on residential charging behaviour could be identified. Whilst household type and economic status are the most important factors for the number of cars per household, the driver’s occupation has the strongest influence on the first departure time and parking time whilst at work. EV use is modelled using an inhomogeneous Markov-chain to sample a sequence of destinations of each car trip, depending (amongst other factors) on the occupation of the driver, the weekday and the time of the day. Probability distributions for the driven kilometres, driving durations and parking durations are used to model presence at a charger and calculate electricity demand. The probability distributions are retrieved from a national mobility dataset of 70,000 car trips and filtered for a set of socio-economic and demographic factors. Individual charging behaviour is included in the model using a logistic function accounting for the sensitivity of the driver towards (low) battery SOC. The model output is compared to the mobility dataset to test its validity and shown to have a deviation in key household mobility characteristics of just a few percentage points. The model is then employed to analyse the impact of uncontrolled charging of EV on the residential load profile. It is found that the absolute load peaks will increase by up to a factor of 8.5 depending on the loading infrastructure, the load in high load hours will increase by approx. a factor of three and annual electricity demand will approximately double.

1. Introduction and objectives In Germany, approx. 33% of end energy consumption in the residential sector is used for fuelling cars [1]. A change of energy carrier in the transportation sector from fossil fuels to electricity will therefore

⁎

strongly impact residential electricity consumption. In January 2016, 25,502 electric vehicles (EV) were registered in Germany [2], by 2030 the German federal government plans the number to be around 6,000,000 [3]. On the one hand EV charging will present new challenges for distribution grid planning and operation [4]. On the other

Corresponding author. E-mail address: [email protected] (D. Fischer).

https://doi.org/10.1016/j.apenergy.2018.10.010 Received 17 May 2018; Received in revised form 4 October 2018; Accepted 8 October 2018 0306-2619/ © 2018 Elsevier Ltd. All rights reserved.

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hand using electric vehicles’ flexible storage capacities can prove a potential solution to integrate high shares of renewable electricity into the power system [5]. To fully evaluate the potential benefits and risks of a large scale deployment of EV in electric distribution grids, accurate models of households, electrical distribution and transport networks are needed. Residential load profiles resulting from battery electric vehicle (BEV) charging have been studied intensively in recent years based on simulations using empirical driving data from mobility surveys, most of the time with a focus on home-charging [6–12]. More recent approaches focus on empirical charging data of fleets [13,14]. However, none of these approaches allow for a detailed examination of driving and charging behaviour differentiated by socio-economic, socio-demographic and temporal aspects. Most mobility survey approaches generally assume charging upon arrival for every parking event and fail to include possible behavioural preferences regarding the charging decision, which have a major influence on the electric vehicle’s charge level at which people typically recharge [15]. This results in synthetic load profiles that may be contradictory to empirical findings on the average number of vehicle charging events per day, the average vehicle’s charged energy per day and location-dependent charging preferences from field trials [16–19]. Focusing on these behavioural aspects of electric vehicle charging, the following research questions are proposed for the present work:

2. Literature review In the past considerable effort has been undertaken to model and evaluate the effect of EV on the power system. In [21] measured charging profiles are used to study the effects on household power demands and concluding that smart-charging methods are needed. Clement-Nyns et al. [22] show that assumptions on how people charge their EV after arriving at home influences the load profile. Furthermore it is shown that uncoordinated charging can lead to voltage problems in the distribution grid. In [23] metered driving and charging data from EV trials is used to evaluate the impact of EV on local urban networks. In [24] EV agents, with different driving profiles, SOCs and charging decision logic were simulated, based on a Swiss survey of mobility behaviour, and differentiated between ‘simple’ and ‘optimal’ charging behaviour as well as static and dynamic price regimes. With flat tariffs, they conclude that substations would not be overloaded, but higher EV penetration levels and dynamic tariffs could indeed overload the substations. The study [24] provides valuable insights into the potential impacts of electric vehicles on distribution grid substations, but it does not generate socioeconomically-differentiated charging profiles for the purposes of energy system modelling. The studies presented in [22,23,25] conclude that uncoordinated charging will lead to problems in the network. To design charging strategies and evaluate their impact detailed models of EV use are needed. This is why another group of studies focusses particularly on modelling EV use. In [11,7] a Markov-chain is used to model the use of electric vehicles based on persons presence at the household. In [26,27] probability density functions are used to model the start time of charging, the required electrical energy, and required power in order to obtain aggregated impact of EV. Data from the American national household travel surveys (NHTS) is used to calibrate the model. A similar approach is followed in [28,29] for the Netherlands and in [30] for Germany. Finally, [31] developed a method to determine the optimal charging strategy of an EV based on the usage profile. The authors present a stochastic model for driving patterns based on inhomogeneous Markov chains, with the charging strategy then being optimized by a stochastic dynamic programming model. The authors find that the optimum strategy depends mostly on the usage profile, the risk-aversion of the end user and the electricity price. The study is highly relevant to the present paper, but it does not differentiate between different types of socioeconomic groups or journeys (urban, rural etc.), only considers home charging, and thus does not enable district level profiles to be generated, instead focussing on individual EVs.

1. How can driving behaviour of private households in Germany be differentiated by socio-economic, socio-demographic and temporal characteristics? 2. How can different charging locations and decisions be considered in synthetic load profile simulation models? 3. What are the characteristics of simulated electric vehicle charging load profiles with location-dependent charging decisions and sociodemographically differentiated driving behaviour compared to models using empirical electric vehicle charging data? In order to answer these questions, a large German mobility dataset (MID [20]) is employed, which distinguishes between four different abstract locations, namely inside/outside town, workplace and home. The individual destinations are used as the spatial components, represented as discrete states of a Markov-chain model. The trips are grouped in three trip index categories to study the relationship between the trip number with the destination (first/last trip of the day, in-between), which influence the transition probability between the destinations. For each trip a purpose is listed (accompanying, business, education, errand, leisure, shopping, work). The dataset was also analysed with respect to different socio-economic and socio-demographic factors as well as seasons and weekdays. Households are categorised into different types based on the number of persons, their age and work patterns. Furthermore household economic status based on net incomes is considered. The place of residence (POR) is categorised into rural, urban and inner-city locations. Car users are differentiated into three categories according to their car usage frequency (daily, weekly, monthly). This paper presents a model, which is able to generate stochastic, socio-economically-differentiated electric load profiles for EVs as well as information about available battery storage capacity and power over time and space. The targeted use for the model is the simulation and optimisation of electric grids, power generation and storage technology on the neighbourhood and city level. Furthermore, the model is used to develop and evaluate different EV charging algorithms. The following section reviews the literature in this area, before Section 3 presents the methodology employed to derive statisticallyrobust and significant relationships with which to model households’ BEV behaviour. Section 4 then explains the algorithmic implementation. Section 5 then presents and discusses the results, and the paper closes in Section 6 with conclusions and an outlook.

2.1. The main aspects that should be covered by an EV model In order to better anticipate the potential impacts of electric vehicles on the electricity systems a fundamental understanding of the of households’ influencing factors is required. In general, the influence can be subdivided into three major domains: first, the behavioural and economic domain, second, the spatial domain and third, the technical domain. However, all three domains are not mutually exclusive so that there are aspects that relate to more than one category. Fig. 1 tries to provide a holistic overview without claiming to be collectively exhaustive. Based on a study of present literature a set of model aspects that should be covered by an EV model was identified. These can be divided into three domains. 2.1.1. Behavioural and economic domain First of all, driving behaviour can considerably influence electric vehicle charging as it influences the amount of energy consumed while driving as well as the time and frequency of recharging intentions. Typical variables to characterize driving behaviour are the number of trips driven per day, the vehicle use frequency (e.g. daily, weekly, monthly), departure and arrival places, the driven distance as well as 645

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Fig. 1. Overview of households’ main influencing factors on electric vehicle impacts for the energy system.

security considerations with respect to the charging locations and its surroundings as well as the accessibility of charging stations in terms of time and distance to the closest idle charging station, can impact electric vehicle charging.

departure and arrival times (or driving and parking times respectively) per trip [32–34]. All variables may be influenced by household, person and trip attributes as well as temporal aspects such as the weekday of the vehicle use. The effects of these and other socio-economic variables on households' driving behaviour was recently analysed with panel data for 14 countries based on a linear regression model [35]. Besides driving behaviour, connection decisions play an important role with respect to the time and frequency of taking charging decisions. An important finding from a field trial in Germany suggests that the initial battery’s SOC upon recharging in combination with other factors such as ’comfortable range’ may explain a large proportion of electric vehicle recharging [15]. Other field trials show preferences for different charging locations and reveal preferences for fast-charging, i.e. charging at high power levels consequently reducing the required time to charge a specific energy amount [19,18,17]. Moreover, the existence and type of control over the charging process can be an important aspect affecting the time, frequency, charged energy and load profile of electric vehicle charging. If there is no exertion of influence on the charging process at all, so that it just results from the driving behaviour and the connection decision of the electric vehicle user, one speaks of uncontrolled charging. However, several ways to control the charging process are conceivable: the control can emanate from an operator or the EV user himself. The former could aim at maximizing revenues from ancillary services or minimizing load peaks. The latter might focus on a minimization of charging costs or CO2 emissions. It should be noted that an optimum for an individual EV user may not necessarily equal a system optimum. There are several behavioural drivers that also relate to the spatial domain such as driving behaviour influenced by the household’s place of residence (e.g. agglomerations such as ‘rural’, ‘urban’, ‘city’). Even though [9] state that neither “national nor regional differences are as significant as the possibility to charge at work” with respect to Germany, and [36] found no significant effect of demographic characteristics on charging behaviour in the USA, the analysis of the household’s place of residence is still open to a quantitative assessment. Furthermore, connection and charging decisions influenced by, e.g.

2.1.2. Spatial domain From a systemic point of view, the expected market penetration of electric vehicles is an important spatial aspect since the majority of EVs will certainly be located, driven and recharged in rather concentrated urban or city areas where distribution grids, e.g. in Germany, are already confronted with challenges due to high shares of intermittent and decentralized electricity production. Analogously, the market penetration of charging stations is not expected to be uniformly distributed in terms of spatial expansion. The German National Electric Mobility Platform (NPE) identified three main locations for charging stations with respect to the expected charging powers: first, private locations such as garages of single family houses or parking lots/basement garages of multi-family houses as well as company car parks or public charging stations for on-street parking with relatively low charging power; second, semi-public charging stations, e.g. at supermarkets, shopping malls or public car parks with relatively high charging power; and, third, fast-charging stations, e.g. along highways [37]. 2.1.3. Technical domain First of all, the most important technical aspects influencing electric vehicle charging is the energy consumption of the EV typically measured in kWh per 100 km. This quantity is physically dependent on e.g. the velocity profile, the rim size, the vehicle weight, the vehicles’ aerodynamics and the use and application of auxiliary devices such as heating and cooling of the passenger cell or heating and cooling of the battery controlled by the battery management system. Furthermore, the maximum velocity and the total battery size (together with the usable battery share) as well as the self-discharging rate of the vehicle influence energy consumption and therefore the recharge frequency. Additionally, the nominal charging power of the electric vehicle’s internal charger and the charging station’s external charger together with their particular efficiencies primarily influence the charging time. 646

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Several technical aspects will most likely influence the charging behaviour. Examples are the practicability of EV-grid connection (e.g. cable charging vs. inductive charging), safety considerations as well as EV driving range and the roll-out and density of fast-charging infrastructure.

involved removing ten households that did not report any information on the availability of cars. In addition, 8856 job-related trips (rbW: regelmäßige berufliche Wege) during working hours by craftsmen, postmen, salesmen etc. were excluded from the approximate total of 193,000 trips, as they lack relevant categorical and numerical information required for the method presented in the next section. Subsequently, 75 double entries were removed, in which the identical values could be found in all 124 fields, except for the unsorted and sorted trip counter variables per person. Likewise, entries which represented vacation trips were removed from the trip dataset and correspondingly all entries from the person dataset as well. A consistency test on the resulting datasets yielded that either all or no trips of a person had been excluded by this procedure. After that, the trip dataset was restricted to trips by persons originating from households with at least one car available, thereby excluding approx. 10,000 entries. A further restriction on trips entirely executed with one of the household’s cars while also providing valid spatial information about the point of departure and destination excluded over 100,000 trips executed by other means of transportation. In a final step, 399 trips where other household members reported themselves as the driver were excluded resulting in the final trip dataset with approx. 70,000 valid data points. Final verification of the reporting driving time, distance, average speed and parking time revealed some negative driving times and the further exclusion of these trips. Subsequently trips were analysed for average speed and trip durations and outliers based on duration (> 16 h) and average speed (< 3.9 and > 180, see [39,40]), zeros and missing values were removed. This threshold was determined by a detailed examination on the first quantile of the average speed distribution before data preparation. It revealed that an average speed of 3.9 km per hour can be regarded as plausible in special congestion situations. For that matter, the driving time as well as the driven distance were replaced with the corresponding moving average, determined as the mean of the three driving time step groups prior to the group of the value in doubt. This was the case for 1525 observations. In a final step, the individual minute values of the driving time as well as the parking time were assigned to 5 min time steps, since a lot of data points accumulated on values with a right-hand digit of 0 or 5.

2.2. Novel contribution of this article The previous studies show the importance of considering technological aspects of EV use such as charging infrastructure and charging strategies in EV models. The presented work extends the technical viewpoint towards behavioural and spatial aspects by considering different charging locations, charging habits and socio-economic household aspects. All of these are shown to have a significant impact on the load profile. The EV model accounts for driving characteristics both on a household level (e.g. number of vehicles per household) as well as characteristics associated with the main user of the vehicle (e.g. vehicle use frequency), the usage of the vehicle (e.g. number of trips per survey day) and the particular trips driven (e.g. trip purpose). This structure, and a diligent statistical analysis of effects resulting from influencing factors on the driving characteristics, provides the model with a detailed socio-economic differentiation. The model extends the behavioural aspects integrated in previous models such as different charging locations and SOC dependent recharging related to findings from [19,15]. The model is used to investigate the influence of the different factors on EV charging profiles. The proposed approach, shown in Section 3 accounts for the diversity of vehicle ownership, usage and charging behaviour. Results presented in Section 5 show that these factors strongly influence the resulting load profile and should be considered in other EV models, too. Furthermore, the presented modelling approach allows an assessment of the flexibility provided by EV, as presence of EV at different locations and the corresponding state of charge (SOC) of the battery are direct outputs of the model. 3. Data analyses and identification of model parameters A core assumption of modelling private EV use is that peoples’ driving behaviour will remain similar when switching from a conventional car to an EV. The underlying idea is that mobility needs are robust in terms of transportation modes. Mobility data for Germany has been surveyed in the MID study.

3.3. Feature generation used to determine model driving behaviour

3.1. Employed data

3.2. Preprocessing of the data

3.3.1. Locations, trip category and trip purpose Besides exchanging various attributes between the household, car and trip datasets by simply joining the respective tables on specific keys, the exact reconstruction of spatial information for every trip posed a bigger challenge. For that matter, information was gathered by taking a combined look at the departure place, destination and trip purpose variables from the trip dataset. From this a set of abstract locations is extracted. Those are somewhere inside city (referred to as “I”), somewhere outside city (referred to as “O”), Home (“H”), Work Place (“W”), other (“O”). From these four locations 16 different combinations of origin and destination are built and form a new variable named trip category. An example of the trip category and the corresponding notation is “H-W” indicating a departure at ‘home’ with destination ‘workplace’. A third variable called trip purpose, which is included in the dataset, is used to differentiate between trips to the same abstract location with different purposes. Those are: accompanying, business, education, errand, leisure, shopping, work. The trip purpose influences parking times and departure times at the different locations.

Before employing the MID dataset to parametrize the developed model, it was necessary to pre-process the data. In particular, this

3.3.2. Trip distance A further variable was added to the dataset specifying three

In the next step, new variables were added to the data-set used for characterising the trips. These were investigated for their importance and statistical interrelation.

Survey data on mobility behaviour in Germany “Mobilität in Deutschland” (MID) from the Federal Ministry of Transportation and Digital Infrastructure [20] builds the foundation for both the probability density functions and the transition probabilities in the Markovchain used to model individual mobility behaviour. The underlying survey was conducted in Germany in 2008 and 2009 with over 40,000 participating household members from more than 20,000 households. The participants reported their mobility behaviour for one day using a mobility diary. The analysed dataset consists of approx. 193,000 oneway trips of which approximately 70,000 were by car. The MID data consists of two files for each of the following units of observation: households, persons (household members), vehicles, trips (covered oneway trips), journeys (vacation trips). This paper is based on the Public Use File (PUF) of the MID dataset, which includes detailed information of the spatial and settlement characteristics for each household [38].

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different trip distance categories, based on their places of origin and destination, those are:

interest with multiple attribute levels is limited to two or three), the accuracy of the test is given. For numerical data, Cliff’s Method [43] is used. Cohen’s categories were used for the classification of Cramer’s V as well as the pairwise effect size by the means of means for Cliff’s. The employed effect size categories for standardized effect size measures k are

• inside city: assigned to the trip categories H-I, I-H, I-I • outside city: assigned to the trip categories H-O, I-O, O-H, O-I • other: assigned to the trip categories H-H, H-W, W-H, W-W, W-I, WO, I-W, O-W, O-O

negligible: 0.0 k < 0.1 small: 0.1 k < 0.3 medium: 0.3 k < 0.5 large: 0.5 k 1

The residual category “other” contains observations, where it was not possible to determine the precise location of the trip. It is, for example, unknown, whether the work place of a participating person is located inside or outside their own city or town. Similarly, driven routes for H-H, W-W and O-O trips were not certainly distinguishable in terms of distance. The trip distance category is used as conditional variable for building probability distributions for the driven distances.

(1)

For the consecutive modelling process only small (+), medium (+ +) and large (+++) effects were considered. Details on the employed methods can be found in [44]. 3.5. Mapping the results to effect size categories

3.3.3. Trip index In order to differentiate between different trips over one day the variable trip index was added to the trip dataset. The following levels were created:

An example overview of a test table resulting from Cliff’s method is shown in Fig. 3. The overview shows the influence of three different factors (see labels on the left side of the figure) on the dependent variable Y. Each factor has a specific number of factor levels (rectangles) which each form a group of observations. The rectangle’s position on the x-axis denotes the relative tendency that the observations in the group have higher/lower values for Y compared to all other factor levels left/right of the factor level of interest. Note that the position of a factor level has only relative informative value compared to the other factor levels of the same row and can therefore not be related to the levels of other factors. The factor label position on the y-axis denotes the largest pairwise effect size calculated, which is always the pairwise effect size of the leftmost and rightmost rectangle in the same row. In case there was not enough horizontal space for aligning all rectangles side by side so that some of them are stacked (or offset to the side due to grouping) the corresponding ambiguous leftmost or rightmost factor labels are denoted with a star (*). Dotted rectangles encircling several factor levels indicate that there was no significant pairwise distinction of all involved factor levels at a local -value of 0.05 or the effect sizes were “negligible”.

• first: assigned to the first trip of the day • last: assigned to the last trip of the day • between: assigned to all trips between the first and the last trip of the day • firstANDlast: assigned to all trips driven on a day with only one trip The trip-index is used as conditional variable for building probability distributions for departure times and the chosen trip category. 3.3.4. Daily vehicle use A variable called vehicle use (by the main user) with two factor levels use or disuse was added to describe car usage patterns. This variable contains the information whether the primary driver of a household’s vehicle used it on the respective household’s survey due day. Use is defined as a day on which the vehicle drove a minimum of one trip. Disuse is defined as a day on which the vehicle drove no trips at all. This variable provides the basis for categorising the households into different car usage categories with respect to their car usage. It is worth noting that the informative value of an average consideration of daily vehicle use is limited, as it does not reflect the certainly complex dependencies. For example, different households might have different weekly use patterns and some households might not have a weekly use pattern at all. In fact, this limitation applies to all variables of the MID dataset, as it only provides information on the mobility behaviour of households for single days (i.e. is not a panel dataset).

3.6. Exemplary application of the method In this section, exemplary results regarding the data analysis of the MID dataset are presented to demonstrate the procedure and better explain the methods. This is done in the following to analyse the impact of socio-economic household characteristics on the number of cars per household. Similar evaluations are carried out for other interaction effects and variables, as documented in [44]. For a representation of households’ driving behaviour in Germany at the system level it is necessary to determine the number of available EV for a specific number of households e.g. in a quarter, city or whole region given a specific EV market penetration rate. Similarly to the general assumption made in terms of driving behaviour, here it is assumed that in the future the purchasing decision of households for EV will not alter significantly from that of (today’s) conventional cars. For reasons of simplification it is also assumed that EV market penetration is equal amongst households. In the subsequent analysis, Figs. 2 and 3, the following labels are employed for household type:

3.4. Statistical methods employed The MID datasets contain categorical data (such as location) and numerical data (such as times and driven kilometres). To investigate which variables influence car ownership and mobility patterns, a set of statistical methods was used to analyse the data. For categorical data, the influence of different factors on the selected dependent variables (see Table 1) was analysed using residualbased shading for visualizing (conditional) independence [41]. Contingency tables and ‘Mosaic plots (as shown exemplarily in Fig. 2) are used to investigate and visualize relative frequencies and effects between categorical variables. For the residual-based shading applied throughout this work, maximum Pearson residuals [41, p. 515–517] were used. Cramer’s V, was also employed to assess the magnitude of a dependency in terms of the nominal effect size measure. The 2 -Test of Independence [42, p. 34–37] for two-way contingency tables was used to test for statistical independence. As sample sizes of the MID data at hand (see Section 3.2) are large (as long as the number of variables of

1A1830 1A3060 1A60p 2Ay1830 2Ay3060 2Ay60p 3 mA 2Am1Cu6 648

one adult, between 18 and 30 years one adult, between 30 and 60 years one adult, 60 years or older two adults, youngest person between 18 and 30 years two adults, youngest person between 30 and 60 years two adults, youngest person 60 years or older three or more adult persons minimum one child under 6 years

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Table 1 Identified parameters. Large (+++), medium (++) and small (+) effect size based on Cramer’s V (for categorical variables) or Cliff’s delta (numerical variables) together with Cohen’s effect size categories.

Cars per HH Car use pattern Car trips per day Trip purpose Distance & driving time (work) Distance & driving time (non-work) First departure time (work) First departure time (non-work) Parking time (work) Parking time (non-work)

HH type

HH econ. status

POR

+++ + +

+++

+

Driver occupation + + + ++ +++

Week day

+ + + + + + + +

Trip index

Trip purpose

+++ +++ +++ +++

2Am1Cu14 minimum one child under 14 years 2Am1Cu18 minimum one child under 18 years SP single parents 3.6.1. Exemplary use of the Mosaic plot Question: “Is the household type dependent on the place of residence?” Fig. 2 shows that the factor “household type” is in general statistically dependent on the factor “place of residence” based on a 2 -test of independence. The overall association between these factors is barely “small” based on Cramer’s V and Cohen’s effect size categories. Examining where the dependency originates from using the mosaic plot, one can see that it is for example more likely that family households (“2A1mCu6”, “2A1mCu14”, “2A1mCu18”) tend to exist in “rural” or “urban” areas (respectively blue shaded tiles in row 2 and 3) than in “cities” (respectively red shaded tiles in row 1) since the width of the tiles (i.e. the conditional probability of observing a specific value in the corresponding contingency table given a specific factor level of the independent variable) differs noticeably, even though not very strongly. 3.6.2. Exemplary use of Cliff’s method Question: “Do households with higher incomes tend to have more cars available?” - Results in Fig. 3 show that the most influential main effect on the number of cars available in a German household is the household

Fig. 2. Household level ( 2 -test and mosaic plot): main effect of place of residence on household type, for details of the figure household type labels see Section 3.6.

Fig. 3. Household level: overview of main effects on number of cars per household. 649

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type, followed by the economic status and the place of residence. The null hypothesis of this research question can be rejected since a higher income is clearly associated with the tendency to have more cars available in a household compared to a household with lower incomes. However, there is no significant distinction between the factor level groups “very low” and “low”. Question: “Do family households tend to have more cars available?” The order of levels for the factor “household type” in Fig. 3 shows that an increasing number of adult household members is associated with the probability of having more cars available in a household. Additionally, age seems to play a role as households with a youngest adult household member between 30 and 60 years have a tendency to have more cars available both for single person and two-person households compared to households with the youngest adult household member under 30 or above 60 years. Households with a minimum of one child under 18 years (family households) distinguish from two-person (as well as from single person and single parent) households significantly, having a clear tendency for more cars per household. As households with a minimum of three adult members are not necessarily family households, and single parent households clearly distinguish from the other family households, the null hypothesis of this research question can not be rejected.

sociodemographic and temporal aspects: main user occupation (+ +), weekday (+) – First departure time per non-work trip is dependent on socio-economic, sociodemographic and temporal aspects: trip purpose (+++), number of trips per use day (+++), weekday (+) – Parking time per work trip is dependent on socio-economic, sociodemographic and temporal aspects: main user occupation (+++), weekday (+) – Parking time per non-work trip is dependent on trip characteristic and temporal aspects: trip purpose (+++), weekday (+) 4. Modelling the electric demand of EV at different charging locations Five central questions are considered for generating the electric load profiles for EV charging: 1. 2. 3. 4. 5.

3.7. Selection process and chosen parameters for the EV model

When and where is the EV arriving? What is the SOC of the battery when arriving? Is the EV connected to the charging station? How long will the EV be present at the charging station? What is the SOC of the battery when leaving?

It can be seen that the answer to all questions is eventually linked to the mobility behaviour of the vehicle driver.

To select the model parameters from the MID data, a subset of all factors was formed that was statistically significant for the variables examined. From these, factors with small (+), medium (++) and large (+++) effect size according to Cohen’s effect size categories, were selected. From this subset, the respective factors were added to the model in descending order (beginning with the strongest effects) as long as the sample size in the respective factor levels/groups was still acceptable (n > = 30). The used effect measures were Cramer’s V for categorical dependent variables and Cliff’s delta for numerical dependent variables. Both measures are standardized and comparable. This procedure leads to a simulation model that depicts the strongest effects and most influencing factors. The main probability distributions used in the model and the conditional variables that have been identified as significantly influencing the parameters are listed in Table 1 and are explained in the following:

4.1. General approach The load profile generator synPRO [45,46] builds the framework in which the EV model is integrated. The consideration of socio-economic and demographic aspects is a main part of the modelling philosophy, which is also applied here. A spatial component is added to the model to account for the fact that EVs are moving towards different locations during the course of the day and can be charged at multiple locations. A Markov-chain is used to sample the sequence of trips between the different locations, depending on the corresponding trip index, trip purpose, weekday and driver occupation. The spatial component influences the electric load profile at each connection point. This is why the modelling focus targets the EV itself rather than the grid connection points. The inputs and outputs of the model are shown in Fig. 4. The inputs can be classified into three main categories:

– Number of available cars per household is dependent on socio-economic, sociodemographic and spatial household characteristics: household type (+++),1 economic status (+++), place of residence (+) – Main user (daily vehicle) use is dependent on temporal aspects: main user (vehicle) use frequency (+), weekday (+) – Number of trips per (vehicle use) day is dependent on socio-economic, sociodemographic and temporal aspects: household type (+), occupation of the primary driver (+), vehicle use frequency of the primary driver (+), weekday of the vehicle use (+) – Trip purpose (Departure and arrival places) is dependent on socioeconomic, sociodemographic and temporal aspects: trip index (++ +), weekday (+), main user occupation (+) – Driven distance and driving time per work trip is dependent on socioeconomic, sociodemographic and temporal aspects: main user occupation (+), weekday (+) – Driven distance and driving time per non-work trip is dependent on trip characteristics and temporal aspects: trip purpose (+++), weekday (+) – First departure time per work trip is dependent on socio-economic,

1. Household characteristics, such as income, age, place of residence and occupancy of the main driver: used to determine the number and type of electric vehicles. The mapping of EV models to status categories was done by common sense, based on the size and price of the vehicle (see [44]). Once the household’s status is determined, an EV model is sampled using a uniform distribution within each household’s status group of EV models. This category determines the subset of the mobility data (see Section 3.1) used to model driving behaviour. Once the number of available EVs per household given a specific market penetration rate is determined, a certain occupation type together with a corresponding vehicle use frequency is assigned to each EV’s primary driver. 2. Technical aspects, such as car type, self discharge-rate of the battery and charging infrastructure: these factors determine specific electricity consumption per km and the charging load profile over time and SOC. Different charging technologies reaching from 3.7 kW single phase charging at the household level up 120 kW DC chargers can be selected and placed at each destination. 3. Mobility data and charging assumptions: this set of inputs is the core of the behavioural model. The data is used to populate the probability distributions, transition matrices of the Markov-Chain and to configure the charging behaviour of the driver. The transition matrices were derived from the MID dataset. The matrices are

1 large (+++), medium (++) and small (+) effect size based Cramer’s V (for categorical variables) or Cliff’s delta (numerical variables) together with Cohen’s effect size categories.

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Algorithm 1. Algorithm to sample car usage, locations and distances 1: function SAMPLE_USAGE_LOCATIONS(socioecs) 2: Sample driven distance from home to work 3: for All days do 4: Sample use and number of trips 5: for Each trip do 6: Sample next trip purpose and next location 7: Sample and assign distance to trip 8: ▷round-trips get the same distance 9: end for 10: end for 11: return locations , distances, trippurpose 12: end function In a first step the use of an EV is sampled for all days of a year depending on the weekday or national holidays. For vacation-trips it is assumed that the car is not used. Next, the total number of trips is sampled for all days where the car is used. The probability distribution is dependent on occupation and the household type. Subsequently, an inhomogeneous first-order Markov chain constructs a sequence of destinations for all trips based on the parking locations (assuming first order Markov-property). The different locations are illustrated in Fig. 5. The transition probabilities between the destinations are dependent on the trip index (number of the trip during the day) and purpose, the weekday and the occupation of the main driver. On workdays full-time and part-time employed primary drivers are assumed to drive to the workplace on the first trip of the day and return home on the last. For each trip a distance is sampled from a distribution dependent on the trip purpose and the occupation of the main driver from the MID dataset. Round-trips between two destinations are identified and are assigned the same distance. The distance from home to work is sampled once for the whole simulation year dependent on the sampled main user occupation and is assigned to all corresponding trips. After calculating all the locations and distances the driving and parking times are sampled iteratively for each day, which is shown in Algorithm 2. As shown in Table 1, they depend on the main user occupation, trip purpose, the weekday and the distance. The time of first departure is sampled as a first step, which is the seed of the day’s driving sequence. At this point a sequence of trips between different locations and the corresponding distance has therefore been sampled for the day. The sequence is accepted if the speed for all trips are below the maximum allowed speed of the EV and the last arrival of the day is before a predefined time (based on normal distributions with a mean of

Fig. 4. Graphical representation of the main model inputs and outputs.

inhomogeneous because, for example, the probability to drive to from home to work on the first trip of the day is significantly higher for an employed person during the week than to drive from home to a place somewhere inside the city. The model thus offers the possibility to configure the charging behaviour of the driver. So preferences at the charging locations (e.g. free charging at work) and behavioural types (e.g. always charge when possible, only charge when needed) can be simulated. The charging behaviour characterised by the recharge probability dependent on the state-ofcharge (SOC) [15] and the recharge probability dependent on charging location [19]. The model output is a time series for each EV and each charging location for the electric charging profile, the presence of the EV at the charging location and the battery’s state of charge (SOC). 4.2. Algorithm to generate synthetic load profiles for EV The core of the model is the algorithm that generates the synthetic electric load profiles for each EV and each location. An inhomogeneous first-order Markov-Chain is used to sample trips between different locations. The algorithm consists of three main parts: 1. Calculation of trip sequences for the EV between the different locations and the corresponding driving distances, speed, and times, 2. Calculation of the charging decision, 3. Calculation of energy, SOC and the corresponding load profile. These are explained in the following sections. 4.2.1. Calculation of locations, driving distances, speed, and times The calculation of locations, driving distances, speed, and times is shown in Algorithm 1 and uses the probability functions based on the evaluation of the mobility data-set presented in Section 3.1 with respect to the different socio-economic factors (socioecs).

Fig. 5. Spatial components of the model represented as states of a Markovchain.

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12 p.m. on working days and 3 a.m. on weekends and a standard deviation of 1 h).

results are shown in Fig. 6. The figure shows the effect of a reduced sensitivity from left to right. It can be seen that a sensitivity of 100% leads to a sharp drop in the curve and an almost certain charging decision for a an SOC below the indifference level (here 75%). Reducing the sensitivity to 14% smooths the decision boundary. The function can be calibrated with empirical findings on the initial SOC upon recharging from [16]. Other aspects that influence the charging decision at different locations are the charging price, the accessibility of alternative charging locations in terms of time and distance, and the practicability of the connection at a certain charging station. This is accounted for by assigning a location dependent calibration factor cl to Eq. (2). In the current version of this model the calibration factor is only used to evaluate different charging scenarios. In future versions it will be calibrated with data on locational dependent charging preferences. The model also allows to define a set-value for the maximum charged SOC to respect, that drivers might not want to charge the EV up to 100% to prolong battery lifetime. This upper value can be dynamically modified. Which allows to evaluate different charging strategies such as to set the target SOC value such as to just reach the next EV charging station. This can be done in the algorithm as distances and speed have been sampled before. This allows using the model to simulate an EV driver with foresight and different EV charging algorithms.

Algorithm 2. Algorithm to generate driving times, parking times and speed 1: function CALC_TIMES_SPEED(locs, dists, trip _purpose , socioecs) 2: for All days do 3: Sample time of the first departure from home 4: for Each trip do 5: Sample speed and driving times 6: Sample parking times 7: end for 8: if Last parking too late then 9: Reset and restart loop 10: end if 11: end for 12: return parkingtimes, drivingtimes, speed 13: end function

4.2.2. Calculation of the charging decision The algorithm used to generate a charging profile is depicted in Algorithm 3. Once an EV is parked the decision has to be taken if it is charged. The decision if the EV is charged depends on several factors. First and most trivial is the presence of the necessary charging infrastructure. The nominal charging power of the charging station at the different locations are sampled once for the whole simulation year based on the respective distributional parametrization provided as model input. For details of the charging infrastructure, see Section 5.4. Second, the parking time needs to be above a certain threshold (here 10 min) for charging to be possible. Third, behavioural aspects and the individual charging ‘style’ of the user is considered. As stated in [15], behavioural aspects such as the ‘comfortable range’, the ‘user-battery-interaction style’ can be used to explain individual charging decisions depending on the current SOC of the battery. A logistic function is used to model charging probability upon SOC,

pcharge (SOC ) = min

1

1 1+e

k (SOC %SOC )

· c l, 1

Algorithm 3. Algorithm to calculate the new SOC of the EV and the load profile at a given charging location. 1: function CALC_SOC_P_EL(loc, dist , speed , times, car ) 2: for All trips do 3: Calculate electricity per trip 4: Calculate SOC upon arrival 5: Get charging infrastructure at location 6: if Parking time sufficient for charging then 7: Sample charging decision (see Eq. (2)) 8: if Charging decision is TRUE then 9: Get charging load trace 10: while SOC < target & t < parking time do 11: Charge 12: end while 13: end if 14: end if 15: Calculate self-discharge of battery 16: Calculate SOC upon departure 17: end for 18: return SOC (t ), Pel,ch (t ) 19: end function

(2)

where k is the gradient of the curve at its midpoint and %SOC is the corresponding midpoint SOC. The midpoint is interpreted as the indifference level where the probability to charge is 50%. The gradient at this point can be interpreted as the sensitivity of the driver towards decreasing SOC values. This allows to account for individual charging preferences according to the SOC and different user types. Exemplary

4.2.3. Calculation of SOC, charging energy and load profile Using the arrival and parking time and the charging decision a charging procedure is initialized. At each destination the SOC of the battery is calculated using the SOC from the last departure, the driven kilometres and the energy consumption rate. Once an EV is plugged into the charging station the power demand to charge the battery is obtained from a charging load-trace. The charging load trace describes the charging power at each time step Pel,ch (t ) as a function of the current SOC.

Pel,ch (t ) = f (SOC (t ))

(3)

The employed load traces f are different for each car type and the charging infrastructure present. This is done to account for differences in battery capacity and the maximum allowed charging power. The characteristic shape of the curve is maintained and scaled accordingly,

Fig. 6. Example for different curve shapes for the logistic charging likelihood function. 652

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thereby allowing to generate a loading profile for each car, at different charger types for a given SOC. The required charging energy for the EV’s battery over time is calculated with:

Ech = Edrive + Esdc

(4)

Eiml

It is the sum of charged electricity Ech at the charging destinations explained in the previous section and Eiml electricity charged at intermediate locations. Eiml is used to account for intermediate charging on long trips (e.g. at chargers on the highway), where the trip distance exceeds the nominal range of the EV. Eiml is chosen such that the EV arrives at its destination with an empty battery. The required electricity to charge the EV has two components: 1) Esdc being the self-discharged energy for the total parking time and 2) Edrive being the specific consumption per driven kilometre. Self-discharged is modelled non-linear for the first 24 h and then linear for the following 30 days as indicated in [47]. The resulting SOC over the remaining parking-time after charging was completed is calculated with:

SOC24h + SOC = SOC 24h

1

SOC24h t+1 1 SOC24h ·( 29·24

for t

t

24h

24) for 24 h< t

0.9

30d (5)

for30d < t

SOC24h is a user specified target SOC value (0.95 for lithium battery systems [47]). t is the timespan in hours between the end of the charging process and the current parking time. For lithium batteries this leads to a self-discharge of 5% in the first 24 h and another 5% in the following month. The required electricity for charging further includes the energy consumption of all auxiliary devices such as air-conditioning/heating of the interior and cooling/heating of the battery, and is therefore dependent on the outdoor temperature TAir . The temperature-dependent specific consumption Edrive (TAir ) is calculated to:

1.12 0.01· TAir for TAir < 15 Edrive (TAir ) = 1 for 15 TAir Edrive,0 0.63 + 0.02· TAir for TAir > 20

Fig. 7. Comparison of the arrival time probability distribution. Data standardized to have a cumulated probability of 100%. The horizontal axis has a 1 min resolution.

is sampled with a mean absolute error of 3.5%. The average distance per trip as a weighted mean deviates by −2.1% and the total annual km differ by 2.1%. Fig. 7 shows the distribution of arrival times at the home parking station obtained from a simulation of 600 households using the synPRO model and the corresponding results of the MID study. It can be seen that on a working day (see Fig. 7(a)) the distributions match until the lunch peak at midday. The following valley in the MID data set together with the afternoon peak at 17:00 are less pronounced in the synPRO model. Overall the correlation of the two curves is 98.7% on mean working days. On the weekend (see Fig. 7(b) and (c)), the correlation between simulated arrival times and those reported in MID are 98.3% for Saturdays and 97.6% on Sundays. It shows that the model quality is higher for working days than it is on the weekend. A reason for this might be the fact that, for most people, working days follow more distinct routines and patterns than weekends. The high correlation of the daily arrival times and the correspondence in number of trips and distances between the model and the MID data, show that the algorithm can be used to generate representative EV driving profiles and the corresponding electric load profiles.

20 (6)

It depends on the EV model’s nominal consumption Edrive and a temperature-dependent scaling factor for cabin and battery temperature control based on [48]. The corresponding equation consists of a piecewise linear regression for cooling (TAir > 20 °C) and heating (TAir < 15 °C), framing the constant nominal consumption in the range between these two temperatures. 4.3. Model validation For the investigation of the electric load profile, the required electrical energy and the charging times are of particular interest. Therefore the validation focusses on number of trips, trip lengths and the corresponding arrival times at home. To validate the EV model, the results of the algorithm are compared to the MID data-set, which is seen as representative. A pool of 600 households is sampled using the same distribution of households’ socio-economic factors as in the survey data. The results presented in Table 2 show that the number of daily car trips

5. Impact of EVs on households’ electric load profiles The model is now used to evaluate the impact of EV on residential load profile with respect to the different factors covered by the EV model. For this purpose a set of 300 households is simulated for each scenario. Unless stated differently, the employed charging infrastructure and car-type are sampled based on the socio-economic factors of the corresponding households. Simulation, evaluation and plots are done with a one minute time resolution. Three indicators are of particular interest: (1) Absolute annual electricity demand, (2) The change in the daily load profile and (3) Electricity peaks on an individual household level.

Table 2 Comparison of simulated data and survey data for a car pool of 600 cars given the same household characteristics (synPRO: mean values ± standard deviation, MiD: mean values).

Car trips per day Dist. per trip in km Dist. p.a. in km

synPRO

MiD

Deviation (%)

2.06 ± 0.44 15.28 ± 5.78 11, 573 ± 5, 171

1.99 15.6 11,333

3.5 −2,1 2.1

5.1. Impacts on an exemplary 4 person household To illustrate the impact of electric vehicles we start with an 653

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Table 3 Results for one family with two adults and one child and different electric vehicles and charging infrastructure. Annual values

Family 4 pers. without EV with small EV (3.7 kW) with midsize EV (11 kW) with large EV (22 kW)

Average daily profile

Peak Load [W]

Electric Demand [kWh]

Peak Load [kW]

Electric Demand [kWh]

Pmax/ Pmean [ ]

7968 8433 16,713 28,683

3992 4669 6013 8237

0.829 1.046 1.717 2.829

10,938 12,792 16,473 22,568

1.82 1.96 2.50 3.01

Fig. 9. Comparison of duration curve for a family with different car types and home charging station.

example. In this case an individual 4 person household is equipped with different electric car models and a representative charging station located at the house. For this purpose three different car models referred to as ‘large’, ‘mid-size’ and ‘small’ are added to the household. The three exemplary cars selected correspond to a Tesla Model X P100D, Opel Ampera and Renault Twizy Urban Life. From Table 3, which provides an overview of the results, it can be seen that annual electricity consumption is increased by a factor of 1.2–2.1. The increase in electric demand strongly depends on the specific consumption for each EV. The annual load peaks are increased by a factor of 1.06–3.6. Note that the annual peaks are strongly influenced by the type of charging infrastructure present, which is studied in more detail later in this section. Fig. 8 shows the average daily load profile for the given household. It can be seen that when electric vehicles are added the shape and the absolute load profile change. Adding EV to the household increases the duration of the hours of high load during the evening and the absolute load by factor of 1.2–3.4. Further, the load increase during evening hours begins approximately 45 min earlier, as EVs are plugged in when persons arrive at home. We further see that the average load peak is approximately 0.1 times the annual load peak independent of the presence and the type of an EV. This shows that smoothing patterns for the load profile with and without an EV result in the same characteristic peak ratios. In case of a household with an EV the probability of the EV charging at 6 pm is 10%. This characteristic takes into account different arrival times and charging durations and is strongly dependent of the usage pattern of the EV user. Fig. 9 shows the sorted hourly load distribution for the year. From this the effects of peak increase and increase in high load hours for the exemplary household become clearly visible. Adding a small EV with a 3.7 kW charging station only mildly effects the load characteristics, whereas adding a mid-sized or large EV with the corresponding charging infrastructure increases the hours of high load - corresponding to more than a factor of 1.4 of today’s load peaks - up to over 200 h per year.

Fig. 10. Influence of main car user occupancy on the residential electric load profile. Mean value in solid lines and 0.25/0.75 percentiles shaded.

5.2. Influence of socio-economic factors To investigate the influence of different socio-economic factors on the load profiles, 7 different user-groups, defined by their main occupation are simulated. The influence of lifestyle and daily routines strongly

Fig. 8. Comparison of mean day load trace for a family with different car types and home charging station. 654

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influences when electricity is consumed, which can be seen in Fig. 10. Fig. 10(a) shows the annual electricity consumption for charging the EV at home for the different user groups. The difference between workers, which need to reach their workplace on a daily basis, and nonworkers is clearly visible. An exception here are apprentices, which typically use their own car (if any) less often. It can be seen in Fig. 10(c) that the introduction of EV intensifies the hours of high load, particularly for workers’ households. When people arrive at home after work, start charging the EV and carrying out their daily routines, the electric demand adds up. This leads to a more volatile demand curve, which on a power system level might lead to the need for more power plant capacity. It further shows that on working days there is a difference between full-time workers, part-time workers and non workers. While full-time workers tend to come home after an 8 h day at around 5 p.m., part-time workers arrive at home mostly during the second half of the day, depending more on individual working patterns. The highest load peaks are caused by full-time workers as they show the highest simultaneity. This can also be experienced in urban traffic during rush hour. Unemployed households show a less clear pattern in their mobility. Hence additional load due to charging procedures is more evenly distributed over the day. During Sundays the EV use seems hardly dependent on the occupation of the main driver as can be seen in Fig. 10(b). 5.3. Influence of the charging location To show the influence of the charging locations, 300 profiles for fulltime workers are simulated for different charging preferences, defined by the midpoint indifference level at different locations. As explained in Section 4.2.2, the midpoint indifference level equals the SOC at which the person is indifferent about charging (50% probability to charge). The indifference levels for home (H) and work (W) were set to simulate one scenario where just charging at home is permitted and the vehicle is always charged whenever it arrives home (H: 100, W: 0), a second and third scenario in which charging at home and work are done whenever the battery SOC is under 50% or 100% (H: 50, W:50; H:100, W:100) and a fourth scenario in which charging at work is forced (H: 0, W: 100). All other charging locations were set to have an indifference level of 0, meaning that charging only occurs when the battery is empty. Connecting sensitivity is set to 1 for all cases. Fig. 11 shows the impact of the different charging preferences on the annual electricity demand (Fig. 11(a)), as well as on the daily load profile for the two locations home (Fig. 11(b)) and work (Fig. 11(c)). In the case that only home charging is permitted, 90% of the electricity is charged at home, while for charging at work the work share is 80%. The missing percentages indicate that the charged electricity at just one charging station is not sufficient to supply all car trips, resulting in additional charging procedures at other locations. This is the case for long distance trips or, in case of the charging at work scenario, at home during the weekends. Comparing the H50W50 case to the H100W100 (always charge upon arrival), the effect of self-discharge of a full battery becomes visible: in the latter case the battery is charged more frequently and therefore is more frequently at 100% SOC, which leads to higher self-discharged electricity, explaining the difference in annual electricity consumption between the two cases. The same explanation holds for the difference in annual charged electricity for the H0W100 and the H100W0 cases, as during the course of the year there are more arrivals at home than at work. While the overall electricity charged is not affected by the place of charging, the effects on charging times and therefore on the occurrence of daily load peaks need to be considered. In 11(b) the charging profile at the home charging station for a mean working day shows that for a vehicle just charged at home (green2 line), load peaks occur in the late

Fig. 11. Influence of charging location on the residential electric load profile. Mean value in solid lines and 0.25/0.75 percentiles shaded. H: Home, W: Work, the number represents the connected midpoint indifference (threshold of SOC when the EV is charged).

afternoon around 5 p.m. For a vehicle charged at home and work with equal probability, the afternoon load peak can be halved. In Fig. 11(c) the mean working day for a charging station at work can be seen. The load peak is shifted to the morning hours between 8 a.m. and 9 a.m. Furthermore the peak is about 1.8 times as high as the corresponding home charging peaks. This is due to a higher simultaneity concerning arrival times at work than arrival times back home after a working day. As a general finding for uncontrolled charging strategies it can be stated that a higher number of charging locations results in a lower chance to accumulate load peaks. Charging with lower SOC as in the H50W50 case leads to a smoothing of the load curve at the two locations. 5.4. Influence of charging infrastructure To investigate the influence of the charging infrastructure on the household load profiles three different charger types are simulated covering the range implemented in the model. Those are:

2

For interpretation of color in Fig. 11, the reader is referred to the web version of this article. 655

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5.5. Influence of the EV model To investigate the influence of different car models on the electric load profile, 300 households are simulated and three different car types are explicitly used. Those are:

• Large: Tesla Model X P100D • Midsize: Opel Ampera • Small: Renault Twizy Urban Life For better comparison only charging at home was allowed. The charging power was set to be 3.7 kW for all simulated cars, ignoring differences in charging infrastructure depending on the car model, thereby putting the focus on the vehicle itself. The resulting annual electricity demand was 3200 kWh/a for the large, 2100 kWh/a for the mid-sized and 700 kWh/a for the small car. Hence the large car requires 3.9 times as much electricity as the smallest car in the pool. The main reason for this is the 3.1 times higher specific consumption per km of the large vehicle. The remaining difference is found in the self discharged energy, which is higher (in absolute terms) for larger battery capacities. The absolute value of self-discharged energy for the largest vehicle is 11.3 times as high as the one for the smallest. This also means that, for an average distance driven of 15.3 km, the discharged energy for a large vehicle reaches a share of 29% of the totally consumed and lost energy, while for a small vehicle this share is just 10%. The smaller the distances driven, the less efficient larger battery sizes become. Note that the self-discharged electricity is strongly dependent on the charging strategy. In this simulation it is assumed that the EV is always fully charged leading to a high self-discharge loss, particularly in the SOC range of 95% to 100%. Charging only up to about 90% can reduce these losses and can be investigated using the model. When investigating the daily load profiles shown in Fig. 13 it can be seen that, the larger the car, the higher the demand during the entire time. Comparing the large and mid-size car it can be seen that the large car leads to a smoother load profile for working days and particularly

Fig. 12. Influence of charging infrastructure on the residential electric load profile. Mean value in solid lines and 0.25/0.75 percentiles shaded.

• 3.7 kW nominal power corresponding to a 16A, 230 V 1-phase AC charger, • 11 kW nominal power corresponding to a 16A, 230 V 3-phase AC charger • 120 kW nominal power corresponding to a 250A, 480 V DC Tesla super charger.

The midpoint indifference levels and charging sensitivities are set so that the EV is always charged when arriving home, while all other locations are not being used. Fig. 12 shows the average daily load profile for the household connection point. It can be seen that a lower nominal power of the charger leads to a smoothed load profile, due to longer charging durations. Increasing nominal power leads to more volatile average load profile and also fluctuations as visualized by the 25% and 75% areas around each mean line increase. This makes forecasts of charging peaks more difficult. With increased power of the charger the aggregation effect of simultaneity between arrivals is increased. This leads to earlier and more pronounced high load hours for households. In general the higher the simultaneity of arrivals over a given timespan, the higher the load peak. High charging powers will result in high load peaks directly after arrival, whereas smaller charging powers lead to longer duration of charge and add up during the end of given time windows. Clearly the nominal charging power strongly influences the annual load peak and the needed connection capacity. The above example highlights the influence of charging power on simultaneity. For the case of uncontrolled charging it is shown that with 120 kW fast chargers the average simultaneity on a daily base is about 1% as charging duration is short. On the day with the highest load from EV charging a simultaneity of 9% and a peak of 1078 kW is observed for the 120 kW chargers. 11 kW chargers lead to 35% simultaneity (388 kW peak) and 3.7 kW chargers to 70% simultaneity (259 kW peak) respectively. Fig. 13. Influence of car model on the residential electric load profile. Mean value in solid lines and 0.25/0.75 percentiles shaded. 656

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higher loads during late evening and night hours. As battery size and absolute electricity demand are higher for the larger car, this leads to a longer charging durations and a smoothing of the load. For the simulated charging infrastructure this leads to a simultaneity of 21% for large cars, 17% for midsize and 6% for small cars, on the average working day at peak time (at 18:00).

Bestand/Umwelt/>; 2016. [3] BMWi, BMVBS, BMU, and BMBF. Regierungsprogramm Elektromobilität, Technical report, Die Bundesregierung; 2011. [4] Richardson David B. Electric vehicles and the electric grid: a review of modeling approaches, impacts, and renewable energy integration. Renew Sustain Energy Rev 2013;19:247–54. [5] Lund Peter D, Lindgren Juuso, Mikkola Jani, Salpakari Jyri. Review of energy system flexibility measures to enable high levels of variable renewable electricity. Renew Sustain Energy Rev 2015;45:785–807. [6] Wang Hewu, Zhang Xiaobin, Ouyang Minggao. Energy consumption of electric vehicles based on real-world driving patterns: a case study of Beijing. Appl Energy 2015;157:710–9. [7] Munkhammar Joakim, Widén Joakim, Rydén Jesper. On a probability distribution model combining household power consumption, electric vehicle home-charging and photovoltaic power production. Appl Energy 2015;142:135–43. [8] García-Villalobos J, Zamora I, Eguia P, San Martín JI, Asensio FJ. Modelling social patterns of plug-in electric vehicles drivers for dynamic simulations. In: 2014 IEEE international electric vehicle conference, IEVC 2014; 2014. [9] Babrowski Sonja, Heinrichs Heidi, Jochem Patrick, Fichtner Wolf. Load shift potential of electric vehicles in Europe. J Power Sources 2014;255:283–93. [10] Grahn Pia, Alvehag Karin, Soder Lennart. PHEV utilization model considering typeof-trip and recharging flexibility. IEEE Trans Smart Grid 2014;5(1):139–48. [11] Grahn Pia, Munkhammar Joakim, Widen Joakim, Alvehag Karin, Soder Lennart. PHEV home-charging model based on residential activity patterns. IEEE Trans Power Syst 2013;28(3):2507–15. [12] Su Wencong, Wang Jianhui, Zhang Kuilin, Yuen Chow Mo. Framework for investigating the impact of PHEV charging on power distribution system and transportation network. In: IECON proceedings (industrial electronics conference); 2012. [13] Xydas Erotokritos, Marmaras Charalampos, Cipcigan Liana M, Jenkins Nick, Carroll Steve, Barker Myles. A data-driven approach for characterising the charging demand of electric vehicles: a UK case study. Appl Energy 2016;162:763–71. [14] Wieland Thomas, Reiter Michael, Schmautzer Ernst, Fickert Lothar, Fabian Jürgen, Schmied Robert. Probabilistische Methode zur Modellierung des Ladeverhaltens von Elektroautos anhand gemessener Daten elektrischer Ladestationen Auslastungsanalysen von Ladestationen unter Berücksichtigung des Standorts zur Planung von elektrischen Stromnetzen, Elektrotechnik und Informationstechnik; 2015. p. 160–7. [15] Franke Thomas, Krems Josef F. Understanding charging behaviour of electric vehicle users. Transport Res Part F: Traffic Psychol Behav 2013;21:75–89. [16] Schäuble Johannes, Kaschub Thomas, Ensslen Axel, Jochem Patrick, Fichtner Wolf. Generating electric vehicle load profiles from empirical data of three EV fleets in Southwest Germany. J Cleaner Prod 2017. [17] Morrissey Patrick, Weldon Peter, O’Mahony Margaret. Future standard and fast charging infrastructure planning: an analysis of electric vehicle charging behaviour. Energy Policy 2016;89:257–70. [18] Azadfar Elham, Sreeram Victor, Harries David. The investigation of the major factors influencing plug-in electric vehicle driving patterns and charging behaviour. Renew Sustain Energy Rev 2015;42:1065–76. [19] Jabeen Fakhra, Olaru Doina, Smith Doina, Braunl Thomas, Speidel Stuart. Electric vehicle battery charging behaviour: findings from a driver survey. In: Australasian Transport Research Forum 2013 Proceedings; 2013. p. 1–15. [20] Infas Institut für angewandte Sozialwissenschaften and Deutsches Zentrum für Luftund Raumfahrt e.V. Institut für Verkehrsforschung, Mobilität in Deutschland 2008; 2009. [21] Estes Hunter, Santoso Surya, Fisher Grant. Analysis of high-resolution electric vehicle charging on time-of-use grid demands. In: IEEE Power and energy society general meeting, 2015-Septe; 2015. [22] Clement-Nyns Kristien, Haesen Edwin, Driesen Johan. The impact of charging plugin hybrid electric vehicles on a residential distribution grid. IEEE Trans Power Syst 2010;25(1):371–80. [23] Neaimeh Myriam, Hill Graeme, Blythe Phil, Wardle Robin, Yi Jialiang, Taylor Phil. Integrating smart meter and electric vehicle charging data to predict distribution network impacts. In: 2013 4th IEEE/PES innovative smart grid technologies Europe, ISGT Europe 2013; 2013. p. 1–5. [24] Salah Florian, Ilg Jens P, Flath Christoph M, Basse Hauke, Dinther Clemens van. Impact of electric vehicles on distribution substations: a Swiss case study. Appl Energy 2015;137:88–96. [25] Pinter Laszlo, Farkas Csaba. Impacts of electric vehicle chargers on the power grid. In: IYCE 2015 - proceedings: 2015 5th international youth conference on energy; 2015. p. 1–6. [26] Darabi Zahra, Ferdowsi Mehdi. Aggregated impact of plug-in hybrid electric vehicles on electricity demand profile. IEEE Trans Sustain Energy 2011;2(4):501–8. [27] Darabi Zahra, Ferdowsi Mehdi. Extracting probability distribution functions applicable for PHEVs charging load profile. In: Proceedings of the IEEE power engineering society transmission and distribution conference; 2012. p. 1–6. [28] Verzijlbergh Remco, Lukszo Z, Ilić MD. Comparing different EV charging strategies in liberalized power systems. In: 9th International conference on the European energy market, EEM 12; 2012. [29] Verzijlbergh Remco, Lukszo Z, Veldman E, Slootweg JG, Ilic M. Deriving electric vehicle charge profiles from driving statistics. In: IEEE power and energy society general meeting; 2011. p. 1–6. [30] Fischer David, Scherer Johannes, Flunk Alexander, Kreifels Niklas, Byskov-Lindberg Karen, Wille-Haussmann Bernhard. Impact of HP, CHP, PV and EVs on households’ electric load profiles. In: 2015 IEEE Eindhoven PowerTech, Eindhoven; 2015. p. 2–7. [31] Iversen Emil B, Morales Juan M, Madsen Henrik. Optimal charging of an electric

6. Conclusion and outlook The presence of electric vehicles will lead to new challenges in the power system but will also offer possibilities for load shifting. Simulation models are needed in order to study the effects of EV on electricity networks and develop adequate solutions in terms of system design and operation. This paper has presented and validated a stochastic bottom-up model of residential EV use, charging behaviour and resulting electrical load profiles. The central innovation lies in the consideration of socio-economic, technical and spatial factors, all of which influence charging behaviour and location. Based on a detailed statistical analysis of a large dataset on German mobility, the most statistically significant influencing factors on residential charging behaviour could be identified. Whilst household type and economic status are the most important factors for the number of cars per household, the driver’s occupation has the strongest influence on the first departure time and parking time whilst at work. Hence the charging behaviour with respect to spatial preferences (such as home or work) and with respect to the battery SOC is a main feature of the model and has proven to strongly influence the resulting load profile. The results suggest that the working pattern of the main driver, weekday, car-type and charging infrastructure should be considered when modelling the use of EV and planning for their integration into the electric grid. The model was employed to study the effects of EV on individual households’ electric load profiles. It was shown that load peaks strongly depend on the deployed charging infrastructure and can easily increase by up to 3.6 times (4 person household, 22 kW charger, large car), compared to today. Not only are the overall peaks higher for higherpower EV and charging infrastructure, the variability of the charging profile is higher, which could lead to problems in the distribution grid. It was shown that EV will lead to an intensification and an approximately 45 min earlier start of high load hours during evenings in particular for working households. The results clearly show that in all investigated cases improved charging strategies are needed to ease the effects of high simultaneity and thus high load peaks. This paper represents the latest extension to a stochastic multi-energy simulation model for residential buildings (synPRO). Whilst the paper has concentrated on the model implementation, validation and implications for residential load profiles, future work will go beyond this. In particular, the synergies between decentralised heat and power generation technologies with EV should be analysed in the context of sector coupling on a building and district scale. Further work should also examine the scope of demand response (DR) to exploit demandside flexibility and enable an efficient integration of EV into distribution networks, possibly by also incorporating a more explicit spatial dimension. Acknowledgement This work was supported by the German Federal Ministry for Economic Affairs and Energy as well as the Federal Ministry of Education and Research within the project “EnStadt:Pfaff”. References [1] Bdew. Energieverbrauch im Haushalt - BDEW-Datenkatalog. Bdew 2010. [2] Kraftfahrtbundesamt. Bestand an Pkw in den Jahren 2007 bis 2016 nach ausgewählten Kraftstoffarten.
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D. Fischer et al. vehicle using a Markov decision process. Appl Energy 2014;123:1–12. [32] Follmer Robert, Lenz Barbara, Jesske Birgit, Quandt Sylvia. Mobilität in Deutschland 2008 Ergebnisbericht; 2010. p. 214. [33] Wermuth Manfred, Neef Christian, Wirth Rainer, Hanitz Inga, Löhner Holger, Hautzinger Heinz, et al. Mobilitätsstudie “Kraftfahrzeugverkehr in Deutschland 2010” (KiD 2010 ) – Schlussbericht, 2010(70); 2012. [34] Weiss Christine, Chlond Bastian, Hilgert Tim, Vortisch Peter, Streit Tatjana, Chlond Bastian, et al. Deutsches Mobilitätspanel (MOP) - Wissenschaftliche Begleitung und Auswertungen Bericht 2012/2013: Alltagsmobilität und Fahrleistungen; 2014. p. 1–138. [35] Li Xiaomin, Chen Pu, Wang Xingwu. Impacts of renewables and socioeconomic factors on electric vehicle demands - panel data studies across 14 countries. Energy Policy 2017. [36] Harris Chioke B, Webber Michael E. An empirically-validated methodology to simulate electricity demand for electric vehicle charging. Appl Energy 2014. [37] Nationale Plattform Elektromobilität, Ladeinfrastruktur für Elektrofahrzeuge in Deutschland - Statusbericht und Handlungsempfehlungen 2015, Gemeinsame Geschäftsstelle Elektromobilität der Bundesregierung (GGEMO); 2015. p. 36. [38] Follmer Robert, Gurschwitz Dana, Jesske Birgit, Quandt Sylvia, Nobis Claudia, Köhler Katja. Mobilität in Deutschland 2008 - Nutzerhandbuch; 2010. [39] Wie schnell fahren Sie selbst in der Regel auf der Autobahn, wenn kein Tempolimit vorgegeben ist? Technical report, EARSandEYES GmbH; 2008.

[40] Cookson Graham, Pishue Bob. INRIX Global Traffic Scorecard; 2017. [41] Zeileis Achim, Meyer David, Hornik Kurt. Residual-based shadings for visualizing (conditional) independence. J Comput Graph Stat 2007;16(3):507–25. [42] Agresti Alan. An introduction to categorical data analysis, vol. 22; Wiley, Hoboken, New Jersey; 2007. [43] Cliff Norman. Ordinal methods for behavioral data analysis; Lawrence Erlbaum Associates, Mahwah, New Jersey; 1996. [44] Harbrecht Alexander, McKenna Russell, Fischer David, Fichtner Wolf. Behaviororiented modeling of electric vehicle load profiles: a stochastic simulation model considering different household characteristics, charging decisions and locations. Working Paper Series in Production and Energy KIT, (29); 2018. https:// publikationen.bibliothek.kit.edu/1000082537. [45] Fischer David, Härtl Andreas, Wille-Haussmann Bernhard. Model for electric load profiles with high time resolution for German households. Energy Build 2015;92:170–9. [46] Fischer David, Wolf Tobias, Scherer Johannes, Wille-Haussmann Bernhard. A stochastic bottom-up model for space heating and domestic hot water load profiles for German households. Energy Build 2016;124:120–8. [47] Cadex Electronics. BU-802b: what does elevated self-discharge do?; 2017. [48] Lindgren Juuso, Lund Peter D. Effect of extreme temperatures on battery charging and performance of electric vehicles. J Power Sources 2016;328:37–45.

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