Consumer behaviour for electromobility and charging strategies in TIMES Local - influence on the network load in an urban energy system

Source: https://www.audi.com/de/experience-audi/mobility-and-trends/e-mobility/e-tron-charging-service.html
Consumer behaviour for
electromobility and
charging strategies in
TIMES Local - influence on
the network load in an
urban energy system
Lukasz Brodecki
1

1 2 3 4 5
Agenda
03.07.2020 2
• Motivation
• Simulation Approach (Markov-Chain-Monte-Carlo)
• TIMES Local: Model, Scenarios, Results
• Discussion and Summary
• References
Consumer behaviour for electromobility and charging strategies in TIMES Local

1 2 3 4 5
Motivation
03.07.2020Consumer behaviour for electromobility and charging strategies in TIMES Local 3
Mobility / Transportation transition
Energy System Transition – „Energiewende“
Intelligent Charging/Loadmanagement
Load[kW]
Time
4
5
2
3
Source [6]

1 2 3 4 5
Approach
03.07.2020Consumer behaviour for electromobility and charging strategies in TIMES Local 4
Goal: Analysis of the influence of Electromobility and Charging Strategies on the overall Energy System
Data Basis:
Empirical Study „Mobilität in Deutschland 2008“
Stochastic Simulation tool:
Mobility Load Curve
Simulation of Load Curve of charging infrastructure
Application in Energy System Model TIMES Load Curve of charging infrastructure
(Example)
ElectricLoad[kW]
Daytime of type-day
Load Curve of vehicle-kilometres
(Example)
Vehicle-kilometres
[km]
Daytime of type-day
Sources:
Liebhart (2017); Brodecki (2018); Klempp (2018); [7-9]

1 2 3 4 5
Agenda
503.07.2020Consumer behaviour for electromobility and charging strategies in TIMES Local
• Motivation
• References

1 2 3 4 5
Study „Mobilität in Deutschland 2008“
6
Data Basis
• Federal Ministry of Transport and Digital Infrastructure (BMVI)
• 26 000 Households
• 193 000 Ways
• 121 Variables per Way
HH-ID P-ID W-ID Year Month Weekday KW Start Time End Time Purpose Origin Destination
Mean of
Transport
Distance
[km]
Duration
[min]
Speed
[km/h]
…
200811 1 1 2008 5 6 18 9:00:00 9:20:00 shopping At home Out of town car (driver) 14.25 20 42.75 …
200811 1 2 2008 5 6 18 10:00:00 10:30:00 home - At home car (driver) 14.25 30 28.5 ...
200811 1 3 2008 5 6 18 11:00:00 11:03:00 shopping - Within town car (driver) 2.85 3 57 …
200811 1 4 2008 5 6 18 11:27:00 11:30:00 home - At home car (driver) 2.85 3 57 …
200812 1 1 2008 8 7 31 13:30:00 14:30:00 Freetime activity At home Roundway On foot 1.96 60 1.96 …
… … … … … … … … … … … … … … … … …
Sources:
infas, 2010b; Liebhart (2017); [7,10]

1 2 3 4 5
Simulation Tool - Concept
Simulation:
Mobility Load Curves
Simulation:
Load Curves of Charging
Infrastructure
Attributes of area:
• Inhabitants
• Season or month
• State / Type of region
Simulationparameters:
• Period (Day or week)
• Number of Households
• …
User Inputs
„Mobilität in
Deutschland“
2008
Simulationparameters:
• Share of electromobility [%]
• Battery Capacity per Vehicle [kWh]
• Electricity consumption [kWh/100km]
• Charging power per station [kW]
• Places electric car can be charged
• …
User Inputs
Load Curve of vehicle-kilometres
(Example)
Vehicle-kilometres[km]
Daytime of type-day
Load Curve of charging infrastructure
(Example)
ElectricLoad[kW]
Daytime of type-daySources:
Liebhart (2017); Brodecki (2018); Klempp (2018); [7-9]

1 2 3 4 5
Mobility Load Curves - General structure
User Input Household- and Vehicletype simulation
START
Stochastic Calculations
State 0 1 ∑
0 0,6 0,4 1
1 0,2 0,8 1
Markov-Chain-Monte-Carlo-Simulation
Veh.-Status Purpose Destination Speed
Repeat for each timeslice and vehicle
Generate daily profile
Vehicle-
kilometres
Daytime of type-day
Vehiclekilometres
Personkilometres
Generate weekly profiles
Hour index of type-day
Vehicle-
kilometres
Vehiclekilometres
Personkilometres
END
Simulationstool
Electric Load
Rep. for each
day of the
simulation

1 2 3 4 5
Agenda
• Motivation
• References

1 2 3 4 5
 Decoupling of Charging cycle, storage and use of the electric vehicle
 Availability of electromobility and charging profiles based on exogenously given users‘ behaviour
 Coupling of consumers in commercial / housing sector and generation via Vehicle-to-grid
Implementation of Prosumers‘ behaviour patterns
10
Charging
Process
Battery Storage
in Hour 1
Distribution-
process
…
Battery Storage
in Hour 2
Battery Storage
in Hour 24
Electric vehicle
Storage-processes
Mobility
Demand
Charging Process
Technology =
Discharging
process
Distribution-
process
Photovoltaic in
Households
Source:
L. Brodecki (2018)
Photovoltaic in
Commercial
…
03.07.2020

1 2 3 4 5
 Decoupling of Charging cycle, storage and use of the electric vehicle
 Availability of electromobility and charging profiles based on exogenously given users‘ behaviour
 Coupling of consumers in commercial / housing sector and generation via Vehicle-to-grid
Implementation of Prosumers‘ behaviour patterns
11
Charging
Process
Battery Storage
in Hour 1
Distribution-
process
…
Battery Storage
in Hour 2
Battery Storage
in Hour 24
Electric vehicle
Storage-processes
Mobility
Demand
Charging Process
Technology =
Discharging
process
Distribution-
process
Photovoltaic in
Households
Source:
L. Brodecki (2018)
Photovoltaic in
Commercial
…
• Definition of a „tube“ for
charging of electromobility via
FLO_FRaction up/lo
0
0.01
FlowFraction
ofLoad
Charging[%]
FLO_FR UP FLO_FR LO
03.07.2020

1 2 3 4 5
Model description TIMES Local
TIMES Local: Stuttgart
Goal:
• Investigation of the
requirements for the
energy and transport
system in the Stuttgart
area due to increased
electromobility
• Influence of charging
strategies on the network
load in an urban energy
system
Source:
Lukasz Brodecki (2018)

1 2 3 4 5
Scenario description TIMES Local
General scenario framework:
• Linear optimization, bottom-up model
• Perfect foresight and perfect competition
• City of Stuttgart in Germany modelled as
one region
• Focus on supply and demand processes
relevant for a city/district model, all sectors
• Starting point 2010, 5-year-steps until 2050
• Hourly time resolution with 5
representative seasons (original seasons
plus fall peak) adding up to 840 timeslices,
• Endogen investment and dispatch in
eletrical, thermal sevices and mobility
technologies
• Extrapolation of local development based on statistical data
and Masterplan for the city of Stuttgart
• Emission mitigation argets until 2050 as yearly upper bound
(UB)  -95% vs. 1990 with linear interpolation for timesteps
between target years
• Conservative development of the Modal Split
• No sufficiency measures
• Fast increase of market share of electromobility
KLIM
• Mitigation targets based on KLIM scenario
• Adjustment of mobility demand based on explicit
specifications in the Masterplan City of Stuttgart
• Compensation of decreasing demand in motorized private
transport via increasing demand in public transport
• Compensation of the declining commercial traffic through
partial shift to rail transport
KLIMPLUS
• Based on KLIMPLUS scenario
• delayed increase in market share of electric mobility
(xEV low)
KLIMPLUS
-
LOW
Based on [11]

1 2 3 4 5
Charging behaviour of Electromobility and Strategies influence the Network Load: Season comparison
Summer2050
KLIMscenario
Winter2050
KLIMscenario

1 2 3 4 5
Charging behaviour of Electromobility and Strategies influence the Network Load: Year comparison
Summer2030
KLIMPLUSscenario
Summer2050
KLIMPLUSscenario

1 2 3 4 5
Agenda
• Motivation
• References

1 2 3 4 5
Summary, Conclusion and Discussion
1703.07.2020
 Peak loads as well as the resulting grid load are largely dependent on the charging behaviour of the
electric car users and the time of analysis
 Peak loads of households and Charging Load of electromobility can differ in time
 Potential for load peak reduction by system/grid beneficial control mechanisms
 Densely built-up residential areas as a challenge with high Load requirements by electric vehicle
charging in low voltage grid
 High charging energy demand in the industrial/commercial sector (e.g. car park) can be supplied via
charging stations in the medium-voltage grid
 Independently of free transformer capacities, local construction measures within the grid network
can become necessary due to high/increasing charging loads
 Through model coupling (simulation+TIMES), actor behaviour can be considered in “system models”
Consumer behaviour for electromobility and charging strategies in TIMES Local

1 2 3 4 5
Agenda
• Motivation
• References

1 2 3 4 5
References
1 https://www.audi.com/de/experience-audi/mobility-and-trends/e-mobility/e-tron-charging-service.html
2 Daniel Seeger; „Stabilere Stromversorgung durch Kombination von Photovoltaik und Windkraft“; PV Magazine; https://www.pv-
magazine.de/2018/03/06/stabilere-stromversorgung-durch-photovoltaik-und-windkraft/; Foto Conda
3 Bundesministerium für Umwelt, Naturschutz und nukleare Sicherheit; Elektromobilität: Allgemeine Informationen; Foto iStock.com/ewg3D
4 https://www.virta.global/blog/what-is-dynamic-load-management
5 M. Litzlbauer, „Erstellung und Modellierung von stochastischen Ladeprofilen mobiler Energiespeicher“. 11. Symposium Energieinnovation,
Graz, Feb. 2010.
6 M. Nauland; “Quantifizierung der Auswirkungen intelligenter Ladestrategien von batterie-elektrischen Fahrzeugen auf das deutsche
Elektrizitätsversorgungssystem“; IER, Universität Stuttgart, 2019
7 Julian Liebhart, Lukasz Brodecki, Nikolai Klempp; „Simulation hochaufgelöster Mobilitätsganglinien“; IER, Universität Stuttgart; 2017
8 Lukasz Brodecki, Markus Blesl; „Modellgestützte Bewertung von Flexibilitätsoptionen und Versorgungsstrukturen eines Bilanzraums mit hohen
Eigenversorgungsgraden mit Energie “; 15. Symposium Energieinnovation, Graz; 2018
9 D. Schneider, L. Langenbucher, L. Brodecki, M. Blesl, R. Wörner;
Analysis of an emission-free public transport (xEV) ; 33nd Electric Vehicle Symposium (EVS33) Portland, Oregon, June 14 - 17, 2020
10 Institut für angewandte Sozialwissenschaft GmbH. (2010b). Mobilität in Deutschland 2008. Nutzerhandbuch, Bonn und Berlin. Zugriff am
02.05.2017
11 Wörner R.; Bauer P.; Schneider D.; Kagerbauer M.; Kostorz N.; Jochem P.; Märtz A.; Blesl M.; Wiesmeth M.; Mayer D.; Körner C.; Schmalen J.;
„Elektromobilität im urbanen Raum - Analysen und Prognosen im Spannungsfeld von Elektromobilität und Energieversorgung am Fallbeispiel
Stuttgart“, Stuttgart, 2019

e-mail
phone +49 (0) 711 685-
fax +49 (0) 711 685-
Universität Stuttgart
Thank you!
IER Institute for Energy Economics
and Rational Energy Use
Lukasz Brodecki
878 58
878 73
Institut e for Energy Economics and Rational Energy Use (IER)
lukasz.brodecki@ier.uni-stuttgart.de
Heßbrühlstraße 49a, 70565 Stuttgart

Consumer behaviour for electromobility and charging strategies in TIMES Local - influence on the network load in an urban energy system

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