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Source: https://www.audi.com/de/experience-audi/mobility-and-trends/e-mobility/e-tron-charging-service.html
Consumer behav...
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Agenda
03.07.2020 2
• Motivation
• Simulation Approach (Markov-Chain-Monte-Carlo)
• TIMES Local: Model, Scenario...
1 2 3 4 5
Motivation
03.07.2020Consumer behaviour for electromobility and charging strategies in TIMES Local 3
Mobility / ...
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Approach
03.07.2020Consumer behaviour for electromobility and charging strategies in TIMES Local 4
Goal: Analysi...
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Agenda
503.07.2020Consumer behaviour for electromobility and charging strategies in TIMES Local
• Motivation
• S...
1 2 3 4 5
Study „Mobilität in Deutschland 2008“
6
Data Basis
03.07.2020Consumer behaviour for electromobility and charging...
1 2 3 4 5
Simulation Tool - Concept
Simulation:
Mobility Load Curves
Simulation:
Load Curves of Charging
Infrastructure
At...
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Mobility Load Curves - General structure
803.07.2020Consumer behaviour for electromobility and charging strategi...
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Agenda
903.07.2020Consumer behaviour for electromobility and charging strategies in TIMES Local
• Motivation
• S...
1 2 3 4 5
 Decoupling of Charging cycle, storage and use of the electric vehicle
 Availability of electromobility and ch...
1 2 3 4 5
 Decoupling of Charging cycle, storage and use of the electric vehicle
 Availability of electromobility and ch...
1 2 3 4 5
Model description TIMES Local
1203.07.2020Consumer behaviour for electromobility and charging strategies in TIME...
1 2 3 4 5
Scenario description TIMES Local
1303.07.2020Consumer behaviour for electromobility and charging strategies in T...
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Charging behaviour of Electromobility and Strategies influence the Network Load: Season comparison
1403.07.2020C...
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Charging behaviour of Electromobility and Strategies influence the Network Load: Year comparison
1503.07.2020Con...
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Agenda
1603.07.2020Consumer behaviour for electromobility and charging strategies in TIMES Local
• Motivation
• ...
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Summary, Conclusion and Discussion
1703.07.2020
 Peak loads as well as the resulting grid load are largely depe...
1 2 3 4 5
Agenda
1803.07.2020Consumer behaviour for electromobility and charging strategies in TIMES Local
• Motivation
• ...
1 2 3 4 5
References
1903.07.2020Consumer behaviour for electromobility and charging strategies in TIMES Local
1 https://w...
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Consumer behaviour for electromobility and charging strategies in TIMES Local - influence on the network load in an urban energy system

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

  1. 1. 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
  2. 2. 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
  3. 3. 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]
  4. 4. 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]
  5. 5. 1 2 3 4 5 Agenda 503.07.2020Consumer behaviour for electromobility and charging strategies in TIMES Local • Motivation • Simulation Approach (Markov-Chain-Monte-Carlo) • TIMES Local: Model, Scenarios, Results • Discussion and Summary • References
  6. 6. 1 2 3 4 5 Study „Mobilität in Deutschland 2008“ 6 Data Basis 03.07.2020Consumer behaviour for electromobility and charging strategies in TIMES Local • 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]
  7. 7. 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]
  8. 8. 1 2 3 4 5 Mobility Load Curves - General structure 803.07.2020Consumer behaviour for electromobility and charging strategies in TIMES Local 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
  9. 9. 1 2 3 4 5 Agenda 903.07.2020Consumer behaviour for electromobility and charging strategies in TIMES Local • Motivation • Simulation Approach (Markov-Chain-Monte-Carlo) • TIMES Local: Model, Scenarios, Results • Discussion and Summary • References
  10. 10. 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
  11. 11. 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
  12. 12. 1 2 3 4 5 Model description TIMES Local 1203.07.2020Consumer behaviour for electromobility and charging strategies in 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)
  13. 13. 1 2 3 4 5 Scenario description TIMES Local 1303.07.2020Consumer behaviour for electromobility and charging strategies in 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]
  14. 14. 1 2 3 4 5 Charging behaviour of Electromobility and Strategies influence the Network Load: Season comparison 1403.07.2020Consumer behaviour for electromobility and charging strategies in TIMES Local Summer2050 KLIMscenario Winter2050 KLIMscenario
  15. 15. 1 2 3 4 5 Charging behaviour of Electromobility and Strategies influence the Network Load: Year comparison 1503.07.2020Consumer behaviour for electromobility and charging strategies in TIMES Local Summer2030 KLIMPLUSscenario Summer2050 KLIMPLUSscenario
  16. 16. 1 2 3 4 5 Agenda 1603.07.2020Consumer behaviour for electromobility and charging strategies in TIMES Local • Motivation • Simulation Approach (Markov-Chain-Monte-Carlo) • TIMES Local: Model, Scenarios, Results • Discussion and Summary • References
  17. 17. 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
  18. 18. 1 2 3 4 5 Agenda 1803.07.2020Consumer behaviour for electromobility and charging strategies in TIMES Local • Motivation • Simulation Approach (Markov-Chain-Monte-Carlo) • TIMES Local: Model, Scenarios, Results • Discussion and Summary • References
  19. 19. 1 2 3 4 5 References 1903.07.2020Consumer behaviour for electromobility and charging strategies in TIMES Local 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
  20. 20. 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

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