Elektromobilität im Spannungsfeld von Smart Grids

Transcription

1 Elektromobilität im Spannungsfeld von Smart Grids Thomas Loewel, Ralph Ballentin, Frank Ilchmann, Matthias Sund Alcatel-Lucent Bell Labs 10. ITG Fachtagung zur "Zukunft der Netze" 1

2 AGENDA Warum werden Smart Grids benötigt? Welche besondere Rolle haben e-fahrzeuge bzw. die e-mobilität? Was kann die Informations- und Kommunikationstechnik dazu beitragen? 2

3 Why Smart Grids 3

4 The (new) Distribution Network Infrastructure Classical Networks Management: WAN Electricity Quality Applications (Smart) Metering Access New Networks 4

5 Renewable Energy Supply Forecast > 2x in 10 years! Source: > 4x in 10 years! 5

6 Conditions and Requirements Renewable Energies: Fluctuation Unpredictable Storages: Batteries Pumped storage power stations Kinetic (Flywheels) Thermic (Cold storages) Network Expansion : Hydrogen (Electrolysis) Introduction of ICT Extension of the distribution networks ~ 540 GWh of storage capacity is needed Source: KIT New direct current distribution networks 6

7 Role of e-mobility 7

8 The Role of e-car Local Storage: Load Balancing: Private households Companies Car fleets Dynamic and Static Battery Exchange Incentive Systems Moveable Storage: Micro Grids Energy Prediction Movement Pattern Extensive Communication Private households Companies Car fleets Local Renewable Energy 8

9 Interactions between e-cars and Power Grid Study the influence of moveable storage on power grid E-cars with moveable storage generate demands but also provide offers How to model e-car behavior? Idea: Applying telecommunication network modeling techniques to traffic networks (junctions and roads considered as nodes and edges of a telco network) Modeling based on driving pattern with origins and destinations of individual drivers Also driving and stand-still times known Junction and roads extracted from Open Street Map First Step: Analyzing geographical distribution of e-cars (for storage usage) Second step: Analyzing trip length and street load 9

10 Analyzing Geographical Distribution (1) Analysis of raw data of the Chicago area - ~2000 locations covering more than 10,000 sq km - ~75,000 single trips - Three example trips with different behavior shown in picture Analysis done using a network planning tool (graph based) 10

11 Analyzing Geographical Distribution (2) Number of vehicles arriving at one location - Values normalized to maximum number of vehicles, see legend Total amount of vehicle downtime per location (directed to location) - Values normalized to maximum number of standstill time Legend 0%..20% 20%..40% 40%..60% 60%..80% 80%..100% of maximum Legend 0%..20% 20%..40% 40%..60% 60%..80% 80%..100% of maximum 11

12 Analyzing Trip Length and Street Load (1) Chicago - Extracted Geodata Source: 12

13 Analyzing Trip Length and Street Load (2) Chicago Street Load 5am 10am 10am 3pm 13

14 Frequency Analyzing Trip Length and Street Load (3) Chicago Trip Length Frequency = f(trip length) Trip Length [km] Around 90% of all Trips are below 40 km! 14

15 ICT Part 15

16 ICT Enabler Converged Communications Voice, Data, SCADA Reliability Smart Prosumer Home Energy Management Flexible tariffs E-Mobility Privacy Smart Grid Outage protection Data security Smart Metering Usage management Micro Grids Business Processes Smart Metering rollout Billing Operation 16

17 ICT Requirements Efficient Algorithms Data Collection Data Management Data Analysis Intermodal Communication Routing Encryption Distributed Processing Reliability Flexible and Scalable Architectures Real Time Communication Integrity Access Control Trustability Efficient Communication Protocols Real Time Management of Network Elements 17

18 ICT Future Steps Incentive Systems to support the Smart Grid development Involvement of ICT in the planning and realization process Broad rollout of Smart Meter Speedup of the standardization process, especially in the Smart Metering area Clarification of data protection regulations 18

19 Future Scenario Parking lot with Charging Point Power reserve Traffic depending charging Charging scheduling Dynamic Tariffs Metrics: Network status Offering and request Advertising Network Status Monitoring Sensor data recording Control of network elements Forecasts Islanding Local Storage Energy reserve Controlled charging Renewable Energy World Wide Data Access Battery status Current tariff Location services Home automation Univ. of Stuttgart, 2010 Institute for Energy Transmission and HV Technology 19

20 Thank you! 20