• Save
Einführung in Smart Grids
Upcoming SlideShare
Loading in...5
×
 

Like this? Share it with your network

Share

Einführung in Smart Grids

on

  • 1,047 views

 

Statistics

Views

Total Views
1,047
Views on SlideShare
865
Embed Views
182

Actions

Likes
0
Downloads
0
Comments
0

2 Embeds 182

http://www.foerderverein-technische-fakultaet.at 167
http://www.ftf.or.at 15

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Einführung in Smart Grids Presentation Transcript

  • 1. Technische Universität MünchenLehrprobe - Einführung in Smart Grids Dr. Martin Sachenbacher Technische Universität München Institut für Informatik http://www.in.tum.de/energieinformatik14. Mai 2012 M. Sachenbacher 1
  • 2. Technische Universität MünchenElectricity Grid Basics Producers Consumers Conventional Industry Power Plants Households Pumped- Renewable storage Plants Energy Storage14. Mai 2012 M. Sachenbacher 2
  • 3. Technische Universität MünchenElectricity Grid Assumptions   Guiding Principle: Production follows consumption   Basic Assumptions:   Production is deterministic and fully controllable   Consumption is well-understood stochastic process   Mass effects ensure smooth consumer behavior   Grid state is observable (frequency, voltage)   Thus, good prediction and a bit of fine-tuning do the job   Control energy that be subtracted or added to the grid; mostly, pump storage   This was the case for several decades, but now situation changes rapidly14. Mai 2012 M. Sachenbacher 3
  • 4. Technische Universität MünchenUK Electricity Grid Frequency 14. Mai 2012 M. Sachenbacher 4
  • 5. Technische Universität MünchenElectricity Grid in Germany: Fundamental Changes   Absolute priority given to (microgenerated) renewable power   Financial incentives for renewable power above market price   Decision to phase out nuclear power after Fukushima incident14. Mai 2012 M. Sachenbacher 5
  • 6. Technische Universität MünchenChallenges from Integrating Renewable Power   Wind and solar power have much higher volatility, and this volatility is largely uncontrollable   Production is now turning into a stochastic process as well   Volatility may exceed the available control energy   Need mechanisms for grid stabilization14. Mai 2012 M. Sachenbacher 6
  • 7. Technische Universität MünchenRenewable Power affects Grid Stability   Incident on September 6, 2010   Drastically more solar power in the grid than predicted before   Germany at 12 p.m.: Surplus of 7 GW   Entire negative control energy exhausted (- 4.3 GW)   Imported emergency reserve from neighboring countries (- 2.8 GW) to avoid black-out   Number of manual interventions   EWE in 2009: < 1 per week   EWE in 2011: > 1 per day   Bundesnetzagentur during winter 2010/11: 39   Bundesnetzagentur during winter 2011/12: 19714. Mai 2012 M. Sachenbacher 7
  • 8. Technische Universität MünchenWhat can be done? Producers Consumers Conventional Industry Power Plants Households Pumped- Renewable storage Plants Energy Storage14. Mai 2012 M. Sachenbacher 8
  • 9. Technische Universität MünchenWhat can be done? Storage Source: bmvbs.de14. Mai 2012 M. Sachenbacher 9
  • 10. Technische Universität MünchenWhat can be done? Consumers Source: [Wiechmann, VDE Congress 2011]14. Mai 2012 M. Sachenbacher 10
  • 11. Technische Universität MünchenBasic Elements of Demand-Response Systems   Short-range and medium-range prediction techniques for   Electric power demand   Solar and wind generated electric power   Grid capacity and potential grid bottlenecks   Voltage stability, especially for last mile   Measurement and logging infrastructure for state of grid components   Techniques to group elastic customers into clusters, and orchestrate their behavior   Decision support systems for effectuating demand-response mechansims14. Mai 2012 11 M. Sachenbacher
  • 12. Technische Universität MünchenDecentralized Grid Management   Frequency-based distributed control strategy (EN 50438) enforced for microgenerators in Germany in 2007   Must shut off the output if observed frequency overshoots 50.2 Hz   However, observation delays can lead to critical oscillations Source: [Berrang et al. AVACS 2012] no delay 10s delay14. Mai 2012 M. Sachenbacher 12
  • 13. Technische Universität MünchenDecentralized Grid Management   Improved control strategy proposed by VDE (VDE-AR-N 4105)   Linear increase/decrease of output per minute by 10%/40%, if frequency is observed below/above 50.2Hz   Avoids oscillations, but may overshoot target frequency Source: [Berrang et al. AVACS 2012] no delay 10s delay14. Mai 2012 M. Sachenbacher 13
  • 14. Technische Universität MünchenDecentralized Grid Management   Control strategy analogous to internet transmission protocol (TCP) proposed by [Berrang et al. 2012]   Additive increase/multiplicative decrease of output per minute by 10%/0.67, if frequency is observed below/above 50.2Hz   Highly dampened version of on/off-controller behavior Source: [Berrang et al. AVACS 2012] no delay 10s delay14. Mai 2012 M. Sachenbacher 14