AI Techniques for Smart Grids

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These are the slides for my keynote lecture "AI Techniques for Smart Grids" at the 2014 IEEE Innovative Smart Grid Technologies - Asia conference where I discussed the role and potential of self-organization in the smart grid.

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AI Techniques for Smart Grids

  1. 1. AI Techniques for Smart Grids Networked and Embedded Systems Wilfried Elmenreich | 2014-05-22 Keynote lecture, ISGT-ASIA 2014
  2. 2. Introduction • Many AI techniques are already in use – Artificial neural networks (Modeling) – Fuzzy logic (Control) – Evolutionary algorithms, – Swarm algorithms (Optimization) • Now we go for the real thing – should we change the way the system is controlled? Must?
  3. 3. Building Self-Organizing Systems 3 Wilfried Elmenreich Self-Organzing Systems
  4. 4. What is a Self-Organizing System „A self-organizing system (SOS) is a set of entities that obtains global system behavior via local interactions without centralized control.“
  5. 5. Adaptation Robustness Scalability from C. Bettstetter, „Lakeside Labs“ Self-Organizing Systems are Effective! Image: Imgur.com
  6. 6. Adaptation Robustness Scalability from C. Bettstetter, „Lakeside Labs“ Self-Organizing Systems are Effective! Image: Imgur.com
  7. 7. Characteristics • System of many interconnected parts • Degree of difficulty in predicting the system behavior • Emergent properties • Dynamic • Decentralized control • Global behavior from local interactions • Robustness, adaptivity • Non-linearity (small causes might have large effects) 7 Wilfried Elmenreich
  8. 8. SOS and Smart Grid • Why a self-organizing approach? 8 Wilfried Elmenreich Why Self- Organzation? Image: Creative Commons, Wikipedia Figure: Creative Commons, Wikipedia
  9. 9. Transferring control to the network • Counter-arguments – Giving up control makes the system instable, – untrustable, – harder to maintain… • Pro arguments – Stability for complex system can be only achieved by control approach at same complexitiy level – Self-organizing systems are more robust… – and provide inherent scalability • Sometimes you do not have this choice! 9 Wilfried Elmenreich Image: Creative Commons, Wikipedia
  10. 10. Example: Wide Area Synchronous Grids (Interconnections) 10 Wilfried Elmenreich Figure: Creative Commons, transmission data based on European Joint Research Center/Institute for Energy and Transport • Operate at synchronized frequency • UCTE grid (Continental Europe) is largest synchronous grid in the world in terms of generation capacity (667 GW) • Unbundling process of power generation and Transmission System Operators (TSO)  many players
  11. 11. Oscillations in wide area grids On Saturday, 19 February 2011 around 8:00 in the morning, inter-area oscillations within the Continental Europe power system occurred. The highest impact of these 0.25 Hz oscillations was observed in the middle-south part of the system with amplitudes of +/- 100 mHz in southern Italy and related power oscillation on several north-south corridor lines of up to +/- 150 MW and with resulting voltage oscillation on the 400 kV system of +/- 5 kV respectively. ENTSO-E, ANALYSIS OF CE INTER-AREA OSCILLATIONS OF 19 AND 24 FEBRUARY 2011, 2011 Almost the same event reappeared on 24 February 2011 during midnight hours 11 Wilfried Elmenreich
  12. 12. System frequency oscillations 12 Wilfried Elmenreich • Superposition of 0.18 Hz (East-West Mode) and 0.25 Hz (North-South Mode) modes • Frequency and damping continously oscillates Figure: ENTSO-E, ANALYSIS OF CE INTER-AREA OSCILLATIONS OF 19 AND 24 FEBRUARY 2011, 2011
  13. 13. Investigation of the oscillation events • Transmission system operators (TSOs) Amprion, Mavir, TenneT DE, Swissgrid,... exchanged power recordings • Event was not predictable, no single cause • Oscillations started around the change of the hour – Turkey had changed mode displacement • Total system load was low • Absence of industrial load • Dispersed generation (PV, Wind) provides less stabilized inertia than classical generators • Italian system currently more sensitive to oscillation modes – Power system stabilisers in Italy had been reinforced 13 Wilfried Elmenreich
  14. 14. Observations from this example • Liberalization of power market has decreased the scope of control • New approach is to carefully and knowledgeable interact with the system in order to guide it • We can can observe the main properties of a SOS here • Understanding this system in a new way became a necessity 14 Wilfried Elmenreich
  15. 15. 15 Wilfried Elmenreich Another Example Image: Creative Commons, Wikipedia
  16. 16. Smart Meter Rollout • Energy Services Directive (2006/32/EC) and the electricity directive (2009/72/EC) require the implementation of "intelligent metering systems". • Such systems ought to be in place for 80% of electricity consumers by end 2020 16 Wilfried Elmenreich Source: The Smart Grid in Europe, 2012-2016: Technologies, Market Forecasts and Utility Profiles (GTM Research), August 2011
  17. 17. The Smart Grid, as the Providers Envision it • Smart meters – Read meters remotely (save money for data acquisition) – Get metering data at a high resolution • Controllability of the loads – Send „off“ signals to customer appliances at peak load situations – Cut off a customer that does not pay the bill • Having a system supporting different types of energy sources and storage in overall: get more comprehensive information and control over the system 17 Wilfried Elmenreich
  18. 18. The Smart Grid, as the Customers want it • Magically save energy / reduce bill • Connect own generators (plug-in PV system) • Get more reliable energy service • Get green energy • Don‘t give up privacy or control in overall: only positive things should arise, nothing must get worse 18 Wilfried Elmenreich
  19. 19. How Self-Organization can help • Handling complexity: Provides scalable approaches for a high number of interacting components  providers will like that • „Bossless structure“: Allow bottom-up processes, keep responsibility and decisions at customer („I can decide“)  customers will like that Building Self-Organizing Systems 19 Wilfried Elmenreich
  20. 20. What holds us? • Reluctance to give up (central) control • Hard to understand – hard to trust – Many proponents miss a Non-linear thinking (© Alessandro Vespignani), a.k.a. complex system goggles • How can be design self-organizing systems? This is our quest: • Provide models, proofs, case studies, etc. showing that self- organizing approaches work – Sufficiently large, realistic case studies Building Self-Organizing Systems 20 Wilfried Elmenreich
  21. 21. Building a self- organizing system Image: Creative Commons, Wikipe
  22. 22. Rules of an SOS may be simple… • ..but finding the right rules is difficult! • Complex systems are hard to predict • Counter-intuitive dependencies 22 Image: USGOV-NOAA (Public Domain) Wilfried Elmenreich – Building Self-Organizing Systems
  23. 23. Evolutionary Design Approach Building Self-Organizing Systems 23 Wilfried Elmenreich • Evolution applied during design phase • We don‘t refer to evolution/development of a system at run time
  24. 24. Search Algorithm Building Self-Organizing Systems 24 Wilfried Elmenreich • Figuratively and literally a zoo on metaheuristic optimization algorithms • Ability to find global optimum • Number of tweaking parameters?
  25. 25. FREVO: A Software for Designing SOS • FREVO (Framework for Evolutionary Design) • Operates on a simulation of the problem • Interface for sensor/actuator connections to the agents • Feedback from a simulation run -> fitness value • Open-source, system-independent http://frevo.sourceforge.net
  26. 26. System architecture Building Self-Organizing Systems 26 Wilfried Elmenreich 6 major components: task description, simulation setup, interaction interface, evolvable decision unit, objective function, search algorithm
  27. 27. Application examples Image: Creative Commons, Wikipedia
  28. 28. Application example: Trader (1) • Evolving an energy trader algorithm at consumer/prosumer level • Simulation • Java module added to FREVO • Market rules • Simulated Market • Agent • No initial knowledge about market rules • Trader rules are learned implicitly • This way also counter-intuitive strategies are considered
  29. 29. Application example: Trader (2) • Tradeoff between performance, complexity and comprehensibility  There is no free lunch! Performance of evolved market agents
  30. 30. WiP: Evolving system of device-level traders • Model HEMS devices as agents with independent controllers • Constraints are given by a budget per device and the importance of a device for the user
  31. 31. Summary • AI techniques can be used as a tool but as well contribute to a change in system design • Self-organizing systems are promising for handling complex systems • Design challenge – Evolutionary approach in combination with modelling techniques • Validation challenge – Verification techniques, simulation – Need for more case studies 31 Wilfried Elmenreich
  32. 32. Thank you very much for your attention!  Building Self-Organizing Systems 32 Wilfried Elmenreich Thank you very much for your attention! Image: Creative Commons, Wikipedia

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