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AI Techniques for Smart Grids
Networked and Embedded Systems
Wilfried Elmenreich | 2014-05-22 Keynote lecture, ISGT-ASIA 2014
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?
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Wilfried Elmenreich
Self-Organzing Systems
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.“
Adaptation Robustness
Scalability
from C. Bettstetter, „Lakeside Labs“
Self-Organizing Systems are Effective!
Image: Imgur.com
Adaptation Robustness
Scalability
from C. Bettstetter, „Lakeside Labs“
Self-Organizing Systems are Effective!
Image: Imgur.com
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)
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SOS and Smart Grid
• Why a self-organizing approach?
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Why Self-Organzation?
Image: Creative Commons, Wikipedia
Figure: Creative Commons, Wikipedia
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!
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Image: Creative Commons, Wikipedia
Example: Wide Area Synchronous Grids (Interconnections)
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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
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
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System frequency oscillations
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• 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
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
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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
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Another Example
Image: Creative Commons, Wikipedia
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
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Source: The Smart Grid in Europe, 2012-2016: Technologies, Market Forecasts and Utility Profiles (GTM Research), August 2011
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
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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
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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
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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
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Building a self-organizing system
Image: Creative Commons, Wikipedia
Rules of an SOS may be simple…
• ..but finding the right rules is difficult!
• Complex systems are hard to predict
• Counter-intuitive dependencies
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Image: USGOV-NOAA (Public Domain)
Wilfried Elmenreich – Building Self-Organizing Systems
Evolutionary Design Approach
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• Evolution applied during design phase
• We don‘t refer to evolution/development of a system at run time
Search Algorithm
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• Figuratively and literally a zoo on metaheuristic optimization algorithms
• Ability to find global optimum
• Number of tweaking parameters?
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
System architecture
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6 major components: task description, simulation setup, interaction interface, evolvable decision unit, objective function, search algorithm
Application examples
Image: Creative Commons, Wikipedia
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
Application example: Trader (2)
• Tradeoff between performance, complexity and
comprehensibility
There is no free lunch!
Performance of
evolved market agents
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
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
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Thank you very much for your attention!
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Thank you very much for your attention!
Image: Creative Commons, Wikipedia
