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?

Building Self-Organizing Systems 3

Wilfried Elmenreich

Self-OrganzingSystems

What is a Self-Organizing System

„A self-organizing system (SOS) is a set ofentities 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 controlapproach 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 ofpower 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 duringmidnight 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, TenneTDE, 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 ofcontrol

• New approach is to carefully and knowledgeable interact withthe 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 sourcesand storage

in overall: get more comprehensive information and controlover 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, keepresponsibility 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 hardto predict

• Counter-intuitivedependencies

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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 systemat run time

Search Algorithm

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• Figuratively and literally a zoo on metaheuristicoptimization 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

Applicationexamples

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 wellcontribute to a change in system design

• Self-organizing systems are promising for handlingcomplex 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 muchfor your attention!

Image: Creative Commons, Wikipedia