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5 June 2009 © David J Hill The Australian National University Massive Networks 112 October 2009 © David J Hill The Australian National University Adaptive Grids 1

Short Course on Future Trends for Power Systems, The University of Sydney, 12th October, 2009

Adaptive Power Grids:Responding to Generation Diversity

David J HillResearch School of Information Sciences

and EngineeringThe Australian National University

12 October 2009 © David J Hill The Australian National University Adaptive Grids 3

Outline

Future grids

Challenges

New control ideas

Example: Voltage control

Conclusions


Australian Transmission Network


Diverse Generation in Australia

• New– Wind – Solar – Bioenergy– Geothermal– Nuclear

• Old– Coal– Hydro

New Power Grids

• Diverse loads

• Diverse generation New

• Diverse storage New

• All– Distributed– Multi-level– Multi-scale– Multi-type– Volatile


Ref: J.Fan and S.Borlase, IEEE Power & Energy Magazine, Special Issue on the Next-Generation Grid, Vol.7, No.2, 2009

Big Changes

• Old model – variable load, adjust generation

• New models – variable load and generation

End-to-end control, i.e. generation, demand management, storage


Changes

• The existing grids typically do not have the right structure and capacities for large-scale renewables, e.g. will need wind and solar hubs quickly

• Generation is much more volatile, i.e. now on both sides of the generation = load equation

• Major new need is demand management

• New loads on horizon, e.g. plug-in (hybrid) electric vehicles (PHEV)

MUCH MORE UNCERTAINTY FOR THE GRID

Need ADAPTIVE end-to-end control

Uncertainty for Wind Generation

• Dependence on nature gives unpredictability

• Companies do not want to disclose their data, controls (IP for market)

• Manufacturers can disappear but their turbines keep operating

• Manufacturer models are very detailed, but need simpler models for grid studies

Challenges of Complexity

• Planning vs control

• Decision and control (performance, security)

• Massive amounts of data

• Optimizing (planning, control) on such a scale

• Validation


What is “Smart Grid”?

• Concept emerged in Europe; named in USA Energy Act 2007, Obama stimulus package

• Now a buzzword which captures other ideas: Intelligent Grid, EPRI; iGrid, Australia etc

• But Aus budget just gave A$100 million, US$4.6 billion in USA, so much anticipation


Smart Grid Targets

• Meet environmental targets

• Accommodate greater emphasis on demand management

• Support new loads, e.g. PHEVs

• Support distributed generation and storage

• Maintain a level of availability, performance and security


More Monitoring, Computing and Control

Ref: A.Ipakchi and F.Albuyeh, IEEE Power & Energy Magazine, Special Issue on the Next-Generation Grid, Vol.7, No.2, 2009

“Smart Grid” as Control Engineering

• Large network of sensors

• Massive amounts of data, i.e. measurements, availability etc

• Distributed control operating at many levels, c.f. Internet

Thinking like the Internet

• Things just have to happen in time, e.g. the TV immediate, the toaster within 1 minute, but allow some scale

• A vision of a “plug and play” capability for the whole grid

• All controlled in (seven) layers

• Congestion handled by protocols, AQM, delays

• Major problems by re-routing

What looks useful

• Computer science– Machine learning– Planning and diagnosis, etc

• Automatic control

• Communications

• Mathematical algorithms

All working together have the tools to make major advances.


Outline

Future grids

Challenges

New control ideas


Conclusions


Big Questions

Diverse generation makes planning, analysis and control all harder

• What level of renewables (or any given energy mix) can a given network support?

• How do we plan and control the power grid given all challenges?


Many Challenges

• Protocols for access– cf. Internet “plug and play”

• Affect on system dynamics, collapse– Blackouts due to weak points

• Wide-area control architectures– How to coordinate 1000’s of controls at multiple levels– Lot more uncertainty

• Sensing technology and architectures


Voltage Collapse


Blackout 2003 USA-Canada

South Australia Wind Power Case*

1200MW wind scenario

Wind Generation Scenarios

South Australia Wind Power Case* --- continued

Long term voltage stability limits

* NEMMCO Report: Assessment of Potential Security Risks due to High Levels of Wind Generation in South Australia

Locations of Wind Turbines

0 20 40 60 80

0.20

0.25

0.30

0.35

0.40

Number of Turbines

Crit

ical

Cle

arin

g Ti

me

DFIG at G3Constant Speed at G3DFIG at G1Constant Speed at G1

G1

G2

G3

G4 WW

Ref: Bennett, Hill and Zhang, in prep

Comments and Conjectures

• All stability types affected

• Locations of generation types important

• Structure of network important

• More flexible (adaptive) control must be used


ARC LP Project 2009- 2012

1. Investigate proper, possibly generic, models for diverse generators and their local controls in different power networks and voltage levels;

2. Determine what kinds of stability or security issues could arise due to characteristics of renewable energy resources;

3. Check the limitations of available control mechanisms to guarantee power system quality of supply and stabilities;

4. Otherwise, design proper coordinated control schemes to maximize the stability margin of the power system;

5. Given the improved technology, implemented at some generic level, develop methods to assess what level of renewable generation could be supported at different sites;

6. Investigate whether available control in power system with diverse generation can guarantee levels of security and quality of supply for increasing levels of mandatory targets for certain technologies.


Outline

Future grids

Challenges

New control ideas


Conclusions


Control Challenge

• A multi-level version of distributed adaptive control

• Attends to local and system control needs

• Reconfigurability plus tuning, i.e. can attack problems as they arise in staged response

Call it global control


From Ian Hiskens, Cornell Uni

We already do well but we can do more!• Currently SCADA has real-time data every 2 secs, state estimation,

optimal power flow, security analysis etc – that’s already “smart”

• But this is forty year old concept (following 1965 blackout etc)

• Also its confined to generation-transmission system level

• And tends to treat problems separately, e.g. angle stability, voltage stability

• We now have PMUs which can give data in millisecs

• And major advances in technology especially ICT, power electronics

• With whole ICT repertoire we can do control at all levels for distributed generation, load and storage

• And we can coordinate a lot better, e.g. use refined load control to help system stabilities in a cascading situation


Global Control Framework (Leung, Hill and Zhang, 2009)


Other Ideas

• Our view of control “is autistic”; for massive systems get “cognitive overload”;

• Maybe just viewing the problem as computation reduction is inadequate;

• Will need more than just using structure better;

• In global control used ‘indicators’ and switching, c.f. economic control;

• Computer scientists have ad hoc techniques for ‘planning’ in large systems; we have systematic techniques for simple systems?

Comments by ANU Computer Scientist

• The machine learning area has learned a lot from the control area in the past

• We see adaptive control as a precise way to deal with simple systems

• Machine learning has a lot of tools and tricks, a bit ad hoc, but does deal with complex systems

• Maybe its time to see how machine learning can help control?

• Hewitt:• I've been training

extremely hard, putting in a lot of hours on the court …… (BBC Sports)

• An example of• “Learning by

doing”• Fast responses

needed

Learning-based Control

• Improves its performance based on past experiences (Fu, 1969; Farrell and Baker, 1993)

• Effectively recall and reuse the learned knowledge

• Use stability robustness to handle mismatch

• Can be used to reduce space for optimization

Pattern-based Control

• - used in large systems, e.g. Lissajous recordings of faults power systems

• - not developed in control area

• Dynamic pattern recognition• Switching/tuning control between different patterns

– patterns as local models– stability issues

Ref: Wang and Hill, Deterministic Learning Theory for Identification, Recognition and Control, CRC Press, 2009.

Towards development of a human-like learning and control methodology

Ref: Yusheng Xue, PSCC 2005

Aim: Maintain steady voltages at all buses.

Control devices: Tap changers, capacitors, load shedding

Voltage controlG G

GG

G

GG GG G

30

39

1

2

2537

29

17

26

9

338

16

5

4

18

27

28

3624

35

22

21

20

34

2319

3310

11

13

14

15

8 31

126

32

7

The New England 39-bus Power System

Coordinated Voltage Control

Coordinated Voltage Control (CVC)CVC is a scheme relies upon the simulated performance of a power system, coordinatedscheduling and switching voltage control devices

• sequencing: decide the order of control actions• timing: decide the switching time of each control

action• tuning: decide the values of the adjustable

parameters of each control action

CVC include three aspects of system design:

Coordinated Voltage Control

On-line Multi-Objective CVC System

MCVC System:

• Off-line global search

• On-line flexible control

• On-line learning

irefiti t

vi vvJ −=∑ ∑∑min

cact nJ min=

∑=k

loadload knJ min

Mid-term

On-line Learning

Objective functions:

Power System

Output

Global Search: Get non-dominated solutions

Data Base: 1.faults, 2.order of effective controllers 3.objective values of non-dominated solutions

Short term

Local Search: 1.Get available controllers, 2.Searching neighborhood

Control

Get

Ava

ilabl

e C

ontr

olle

rs

Get from Database

Off-line SearchingOn-line Adaptive Control

Evaluation

Some Possible Faults

Multiple Criteria Decision Making

Learning Mid-term

Short term

Case Study

Generator32 tripped at 15sG G

GG

G

GG GG G

30

39

1

2

2537

29

17

26

9

338

16

5

4

18

27

28

3624

35

22

21

20

34

2319

3310

11

13

14

15

8 31

126

32

7

Case1: Tripping Generator 32

Case1: Tripping Generator36 and Line2-3

Case Study

No. 1 2 3 4 5 6 7 8 9

Ctrl Ltc31 Ltc30 Ltc35 Ltc11 Ltc12 Ltc33 C13 C13 Ltc37

move +1 +1 +1 +1 +1 +1 +0.15 +0.30 +1

No. 10 11 12 13 14 15 16 17 18

Ctrl C7 C7 C8 C8 C4 C4 Ltc38 Ltc36 Ltc34

move +0.15 +0.30 +0.15 +0.30 +0.15 +0.30 +1 +1 +1

No. 19 20 21 22 23 24 25 26 27

Ctrl C15 C15 C3 C3 C18 C18 C16 C16 Ltc39

move +0.15 +0.30 +0.15 +0.30 +0.15 +0.30 +0.15 +0.30 +1

No. 28 29 30 31 32 33 34 35 36

Ctrl C24 C24 C27 C27 C21 C21 C26 C26 C25

move +0.15 +0.30 +0.15 +0.30 +0.15 +0.30 +0.15 +0.30 +0.15

No. 37 38 39 40 41 42 43 44 45

Ctrl C25 C23 C23 C28 C28 C29 C29 C20 C20

move +0.3 +0.15 +0.30 +0.15 +0.30 +0.15 +0.30 +0.15 +0.30

Case1: Tripping Generator 32

Order of effective controllers:

Control preferences:

System performance:

(1) the solution which can recover bus voltages very fast is the most desirable one. Totally 28 controllers, 39 movements of control are used.

(2) a solution uses less control actions is the best one. Totally 26 controllers, 29 movements of control are used.

Case Study

Control Scenario

Time Event

30s Line3-2 tripping

60s G36 tripping

180s Line3-2 and G36 reconnection

540s Line3-2 and G36 tripping together

660s Line3-2 and G36 reconnection

1140s Line3-2 and G36 tripping together

Case2: Tripping Generator 36 and Line 2-3

System performance:


Outline

Future grids

Challenges

New control ideas


Conclusions

Future Work

• Combine– Computer science for learning, planning,

diagnosis, visualization, data structures etc– Networks for structure – Control for dynamics

to give algorithms which scale

• Link to other levels: power electronics, economics

adaptive power gridshiskens/short_courses/... · adaptive power grids: responding to generation...

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