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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
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12 October 2009 © David J Hill The Australian National University Adaptive Grids 3
Outline
Future grids
Challenges
New control ideas
Example: Voltage control
Conclusions
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5 June 2009 © David J Hill The Australian National University Massive Networks 412 October 2009 © David J Hill The Australian National University Adaptive Grids 4
Australian Transmission Network
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12 October 2009 © David J Hill The Australian National University Adaptive Grids 5
Diverse Generation in Australia
• New– Wind – Solar – Bioenergy– Geothermal– Nuclear
• Old– Coal– Hydro
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New Power Grids
• Diverse loads
• Diverse generation New
• Diverse storage New
• All– Distributed– Multi-level– Multi-scale– Multi-type– Volatile
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5 June 2009 © David J Hill The Australian National University Massive Networks 712 October 2009 © David J Hill The Australian National University Adaptive Grids 7
Ref: J.Fan and S.Borlase, IEEE Power & Energy Magazine, Special Issue on the Next-Generation Grid, Vol.7, No.2, 2009
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Big Changes
• Old model – variable load, adjust generation
• New models – variable load and generation
End-to-end control, i.e. generation, demand management, storage
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12 October 2009 © David J Hill The Australian National University Adaptive Grids 9
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
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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
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Challenges of Complexity
• Planning vs control
• Decision and control (performance, security)
• Massive amounts of data
• Optimizing (planning, control) on such a scale
• Validation
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12 October 2009 © David J Hill The Australian National University Adaptive Grids 12
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
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5 June 2009 © David J Hill The Australian National University Massive Networks 1312 October 2009 © David J Hill The Australian National University Adaptive Grids 13
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
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12 October 2009 © David J Hill The Australian National University Adaptive Grids 14
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
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“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
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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
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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.
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12 October 2009 © David J Hill The Australian National University Adaptive Grids 18
Outline
Future grids
Challenges
New control ideas
Example: Voltage control
Conclusions
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12 October 2009 © David J Hill The Australian National University Adaptive Grids 19
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?
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12 October 2009 © David J Hill The Australian National University Adaptive Grids 20
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
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5 June 2009 © David J Hill The Australian National University Massive Networks 2112 October 2009 © David J Hill The Australian National University Adaptive Grids 21
Voltage Collapse
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5 June 2009 © David J Hill The Australian National University Massive Networks 2212 October 2009 © David J Hill The Australian National University Adaptive Grids 22
Blackout 2003 USA-Canada
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South Australia Wind Power Case*
1200MW wind scenario
Wind Generation Scenarios
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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
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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
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Comments and Conjectures
• All stability types affected
• Locations of generation types important
• Structure of network important
• More flexible (adaptive) control must be used
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12 October 2009 © David J Hill The Australian National University Adaptive Grids 27
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.
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12 October 2009 © David J Hill The Australian National University Adaptive Grids 28
Outline
Future grids
Challenges
New control ideas
Example: Voltage control
Conclusions
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12 October 2009 © David J Hill The Australian National University Adaptive Grids 29
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
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12 October 2009 © David J Hill The Australian National University Adaptive Grids 30
From Ian Hiskens, Cornell Uni
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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
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5 June 2009 © David J Hill The Australian National University Massive Networks 3212 October 2009 © David J Hill The Australian National University Adaptive Grids 32
Global Control Framework (Leung, Hill and Zhang, 2009)
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12 October 2009 © David J Hill The Australian National University Adaptive Grids 33
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?
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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?
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• 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
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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
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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
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Ref: Yusheng Xue, PSCC 2005
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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
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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
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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
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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
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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.
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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:
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12 October 2009 © David J Hill The Australian National University Adaptive Grids 45
Outline
Future grids
Challenges
New control ideas
Example: Voltage control
Conclusions
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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