Adaptive Power Gridshiskens/short_courses/... · Adaptive Power Grids: Responding to Generation Diversity David J Hill Research School of Information Sciences ... and capacities for
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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.
• 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
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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