1. ee392n - Spring 2011
Stanford University
Intelligent Energy Systems 1
Lecture 3
Intelligent Energy Systems:
Control and Monitoring
Basics
Dimitry Gorinevsky
Seminar Course 392N ● Spring2011
2. Traditional Grid
• Worlds Largest Machine!
– 3300 utilities
– 15,000 generators, 14,000
TX substations
– 211,000 mi of HV lines
(>230kV)
• A variety of interacting
control systems
ee392n - Spring 2011
Stanford University
2
2
Intelligent Energy Systems
3. Smart Energy Grid
3
Conventional Electric Grid
Generation
Transmission
Distribution
Load
Intelligent Energy Network
Load IPS
Source IPS
energy
subnet
Intelligent
Power Switch
Conventional Internet
ee392n - Spring 2011
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Intelligent Energy Systems
4. Business Logic
Intelligent Energy Applications
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Intelligent Energy Systems 4
Database
Presentation Layer
Computer
Tablet Smart
phone
Internet
Communications
Energy Application
Application Logic
(Intelligent Functions)
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Intelligent Energy Systems 5
Control Function
• Control function in a systems perspective
Physical system
Measurement
System
Sensors
Control
Logic
Control
Handles
Actuators
Plant
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Intelligent Energy Systems 6
Analysis of Control Function
• Control analysis perspective
• Goal: verification of control logic
– Simulation of the closed-loop behavior
– Theoretical analysis
Control
Logic
System
Model
Control Handle
Model
Measurement
Model
7. Key Control Methods
• Control Methods
– Design patterns
– Analysis templates
• P (proportional) control
• I (integral) control
• Switching control
• Optimization
• Cascaded control design
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Intelligent Energy Systems 7
8. Generation Frequency Control
• Example
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Intelligent Energy Systems 8
Controller
Turbine /Generator
sensor measurements
control command
Load
disturbance
9. Generation Frequency Control
• Simplified classic grid frequency control model
– Dynamics and Control of Electric Power Systems, G. Andersson, ETH Zurich, 2010
http://www.eeh.ee.ethz.ch/en/eeh/education/courses/viewcourse/227-0528-00l.html
ee392n - Spring 2011
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Intelligent Energy Systems 9
e
m P
P
I
Swing equation:
d
u
x
load
e P
P
d
I
P
u
I
P
x
e
m
/
/
10. P-control
• P (proportional) feedback control
• Closed –loop dynamics
• Steady state error
ee392n - Spring 2011
Stanford University
Intelligent Energy Systems 10
d
u
x
x
k
u P
d
x
k
x p
)
1
(
1
0
t
k
p
t
k p
p
e
d
k
e
x
x
p
s
s k
d
x /
frequency droop
0 2 4
0
0.2
0.4
0.6
0.8
1
x(t)
Step response
frequency
droop
x+0
u
11. AGC Control Example
• AGC = Automated Generation Control
• AGC frequency control
ee392n - Spring 2011
Stanford University
Intelligent Energy Systems 11
Load
disturbance
AGC
generation command
frequency measurement
12. AGC Frequency Control
• Frequency control model
– x is frequency error
– cl is frequency droop for load l
– u is the generation command
• Control logic
– I (integral) feedback control
• This is simplified analysis
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Intelligent Energy Systems 12
,
l
c
u
g
x
x
k
u I
13. ee392n - Spring 2011
Stanford University
Intelligent Energy Systems 13
P and I control
• P control of an integrator
• I control of a gain system. The same feedback loop
d
bu
x
x
k
u P
g
-kI
cl
x
x
k
u I
,
l
c
u
g
x
-kp
b
d
x
14. ee392n - Spring 2011
Stanford University
Intelligent Energy Systems 14
outer loop
Cascade (Nested) Loops
• Inner loop has faster time scale than outer loop
• In the outer loop time scale, consider the inner loop as
a gain system that follows its setpoint input
inner loop
-
inner loop
setpoint
output Plant
Inner Loop
Control
Outer Loop
Control
-
outer loop
setpoint
(command)
15. ee392n - Spring 2011
Stanford University
Intelligent Energy Systems 15
Switching (On-Off) Control
• State machine model
– Hides the continuous-time dynamics
– Continuous-time conditions for switching
• Simulation analysis
– Stateflow by Mathworks
off on
70
x
71
x
69
x
passive
cooling
furnace
heating
setpoint
16. Optimization-based Control
• Is used in many energy applications, e.g., EMS
• Typically, LP or QP problem is solved
– Embedded logic: at each step get new data and compute
new solution
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Intelligent Energy Systems 16
Optimization Problem
Formulation
Embedded Optimizer
Solver
Measured
Data
Control
Variables
Plant
Sensors Actuators
17. Cascade (Hierarchical) Control
• Hierarchical decomposition
– Cascade loop design
– Time scale separation
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Intelligent Energy Systems 17
18. Hierarchical Control Examples
• Frequency control
– I (AGC) P (Generator)
• ADR – Automated Demand Response
– Optimization Switching
• Energy flow control in EMS
– Optimization PI
• Building control:
– PI Switching
– Optimization
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Intelligent Energy Systems 18
19. Power Generation Time Scales
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Intelligent Energy Systems 19
Power Supply
Scheduling
• Power generation and distribution
• Energy supply side
Time (s)
1/10 10 1000
1 100
http://www.eeh.ee.ethz.ch/en/eeh/education/courses/viewcourse/227-0528-00l.html
20. Power Demand Time Scales
• Power consumption
– DR, Homes, Buildings, Plants
• Demand side
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Intelligent Energy Systems 20
Demand Response
Home Thermostat
Building HVAC
Enterprise Demand
Scheduling
Time (s)
100 1,000 10,000
21. Research Topics: Control
• Potential topics for the term paper.
• Distribution system control and optimization
– Voltage and frequency stability
– Distributed control for Distributed Generation
– Distribution Management System: energy optimization, DR
ee392n - Spring 2011
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Intelligent Energy Systems 21
22. ee392n - Spring 2011
Stanford University
Intelligent Energy Systems 22
Monitoring & Decision Support
Physical system
Monitoring
& Decision
Support
Physical
system
Measurement
System
Sensors
Data
Presentation
• Open-loop functions
- Data presentation to a user
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Intelligent Energy Systems 23
Monitoring Goals
• Situational awareness
– Anomaly detection
– State estimation
• Health management
– Fault isolation
– Condition based maintenances
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Intelligent Energy Systems 24
Condition Based Maintenance
• CBM+ Initiative
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Intelligent Energy Systems 25
SPC: Shewhart Control Chart
• W.Shewhart, Bell Labs, 1924
• Statistical Process Control (SPC)
• UCL = mean + 3·
• LCL = mean - 3·
Walter Shewhart
(1891-1967)
sample
3 6 9 12
12 15
mean
quality
variable
Lower
Control
Limit
Upper
Control
Limit
26. ee392n - Spring 2011
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Intelligent Energy Systems 26
Multivariable SPC
• Two correlated univariate processes
y1(t) and y2(t)
cov(y1,y2) = Q, Q-1= LTL
• Uncorrelated linear combinations
z(t) = L·[y(t)-]
• Declare fault (anomaly) if
2
2
1
2
~
y
Q
y
z
T
2
1
y
y
y
2
1
2
1
c
y
Q
y
T
27. ee392n - Spring 2011
Stanford University
Intelligent Energy Systems 27
Multivariate SPC - Hotelling's T2
• Empirical parameter estimates
y
t
y
t
y
n
Q
X
E
t
y
n
T
n
t
T
n
t
cov
)
)
(
)(
)
(
(
1
ˆ
)
(
1
ˆ
1
1
• Hotelling's T2 statistics is
• T2 can be trended as a univariate SPC variable
)
(
ˆ
)
( 1
2
t
y
Q
t
y
T
T
Harold Hotelling
(1895-1973)
28. Advanced Monitoring Methods
• Estimation is dual to control
– SPC is a counterpart of switching control
• Predictive estimation – forecasting, prognostics
– Feedback update of estimates (P feedback EWMA)
• Cascaded design
– Hierarchy of monitoring loops at different time scales
• Optimization-based methods
– Optimal estimation
ee392n - Spring 2011
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Intelligent Energy Systems 28
29. Research Topics: Monitoring
• Potential topics for the term paper.
• Asset monitoring
– Transformers
• Electric power circuit state monitoring
– Using phasor measurements
– Next chart
ee392n - Spring 2011
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Intelligent Energy Systems 29
30. Optimization Problem
Measurements:
• Currents
• Voltages
• Breakers, relays
State estimate
• Fault isolation
Electric
Power System
model
Electric Power Circuit Monitoring
v
Df
Cx
y
w
Bf
Ax
0
ee392n - Spring 2011
Stanford University
30
Intelligent Energy Systems
ACC, 2009
31. End of Lecture 3
ee392n - Spring 2011
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Intelligent Energy Systems 31