EE392n_Lecture3apps (1).ppt

ee392n - Spring 2011
Stanford University
Intelligent Energy Systems 1
Lecture 3
Intelligent Energy Systems:
Control and Monitoring
Basics
Dimitry Gorinevsky
Seminar Course 392N ● Spring2011

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
Stanford University
2
2
Intelligent Energy Systems

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
Stanford University

Business Logic
Intelligent Energy Applications
Stanford University
Database
Presentation Layer
Computer
Tablet Smart
phone
Internet
Communications
Energy Application
Application Logic
(Intelligent Functions)

Stanford University
Control Function
• Control function in a systems perspective
Physical system
Measurement
System
Sensors
Control
Logic
Control
Handles
Actuators
Plant

Stanford University
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

Key Control Methods
• Control Methods
– Design patterns
– Analysis templates
• P (proportional) control
• I (integral) control
• Switching control
• Optimization
• Cascaded control design
Stanford University

Generation Frequency Control
• Example
Stanford University
Controller
Turbine /Generator
sensor measurements
control command
Load
disturbance

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
Stanford University
e
m P
P
I 




 
Swing equation:
d
u
x 


load
e P
P 


d
I
P
u
I
P
x
e
m










/
/



P-control
• P (proportional) feedback control
• Closed –loop dynamics
• Steady state error
Stanford University
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

AGC Control Example
• AGC = Automated Generation Control
• AGC frequency control
Stanford University
Load
disturbance
AGC
generation command
frequency measurement

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
Stanford University
,
l
c
u
g
x 



x
k
u I




Stanford University
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

Stanford University
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)

Stanford University
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

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
Stanford University
Optimization Problem
Formulation
Embedded Optimizer
Solver
Measured
Data
Control
Variables
Plant
Sensors Actuators

Cascade (Hierarchical) Control
• Hierarchical decomposition
– Cascade loop design
– Time scale separation
Stanford University

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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Power Generation Time Scales
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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

Power Demand Time Scales
• Power consumption
– DR, Homes, Buildings, Plants
• Demand side
Stanford University
Demand Response
Home Thermostat
Building HVAC
Enterprise Demand
Scheduling
Time (s)
100 1,000 10,000

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
Stanford University

Stanford University
Monitoring & Decision Support
Physical system
Monitoring
& Decision
Support
Physical
system
Measurement
System
Sensors
Data
Presentation
• Open-loop functions
- Data presentation to a user

Stanford University
Monitoring Goals
• Situational awareness
– Anomaly detection
– State estimation
• Health management
– Fault isolation
– Condition based maintenances

Stanford University
Condition Based Maintenance
• CBM+ Initiative

Stanford University
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

Stanford University
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


 



Stanford University
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)

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
Stanford University

Research Topics: Monitoring
• Potential topics for the term paper.
• Asset monitoring
– Transformers
• Electric power circuit state monitoring
– Using phasor measurements
– Next chart
Stanford University

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
Stanford University
30
ACC, 2009

End of Lecture 3
Stanford University

EE392n_Lecture3apps (1).ppt

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