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TRANSCRIPT
Dynamic Paradigm for Grid Operations
Henry Huang
Pacific Northwest National Laboratory
Collaborating Organizations: Binghamton University, Powertech Labs
June 17, 2014
1
Presentation outline
• Project Purpose: Capturing Dynamics
• Significance and Impact
• Technical Approach – A Dynamic Paradigm for Operation
• Technical Accomplishments
– Dynamic State Estimation
– Look-Ahead Dynamic Simulation
– Dynamic Contingency Analysis
• Transient Stability
• Voltage Stability
• Synergistic Work
• Summary
• Contacts
2
Capturing dynamics: Enable grid operations from reactive to predictive
3
Today’s paradigm:Slow data
Static modeling
State Estimation
Contingency AnalysisSCADA
DynamicState
Estimation
Look‐ahead Dynamic Simulation
Dynamic Contingency Analysis
Graphical Contingency Analysis
Future paradigm:High‐speed dataDynamic modeling
Phasor
Predictive
Reactive
Data cycle
Time1/30 sec 2/30 sec 3/30 sec
Dynam
ic States
Look-ahead dynamic simulation
1 min
Dynamic contingency analysis
Dynamics state estimation
5 10 15 200
500
1000
1500
2000
2500
Time, hour
Tra
nsfe
r lim
it of
a c
ritic
al p
ath,
MW Real-time Path Rating
Offline path rating, current practice
25.74% more energy transferusing real-time path rating
5 10 15 200
500
1000
1500
2000
2500
Time, hour
Tra
nsfe
r lim
it of
a c
ritic
al p
ath,
MW Real-time Path Rating
Offline path rating, current practice
25.74% more energy transferusing real-time path rating
Real‐Time Path Rating
Other Applications
Projects and teams
• Dynamic Paradigm for Grid Operations– Henry Huang, Ning Zhou (Binghamton University), Steve Elbert,
Shuai Lu, Da Meng, Shaobu Wang, Ruisheng Diao
• Look-Ahead Dynamic Simulation– Shuangshuang Jin, Ruisheng Diao, Di Wu, Yousu Chen
• Dynamic Contingency Analysis– Yousu Chen, Mark Rice, Shuangshuang Jin, Kurt Glaesemann
• Non-Iterative Voltage Stability Analysis– Yuri Makarov, Bharat Vyakaranam, Da Meng, Pavel Etingov,
Tony Nguyen, Di Wu, Zhangshuan (Jason) Hou, Shaobu Wang,Steve Elbert, Laurie Miller
4
See project details on posters…
2007 2,008 2,009 2,010 2,011 2,0120.01
0.011
0.012
0.013
0.014
0.015
0.016
0.017
0.018
0.019
0.02
Year
Roo
t Mea
n S
quar
e E
rror
of F
requ
ency
Sig
nal,
Hz
0.005
0.01
0.015
Mea
n of
Abs
olut
e F
requ
ency
Dev
iatio
n, H
z
Trend of frequency variance
5
Freq
uen
cy
60 Hz
• Traditional state estimation does not accurately capture the system’sdynamic status
– During emergency situations, frequency is significantly off nominal 60Hz
– During normal operation, frequency tends to deviate more often from 60Hz
Challenges in future power grid operations
“Grid evolution meets information revolution” • Grid Evolution –
stochastic & dynamic– Generation: intermittent
renewable energy, distributed generation
– Demand: smart loads, plug-inhybrids
– Other: storage, new marketdesign/incentives
• Information Revolution –data rich but information scarce– Large number of phasor
measurement units, smart meters, and intelligent devices
– Requirements of cybersecurity
Transmission Grid
6
Dynamic paradigm for grid operation
• National Driver: clean and efficient power grid as well as being affordable,reliable, and secure dynamic and fast operation
• Technical Approach: combine model prediction and measurementobservations to determine where we are, where we are going, and what-ifs
– Fuse models and data with nonlinearity, discontinuity, model deficiency, and datasparsity
– Develop Advanced Kalman Filter and HPC codes to estimate states and models
– Solve a large number of ODE systems to predict future states and alternativestates
7
Data collection cycle
Time1/30 sec 2/30 sec 3/30 sec
Dynam
ic States
Look-ahead dynamic simulation
1 min
Dynamic contingency analysis
Dynamic state estimation
Better Reliability
Clean Energy
Integration
Better Asset
Utilization
Enabling factor ─
Phasor measurement
• Time-synchronized, high-speed measurement at 30 samples persecond, able to capture the majority of grid dynamics
8
NASPI: www.naspi.org
Enabling factor –
Computing technology
• CMOS technology hitting its limits due to power dissipation for theincreased clock speed (4-6 GHz)
• Moore’s Law still applies – multiple-core processors (parallelcomputers)
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– PCs: Two-, four-, andeight-cores
– HPCs: 100s~1,000s cores
– Forthcoming high-endparallel systems expectedto offer million cores
• Parallel computingbecomes a fundamentaltechnique
Advanced Kalman Filter for dynamic state estimation
10HPC Platform
x(k‐1)
z(k)
x(k)x’(k) “Correction”Measurement Eq’s
z = h(x, α) +
R
“Prediction”Dynamic Simulation
dx/dt = f(x, α)
Q
Standard KF
Correction Cycle ~1/30 second
Sensor Placement
Sequential Estimation
Measurement Selection
NonlinearityModel deficiency DiscontinuityData sparsityLarge dimension
Adaptive Tuning
P
dα/dt = g(x, α)
,α’(k) ,α(k),α(k‐1)
Prediction Cycle milliseconds
0 5 10 15 200.5
1
1.5
States tracking (Basic EnKF)
Re
lativ
e R
oto
r A
ng
le (
rad
)
0 5 10 15 20
0
5
10
15
x 10-3
Sp
ee
d D
evi
(p
u)
TrueMeanMean+/-3*Std100 MC Ests
0 5 10 15 20
0.85
0.9
0.95
Eq
' (p
u)
Time (sec)0 5 10 15 20
0.5
0.55
0.6
0.65
Ed
' (p
u)
Time (sec)
Performance evaluation – estimation accuracy
• Excellent tracking with realistic evaluation conditions– 3% measurement noise; 40 ms measurement cycle (phasor measurement)
– 5 ms interpolation cycle; modeling errors considered; unknown inputs;unknown initial states
11
Blue/green tracking red is the goal
Filtering technology assessment
12
Extended
Kalman Filter
Unscented
Kalman Filter
Ensemble
Kalman FilterParticle Filter
Accuracy
The 2nd best
with 0%
diverged
33% divergedThe best with
0% diverged
20% diverged
(PF 2000)
Efficacy of
interpolationHigh High Low High
Number of samples
neededNone Small Medium Large
Sensitivity to missing
dataLow Low Low Low
Sensitivity to outliers Low Low Medium High
Computation time
(non‐parallel)Shortest
Same order
as EKF
longer than
EKF
Same order
as EnKF
Computational performance – scalability
• Current codes scale to ~1,000 cores
• Current computational performance meets the real-time requirement forregional systems
• Performance is expected to be real-time soon for interconnection-scalesystems
13
0.0001
0.001
0.01
0.1
1
10
100
1000
10000
0.003 0.030 0.300 3.000 30.000
TeraFLOPS
Seconds
2017?
30 ms
Oct 2012
June 2014
Oct 2012
June 2014
Computational time per estimation step
Look‐Ahead Dynamic Simulation
• Performance evaluation with the WECC system
• 13x faster compared to a commercial tool with sequential computing
Time distribution for WECC system(Simulation length: 30s with 0.005s time step)
# of threads
Algebraic Equation
Integration Total
1 13.26 218.87 232.14
2 8.99 111.97 120.95
4 6.46 57.49 63.96
8 5.07 29.58 34.65
16 4.58 10.64 15.22
32 4.14 6.41 10.55
64 4.16 4.88 9.04
14
Dynamic Contingency Analysis
• Solving a large set of Differential Algebraic Equations
• Computational challenge is load balancing – dynamic load balancingvs. static load balancing
15
0
2000
4000
6000
8000
10000
12000
0 2000 4000 6000 8000 10000
Speedup
Number of cores
0 50 100 150 2000
10
20
30
40
50
number of cores
spee
dup
Speedup vs. number of cores
Commercial Tool on HPCvia Virtual Machine
Native HPC Implementation
Voltage Stability Analysis
16
• Objectives:– Generate voltage stability boundary (VSB) accurately and quickly
– Use parallel computing to further enhance speed
– Provide connectivity to commercial tools such as PowerWorld
• A combination of methods toexplore the VSB:– Continuation power flow (CPF)
– New PNNL method based on theX-ray theorem (XR)
– Orbiting method (OM) - a directVSB tracing procedure
– High-order numerical methods(HO)
Voltage Stability Analysis –
Computational performance
• Minimizing the CPF runs results in great improvement incomputational performance
– 5.6x speedup for WECC-sized system in this example
• Parallel computing version is being implemented and expected tofurther improve the performance
17
1Boundary Points2Average Time per Run (s)
Current Operating Point
Boundary Points
Use cases of dynamic paradigm
• Real-time predictive gridoperation with calibrated modeland parameters for fasterresponse and control
• Effective management of large-scale integration of smart gridtechnologies such as renewablegeneration, demand response,electric vehicles, and distributedgeneration
• Better asset utilization tomaximize power transfercapabilities and defertransmission expansion
18
Phasor measurement
Dynamic state estimation
Base case
analysis
Contingencycase
analysis
Violations?
Contingencylist
Reporting
Real‐timetransferlimits
Real‐timetransferlimits
Advanced computing platform
1
2
3
Real‐time calibrated model and parameters
Synergistic work
• M2ACS Predictive Modeling, DOE-ASCR– Stochastic modeling and data assimilation
• GridPACK™, DOE-OE– Parallel computing library for efficient development and better
compatibility
• Non-Wire Methods for Congestion Management, DOE ARPA-E– Transient and voltage stability analysis
– Asset utilization improvement through real-time path rating
See project details on posters…
19
Summary
• Grid evolution meeting information revolution grid operationstransition from static & slow to dynamic & fast– Enabling technologies are computation advancement and data
development
• A dynamic paradigm is necessary to capture emerging dynamics andunderstand where the grid is, where the grid is going, and where thegrid could end up– Dynamic state estimation based on Advanced Kalman Filter
– Look-ahead dynamic simulation
– Dynamic contingency analysis of transient and voltage stability
• This paradigm is expected to facilitate integration of new generationand load for a more reliable, efficient, and cleaner power grid
20
Capturing dynamics: Enable grid operations from reactive to predictive
21
Today’s paradigm:Slow data
Static modeling
State Estimation
Contingency AnalysisSCADA
DynamicState
Estimation
Proven math for large‐scale system
Look‐ahead Dynamic Simulation
13X speedup Sec’s‐Min’s ahead
Dynamic Contingency Analysis
45X speedup on 64 cores Scalable performanceDays to hours
Graphical Contingency Analysis
Future paradigm:High‐speed dataDynamic modeling
Phasor
Predictive
Reactive
Data cycle
Time1/30 sec 2/30 sec 3/30 sec
Dynam
ic States
Look-ahead dynamic simulation
1 min
Dynamic contingency analysis
Dynamics state estimation
5 10 15 200
500
1000
1500
2000
2500
Time, hour
Tra
nsfe
r lim
it of
a c
ritic
al p
ath,
MW Real-time Path Rating
Offline path rating, current practice
25.74% more energy transferusing real-time path rating
5 10 15 200
500
1000
1500
2000
2500
Time, hour
Tra
nsfe
r lim
it of
a c
ritic
al p
ath,
MW Real-time Path Rating
Offline path rating, current practice
25.74% more energy transferusing real-time path rating
Real‐Time Path Rating
Other Applications
Contacts
• Dynamic Paradigm for Grid Operations– Henry Huang, 509-372-6781, [email protected]
• Look-Ahead Dynamic Simulation– Shuangshuang Jin, 206-528-3061, [email protected]
• Dynamic Contingency Analysis– Yousu Chen, 206-528-3062, [email protected]
• Non-Iterative Voltage Stability Analysis– Yuri Makarov, 509-372-4194, [email protected]
22
Questions?
23
Backup slides
24
Capturing dynamics: Enable grid operations from reactive to predictive
25
Today’s paradigm:Slow data
Static modeling
State Estimation
Contingency AnalysisSCADA
DynamicState
Estimation
Proven math for large‐scale system
Look‐ahead Dynamic Simulation
13X speedup Sec’s‐Min’s ahead
Dynamic Contingency Analysis
45X speedup on 64 cores Scalable performanceDays to hours
Graphical Contingency Analysis
Future paradigm:High‐speed dataDynamic modeling
Phasor
Predictive
Reactive
Data cycle
Time1/30 sec 2/30 sec 3/30 sec
Dynam
ic States
Look-ahead dynamic simulation
1 min
Dynamic contingency analysis
Dynamics state estimation
5 10 15 200
500
1000
1500
2000
2500
Time, hour
Tra
nsfe
r lim
it of
a c
ritic
al p
ath,
MW Real-time Path Rating
Offline path rating, current practice
25.74% more energy transferusing real-time path rating
5 10 15 200
500
1000
1500
2000
2500
Time, hour
Tra
nsfe
r lim
it of
a c
ritic
al p
ath,
MW Real-time Path Rating
Offline path rating, current practice
25.74% more energy transferusing real-time path rating
Real‐Time Path Rating
Other Applications
Mitigate intermittency of renewable energy
Source: BPA Fact Sheet, “BPA’s wind power efforts surge forward,” March 2010
500MW
26
Thermal rating
(N‐0 = 10,500 MW)
(N‐1 = 6000 MW)
Stability Rating
(N‐1, N‐2 = 4,800 MW)
WECC/NERC Criteria
Transfer Capacity Example – California Oregon Intertie (COI)
U75 – % of time flow exceeds 75% of OTC (3,600 MW for COI)
U90 ‐ % of time flow exceeds 90% of OTC (4,320 MW for COI)
U(Limit) ‐ % of time flow reaches 100% of OTC (4,800 MW for COI)
WECC
Path Ratings U75, U90 and U(Limit)
% of OTC
75% 90% 100%
Source : Western interconnection 2006 congestion management study
Improve asset utilization
27
Today’s power grid operation paradigm
~1 min
2‐5 mins
• Steady-state model based: static and slow paradigm
28
Computers are more affordable
29
Data Source: http://en.wikipedia.org/wiki/FLOPS
1¢
2005 (8 years later)
Sony VAIO GRT-290Z6 GFLOPS, ~$2,000
2012 (15 year later)
LG Optimus 4X HD P88024 GFLOPS, $600
1997
IBM Deep Blue11.38 GFLOPS, >$100M