!overview!of!micro,synchrophasors! in!distribu8on!and ... ·...
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Overview of Micro-‐Synchrophasors in Distribu8on and Smart Grids
Alexandra “Sascha” von Meier Co-‐Director, Electric Grid Research, California Ins8tute for
Energy and Environment (CIEE)
Adjunct Associate Professor, Dept. of Electrical Engineering and Computer Science, UC Berkeley
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Acknowledgment: The informa5on, data, or work presented herein was funded in part by the Advanced Research Projects Agency-‐Energy (ARPA-‐E), U.S. Department of Energy, under Award Number DE-‐AR0000340. Disclaimer: The informa8on, data, or work presented herein was funded in part by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any informa8on, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily cons8tute or imply its endorsement, recommenda8on, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
the small phase angle δ between different loca8ons on the grid drives a.c. power flow
δ = 0
δ
power flows from Unit 1 toward Unit 2
Synchrophasors compare voltage phase angle at different loca5ons
Synchrophasors compare voltage phase angle at different loca5ons
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30 60 90 120 150 180 210 240
Time - seconds
Vol
tage
- kV
John Day Malin Summer L Slatt McNary
Grizzly reactor #2
Grizzly reactor #3
Ashe reactor
Phasor Measurement Units (PMUs)
synchronous data
useful real-‐5me informa5on for system operators
Transmission PMUs in North America
NASPI 2010
Transmission PMUs in North America
NASPI 2012
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µPMU concept
TRADITIONAL PMU NETWORK Transmission (Bulk) System
PROPOSED µPMU NETWORK Distribution System
Phase Angle
Substa8on
8
What do we want?
• Be[er visibility and situa8onal awareness for operators • Support faster service restora8on
• Accommodate impacts of more ac8ve devices: • reverse power flow • greater variability and uncertainty • dynamic interac8ons (oscilla8ons)
• Leverage opportuni8es to recruit distributed resources: • volt-‐VAR op8miza8on • real power control • microgrids • resilience
today
tomorrow
yesterday
When do we want it?
9
Challenges for distribu5on synchrophasor measurements and their interpreta5on, compared to transmission
• smaller voltage angle differences
• more noise in measurements
• different X/R ra8os
• unbalanced three-‐phase systems
• few measuring points compared to network nodes
• need lower cost per PMU to make business case
P12 ≈V1V2Xsinδ
10
Challenges for distribu5on synchrophasor measurements and their interpreta5on, compared to transmission
• smaller voltage angle differences
• more noise in measurements
• different X/R ra8os
• unbalanced three-‐phase systems
• few measuring points compared to network nodes
• need lower cost per PMU to make business case
11
Challenges for distribu5on synchrophasor measurements and their interpreta5on, compared to transmission
• smaller voltage angle differences
• more noise in measurements
• different X/R ra8os
• unbalanced three-‐phase systems
• few measuring points compared to network nodes
• need lower cost per PMU to make business case
Time scales in electric grid opera5on
µPMUs and power quality measurements: Time resolu5on in perspec5ve
512 samples per cycle
10-6 10-3 103 seconds
hour day
accuracy of GPS time stamp: differential absolute
clock accuracy
0.1o 1o 1 cycle 12 cycles µPMU data buffer and notifications
temperature, humidity
min/avgas/max recording
W, VAR, VA +/-/0 sequence
imbalance
voltage and current harmonics RMS sags, swells,
interruptions
impulse capture
angular resolution
frequency, dF/dt, angle meas.
interval
10-9 100 106
nanosecond microsecond millisecond second minute month
waveform changes proposed µPMU measurements
proposed device capabilities
reference magnitudes
Micro-‐Synchrophasors in Distribu8on and Smart Grids
ARPA-‐e Project Update
Alexandra “Sascha” von Meier Co-‐Director, Electric Grid Research, California Ins8tute for
Energy and Environment (CIEE)
Adjunct Associate Professor, Dept. of Electrical Engineering and Computer Science, UC Berkeley
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Research Project Overview Three-‐year, $4.4 M project began in 2013
Research partners: CIEE, UC Berkeley, Lawrence Berkeley Na8onal Laboratory, Power Standards Lab
Prospec5ve u5lity and field site partners: Southern California Edison, Sacramento Municipal U8lity District, Southern Company, Na8onal Renewable Energy Laboratory, UC San Diego
Project Objec5ves • develop a network of high-‐precision phasor measurement units
(µPMUs) to measure voltage phase angle to within < 0.05o
• understand the value of voltage phase angle as a state variable on power distribu8on systems
• explore applica8ons of µPMU data for distribu8on systems to improve opera8ons, increase reliability, and enable integra8on of renewables and other distributed resources
• evaluate the requirements for µPMU data to support specific diagnos8c and control applica8ons
• advance the adop8on of this technology and its successful applica8ons through technology transfer and outreach
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Key ac5vi5es in 2014
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Install 90+ µPMUs at field sites (about 10 per circuit) in collabora8on with research partners
Choosing field deployment sites where…
• the circuit has unique characteris8cs that the u8lity wishes to be[er understand (e.g., extremely high DG penetra8on, some unexpected behavior, etc.).
• the circuit presents a rich opportunity to study one or more diagnos8c applica8ons for distribu8on PMU data that the project is considering.
• the circuit has other monitoring equipment installed against which µPMU measurements can be compared and validated.
Key ac5vi5es in 2014
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Install 90+ µPMUs at field sites (about 10 per circuit) in collabora8on with research partners
Compare µPMU measurements against circuit models for mutual valida8on
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Modeling and planning tool selec5on to address u5lity concerns
Issue Timeframe SoZware op5ons Opera5onal Op5ons FIDVR milliseconds, sub-‐cycle none Data input to
opera8ons
Dynamic Load Behavior similar to FIDVR CymDist, PSCAD, DEW, DigSilent
Data input to opera8ons
Dynamic Inverter Behavior
milliseconds, Seconds CymDist, PSCAD, DEW, DigSilent
Network model representa8on
Controllable inverter behavior (Volt/VAR)
seconds, minutes, hours
CymDist, DEW, DigSilent
Data input to opera8ons
Transient switching impacts (microgrids)
subcycle PSCAD, PSS/Sincal, DigSilent
Preven8ve in planning and protec8on analysis
Load flow – reverse power, voltage profiles etc
hours, minutes, steady state
CymDist, DEW, SynerGEE Electric
Data input to opera8ons
Protec8on in high penetra8on scenarios
milliseconds DEW, CymDist, SynerGEE Electric
Preven8ve in planning and protec8on analysis
Key ac5vi5es in 2014
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Install 90+ µPMUs at field sites (about 10 per circuit) in collabora8on with research partners
Work on algorithms for selected applica8ons
Experiment with networking, examine latencies
…but first: Get some actual measurements!
Study feasibility of instrumen8ng the secondary side, to view primary through distribu8on transformer
Compare µPMU measurements against circuit models for mutual valida8on
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Pilot site development at LBNL
• Installing µPMU devices in 4 locations at LBNL from Grizzly substation to Building 90
• Will measure high fidelity phase angle data, voltage and current to validate the LBNL CymDist Model
• Challenges: communications, calibration, and commissioning
Grizzly Substa5on
feeds LBNL and UC Berkeley campus 115kV from PG&E 12.47kV distribu8on
© 2014 Power Sensors Ltd – All rights reserved 24
µPMU antennas, receivers
• Calibrated GPS Receiver, and dual 4G modem antennas • Magne8c mounts for all – no roof penetra8on
© 2014 Power Sensors Ltd – All rights reserved 25
Installa5on “proof of concept”
• Temporary power, measurement from extension cord • Good signals, good phase lock • Need to have further discussion about real signals
© 2014 Power Sensors Ltd – All rights reserved 26
© 2014 Power Sensors Ltd – All rights reserved 27
17 min on a Friday lunch8me
UCB Soda Hall Lab
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Very first µPMU network setup at UC Berkeley
Internet
Gateway
4G LTE
POE ethernet
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4G TE modem
Campus LAN
Cloud services
Primary archiver
Redundant archivers
Broker (MQTT)
SODA1
SODA2 GRIZZLY PEAK
4G TE modem
Ini5al data transfer
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Phase angle difference between Grizzly Sub and Soda Hall
10 min
0.2o
-‐ 0.2o
-‐ 0.4o
0o
The first four µPMU’s connected…
• "Oakland” 37.830345,-‐122.256077
• Two at “PSL-‐Alameda North” 37.785826,-‐122.273673
• "Alameda South” 37.762047,-‐122.251099
© 2013 Power Sensors Ltd – All rights reserved 31
The first four µPMU’s connected…
• "Oakland” 37.830345,-‐122.256077
• Two at “PSL-‐Alameda North” 37.785826,-‐122.273673
• "Alameda South” 37.762047,-‐122.251099
© 2013 Power Sensors Ltd – All rights reserved 32
Oakland – Alameda rela5onship
© 2013 Power Sensors Ltd – All rights reserved 33
© 2014 Power Sensors Ltd – All rights reserved 34
-‐20
-‐15
-‐10
-‐5
0
5
10
15
20
Angle in degrees
1 minute of data -‐ 7:32:00UTC 2/6/2014 (7200 points)
Alameda(South)-‐to-‐PSL(Alameda North) angle
1 min
© 2014 Power Sensors Ltd – All rights reserved 35
-‐20
-‐15
-‐10
-‐5
0
5
10
15
20
Angle in degrees
1 minute of data -‐ 7:32:00UTC 2/6/2014 (7200 points)
Oakland-‐to-‐PSL(Alameda North) angle
© 2013 Power Sensors Ltd – All rights reserved 36
-‐20
-‐15
-‐10
-‐5
0
5
10
15
20
Angle in degrees
1 minute of data -‐ 7:32:00UTC 2/6/2014 (7200 points)
PSL-‐to-‐PSL angle
Should be approximately zero degrees – two µPMU’s connected to same signal
© 2014 Power Sensors Ltd – All rights reserved
© 2014 Power Sensors Ltd – All rights reserved 38
59.978
59.98
59.982
59.984
59.986
59.988
59.99
59.992
59.994
59.996
Freq
uency in Hz
1 minute of data -‐ 7:32:00UTC 2/6/2014 (7200 points)
Frequency, derived from angle progression
PSL1 freq
PSL2 freq
ALS freq
OAK freq
Illustra8on: Michael Sowa
Extra Slides
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Categories of applica5ons
• diagnos8c vs. control applica8ons
• (quasi-‐) real-‐8me vs. off-‐line
• based on observa8on of steady-‐state vs. dynamic behavior
Research objec5ve:
• iden8fy benefits of explicit voltage angle measurements
• es8mate data requirements to support various applica8ons
• begin to develop algorithms
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Possible diagnos5c applica5ons supported by micro-‐synchrophasors
• topology detec8on: switch status, uninten8onal islanding, phase iden8fica8on
• state es8ma8on: voltage profile, reverse power flow
• fault loca8on, high-‐impedance fault detec8on
• oscilla8on detec8on • characteriza8on of dynamic behavior: loads, distributed
generators, iner8a, FIDVR risk detec8on, unmasking loads behind net metered DG?
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Possible control applica5ons supported by micro-‐synchrophasors
• protec8on: adap8ve relaying • Volt-‐VAR op8miza8on
• microgrid balancing: load, DG and storage control
• ancillary services coordina8on • inten8onal islanding
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Ini5al thoughts about data requirements
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Ini5al thoughts on the value of δ for various applica5ons
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Ini5al thoughts on the value of δ for various applica5ons