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P R E S E N T E D B Y
Sandia National Laboratories is a multimission
laboratory managed and operated by National
Technology & Engineering Solutions of Sandia,
LLC, a wholly owned subsidiary of Honeywell
International Inc., for the U.S. Department of
Energy’s National Nuclear Security
Administration under contract DE-NA0003525.
Towards High Fidelity Modeling of DER Integration to Distribution Grids
Mat thew Lave
Smar t Gr id Edg e Ana ly t i cs Workshop
At l anta , GA
June 5 , 2019
SAND2019 -6176 C
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What do we mean by “high fidelity”?
High-fidelity – a reproduction faithful to the original
For DER integration, we mean “accurately modeled,” including:
1) Granular Distribution Grid Modeling
◦ Phase identification
◦ Topology identification and parameter estimation
2) Detailed Locational Impact Analysis◦ Locational hosting capacity
◦ Synthetic Cloud Fields
3) Long-term Timeseries Analysis◦ Daily/seasonal variability in generation and impact to grid operations
◦ Rapid QSTS
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Granular Distribution Grid Modeling
• Phase identification
• Topology identification and parameter estimation
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Phase Identification
Use Machine Learning on voltage profiles from AMI meters to cluster by phase.
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Concept
Method
Result
L. Blakely, M. J. Reno and W. Feng, "Spectral Clustering for
Customer Phase Identification Using AMI Voltage Timeseries," 2019
IEEE Power and Energy Conference at Illinois (PECI), Champaign, IL,
USA, 2019, pp. 1-7.
doi: 10.1109/PECI.2019.8698780
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8698780
&isnumber=8698776
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Phase Identification
May confirm utility model:
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Phase
Identification:
Phase B
Utility Model Phase Identification Street View
Phase B ✓ Phase B ✓ Phase B ✓
Utility Model Street View
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Phase Identification
Or may correct model:
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Phase
Identification:
Phase B
Utility Model Phase Identification Street View
Phase C Phase B ✓ Phase B ✓
Utility Model
Utility Model
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Topology and Parameter Estimation
Use AMI voltage and power measurements to derive secondary system topology and impedances.
Linear regression to find R1, R2, X1, X2 values which best fit the V1-V2 fluctuations
𝑽1 − 𝑽2 = 𝑰𝑅1𝑅1 + 𝑰𝑋1𝑋1 + 𝑰𝑅2𝑅2 + 𝑰𝑋2𝑋2 + 𝝐
Known Unknown
M. Lave, M. J. Reno and J. Peppanen, "Distribution
System Parameter and Topology Estimation Applied
to Resolve Low-Voltage Circuits on Three Real
Distribution Feeders," in IEEE Transactions on
Sustainable Energy.
doi: 10.1109/TSTE.2019.2917679
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&ar
number=8718261&isnumber=5433168
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Topology and Parameter Estimation
Topology and parameter estimation (DSPE) matched well with the (good) utility secondary model.
Parameter/Topology Est. Utility Secondary Model
Comparison
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Topology and Parameter Estimation
Topology and parameter estimation (DSPE) matched well with the (good) utility secondary model, even for complicated topologies.
Parameter/Topology Est. Utility Secondary Model
Comparison
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Topology and Parameter Estimation
For bad or nonexistent utility secondary models (common), topology and parameter estimation can be used to develop a high-fidelity secondary model.
Common assumption
when no secondary model
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Detailed and verified utility secondary model.
11 Result of Granular Modeling
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Detailed Locational Impact Analysis
• Locational hosting capacity
• Synthetic Cloud Fields
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Locational Hosting Capacity
Concept –add more and more PV at a single node until it causes a problem –voltage, line loading, or transformer loading. The largest amount of PV that does not cause a problem is the locational hosting capacity.
Reno, Matthew J., et al. Novel Methods to Determine Feeder
Locational PV Hosting Capacity and PV Impact Signatures.
No. SAND2017-4954. Sandia National Lab.(SNL-NM),
Albuquerque, NM (United States), 2017.
URL: https://www.osti.gov/biblio/1367426
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Locational Hosting Capacity
Can also apply to electric vehicles – for EVs, line loading, transformer loading, or under voltage may be more common (EVs are essentially additional loads).
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Locational Hosting Capacity
Can be applied to facilitate interconnection requests across a feeder.
substation
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Synthetic Cloud Fields
Modeling many PV interconnections on a feeder is difficult: usually only 1 (or no) solar irradiance measurement available near a feeder.
Challenge Impact
Lave, Matthew Samuel, Matthew J. Reno, and Robert Joseph Broderick. Creation and
Value of Synthetic High-Frequency Solar Simulations for Distribution System QSTS
Simulations. No. SAND2017-5646C.
URL: https://www.osti.gov/servlets/purl/1458093
Solution: synthetic cloud fields
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Synthetic Cloud Fields
Synthetic PV used as input to OpenDSS to find voltage profile.
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Synthetic Cloud Fields
Synthetic PV used as input to OpenDSS to find voltage profile.
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Long-Term Timeseries Analysis
• Daily/seasonal variability in generation and impact to grid operations
• Rapid QSTS
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Variability in Generation and Impact to Distribution Grid
Many DERs have variable output that is not controlled by the grid operator.
Solar
Wind
EVs
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Variability in Generation and Impact to Distribution Grid
Timeseries analysis shows temporal impacts (e.g., voltage regulator tap change operations), and allows for full consideration of daily/seasonal trends.
Lave, Matthew, Matthew J. Reno, and Robert J.
Broderick. "Characterizing local high-frequency
solar variability and its impact to distribution
studies." Solar Energy 118 (2015): 327-337.
URL:
https://www.sciencedirect.com/science/arti
cle/pii/S0038092X15002881
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Rapid QSTS
Speed up Quasi-Static Time Series (QSTS) analysis to enable running 1-year distribution grid simulations on a desktop computer.
Rapid QSTS
Circuit reduction
Power Flow Improvements
Vector quantization
Variable time-step
Event-Based
Simulation
Time taken by QSTS:
Time taken
by a single PF
Total PFs
solved
𝑡𝑠𝑜𝑙 =𝑡𝑃𝐹 ∗ 𝑁𝑃𝐹
𝐶𝑃Computational
Power
CP
Temporal Parallelization
Diakoptics
Reno, Matthew J. Rapid QSTS Simulations for
High-Resolution Comprehensive Assessment
of Distributed PV. No. SAND2018-3899C.
URL:https://www.osti.gov/servlets/purl/1507939
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Rapid QSTS
Speed up Quasi-Static Time Series (QSTS) analysis to enable running 1-year distribution grid simulations on a desktop computer.
Extreme
Voltages
Thermal
Loading
Regulators
Tap
Changes
Capacitor
Switching
Time
outside
ANSI
LossesComputation
Time1
Snapshot Good Good - - - - <1 sec
Hourly Timeseries Great Great - - Good Great 5 sec
1 day QSTS Poor Poor Decent Decent Poor Poor 5 minutes
1 year QSTS Great Great Great Great Great Great 36 hours
New Rapid QSTS
AlgorithmsGreat Great Great Great Great Great 30 sec
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Concluding thoughts
• Historic methods (snapshot, no secondary, limited measurements) are insufficient.
• AMI data and other grid edge sensing provide opportunities for deriving and validating system models.
• Developments in modeling methods are simultaneously enabling more accurate distribution grid modeling and faster simulations.
• Upcoming: “QSTS hosting capacity” will simultaneously consider locational and temporal impacts of DER integration.