probabilistic look ahead contingency analysis and dynamic ... · real-time path rating (rtpr)...
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Probabilistic Look Ahead Contingency Analysis and Dynamic Security Assessment YOUSU CHEN Sept. 12, 2016 Scottsdale, Arizona
PNNL-SA-121074
Background
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Current challenges: Increasing dynamic nature of the grid
more uncertainty Lack of algorithm/tool to do multiple contingency analyses for forecasting
large number of sampling sets with uncertainty
Time consuming to complete single Dynamic Simulation (DS) and Dynamic Security Assessment (DSA) Required increased transmission capacity
multiple applications involved
Technologies needed: Sampling methods to significantly reduce the size of samples to cover probability space High performance computing techniques to reduce computational time Probabilistic analysis methods to analyze system future condition Integrated platform that facilitates data communication among applications
Objectives
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Develop smart sampling algorithms to increase the efficiency and accuracy for look-ahead contingency analysis Implement high performance computing (HPC) technology to increase computational speed for Contingency Analysis (CA), Dynamic Simulation (DS) and Dynamic Security Assessment (DSA) functions Enable Real-Time Path Rating (RTPR) computation Develop integrated tool suite to enhance functionalities and improve programming productivity
Sampling Design Comparison Two major criteria for a sampling design
Space-filling: being exploratory of the full multi-parameter space (better coverage) Non-collapsing: no two design points coincide when projected onto a lower number of dimensions (no redundancies)
Sampling methods: General Random Sampling (GRS) Latin Hypercube Sampling(LHS) Quasi-Monte Carlo (QMC)
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Smart Sampling
Smart Sampling Efficiency Comparison
Needed to address slow convergence of the Monte Carlo method More efficient and accurate than general sampling approach (GRS) Number of samples needed for convergence (for desired distributions)
Smart sampling techniques (LHS and QMC): ~50 General random sampling (GRS): >1000 samples (~20 times)
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Smart Sampling Framework
Objective: create a small set of cases to fully represent uncertainty in the grid status associated with the forecast errors Inputs: forecast data and actual data with the same time resolution Approaches:
Study forecast errors based on historical data using Auto Regressive Integrated Moving Average (ARIMA) method
Fit models to historical data Predict the future points
Evaluate the data dependency structure and map it to smart samples
Outputs: smart sampling realizations (reduced size)
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Actual Data
Forecast Error
Smart sampling
Data dependency structure
ARIMA prediction
Realizations
Forecast Data
Smart Sampling + HPC
Objective: further reduce computational time using HPC technologies Send realizations to HPC applications for various probability analyses
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PF/CA/DS…
Probability Analyses
Actual Data
Forecast Error
Smart sampling
Data dependency structure
ARIMA prediction
Realizations
Forecast Data
PF/CA/DS…
PF/CA/DS…
… … HPC
Look-ahead Contingency Analysis Architecture and Design
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Extract actual data from operational models and map to planning model to create smart sampling inputs Smart sampling can be applied to contingency to further reduce computational time Currently using in-house solver Next step: integrate with Alstom tool
Database
PF/CA Solver
Calculate probability density function of power flow and bus voltages
Smart Sampling
Realizations
Forecast Information
Actual Information
Contingency List
PF/CA Solver PF/CA Solver …
HPC Deployment (Task Manager)
Smart Sampling
Reduced Contingencies
Extraction /mapping
On-line /offline
Data Extraction/Mapping
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Goal: to create historical forecast data and actual data with the same time resolution for smart sampling technologies Challenges:
Different naming schemes in operation model/planning model Station ID, voltage, node ID, area
Operational model in PowerWorld format in different database (DB) versions Inconsistent naming between DB versions
Discrepancy of area assignment between mapping table and operating mode
Smart Sampling Real Data Challenges/Solutions
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Challenges Forecast and actual data have different time resolutions
Wind forecast: hourly Load forecast: 5-minute interval Actual data: 15-minute interval
Forecast data is not always available, especially for wind Actual data does not follow the exact 15-minute interval, sometimes does not exist
Solutions: Forecast: down sampling with interpolation is applied on the forecast data to get 15-minute resolution dataset Actual: find the closest actual wind timestamp for forecast data
Wind Data Example
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An example of down sampled forecast data, original forecast data and actual data for a wind farm
Between April 18th to 20th April
Test on ESCA-60 Bus System
1000 realizations on 4 wind farms and 41 loads Smart sampling with contingencies further reduce computational time Computational time (4-core)
275 seconds for full CA 12 seconds for 2 sampled contingencies
Results with full contingency Results with contingency sampling
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Performance Evaluation Smart Sampling (SS) General Random Sampling (GRS)
SS is more efficient than GRS: less number of realizations needed to get the mean close to reference (red line) The uncertainty range of smart sampling is smaller than GRS
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Test on BPA System
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1000 realizations on 44 wind gens and total load (distributed to ~600 loads) Computational time
4-core: ~9,900 seconds for 166 contingencies and ~240 seconds with 4 sampled contingencies 320-core: 110 seconds for 166 contingencies and ~3 seconds with 4 sampled contingencies
Max base PF
Use Real-time Path Rating
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Thermal rating might not be good Real-time path rating (RTPR) is more realistic for operation
Dynamic based on real-time operating conditions Leads to maximum use of transmission assets Relieves transmission congestion
RTPR
Real-Time Path Rating
Current Path Rating Practice and Limitations Offline studies with worst-case scenario Ratings are static for the operating season conservative rating
To enable Real-Time Path Rating Increase computational speed for dynamic simulation and dynamic security assessment Need an integrated platform that facilitates data communication among applications
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Enabling Technologies for Real-Time Path Rating
HPC technology to increase computational speed for dynamic simulation, dynamic security assessment, and voltage stability Requires an integrated platform that facilitates data communication among applications, including visualization
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VSA SE/PF/CA
Measurements Contingencies
DSA …
HPC Task manager
Visualization
Integrated Platform
System data
Database
RTPR
Single DS Performance - Speed 30-second dynamic simulation on BPA WECC system Classical model Detailed model supported: GENSAL, ESST1A, EXDC1, WSIEG1,
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Classical model Detailed model
Validation of Simulation Accuracy with TSAT
0 1 2 3 4 5 6 7 8 9 100.1
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TSATParallel Implementation with API
Call TSAT API to initialize dynamic simulation Initial states (embedded in API) Mapping between generators and buses Generator Norton impedances
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Speedup Performance of the WECC Model
20s simulation, with 0.005s time step 2,600+ synchronous generator models (in service) 16,000+ buses
CPU speed: 1.2 GHz
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imul
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no bus voltages saved16K bus voltages saved
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Dynamic Security Assessment (DSA)
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Run dynamic simulation with multiple contingencies Transient stability is need for path rating Two-level parallelism
Individual tasks are distributed to groups (first level of parallelism) Run in parallel within the group (second level of parallelism)
CPU 4
CPU 2
CPU 3
CPU 1
CPU 4
CPU 2
CPU 3
CPU 1
CPU 4
CPU 2
CPU 3
CPU 1
CPU 4
CPU 2
CPU 3
CPU 1
CPU Group 1 CPU Group 2 CPU Group 3 CPU Group 4
Global Task Manager
HPC DSA Performance
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100
1000
1 10 100 1000
Tim
e (s
econ
ds)
Number of Processors
Total
Linear Solver
DS Solution
Running 30-second dynamic simulation with 16 contingencies Computational time: 33 seconds
Identify available operating margins using HPC – Voltage Stability Boundary
Path 2 MW
Path 1 MW
Base Case
Other Boundary Cases (voltage violation criterion)
Other voltage Boundary Cases
PF = Power Flow; MCA = Massive Contingency Analysis; DS = Dynamic Simulation
Parallelism: (1) PF MCA: parallel over contingencies (2) Orbiting for each contingency: Parallel over contingencies (3) DS test: parallel over boundary points and contingencies (two-level)
Reference: Y. Makarov, D. Meng, B. Vyakaranam, R. Diao, B. Palmer and Z. Huang, “Direct methods to estimate the most limiting voltage level and thermal violations in coordinates of power transfers on critical transmission paths,” in Proceedings of the 49th HICSS conference.
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Voltage Stability Boundary
Identify available operating margins using HPC – Transient Stability Boundary
Path 2 MW
Path 1 MW
Base Case
Boundary Case (transient stability criterion)
Voltage Stability Boundary
Other Boundary Cases (voltage violation criterion)
Other voltage Boundary Cases
PF = Power Flow; MCA = Massive Contingency Analysis; DS = dynamic simulation
Reference: Y. Makarov, D. Meng, B. Vyakaranam, R. Diao, B. Palmer and Z. Huang, “Direct methods to estimate the most limiting voltage level and thermal violations in coordinates of power transfers on critical transmission paths,” in Proceedings of the 49th HICSS conference.
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Parallelism: (1) PF MCA: parallel over contingencies (2) Orbiting for each contingency: Parallel over contingencies (3) DS test: parallel over boundary points and contingencies (two-level) (4) PF: parallel over interior points (5) DS MCA: parallel over boundary points and contingencies (two-level)
Parallelism: (1) PF MCA: parallel over contingencies (2) Orbiting for each contingency: Parallel over contingencies (3) DS test: parallel over boundary points and contingencies (two-level)
A Web-browser based GUI to Display Real-time TTC
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HPC simulations on Linux Real-time display on Windows BPA real-time case with 405 contingencies and 4 nomogram points Total number of dynamic simulations is 4 (nomogram points on a 2D plane) X 4 (number of dynamic contingencies) = 16 per slice 254~370s to complete RTPR for one case using 125 cores on 25 nodes
Architecture of Integrated Look-ahead CA, DSA & RTPR
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Integrate smart sampling techniques and probability analyses functions with the platform RTPR rating can be fed in look-ahead CA as real-time rating to aid operation
Actual/Forecast data Contingencies
HPC Task manager
Probability Analyses Functions
Integrated Platform
System data
Database
Realizations Created by Smart Sampling
Visualization
Sampled data
VSA SE/PF/CA DSA … RTPR
Acknowledgement
Funding support provided by DOE-OE, Advanced Grid Modeling Program and BPA TI
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Project team
Power system engineers Ruisheng Diao Pavel Etingov Yuri Makarov Tony Nguyen
HPC
Bruce Palmer Shuangshuang Jin
Statistician
Huiying Ren Jason Hou
Middleware Poorva Sharma
Visualization Erin Fitzhenry
Advisor Zhenyu Huang Mark Morgan