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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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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1000

1 10 100 1000

Tim

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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

Questions?

Yousu Chen, 206-528-3062, yousu.chen@pnnl.gov

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