Sustainable Data Evolution Technology (SDET) for …...Sustainable Data Evolution Technology (SDET)...
Transcript of Sustainable Data Evolution Technology (SDET) for …...Sustainable Data Evolution Technology (SDET)...
Sustainable Data Evolution Technology
(SDET) for Power Grid Optimization
Zhenyu (Henry) Huang, Ph.D., P.E.
Chief Engineer, Team Lead
Pacific Northwest National Laboratory
March 30, 2016
Team Organization
1SDET Project Plan
PNNL
(prime)
Huang (PI),
Makarov, Diao,
Rice, Elbert,
Halappanavar,
Fuller
NRECA
(second prime)
Miller (Site PI),
Pinney
GE Grid
Solutions
Kadankodu
(Site PI), Sun
Advisory
Board
Research Council
PI: Huang; Site PIs: Miller,
Kadankodu, Tong, Loutan, Kirkeby
Management
Meetings
Monthly
TeleconsAnnual
Review
Meetings
Subcontract
PJM
Tong (Site PI)
Avista
Kirkeby (Site PI)
CAISO
Loutan (Site PI)
Capabilities, Facilities, Equipment, Information
‣ PNNL EIOC
– Modeling, simulation and data host for
Pacific Northwest Smart Grid Demonstration
– PMU streams from Western Interconnect
– Alstom/GE E-terra Platform
‣ PNNL Institutional Computing (PIC)
– HPC platform with ~23K cores
‣ NRECA OMF: production system and user
community
‣ GE Grid Solutions EMS/DMS Tools
‣ Available datasets (T+D models, Market
data) and industry experience at NRECA,
PJM, CAISO, and Avista
‣ Natural connection to one data repository
team through personnel and facility
2SDET Project Plan
Project Summary
‣ Objective: to deliver large-scale, realistic, evolvable
datasets and data creation tools for optimization problems
such as AC OPF and VVO
– Derive data features/metrics for real T+D systems
– Develop tools to generate large-scale, open-access, realistic
synthetic datasets
– Validate the created datasets using industry tools
– Integrate with GRID DATA repositories
‣ Cost: $1,485,000 federal funds and $339,000 cost share
‣ Timeline: 24 months
3SDET Project Plan
Evolvable open-access large-scale datasets to accelerate the
development of next-generation power grid optimization.
Project Impact
‣ A novel concept “data evolution”, with long-lasting impact
– Disrupt the current ad hoc cycles of static dataset generation.
– Enable the datasets to evolve with the increasing grid complexity.
– Accelerate development and adoption of grid optimization methods.
– Improve the reliability, resiliency and efficiency of the power grid.
4SDET Project Plan
Use Cases and Relationship to Data Repository
5DR POWER Project Plan
April
22,
2016
Data Tools
Web PortalData Repository
Download
Processing
Generation
Processing
User
Use
existing
datasets
Generate
new datasets
Submit
datasets
Submission
processing
Validated
models &
scenarios User-generated
models &
scenarios
Existing
models &
scenarios
Dataset
Generation/anon
ymization
Methods
Dataset
Metrics/Val
idation
Download
request
Case
configuration
3rd-party
datasets
Private
Datasets
Data Repository
e.g. DR POWER
SDET
Data Creation Tools
Data Anonymization
Topology Generation
Parameter Population
Dataset Metrics T+D
Bas
ecas
e G
ener
atio
n
Scen
ario
G
ener
atio
n
Data Validation
Tools
Data Repository
(beyond this
project)
Ind
ust
ry T
ran
s an
d
Dis
triD
atas
ets Open-Access Datasets
Datasets & Data
Creation Tools
>>>>>>> Year 1 >>>>>>>>>>>>> Year 2 >>>>>>>
Small Datasets Large Datasets
Industry Partners (NRECA, PJM, CAISO, Avista)
NRECA AlstomPNNL
Data Validation
Industry review
Professional
communities –
FERC, NERC,
IEEE
Validation criteria
Tool refinement
Tasks and Dependency
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Datasets Requirements
• Large-scale
• Realistic
• Open-access
• Sustainable (ARPA-E independent)
• Evolvable (datasets are not static)
Deliverables
• Datasets
• Dataset creation tools
SDET Project Plan
Proposed Technologies
‣ Development of Data Creation Tools
– Develop metrics for topology, parameter, composition, consistency of real-world datasets
– Topology generation: graph theory based algorithms
– Parameter population: deterministic and probabilistic approaches
– Data anonymization
‣ Generation and validation of open-access datasets
– Base cases of small-scale and large-scale models
– Time-series scenarios
– Three-level validation: component, system and application
7SDET Project Plan
Difficulty in modeling topology as a single graph
Graph type Vertices,
edges
Clustering
coefficient
ASPL* Diameter
Western Interconnection 4941, 6594 0.0166 21.75
Random (of Western) 4941, 6594 0.00049 10
Nordic System 4789, 5571 0.0801 18.99
Random (of Nordic) 4789, 5571 0.00054 8.7
Eastern Interconnection 49597,
62966
0.071 35.8 96
Pref. attach (Eastern) 49597,
62966
0.0006 7.2 18
Small world p=0.0882
(Eastern)
49597,
62966
0.27 36.6 96
*Average shortest-path length
‣ Small world does best, but clustering coefficient is off
‣ All others do not fit any metric well
Modeling power grid topology as NoN graphs
500kVWestern
Interconnection
Transformers
= + + …
NoN = Network of Networks
‣ Example: Line Impedance Distribution
-0.1 0 0.1 0.2 0.3 0.4 0.50
1000
2000
3000
4000
5000EI Impedance
-0.1 0 0.1 0.2 0.3 0.4 0.5
EI Line Impedance (p.u.)
2000
1000
0
0 0.1 0.2 0.3 0.4 0.50
200
400
600
800
1000
1200
1400
1600WECC Impedance
WECC Impedance, pu
0 0.1 0.2 0.3 0.4 0.5
WECC Line Impedance (p.u.)
400
200
0
-0.05 0 0.05 0.1 0.15 0.2 0.25 0.3 0.350
200
400
600
800
1000
1200
ERCOT Impedance, pu
0 0.1 0.2 0.3 0.4
ERCOT Line Impedance (p.u.)
200
100
0
Statistical distribution of parameters
10
y axis is the number of
lines that have the
corresponding
impedance value.
Technology to Market and Outreach
‣ T2M Strategy
– Expected products: datasets and data tools
– Transition facilities: EIOC and PIC
– Training and workshops
– Tool adoption: offer datasets and tools to GRID DATA
data repository
– Community engagement: e.g. IEEE PES.
‣ Intellectual Property
– New software tools to be generated, protected by BSD-
style open-source licenses
– Potential patents
11SDET Project Plan
Opportunities for GRID DATA Collaborations
‣ Change perspective: competition collaboration.
‣ Data format coordination and convergence: early
engagement is critical to the success of all model projects.
‣Metrics of realism: information exchange to maximize
consistency and success.
‣ Cross-validation of datasets
‣ Joint workshop(s)
‣ Joint force for outreach, e.g. IEEE Power and Energy
Society
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Conclusions
‣Making datasets evolving is important to keep up with grid
development and enable technology advancement
‣ Delivering datasets is important, but delivering data creation
tools can enable data evolution
– Topology generation tool
– Parameter population tool
– Data anonymization tool
‣ Datasets and data creation tools are to be shared through
GRID DATA repositories and professional communities
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Questions?
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