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

6

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

12

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

13

Questions?

14