LISBON 2013

1.4 Information Technologies for the Smart Grid

Advanced Utility Analytics with Object-Oriented Database Technology

Paul Myrda, John Simmins, Bert Taube

USA

Paper Reference 144

LISBON 2013 Overview

• Uniqueness of the utility data context

• Data provides a “menu” of opportunities for improvement

• Focus of EPRI research in the area of “Big Data”

• Lack of situational awareness and analytics in power transmission demonstrated by August 14, 2003, blackout

• Demo showing an effective analytic approach to create situational awareness and decision making in transmission

• Data challenge and technology solution to create real-time situational awareness and analytics based on PMU data

LISBON 2013 Big Utility Data Management

and Analytics – Data Diversity Challenge

Big Energy Data Features Energy Data Types Energy Data Sample Rates

µs – Range

(e.g. HF Switching Devices)

ms – Range

(e.g. PMU Devices)

sec – Range

(e.g. DER Output Variations)

min – Range

(e.g. Service Restoration)

hour – Range

(e.g. Demand Response)

Day – Range

(e.g. Day-ahead Scheduling)

Year – Range

(e.g. Life of IT Asset)

Decade – Range

(e.g. Life of OT Asset)

Data Volume

(e.g. TBytes per Day)

Data Velocity

(e.g. 300,000 Data Objects/sec)

Data Variety

(e.g. Large Variety of Data

Object Types/Classes)

Data Validity

(e.g. Large Variety in Data

Object Shelf Life)

Data Veracity

(e.g. Large Variety of Data

Objects with different Data

Quality)

Telemetric Data

(e.g. in SCADA Systems;

normally in Historian)

Oscillographic Data

(e.g. in Power Quality Monitor;

normally in Historian)

Usage Data

(e.g. in Meter Data Management

System; normally in RDBMS)

Asynchronous Event Messages

(e.g. in Distribution Managem.

System; normally in RDBMS)

Meta Data

(e.g. in Geospatial Info System;

normally in RDBMS)

LISBON 2013 Building Business Value through Increased Situational Awareness

• Telemetry

• Oscillography

• Usage Data

• Asynchronous Event Messaging

• Meta-data

• Leveraging Asset

• Increased Reliability

• Increased Efficiency (Workforce)

• Increased Efficiency (Grid)

• Silo-busting

LISBON 2013 Situational Awareness & Analytics in Power Transmission – August 2003

Largest ever North American Power Grid Blackout on August 14, 2003 (after 4PM)

Affected 50 million people and more than 70GW of electrical load in OH, MI, NY, PA, NJ,

CT, MA, VT, and the Canadian provinces Ontario and Quebec. Some areas were left

without power for several days with rotating blackouts for up to 2 weeks.

Final NERC Report (7/13/2004)

Lack of situational Awareness of

Line Outages and degraded

Conditions (Group 1 Cause)

No effective Management of

Vegetation in Transmission

Rights-of-Way (Group 2 Cause)

No Provision of effective diagnostic

Support (Group 3 Cause)

DOE/FERC Report (2/2006)

Recommendation to establish a

Real-Time Transmission

Monitoring System (based on

Synchrophasor Measurement

Network)

Recommendation to develop

Computer-based operational and

Management Tools

LISBON 2013 Prior to the August 2013 Blackout

Generation, Demand, Interregional Power Flows on

August 14 before start of more severe part of Blackout

Scenario at 15:05PM

LISBON 2013 Evolution of August 2003 Blackout

Power Flows at 16:05:57, Prior

to the Sammis-Star Trip (25deg)

Power Flows at 16:05:58, After

the Sammis-Star Trip (12deg)Power Flows at 16:09:25

(45deg)

Power Flows at 16:10:44

(115deg)

Power Flows at 16:10:45

(115deg)

Changes in Direction of Power Flow during the Course of Blackout Events correlated

with severe Changes in Phase Angular Differences between Locations involved!

LISBON 2013 Divergent Phase Angle

LISBON 2013 Phase Angle Divergence – Time Series

LISBON 2013 Synchrophasor Measurement Networks

– The Real Big Data Challenge

TimePast (August 14, 2003) Present Future

15 PMUs 150 PMUs 1,000 PMUs

Generated Data

Data Volume

470 – 620 Million

Data Points per Day

Data Size

2.3 – 6.0

GByte per Day

Generated Data

Data Volume

4.7 – 6.2 Billion

Data Points per Day

Data Size

22.6 – 60.3

GByte per Day

Generated Data

Data Volume

31.1 – 41.5 Billion

Data Points per Day

Data Size

150.6 – 401.7

GByte per Day

LISBON 2013 Synchrophasor Measurement

Networks – The Real Big Data Solution

JPA

(IEC CIM Model) Integration Layer

Server Network I/O Layer

V/OD1

(IEC CIM)

Osiris Server

Network/Cloud

Data

Ingestion API

Data Streams

In-server

Analytics API

Sensor/Machines

(e.g. Synchrophasor)

Networks

(e.g. Transmission)

Hadoop/MapReduce

(e.g. AMI/GIS)

Server Network I/O Layer

R/Revolution Analytics

(e.g. Time Series)

Graph Analysis

(e.g. Suppl. Cost Opti.)

Complex Event Proc

(e.g. Real-Time Oper.)

Data Analysis

V/OD1

(IEC CIM)…

Transactional Apps

(e.g. Blackout Demo)

Analytical Apps

(e.g. R Desktop)

Client Network I/O Layer

Network/Cloud

LISBON 2013 JPA Performance Benchmarking Demo:

Linked NoSQL vs. Hibernate Postgres

Database Performance Benchmarking Test Results: Cost

Data Ingestion Test for IEC CIM

End Device Event Profile Objects

3 Years 5 Years T

Cost

$777k

$4,504k

Linked NoSQL

Hibernate Postgres

F = 5.8

10 Years

In-Database Mixed Workload Test for IEC CIM

End Device Event Profile Objects

3 Years 5 Years T

Cost Linked NoSQL

Hibernate Postgres

10 Years

$948k

$7,148k

F = 7.5

$13,758k

$1,378k

F = 10

$2,002k

$4,023k

F = 2 $2,943k

$7,276k

F = 2.5 $5,295k

$15,409k

F = 2.9

Assumptions: (i) 1 DB Server requires 2 App Servers (Linked NoSQL) and

(ii) 1 DB Server requires 5 App Servers (Hibernate Postgres)

LISBON 2013 Building Business Value through Increased Situational Awareness