EE392N Intelligent Energy Systems: Big Data and Energy€¦ · Intelligent Energy Systems: Big Data and Energy April 2, 2013 Dan ONeill Dimitry Gorinevsky Seminar Course 392N ...

EE392N Intelligent Energy Systems:

Big Data and Energy

April 2, 2013

Dan O’Neill Dimitry Gorinevsky

Seminar Course 392N ● Spring2013

ee392n - Spring 2013 Stanford University

Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

1

Today’s Program

• Class logistics

• Introductory lecture on Intelligent Energy Systems: Big Data and Energy



2

Instructors

• Daniel O’Neill, Consulting Professor in EE

– Network Management and Machine Learning in energy

– Executive and Startup experience

– www.stanford.edu/~dconeill

• Dimitry Gorinevsky, Consulting Professor in EE

– Big Data Analytics for energy and aerospace

– Information Decision and Control Applications in many industries

– www.stanford.edu/~gorin



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http://www.stanford.edu/~dconeill

http://www.stanford.edu/~gorin

Class Logistics

• 1 unit graded or CR/NC

– Attendance

– Pre-requisites

– Submitting an one page report on in the end

• Weekly on Tuesdays

– The room and time might change!

– Watch the class website announcements

• Introductory lecture - today

• Nine lectures by industry leaders



4

Planned Lectures

April 2, Introductory Lecture, Dan ONeill and Dimitry Gorinevsky, Stanford

April 9, Feature Discovery in Energy Data, Sachin Adlakha, Ayasdi

April 16, The Industrial Internet, Marco Annunziata, GE Energy

April 23, Risk Analytics Applications and Vision, Chris Couper, IBM

April 30, Power Plant Big Data Optimization, Jim Schmid, GE Energy

May 7, Monitoring of T&D Systems, Paul Myrda, EPRI

May 14, Energy Insights from Big Data, Drew Hylbert and Jeff Kolesky, OPower

May 21, AMI Data Management and Analysis, Aaron DeYonker, eMeter/Siemens

May 28, Smart Grid Analytics, Ed Abbo and Houman Behzadi, C3 Energy

June 4, Data Center Energy Management, Manish Marwah, HP

Intelligent Energy Systems: Big Data

O’Neill and Gorinevsky ee392n - Spring 2013 Stanford University

5

Computing and

Communication

Intelligent Energy Systems



6

Energy System

Analytics: Software Function

Energy Systems

• Power systems

• Physical systems in the focus of the class



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The Traditional Grid

• Worlds Largest Machine! – 3300 utilities

– 15,000 generators, 14,000 TX substations

– 211,000 mi of HV lines (>230kV)

– SCADA control

– Mostly unidirectional

• Capacity constrained graph



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Interconnect



9

• Renewables

• Demand Response

• Power Flow Management

Nearer Term Initiatives



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Renewables: The System Problem

National Renewable Energy Laboratory



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Demand Response

Campus and Buildings Home

• AMI – Advanced Metering Infrastructure • EMS – Energy Management System • Smart devices



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Power Flow Management

• Adjusting supply

• Routing power flow

• Managing demand

– for aggregated users

– for commercial buildings



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Computing and Communications

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Conventional Electric Grid

Generation

Transmission

Distribution

Load

Source IPS

energy subnet

Intelligent Power

Switch

IES

IES

IES IES

Intelligent Energy Network

Conventional Internet



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Power and Data Flow

Generators

Transmission 275-400’s KV

Industrial Commercial Business Residential

Distribution 10-20KV to 120V

ISO

Substations

Fiber and uWave

Promise of AMI

Limited visibility



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http://images.google.com/imgres?imgurl=http://www.caer.uky.edu/images/powergen/power.jpg&imgrefurl=http://www.caer.uky.edu/powergen/home.shtml&h=254&w=250&sz=9&hl=en&start=55&um=1&usg=__g1OV0_NzYrpqRkMei5gKVkiG_GU=&tbnid=6hxp3exdR6UpOM:&tbnh=111&tbnw=109&prev=/images?q=power+generation&start=40&ndsp=20&um=1&hl=en&rlz=1T4SKPB_enUS239US239&sa=N

Today…

• Integrated platforms

– IBM, Oracle. …

– GE, Seimens, SAIC,…

– Itron, Cisco, …

• But

– Analytic tools

– Vertical analytic products

– Data integration



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Visualization

Analytics

Elastic Computing

Data

Comm. Platform

Networking

Business Logic

Analytics

ee392n - Spring 2013

Stanford University


O’Neill and Gorinevsky 17

Database

Presentation Layer

Computer

Tablet Smart

phone

Internet Communications

Energy Application

Analytics

(Intelligent Functions)


Stanford University



Feedback Control Functions

• Closed loop update

Physical system

Measurement System Sensors

Control Logic

Control Handles Actuators

Plant


Stanford University



Monitoring and Decision Support Functions

Physical system

Monitoring & Decision

Support

Physical

system

Measurement System, Sensors

Data Presentation

• Open-loop functions - Results are presented to an operator

Decision Support Applications

Supply

• Asset Management

• Power Quality Monitoring

• Outage Management

• Risk Management

• Renewables Integration – forecasting

Demand

• Energy Efficiency Monitoring

• Revenue Protection


22 Intelligent Energy Systems: Big Data

O’Neill and Gorinevsky

Plant/System Data

Monitoring Approaches

• Most used approach is Exceedance Monitoring

• Example: grid frequency deviation from 60Hz


Stanford University



Monitoring Function: Exceedance

Detected Anomalies

Big Data

• Tens of Tb to Pb range

• Sequential or parallel processing of data chunks. Each chunk fits into memory

• Much of earlier work involved ‘soft’ data

• Energy: Machine-to-Machine (M2M) data

• Monitoring and decision support applications

• Data mining



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Data Mining Functions

• Data Exploration

– Performed interactively

• Model Training

– Known as system identification in control

• Model Exploitation

– Estimation, eg, forecasting

– Decision support, eg, monitoring

– Control, eg, embedded optimization



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EE392N Intelligent Energy Systems: Big Data and Energy€¦ · Intelligent Energy Systems: Big Data and Energy April 2, 2013 Dan ONeill Dimitry Gorinevsky Seminar Course 392N ...

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