1. EE392N Intelligent Energy Systems: Big Data and Energy April 2, 2013
Seminar Course 392N ● Spring2013
EE392N Intelligent Energy Systems: Big Data and Energy April 2, 2013
Dan O’Neill
Dimitry Gorinevsky
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

2. Intelligent Energy Systems: Big Data O’Neill and Gorinevsky
Today’s Program
Class logistics
Introductory lecture on Intelligent Energy Systems: Big Data and Energy
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

3. Intelligent Energy Systems: Big Data O’Neill and Gorinevsky
Instructors
Daniel O’Neill, Consulting Professor in EE
Network Management and Machine Learning in energy
Executive and Startup experience
Dimitry Gorinevsky, Consulting Professor in EE
Big Data Analytics for energy and aerospace
Information Decision and Control Applications in many industries
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

4. Intelligent Energy Systems: Big Data O’Neill and Gorinevsky
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
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

5. Intelligent Energy Systems: Big Data O’Neill and Gorinevsky
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
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

6. Intelligent Energy Systems
Analytics: Software Function
Computing
and
Communication
Energy
System
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

7. Intelligent Energy Systems: Big Data O’Neill and Gorinevsky
Power systems
Physical systems in the focus of the class
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

8. 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
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

9. Intelligent Energy Systems: Big Data O’Neill and Gorinevsky
Interconnect
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

10. Nearer Term Initiatives
Renewables
Demand Response
Power Flow Management
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

11. Renewables: The System Problem
National Renewable Energy Laboratory
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

12. Intelligent Energy Systems: Big Data O’Neill and Gorinevsky
Demand Response
Campus and Buildings
Home
AMI – Advanced Metering Infrastructure
EMS – Energy Management System
Smart devices
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

13. Power Flow Management Adjusting supply Routing power flow
Managing demand
for aggregated users
for commercial buildings
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

14. Computing and Communications
Source IPS
energy subnet
Intelligent Power Switch
IES
Intelligent Energy Network
Generation
Conventional Internet
Transmission
Distribution
Load
Conventional Electric Grid
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

15. Intelligent Energy Systems: Big Data O’Neill and Gorinevsky
Power and Data Flow
Generators
ISO
Transmission ’s KV
Fiber and uWave
Substations
Limited visibility
Distribution 10-20KV to 120V
Because of industry structure, peak prices can be very high.
Generators are de-regulated and the Cal-ISO maintains a market for D/S of energy. At the margin suppliers can bidup prices to very high levels
Promise of AMI
Industrial
Commercial
Business
Residential
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

16. Intelligent Energy Systems: Big Data O’Neill and Gorinevsky
Today…
Integrated platforms
IBM, Oracle. …
GE, Seimens, SAIC,…
Itron, Cisco, …
But
Analytic tools
Vertical analytic products
Data integration
Visualization
Analytics
Elastic Computing
Data
Comm. Platform
Networking
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

17. (Intelligent Functions)
Analytics
Tablet
Smart
phone
Communications
Internet
Computer
Energy Application
Presentation Layer
Analytics
(Intelligent Functions)
Business Logic
Database
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

18. Feedback Control Functions
Closed loop update
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

19. Monitoring and Decision Support Functions
Open-loop functions
Results are presented to an operator
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

20. Decision Support Applications
Supply
Demand
Asset Management
Power Quality Monitoring
Outage Management
Risk Management
Renewables Integration
forecasting
Energy Efficiency Monitoring
Revenue Protection
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

21. Monitoring Approaches
Most used approach is Exceedance Monitoring
Example: grid frequency deviation from 60Hz
Plant/System
Data
Monitoring Function:
Exceedance
Detected
Anomalies
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

22. Intelligent Energy Systems: Big Data O’Neill and Gorinevsky
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
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky

23. Intelligent Energy Systems: Big Data O’Neill and Gorinevsky
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
ee392n - Spring Stanford University
Intelligent Energy Systems: Big Data O’Neill and Gorinevsky