Growing Information Intensity of Energy 2014
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Growing Information Intensity of Energy Peter Evans, PhD Vice President Center for Global Enterprise April 30, 2014 People Machines Data GT Clean Energy Series GT Venture Lab
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All aspects of energy are becoming more information intensive, and will likely become more so in the future. This is true at the machine level, facility level, fleet level and network level. The consequences are significant including the potential for more efficient operations, lower transaction costs, a shift from reactive to predictive maintenance, better safety and finally new regulatory dynamics with new levels of transparency for monitoring and enforcement.
Transcript of Growing Information Intensity of Energy 2014
Growing Information Intensity of Energy
Peter Evans, PhD
Vice President
Center for Global Enterprise
April 30, 2014
People
Machines
DataGT Clean Energy SeriesGT Venture Lab
Information failures = Market failures
• some, or all, of the participants in an economic exchange do not have perfect knowledge
• one participant in an economic exchange knows more than the other, i.e. the problem of asymmetric, or unbalanced, information
Information failure exists when:
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History of the energy sector
Chernobyl Control Panel
Chernobyl NPP, Control Room No. 2 by СмdяСояd
Plagued by information failures
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Spectrum of information availability
Consequences of scarcity
Information scarce Information rich
datadata
Consequences of greater abundance
Reactive maintenance Predictive maintenance
Less efficient operations
High transaction costs Lower transaction costs
More efficient operations
Accident prone Less accident prone
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Why did Google buy Nest?
or
To access data?
To move into devices?
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Forces for changeInformation (packaged as bits) about energy is growing
Source: John Canny, “Designing with Data”, UC Berkeley, EECS, July 2013
Benefits
1. New sources of valuable data at
- machine level - facility level- fleet level - network levels
2. Expanded remote monitoring and analysis
3. Greater potential for fleet optimization
4. Shift from reactive to predictive maintenance
5. Rise of matching digital platforms
- reduce transaction costs- enhance expert networks- expand social --- behavioral change
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Global fleet of major power plants
Fuel type
~6,000 GWs
132,900 units
Source: UDI World Electric Power Plants Database, Platts, 2012
Total
% GW % Units
Machine level
Facility level
Fleet level
Network level
Consider information flows
Large information opportunity and challenge
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52million man-hours to service annually*
Major power plant inspections
• 6 field engineers & 24 craft labor
• Tools, crane, scaffolding,
• 25 – 30 day outage duration
Easier and faster access to critical information
Digitized work
instructions
+
Platform for
collaborationPower plant manuals
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Wireless telemetry + big data analytics
A modern F1 car is fitted
with ~130 sensors, which
send enough information
to fill several telephone
books by the end of a
two-hour race.
Real-time information on
the car’s engine, clutch,
gearbox and tires is
radioed to pit crew and
mission control.
Pushing the boundaries of engineering
Source: Roger Baird, “Telemetry: Every second counts in race for first place”, Financial Times, October 31, 2012.
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Types of platforms
Sources: Gawer, A. Platform Dynamics and Strategies; Baldwin, C. and C. Woodward. The Architecture of Platforms.
Examples
•Credit card payment networks,
shopping malls, search engines
Market Networks
Description
• Products, services, firms, or
institutions that mediate transactions
btw groups of agents
# of participants
• Multi-sided markets
Double-sided
Markets
3
Description
• Products, services, techs that serve
as foundation for other firms to build
complementary products
Examples
•Operating systems; gaming; cloud
Industry
# of participants
• Large clusters or ecosystems of
interdependent firms
Industry
Platforms
2
Description
• Component & subsystem assets
shared across family of products
Examples
•Sony, Boeing/ automobile industry
Product
# of participants
• Single firm/ several firms within
supply chain
Internal / Supply
Chain Platforms
1
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Digital platform dynamics
Platform provider
Components Rules Architecture
Users
Demand side
Users
Supply side
Match service providers to customers
Source: Geoffrey Parker 2009
Leverage networks
Mobile Device Platform Ecosystem
Source: Rahul Basole, 2009
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Solar… internet+ digital imaging
“Solar designers” use satellite
imagery and a sophisticated
set of algorithms to remotely
design solar panel systems.
Speed
Lower cost
Greater reach
Source: Craig Rubens, “Sungevity: Where Solar Rooftops Meet the Internet” Gigaom, April 21, 2008.
From offices in the Bay Area can
size systems in Indiana or India
Reduce inefficient truck rolls
Customer quote within 24 hours
Benefits
Not just panel costs are falling…
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Platform plays in energy efficiency?
Nearly 40 percent of U.S. energy consumption in 2012 was by residential and commercial buildings
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Information in regulatory dynamicsRise of low cost leak detection and mapping technology
Methane leaking from the fracked oil fields of
Kern County, California measured with the
Picarro spectrometer
New Monitoring Capabilities
Accurate
Low cost
Easy to deploy
Mobile
Reports data directly into
Google maps
CH4
Source: Picarro, 2014
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Conclusion
Information failures have historically plagued the energy sector
Information streams about energy is growing and cost is falling Machine level
Facility level
Fleet level
System level
Growing energy intensity can yield important benefits More efficient operations
Lower transaction costs
Shift from reactive to predictive maintenance
Enhanced safety
New regulatory dynamics including new levels of transparency for
monitoring and enforcement
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Growing Information Intensity of Energy
Peter Evans, PhD
Vice President
Center for Global Enterprise
April 30, 2014
People
Machines
DataGT Clean Energy SeriesGT Venture Lab
