Nano, P2P,

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Nano, P2P, Innervation of Energy Infrastructure
Roger Anderson Albert Boulanger The Earth
Institute Columbia University
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Innervation
Distributed Business Adaptive Learning
Simulation Micro Real Options Lean
Workflows Peer-2-Peer
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Digital Convergence
Source Larry Smarr www.jacobsschool.ucsd.edu/lsm
arr/talks
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Plenty of Room at The Bottom Richard
Feynman describing nanotechnology opportunity
Tightly Integrates Levels

• Silicon embeds Adaptive Stochastic Control at
  every enterprise level
• Real Options similar Math to Adaptive Stochastic
  Control

Business
Engineering
Dust Inc. Smart Chips
From Saputelli, L., et al.,, SPE Paper 64621,
2000.
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Examples of Plenty of Room at the Bottom
Shock
Tilt
Motion
Vibration
CPU
Smart Dust
Wi-Fi GPS Web Server
Temperature
Humidity

• Every widget knows its Real Options thru embedded
  Micro Options
• Everything Geo-located
• Peer-2-peer Wireless to/from each component
• Self-healing, aware networks
• Dream Phase uses simulations to Learn how to
  optimize the the system and compute Micro Options
  24/7
• Genetic algorithms is discovery mechanism

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Real Options Have Deep Connections with Nature

• Nature minimizes action -- This principle holds
  true in classical and quantum physics as well
• Quantum physics is governed by the Uncertainty
  Principle. This nondeterministic rule induces an
  uncertainty manifold for the principle of least
  action
• The mathematics of this manifold can be viewed
  as the same type of problem mathematically as
  Real Options (Ito Stochastic Calculus and
  Information Geometry)
• The marketplace is deterministic -- high
  dimensional chaos
• time delay dynamics generates high dimensional
  attractors
• Spooky Action at a Distance occurs in markets
• Wormholes exist in information space -- arbitrage
  violations
• Stochastic Adaptive Control in both cases can use
  Dynamic Programming as an Optimization method
• We use Reinforcement Learning (a type of dynamic
  programming) for Innervation.
• Dynamic Programming has been used in Adaptive
  control of quantum systems
• Quantum Computers being thought of for Real
  Options valuation
• http//quantum.cs.columbia.edu

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Example of Micro Options
Wireless web services, P2P system, with Grid
computing laid on top, to push Real Options and
adaptive learning algorithms to the last mile
The GRID
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Examples of Innervation SignalQuest Alien
Technology
Long RangeBackscatter RFID System Gillette
has just bought a HALF billion -- to eliminate
shipping theft
I/O capability enables the tags to accumulate
data as a remote sensor, and act as long-range
RF-controlled actuator
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Microsoft Smart Personal Objects Technology
SPOT converts everyday objects into information
delivery devices that are time location and
context relevant
DirectBand Network continuous broadcast across
the US and Canada for SPOT
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Align Nano Micro Innervation with Overall
Business Improvement
Target business capabilities most in need of
Fixing
Identify Real Options
Estimate costs, benefits, risks
Monitor w/ Tracking Metrics
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Invest in Downward Distribution of Operational
Business Drivers
Technology
Processes
People
Improved Business Performance
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Example CapitalOne

• Customization for the masses
• Right product to right customer, at right time
  and at right place
• test and learn process
• dynamic programming to identify action plans
  optimal decision paths

Reactions from Competitors Were already doing
that, It cant be done, Too expensive, Data not
good enough, Were different, Its too
Complicated.
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Lean Energy Economic Model
Flow Engineering Design Model
Quality Subsurface Reservoir Model
Lean Mean
Uncertainty
Six Sigma
Modular Flexible
Win/Win
People
Value Real Options Economic Model
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Lean Energy Economic Model
Conceptual Design Definition
Hub Spoke Costs Topside Moorings Drilling
Risers Tiebacks Sub-sea completions Pipelines
3-D Solid Model
Supportability
Parts Supply Chain Models
Assembly Definition
Virtual Manufacture
Subsurface Reservoir Uncertainty Reserves Sizes
Rates, Volumes Compartmentalization Water/Oil/Gas
Ratios Injection Plan Satellite Reservoir
Distances
Production Scenarios
Configurations
Systems Analysis
Modularization
Virtual Plant Operations
Constraints Reservoir Uncertainty Feeds Design
Responses via Real Options
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Innervation of the Global Electric Grid
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Given enough ANTS, you can move a mountain!
david.chassin_at_pnl.gov
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How the Innervated Decision Support Threat
Simulator Responds to Multiple Cascading Threats
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Problem Begins at Canadian - Maine Border
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Causes Buffalo Surge - Spreads to All of Northeast
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Power Shortage Spreads over whole East Coast
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New York has particular Difficulty
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DSTS has Learned Responses to Thousands of
Threat Combinations, some similar to this one
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Foundation is the Simulation Model of the Power
Grid -- This one from PowerWorld
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EPRIs Distribution Engineering Workstation
directs Remediation
Data Inputs
AM/FM System
Engineering data
Visualization of Results
Planning
Customer Information
D E W
Load Research Statistics
Large Customer kW/kVAR
Design
SCADA/Switch Positions
Operations
Outage Reports
Landbase data
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Power Flow is Solved for Continuously by DEW
Power Flow
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How DSTS Learning System Works with DEW
Inputs
Disturbances to the Grid

Critic Functions (Human Critique, Performance
Metrics, Learning Matrices
VQ/RL DEW Simulations used to Learn how to
React to Disturbances
Externalities Human-in-the-Loop Performance
(e.g. scheduling)
DSTS Controller
Real Grid
Recommended Actions
Inputs
Outputs
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Event 2 Transformer Fire at Power Plant
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Problem Spreads to Multiple, Cascading Local
Events
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DSTS leads to Remedial Actions using DEW
Green Excess Generating Capacity to bring On
Line Orange In Trouble Red Outages Blue OK
the Innervated Electric Grid
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DSTS leads to Actions from its Learnings
Problem Expands to Adjoining Districts and
Networks
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DSTS Recognizes Regional Problems and
Coordinates Remediation
But entire Electric Grid must be Innervated
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Nano Technologies might solve Inertia Problems
of the Energy System e.g. the Stranded Gas Problem
Giant Subsea Margarita Machine produces Gas
Oil Hydrate Slurries to load directly to Tankers