Inventing%20an%20Energy%20Internet%20Concepts,%20Architectures%20and%20Protocols%20for%20Smart%20Energy%20Utilization

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Inventing an Energy Internet Concepts,
Architectures and Protocols for Smart Energy
Utilization
Fermi National Accelerator Laboratory Colloquium,
April 29, 2009
Lefteri H. Tsoukalas Purdue University
Consortium for the Intelligent Management of the
Electric Power Grid (CIMEG) Purdue University,
The University of Tennessee, Fisk University,
Exelon, TVA, ANL http//helios.ecn.purdue.edu/cim
eg
Research Sponsored by Grants from EPRI/DOD, NSF,
DOD, DOE
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Outline

• Global Energy Realities
• Conservation
• Smart Energy
• Energy Internet
• Examples/Applications
• Summary

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Global Energy Realities

• World demand for energy approximately 210
  million barrels of oil equivalent (boe) per day
• (7.5 boe 1 mtoe - metric ton of oil
  equivalent).
• World oil demand 85 million barrels of oil per
  day (MM bpd)
• Aggregate world supply 85 million barrels of
  oil per day (MM bpd)
• International markets allocate resources by price
• Need 5 excess capacity for a stable market
• Markets have difficulties directing capital
  towards infrastructural investments
• Are we witnessing the beginning of a series of
  oil-induced market crises?

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Oil Consumption Per Capita
USA 25 barrels/year per capita Japan/S.
Korea 15 barrels/year per capita China (2003)
1.7 barrels/year per capita India (2003) 0.7
barrels/year per capita
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Global Energy Growth

• Nearly 6.6 billion people with modern needs
• Growth in energy demand still has significant
  margins for growth
• 12 of the world uses 54 of all energy
• 33 of the world still has no access to modern
  energy
• The other 45 uses 1/4 of the energy consumed by
  the remaining 12
• Energy use by worlds richest 12
• U.S. 65 boe energy per person
• Japan 32 boe energy per person
• U.K. 30 boe energy per person
• Germany 32 boe energy per person

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Peak (Long Plateau) in Global Oil Production?
Peak 2005(?) 85 mm bpd By 2020
50 mm bpd
Bakhtiari, S. A-M. World Oil Production Capacity
Model Suggests Output Peak by 2006-07 , Oil and
Gas Journal (OGJ), May 2004
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70 of Remaining Reserves
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(No Transcript)
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Future Utopia Dystopia?
Resource Constraints
Financial Crisis
Climate Change
Energy Crisis
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First Do More with Less

• Conservation may be our greatest new energy
  discovery in the near future
• Smart energy can facilitate further convergence
  of IT, power, and, transportation infrastructures
• Smart energy can facilitate integrated
  utilization of new energy carriers
• H2, Alcohols, Biofuels
• Can harvest energy usually wasted
• Ambient energy MEMS

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Electric Power Grid

• Secure and reliable energy delivery becomes a
  pressing challenge
• Increasing demands with higher quality of service
• Declining resources
• The North American electric power grid is
  operating under narrower safety margins
• More potential for blackouts/brownouts
• Efficient and effective management strategy
  needed
• Current energy delivery infrastructure is a super
  complex system (more evolved than designed)
• Lack of accurate and manageable models
• Unpredictable and unstable dynamics
• Can we build an inherently stable energy network?

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US Electric Grid

• Evolved, not-designed
• Developed in the first half of the 20th century
  without a clear awareness and analysis of the
  system-wide implications of its evolution

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Energy Intelligent Systems

• Smart Energy Energy Intelligent Systems
• Smart energy extends throughout the electricity
  value chain
• Smart Generation
• Smart Grid
• Smart Loads (End Use)

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Smart Energy Distribution Management Systems

• Advanced Metering Infrastructure
• Supervisory Control and Data Acquisition (SCADA)
• Capacitor Bank Control

Singapore Smart Energy Vending
HPs Utility Integration Hub for real-time
service integration
Source HPs, Smart Energy Distribution
Management Systems, 2005
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Smart Grids

• Smart grids is an advanced concept
• Detect and correct incipient problems at avery
  early stage
• Receive and respond to a broader range of
  information
• Possess rapid recovery capability
• Adapt to changes and reconfiguring accordingly
• Build-in reliability and security from design
• Provide operators advanced visualization aids
• Most of these features can be found in the
  Information Internet
• The Internet is also a super complex system
• It is remarkably stable
• Can we find an Internet-type network for energy
  systems?

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An Energy Internet

• Benefits of an Energy Internet
• Reliability
• Self-configuration and self-healing
• Flexibility and efficiency
• Customers can choose the service package that
  fits their budget and preferences
• Service providers can create more profits through
  real-time interactions with customers
• Marketers or brokers can collect more information
  to plan more user-oriented marketing strategies
• The regulation agency can operate to its maximal
  capacity by focusing effectively on regulating
  issues
• Transparency
• All energy users are stakeholders in an Energy
  Internet

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Storage and Buffer

• Internet adopts a set of protocols to resolve
  conflicts caused by the competition over limited
  resources (bandwidth)
• What make these protocols feasible is the
  assumption that information transmitted over the
  network can be stored and retransmitted

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Energy Storage

• Similar protocols could be developed for the
  power grid in order to
• Resolve conflicts due to the competition over
  resources (peak demand), and
• Identify and contain problems locally
• IF electricity can be stored in the grid(!)
• Unfortunately, large scale storage of electricity
  is technologically and economically not feasible
• Solution a virtual buffer
• Electricity can be virtually stored if enough
  information is gathered and utilized

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Virtual Buffer Concepts

• Assume predictability of demand
• Based on demand forecast, the desired amount of
  electricity is ordered (by an intelligent agent
  on behalf of the customer) ahead of time
• The supplier receives orders and accepts them
  only if all constraints are met. Otherwise, new
  (higher) price may be issued to discourage
  customers (devices) from consuming too much
  electricity
• Price elasticity is used by the supplier to
  determine the amount of adjustment on price
• Once the order is accepted, from a customer's
  point of view, electricity has been virtually
  generated and stored

Period when electricity is virtually stored
Time
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Virtual Buffer Implementation

• Information can be used to achieve the virtual
  storage of energy
• Two keys for implementation
• know electricity demand for individual customers
  in advance
• Regulate demand dynamically
• Hardware
• An intelligent meter for every customer to handle
  the planning and ordering automatically
• Algorithms
• Demand forecast
• Dynamical regulation via price elasticity

An Intelligent Meter
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Example LAG
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Customer IDs
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Long- and Short-term Elasticity

• Long-term Elasticity
• Average elasticity in within a long period
  (moths, years)
• Usually is an overall index including a large
  number of customers
• Good for long-term strategic planning
• More reliable to estimate
• Short-term Elasticity
• Instant elasticity within a very short period
  (e.g., minutes, hours)
• Can be a local index for a particular customer
• Critical for control of the power flow
• Difficult to estimate

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Managing Short-term Elasticity

• Short-term price elasticity characterizes a
  particular customers nearly instantaneous
  responsiveness to the change of price
• Short-term elasticity can be estimated from
• Historical price-demand data
• Psychological models of customer energy behaviors
• The use of intelligent meters is important for
• Increasing short-term elasticity gt more
  effective for control
• Regulating customers behavior gt more reliable
  for prediction

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Example Approach

• Grid is viewed as polycentric and multilayered
  system
• Customer-driven
• Grid segmented by groups of customers (LAGs)
• Accurate predictions of nodal demand drive the
  system
• Optimal dispatch of units (storage)
• Plug and play tool TELOS

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Local Area Grid - LAG

• Defined as a set of power customers
• Power system divided into Local Area Grids each
  with anticipatory strategies for
• Demand-side management
• Dispatching small units
• Energy storage
• Good neighborly relations

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TELOS Design Requirements

• TELOS Transmission-distribution Entities with
  Learning and On-line Self-healing
• Local Area Grid (LAG)
• Customer-centric
• System Model
• Power System Calculations
• User Interface
• Automated Execution

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Examples of Customers in TELOS
KWh
Hourly data starting at 0000 Monday
Large Commercial /Industrial (LCI) Customer
Hourly Demand (KW-h) for a week
KWh
Residential (RSL) Customer Hourly Demand (KW-h)
for a week
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Intelligent Power Meter
Database
Historical data
Prediction Agent
Power Info
Decision Module
LAG Manager
Effectors
Actions
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TELOS Simulation
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Demand Forecast in TELOS
Argonne National Laboratory (ANL)
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Dynamic Scheduling via Elasticity
Transmission/ Distribution Agent
Power Prediction
Power Flow
Ordering
Customer with Intelligent Meter
N
Elasticity Model
Security Check
Pricing Info
Y
Power Flow
Elasticity Model
N
Capacity Check
N
Backup Power
Y
Scheduling
Y
Generation Agent
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Convergence of IT and Power

• Technological advancements
• More information/data is available
• Transmission and delivery system monitoring
  SCADA, .
• Smart grid/smart meter
• More analytical tools are available.
• Economics models
• Power system analysis tools
• Can we build around current technologies a more
  reliable and efficient infrastructure?
• Utilize complex systems theories (multi-agents)
• Software (information infrastructure) upgrade

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Open- vs Closed-Loop System
Open-Loop System
Storages(t)
Consumptionc(t)
Generationg(t)
Closed-Loop System w/ Anticipation
Pricing p()
Generationg(tt0)
Consumptionc(tt0)
Delivery System
Order f()
Equilibrium is reached at future state!
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Energy Internet Anticipation and Pricing

• Without anticipation

With anticipation and pricing
With anticipation
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Peak Demand and Pricing
average demand
demand (MW)
price (/MWh)
time of day
Source NE ISO Prof. G. Gross, UIUC
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Research Issues

• Multi-agent based methodology
• Asynchronous and autonomous system
• Demand Side Smart Meters
• Anticipation
• Programmable energy management
• Communication and negotiation
• Plug-ins
• Supply Side
• (distributed) Power system analysis
• (distributed) Pricing models
• Renewables (solar, PV, geothermal)
• Vehicle-to-grid

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Outstanding Issues

• Feasibility
• Can system reach equilibrium?
• Efficiency
• How much electricity can be conserved
  (negawatts)?
• Stability
• How does the system react in unexpected events?
• Scalability
• What if the size of the system increases?
• Cost
• How much investment is needed for upgrading
  current infrastructure and evolving towards
  energy internet?

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Summary

• An Internet-like energy network, an Energy
  Internet, representing the smart convergence of
  Power and IT, is a technically plausible next
  step
• Intelligent Systems can provide virtual energy
  storage (via anticipation)
• The Energy Internet may positively shape a
  sustainable future through more transparent
  energy relations
• All sources of primary energy will be needed to
  produce the most easily available energy we have,
  grid electricity
• Nuclear power has a special role as an important
  source of emissions-free electricity with some
  sustainability features
• Important nuclear physics advancements needed to
  enable global standards for future nuclear fuel
  cycles

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Extra SlidesLoad Identification and Wavelets

• Different types of load show a characteristic
  behavior on the change of wavelet coefficients
  with respect to scale
• The type of load is possible to be identified
  with a neural-wavelet approach

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Wavelet Decomposition
RLS
LCI
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Load Identification
LCI
RLS
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Structure of LAG
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LAG Manager

• Collects current demand and supply data
• Checks for grid stability based on predictions of
  individual customer demand and available pathways
• Makes decisions on when to dispatch local units
  and/or manage load based on demand/supply and
  contracts between customers and
  producers/providers
• Sends decisions to individual agents

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Interconnecting LAGs

• Reliable network connections with adequate
  redundancy
• Each LAG connecting to at least two neighboring
  LAGs
• Neighboring LAG Manager should be able to take
  over in case of local fault

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LAG Agent Hierarchy
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Agent Functionalities

• Customer Agent contains a neurofuzzy predictor
  to predict future demand
• Feeder Agent sums up predicted demands of all
  customers connected to the feeder, performs
  internal overload check
• Transformer Agent sums up predictions of all the
  feeders

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TELOS Implementation

• Distribution Agency Propagates Agents Through
  the Network
• Intelligent Meters Contract for Power in a
  Central Database
  

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Intelligent Power Meter
Database
Historical data
Prediction Agent
Power Value
Decision Module
LAG Manager
Effectors
Actions
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Anticipatory Control of Small Units
The objective at time k is to find an open loop
set of constrained control actions u(k) to drive
the plant outputs y(k) along a desired trajectory
with anticipated disturbances v(k).
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Logging Optimal Control Patterns

• Iterate to find the optimal control response
• Log the system trajectory at time 0
• Log the predicted error at time 1
• Log the optimized control action at time 0

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Logging Optimal Control Patterns

• Train a neural network to learn the optimal,
  tuned, anticipatory control response
• Example If the error will be negative and the
  current system trajectory is level, then decrease
  fuel flow rate.

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Anticipatory Control of Small Units
Smoothness of Control - Time Rate of Change of
Fuel Flow
Conventional Control

• 1/25th of Control Effort
• Reliability and Maintenance Benefits
• Energy Savings

Anticipatory Control