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Autonomy-Oriented Mechanisms for Efficient Energy Distribution


... Need exascale computing [7] Hard to provide distribution solutions in specific energy domains! ... like power gird, natural gas pipeline networks. – PowerPoint PPT presentation

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Title: Autonomy-Oriented Mechanisms for Efficient Energy Distribution

Autonomy-Oriented Mechanisms for Efficient Energy
  • Presenter Benyun Shi
  • Principal Supervisor Prof. Jiming Liu
  • Co-Supervisor CHEUNG, William Kwok Wai
  • Department of Computer Science
  • 11th PGDay
  • Mar. 15, 2010

  • The Energy Distribution Problem
  • World Energy Status
  • Challenges
  • Our research focus
  • The Autonomy-Oriented Mechanism
  • Four local behavior-based algorithms
  • Preliminary Simulations
  • Conclusion and Future Work

Energy Status (1)-- Uneven geographical
Middle East
The Worlds Proved Oil Reserves
The Worlds Proved NG Reserves
Data from the Oil and Gas Journal and
International Energy Agency.
Energy Status (2) -- Imbalanced energy
Total primary energy supply of the world from
1971 to 2007. (Adopted from Key World Energy
Statistics 2009)
Worlds total energy use from1965 to 2008.
Data from the British Petroleum.
The General Energy Distribution Problems
  • Efficiently, economically, and reliably
    distribute energy resources either worldwide or
    within a country/region.
  • Issues (how to)
  • Energy price e.g., electricity price in power
    grid 1
  • Distribution infrastructure investment e.g.,
    pipelines, railways 2
  • Cascading control or congestion management in
    Power grid 34
  • Energy markets e.g., world oil market and oil
    futures market
  • Logistics networks of energy resources 56
  • Distribute energy from suppliers to consumers
    under certain physical constraints.
  • Maintain reliable and secure energy distribution
  • Other issues

  • The energy distribution problems are complex in
    terms of
  • Energy supply/demand may dynamically changing
  • Endogenously increase by population or economy
  • Exogenously severe weather
  • The relationships between energy suppliers and
    consumers may evolve over time
  • The information may only be partially available
  • Due to private issues or competitions
  • Suppliers/consumers make decisions based on their
    own benefits

Related Work (1)
  • Systems Dynamics Approach (or Macro-modeling)
  • Issues
  • Understand the relationships among different
    component in energy system
  • Determine roles of energy system in social,
    economic, and environmental systems
  • Simulate the real world
  • Drawbacks
  • Represent relationships based on statistical
  • Hard to represent dynamics, e.g., technology
  • Need exascale computing 7

Predictions can be done in macro-level
Hard to provide distribution solutions in
specific energy domains!
Related Work (2)
  • Static Network Optimization Approach (or
  • Optimize certain objective on static networks
  • Mathematical Optimization
  • E.g., U.S. integrated energy system 56
  • E.g., Economic dispatch in natural gas networks
  • Dynamics-driven Network Optimization Approach
  • Form optimal networks based on certain dynamics
    on the network
  • The by nature open, distributed, and dynamically
    evolving energy distribution system need
    decentralized approaches

Our Research Focus
  • Autonomy-Oriented Mechanism for Energy
  • Entities
  • Represent either suppliers or consumers, or a
    group of them
  • Interactions
  • Entities interact with each other as well as
    their environment to collect information
  • Behavioral rules
  • Exploration behavior
  • Regulation behavior
  • Objectives
  • to characterize the underlying mechanisms of the
    energy distribution system through local
    interactions between entities with different
    behavioral rules
  • to provide scalable distribution solutions

Self-Organized Mechanism
Starting with a Static Energy Distribution Problem
  • A set of energy suppliers/consumers with constant
    energy supply/demand
  • Assume that total supply total demand
  • Represent energy distribution cost among energy
    suppliers and demander as a predefined matrix
  • Symmetric cij cji
  • Triangle inequality cij cjk ? cik
  • Objectives
  • To meet the consumers demand
  • To minimize the global distribution cost
  • Questions to be tackled in this work
  • How does an optimal energy distribution network
    can energy through local dynamic of
    supplier/consumer entities?
  • What kind of local behaviors are crucial for
    achieving final optimal energy distribution

Entities Behaviors
  • Exploration Behavior (Self-avoiding random walk)
  • Explore but do not memorize (Algorithm 1 and 2)
  • Explore and memorize for future utilization
    (Algorithm 3 and 4)
  • Regulation Behavior (decide whom to trade with)
  • First come first serve rule (Algorithm 1)
  • Competition (Algorithm 2 and 3)
  • Proactively send request (Algorithm 4)

Hypothesis by memorizing information for future
utilization, it is much easier to find a path
with small distribution cost.
Hypothesis by proactively regulating trading
partners and sending requests, it is more likely
to find appropriate partners than passively
trading with visitors.
Four Algorithms
  • Algorithm 1 Self-avoiding Random Walk with
  • Explore but not memorize
  • Algorithm 2 Self-avoiding Random Walk with
  • Explore but not memorize
  • Algorithm 3 Self-avoiding Random Walk with
    Information Sharing
  • Explore and memorize for future utilization
  • Algorithm 4 Self-avoiding Random Walk with
    Information Passing
  • Explore and memorize for future utilization
  • Proactively regulate trading partners

Simulations -- Measurements
  • Distribution Cost
  • Global Cost of Energy Flow Network
  • Per Unit Cost of Energy Flow Network
  • Scalability
  • When the number of suppliers/consumers increase,
    can the autonomy-oriented mechanism remain

Distributed quantity along link lij
Observations (1)
Validate hypothesis about exploring with
memory Validate hypothesis about proactively
regulating trading partners
Observations (2)
Scalability The per unit distribution cost of
energy flow network of Algorithm 4 approaches to
optimal solution as the number of nodes increase
form 10 to 1000.
  • The autonomy-oriented mechanism study the energy
    distribution problem from a bottom-up viewpoint
  • Global objectives can be approximately reached
    through local interactions of behavior-based
    autonomous entities
  • Appropriate exploration and regulation behaviors
    play important roles
  • Scalability makes it possible to deal with
    large-scale energy distribution problems, like
    smart grid.

Future Work
  • It can be naturally extended to deal with open,
    distributed, as well as dynamically evolving
    energy distribution problems.
  • How does the energy flow network evolve in an
    open, unpredictable energy distribution system?
  • What kind of local dynamics between supplies and
    consumers can improve the robustness and
    stability of the energy distribution system?
  • What kind of energy trading mechanism (market)
    can be formed? What are the critical factors
    for the stability of the market?

  • 1 M. Bjørdal. Topics on Electricity
    Transmission Pricing. PhD thesis, Norwegian
    School of Economics and Business Administration,
    Bergen, 2000.
  • 2 Oil Division. A compendium of electric
    reliability frameworks across canada. Technical
    report, Petroleum Resources Branch, Canada, 2008.
  • 3 A. E. Motter. Cascade control and defense in
    complex networks. Physical Review Letters,
    93(9)098701, 2004.
  • 4 F. Schweppe, M. Caramanis, R. Tabors, and R.
    Bohn. Spot Pricing of Electricity. Kluwer
    Academic Publishers, Norwell, Massachusetts,
  • 5 A. Quelhas, E. Gil, J. D. McCalley, and S.
    M. Ryan. A multiperiod generalized network flow
    model of the U.S. integrated energy system Part
    I - model description. IEEE Transaction on Power
    Systems, 22(2)829836, May 2007.
  • 6 A. Quelhas and J. D. McCalley. A multiperiod
    generalized network flow model of the U.S.
    integrated energy system Part II - simulation
    results. IEEE Transaction on Power Systems,
    22(2)837844, May 2007.
  • 7 H. Simon, et al. Modeling and simulation at
    the exascale for energy and the environment.
    Technical report, Report on the Advanced
    Scientific Computing Research Town Hall Meetings
    on Simulation and Modeling at the Exascale for
    Energy, Ecological Sustainability and Global
    Security (E3), 2007.
  • 8 K. T. Midthun, M. Bjorndal, and A.
    Tomasgard. Modeling optimal economic dispatch and
    system effects in natural gas networks. The
    Energy Journal, 30(4), 2009.

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