Autonomy-Oriented Mechanisms for Efficient

Energy Distribution

Presenter: Benyun Shi

Principal Supervisor: Prof. Jiming LiuCo-Supervisor: CHEUNG, William Kwok Wai

Department of Computer Science

11th PGDayMar. 15, 2010

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Outline

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

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Energy Status (1)-- Uneven geographical availability

The World’s Proved Oil Reserves

56% 75%

The World’s Proved NG Reserves

Data from the Oil and Gas Journal and International Energy Agency.

Middle East Eurasia

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Energy Status (2) -- Imbalanced energy utilization

World’s total energy use from1965 to 2008.Data from the British Petroleum.

Total primary energy supply of the world from 1971 to 2007. (Adopted from Key World Energy Statistics 2009)

(Mtoe)

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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

[3][4] Energy markets: e.g., world oil market and oil futures market; Logistics networks of energy resources [5][6]

Distribute energy from suppliers to consumers under certain physical constraints.

Maintain reliable and secure energy distribution system Other issues

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Challenges

The energy distribution problems are complex in terms of Energy supply/demand may dynamically changing

Endogenously: increase by population or economy development;

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

benefitsOpen, highly distributed, and dynamically evolving

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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 data; Hard to represent dynamics, e.g., technology

innovations; Need exascale computing [7]Hard to provide distribution solutions in specific energy domains!

Predictions can be done in macro-level

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Related Work (2)

Static Network Optimization Approach (or Micro-modeling) Optimize certain objective on static networks Mathematical Optimization

E.g., U.S. integrated energy system [5][6] E.g., Economic dispatch in natural gas networks [8]

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

Centralized approach

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Our Research Focus

Autonomy-Oriented Mechanism for Energy Distribution 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

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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 CMatrixn*n={cij} 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 network?

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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.

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Four Algorithms

Algorithm 1: Self-avoiding Random Walk with First-come-first-serve; Explore but not memorize

Algorithm 2: Self-avoiding Random Walk with Competition; 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

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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 efficient?

Distributed quantity along link lij

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Observations (1)

0

5

10

15

20

25

30

35

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10 50 100 500 1000

Mill

ion

s

Number of Nodes

Glo

ba

l Co

st

First-Come-First-Serve Competition Information Sharing

Information Passing Optimal

0

1

2

3

4

5

10 50 100 500 1000

Mill

ion

s

Validate hypothesis about exploring with memory

Validate hypothesis about proactively regulating trading partners

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Observations (2)

0

200

400

600

800

1000

1200

1400

1600

10 50 100 500 1000

Number of Nodes

Per

Un

it C

ost First-Come-First-Serve

Competition

Share Information

Pass Information

Optimal

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.

0

20

40

60

80

100

120

140

160

180

10 50 100 500 1000

Pass Information

Optimal

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Conclusions

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.

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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?

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References[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, 1988.[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):829–836, 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):837–844, 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.