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Analysis of Energy System by Energy Model Formulated as Multiagent Simulation

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Actor-Critic Method of reinforcement learning. Formulation of Agent-based Model ... This case can be reduced to a simple one-dimension search problem ... – PowerPoint PPT presentation

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Title: Analysis of Energy System by Energy Model Formulated as Multiagent Simulation


1
Analysis of Energy Systemby Energy
ModelFormulated as Multi-agent Simulation
  • IEW2005July 5th-7th, 2005
  • Yumiko WATANABE, Takeshi SHINOHARA,
  • Taketo HAYASHI, Yasumasa FUJII, Kenji YAMAJI
  • the University of Tokyo, Japan

2
Outline of Presentation
  • Objectives
  • Methodology -Formulation of models-
  • Common structure of our global energy models
  • Formulation of conventional optimization energy
    model
  • Formulation of new agent-based energy model
  • Analysis results
  • 4 case settings for calculations
  • Difference of results between 4 cases
  • Summary

3
Outline of Presentation
  • Objectives
  • Methodology -Formulation of models-
  • Common structure of our global energy models
  • Formulation of conventional optimization energy
    model
  • Formulation of new agent-based energy model
  • Analysis results
  • 4 case settings for calculations
  • Difference of results between 4 cases
  • Summary

4
Objectives
  • To develop agent-based global energy model
  • And
  • To conduct preliminary analysis
  • Conventional global energy model
  • Formulated as optimization model
  • Suitable to analyze global energy policies
  • Problem Unsuitable to analyze conflicts of
    domestic policies such as energy security and
    emission trading
  • ?New formulation of energy model is needed

5
Outline of Presentation
  • Objectives
  • Methodology -Formulation of models-
  • Common structure of our global energy models
  • Formulation of conventional optimization energy
    model
  • Formulation of new agent-based energy model
  • Analysis results
  • Case settings for calculations
  • Calculation results
  • Summary

6
Common Structureof Global Energy Models (1)
  • To calculate energy supply system under given
    demand based on SRES B2 Scenario
  • Succeeding DNE21 model (Fujii and Yamaji, 1998)
  • Geographical coverageWhole world disaggregated
    into 54 regions, 82 nodes
  • Time horizon (original) 2000-2100 at 10-year
    intervals

City nodes (production, conversion,
consumption) Production nodes (production,
conversion)
Geographical Coverage
7
Common Structureof Global Energy Models (2)
8
Formulation of Conventional Energy Model
  • Formulated as optimization calculation
  • Objective to minimize global energy system cost
  • Huge but simple linear programming
  • Facility costs
  • Operation costs
  • Efficiency data
  • Other factors
  • Resource reserve
  • Demands

Input Data
  • Objective function
  • Constraints
  • Resource constraints
  • Balance constraints

Optimization Calculation
Output Variables
  • Production
  • Conversion
  • Transportation
  • Consumption
  • Facility
  • CO2 emission

9
Formulation of NewAgent-based Global Energy Model
  • Agents
  • Have objective to maximize own reward
  • Find strategy through iterated calculation
    (reinforcement learning)
  • Take action up to the strategy
  • Market
  • Allocate energy system to agents with
    optimization calculation

AGENT
AGENT
MARKET
Input Data
Strategy
Act
AGENT
Update
Allocating Calculation
Objective
Reward
Output Variables
10
Formulation of NewAgent-based Global Energy Model
  • Assumption for preliminary analysis
  • Target year 2010 (static decision making)
  • Focused attention to Oil trading
  • Agents Groups of regions (ex. OPEC, EU, )
  • Objective To minimize their energy system cost
  • Strategy To manipulate oil trading priceTo
    charge premium on oil export / tariff on oil
    import

Premium
Agent 2
Tariff
Region A
Region E
Region G
Region B
Region D
Region F
Region C
Region H
Agent 1
11
Outline of Presentation
  • Objectives
  • Methodology -Formulation of models-
  • Common structure of our global energy models
  • Formulation of conventional optimization energy
    model
  • Formulation of new agent-based energy model
  • Analysis results
  • 4 case settings for calculations
  • Difference of results between 4 cases
  • Summary

12
Case Settings for Calculations
  • 4 cases with different sets of agents

1 agent
2 agents
6 agents
Regional names were given for convenience (not
corresponding to the actual groups of nations)
8 agents
13
Calculation ResultsProgress of Learning Oil
Price Strategies
  • The price strategy in 1 agent case is zero
  • As strategical act,
  • OPEC charges premium price on its export
  • EU and Japan/Korea impose tariff on import

Positive value Oil tariff on import Negative
value Oil premium on export
1 agent
2 agents
6 agents
8 agents
14
Calculation ResultsTotal Energy System Cost
  • Total cost in 1 agent case is the
    lowest(corresponding to conventional model)
  • With strategic acts, energy system becomes
    inefficient
  • With enough agents, energy system might recover
    efficiency

Case setting
15
Calculation ResultsAmounts of Oil Production
  • Oil production in 1 agent case is the highest
  • With strategic acts, total oil production
    decreases
  • OPECs share decreases andLatin America and
    Africas share increase

Case setting
16
Calculation ResultsWorld Oil Price
  • World oil price appears as export shadow price of
    OPEC
  • Oil price is 22/bbl in 1 agent case
  • Oil price shifts due to agents strategic action,
    andbecomes higher by 4/bbl in 6 and 8 agents
    case
  • Domestic oil prices in importing nations are
    higher by their tariff

17
Outline of Presentation
  • Objectives
  • Methodology -Formulation of models-
  • Common structure of our global energy models
  • Formulation of conventional optimization energy
    model
  • Formulation of new agent-based energy model
  • Analysis results
  • 4 case settings for calculations
  • Difference of results between 4 cases
  • Summary

18
Summary
  • New agent-base global energy model was developed,
    which allows us to treat conflicts of domestic
    policies
  • The preliminary analysis shows
  • Solution of 1 agent case in new agent-based model
    actually corresponds to optimal solution in
    conventional model
  • As number of agents increases, energy system
    becomes inefficient
  • With sufficient agents, the energy system might
    achieve the efficiency again
  • Future works
  • To introduce more segmentalized agents and
    dynamism
  • To examine the realities of calculation results
  • To deal with carbon dioxide trading

19
(No Transcript)
20
Appendix
21
Common Structureof Our Global Energy Models
  • Energy transportations between nodes

22
Case Setting for Calculation
Grouping together regions into an agent
Setting of Agents
World Division
23
Formulation of NewAgent-based Global Energy Model
  • The calculation flow

Reinforcement Learningby agents
1.Allocating Calculation
2. Calculate Reward
2. Calculate Reward
3. Update Value function / Error function
3. Update Value function / Error function
4. Update strategy
4. Update strategy
5. Decide Action
5. Decide Action
Agent 1
Agent 2
Action
Action
24
Formulation of Agent-based Model(1) Allocating
calculation
  • Optimization calculation Utilizing conventional
    model

25
Formulation of Agent-based Model(2) Calculate
Rewards
  • Calculate reward of each agent

26
Formulation of Agent-based Model(3) Update Value
function / Error function
  • Actor-Critic Method of reinforcement learning

27
Formulation of Agent-based Model(4) Update
strategy / (5) Decide Action
  • Actor-Critic Method of reinforcement learning

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28
Calculation ResultsOil Trading
2 agents
1 agent
6 agents
8 agents
29
The validity of New Agent-based Model (1)
  • OPEC monopolization case
  • Case Setting OPEC, Others (2 agents)
  • Focused attention on oil trading
  • Only agent OPEC charges premium on its export,
    and agent Others has no countermeasure
  • ? This case can be reduced to a simple
    one-dimension search problem
  • ? We can calculate the theoretical monopoly price
    using conventional model

30
The validity of New Agent-based Model (1)
  • Learning solution is equilibrium solution in
    multi-agent formulation and corresponding to one
    of local minimum points
  • ? We can conclude that these results demonstrate
    that the new multi-agent based model works as
    designed

Result of one-dimension search
Result of Agent-based Learning
Theoretical Best Premium 690/TOE
Iteration of Calculation
Premium Price
OPECs Strategy
Energy System Cost
Learning Solution
Learning Solution 632/TOE
Relationsihp between OPECs premium and its
Energy System Cost
Progress of leaning strategy of OPEC
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