Transcript The AgentMatcher Architecture Applied to Power Grid Transactions Riyanarto Sarno Faculty of Information Technology, Sepuluh Nopember Institute of Technology Surabaya, 60111 Indonesia Lu Yang, Virendra C.

The AgentMatcher Architecture Applied to
Power Grid Transactions
Riyanarto Sarno
Faculty of Information Technology,
Sepuluh Nopember Institute of Technology
Surabaya, 60111
Indonesia
Lu Yang, Virendra C. Bhavsar
Faculty of Computer Science, University of New Brunswick
Fredericton, NB, E3B 5A3
Canada
Harold Boley
Lu Yang
Institute for Information Technology e-Business
National Research Council of Canada
Fredericton, NB, E3B 9W4
Canada
1
Outline
•
Introduction
•
A Multi-Agent System for Power Plant Transactions:
Transactions consist of determining the most
economical power plants to satisfy electricity
demands and operating constraints
The AgentMatcher Architecture
– Similarity Computation
– Ranked Pairing
– Focused Negotiation
Conclusion
•
•
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2
Geographical Regions of Indonesia
Jawa-Bali Island
Power Plant
Power
Distributor
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4
Introduction
• Power grids: electricity sellers and buyers
• Computational grids: can support power
grids
• Vision: Intelligent power grids compute
their own transactions
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3
Scenario of Power Plant Application of
our Multi-Agent System
Power
Distributor 1
Power Plant 1
Power Plant 2
Power
Sellers
.
.
Virtual
Power
Grid
Market
.
.
.
.
Power
Distributor n
Power Plant m
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Power
Distributor 2
5
Power
Buyers
The AgentMatcher Architecture
Similarity Computation
ranking similarity table
Ranked Pairing
pairs of buyer and seller agents
Focused Negotiation
finalized transaction
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Similarity Computation
Power
Power
availability
quality
0.1 capacity price 0.2
0.4
0.3
Parameters
Load
Small
Fast
100%
75% freq
0.3 0.3phasevoltage
0.4
25%
0.5
0.1 50%
0.2
0.2
Good
High
Low Low Low Bad Average
availability
quality
0.3 capacityprice 0.1
0.2
0.4
Load
Fast
Parameters
Large
100%
75% freq
0.1 0.8phasevoltage
0.6
25%
0.1
0.2 50%
0.1
0.1
Low
Good
Bad
Good
Low
High
High
Seller Tree
Buyer Tree
Similarity: 0.9328
 (A(Si ) (wi + w'I )/2)
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Ranked Pairing
Initial
Table
(b3, s4)
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Rank
b1
b2
b3
b4
1
s1
0.84
s2
0.75
s4
0.96
s5
0.69
2
s4
0.80
s5
0.72
s1
0.87
s4
0.67
3
s2
0.63
s3
0.72
s2
0.80
s1
0.60
4
s3
0.55
s1
0.53
s3
0.71
s3
0.55
5
s5
0.41
s4
0.38
s5
0.67
s2
0.52
Rank
b1
b2
b3
b4
1
s1
0.84
s2
0.75
s4
0.96
s5
0.69
2
s4
0.80
s5
0.72
s1
0.87
s4
0.67
3
s2
0.63
s3
0.72
s2
0.80
s1
0.60
4
s3
0.55
s1
0.53
s3
0.71
s3
0.55
5
s5
0.41
s4
0.38
s5
0.67
s2
0.52
8
Ranked Pairing
(b1, s1)
(b2, s2)
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Rank
b1
b2
b3
b4
1
s1
0.84
s2
0.75
s4
0.96
s5
0.69
2
s4
0.80
s5
0.72
s1
0.87
s4
0.67
3
s2
0.63
s3
0.72
s2
0.80
s1
0.60
4
s3
0.55
s1
0.53
s3
0.71
s3
0.55
5
s5
0.41
s4
0.38
s5
0.67
s2
0.52
Rank
b1
b2
b3
b4
1
s1
0.84
s2
0.75
s4
0.96
s5
0.69
2
s4
0.80
s5
0.72
s1
0.87
s4
0.67
3
s2
0.63
s3
0.72
s2
0.80
s1
0.60
4
s3
0.55
s1
0.53
s3
0.71
s3
0.55
5
s5
0.41
s4
0.38
s5
0.67
s2
0.52
9
Ranked Pairing
(b4, s5)
Rank
b1
b2
b3
1
s1
0.84
s2
0.75
s4
0.96
s5
0.69
2
s4
0.80
s5
0.72
s1
0.87
s4
0.67
3
s2
0.63
s3
0.72
s2
0.80
s1
0.60
4
s3
0.55
s1
0.53
s3
0.71
s3
0.55
5
s5
0.41
s4
0.38
s5
0.67
s2
0.52
Paired buyer and seller agents
buyers sellers similarity
s4
b3
0.96
s1
b1
0.84
s2
b2
0.75
s5
b4
0.69
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b4
10
Ranked Pairing
Special Case 1
Rank
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b1
b2
b3
b4
1
s1
0.84
s2
0.88
s4
0.61
s5
0.88
2
s4
0.80
s5
0.79
s1
0.58
s4
0.67
3
s2
0.63
s3
0.77
s2
0.43
s1
0.60
4
s3
0.55
s1
0.68
s3
0.34
s3
0.55
5
s5
0.41
s4
0.63
s5
0.20
s2
0.52
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Ranked Pairing
Special Case 2
Rank
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b1
b2
b3
b4
1
s3
0.89
s1
0.84
s3
0.89
s3
0.76
2
s4
0.50
s3
0.79
s5
0.88
s4
0.70
3
s2
0.47
s5
0.77
s1
0.76
s2
0.67
4
s1
0.39
s4
0.68
s4
0.73
s1
0.55
5
s5
0.28
s2
0.63
s2
0.65
s5
0.52
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Focused Negotiation-An Example (I)
Priority
b2
b1
1
s1
0.84
s2
2
s3
0.80
3
s6
0.75
0.75
b3
0.96
s5
0.69
s12 0.74
s10 0.85
s8
0.67
s9
s7
s11 0.64
0.70
s4
b4
0.76
Power
availability
quality
price
0.1
capacity 0.4 0.2
0.3
Parameters
Load
Middle Large
…
…
Suppose that the total demand of capacity is:
125 MW
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Focused Negotiation-An Example (II)
P 1:
sellers capacity(MW) price($)
s1
s2
s4
s5
20
10
20
20
∑=70
sellers capacity(MW) price($)
P 3:
sellers capacity(MW) price($)
5
8
3
4
total demand
125 MW
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P 2:
14
s3
s12
s10
s8
s6
s9
s7
s11
10
5
20
5
∑=40
5
5
10
15
∑=35
2
10
7
20
6
3
12
18
Focused Negotiation-An Example (III)
P 3:
Price ($/MWh)
12
Clearing Price
6
3
P7
Q7
s9
s6
s7
s11
Q6
Q9
P9
P6
-Q
110 115 120 125 130
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sellers capacity(MW) price($)
15
Capacity
(MW)
5
5
10
15
∑=35
3
6
12
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Conclusion
•Tree similarity is the basis for subsequent
ranked pairing and focused negotiation
•The AgentMatcher architecture has been
implemented in java for similarity computation and
ranked pairing
• A capacity/price-focused negotiation algorithm has
been developed for transactions in power grids
• This negotiation algorithm can be extended for
further power attributes
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“The Computational Grid” is
analogous to Electricity (Power) Grid
and the vision is to offer a (almost)
dependable, consistent, pervasive,
and inexpensive access to high-end
resources irrespective their location
of physical existence and the
location of access.
tree t
tree t´
Car
Make
0.3
Ford
1
Year
0.7
2002
Car
Make
0.3
Year
0.7
Ford
1999
0
tree t
tree t´
vehicle
autumn
0.5
summer
0.5
vehicle
autumn
0.5
summer
0.5
auto
auto
auto
auto
year
year
make
year make
make
year make
0.3334
0.3333 0.3334 model
0.3333
0.3334 model 0.33330.3334
0.3333 model
model
0.3333
0.3333
0.3333
0.3333
ford van
ford truck
ford van
ford van
1999
1999
2000
2001
mini
big
big
big
mini
mini
0.5
0.5
0.5
0.5
0.5
0.5
e-series free
e-series
free montery free
wagon star
wagon
star
star
 (A(si)(wi + w'i)/2)
PowerPlant
capacity
0. 2
PowerPlant
capacity
0. 2
Middle
Middle
price
0.8
t1
price
0.8
t2
High
PowerPlant
High
price
0. 2
capacity
0.8
High
Middle
t3
n
variance { ((wi  wi ) 2  ( wil  wi ) 2 )}/ n
i 1
where
wi  (wi wil ) / 2
n = the number of weight pairs