1. 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 Institute for Information Technology e-Business National Research Council of Canada Fredericton, NB, E3B 9W4 Canada Lu Yang 1

2. Outline 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 Lu Yang Introduction 2

3. Geographical Regions of Indonesia Lu Yang 4 Jawa-Bali Island Power Plant Power Distributor

4. Introduction Computational grids: can support power grids Vision: Intelligent power grids compute their own transactions Lu Yang Power grids: electricity sellers and buyers 3

5. Scenario of Power Plant Application of our Multi-Agent System Power Plant m Power Distributor n... Virtual Power Grid Market Power Plant 2 Power Distributor 2... Power Plant 1 Power Distributor 1 Power Sellers Power Buyers Lu Yang 5

6. The AgentMatcher Architecture buyer agents seller agents ranking similarity table pairs of buyer and seller agents Similarity Computation Ranked Pairing Focused Negotiation finalized transaction Lu Yang 6

7. Similarity Computation  (A(S i ) (w i + w' I )/2) Lu Yang 7 Similarity: 0.9328

8. Ranked Pairing Rank b 1 b 2 b 3 b 4 1 s1 s1 0.84 s2 s2 0.75 s4 s4 0.96 s5 s5 0.69 2 s4 s4 0.80 s5 s5 0.72 s1 s1 0.87 s4 s4 0.67 3 s2 s2 0.63 s3 s3 0.72 s2 s2 0.80 s1 s1 0.60 4 s3 s3 0.55 s1 s1 0.53 s3 s3 0.71 s3 s3 0.55 5 s5 s5 0.41 s4 s4 0.38 s5 s5 0.67 s2 s2 0.52 Rank b 1 b 2 b 3 b 4 1 s1 s1 0.84 s2 s2 0.75 s4 s4 0.96 s5 s5 0.69 2 s4 s4 0.80 s5 s5 0.72 s1 s1 0.87 s4 s4 0.67 3 s2 s2 0.63 s3 s3 0.72 s2 s2 0.80 s1 s1 0.60 4 s3 s3 0.55 s1 s1 0.53 s3 s3 0.71 s3 s3 0.55 5 s5 s5 0.41 s4 s4 0.38 s5 s5 0.67 s2 s2 0.52 (b 3, s 4 ) Initial Table Lu Yang 8

9. Ranked Pairing (b 2, s 2 ) (b 1, s 1 ) Rank b 1 b 2 b 3 b 4 1 s1 s1 0.84 s2 s2 0.75 s4 s4 0.96 s5 s5 0.69 2 s4 s4 0.80 s5 s5 0.72 s1 s1 0.87 s4 s4 0.67 3 s2 s2 0.63 s3 s3 0.72 s2 s2 0.80 s1 s1 0.60 4 s3 s3 0.55 s1 s1 0.53 s3 s3 0.71 s3 s3 0.55 5 s5 s5 0.41 s4 s4 0.38 s5 s5 0.67 s2 s2 0.52 Rank b 1 b 2 b 3 b 4 1 s1 s1 0.84 s2 s2 0.75 s4 s4 0.96 s5 s5 0.69 2 s4 s4 0.80 s5 s5 0.72 s1 s1 0.87 s4 s4 0.67 3 s2 s2 0.63 s3 s3 0.72 s2 s2 0.80 s1 s1 0.60 4 s3 s3 0.55 s1 s1 0.53 s3 s3 0.71 s3 s3 0.55 5 s5 s5 0.41 s4 s4 0.38 s5 s5 0.67 s2 s2 0.52 Lu Yang 9

10. (b 4, s 5 ) Rank b 1 b 2 b 3 b 4 1 s1 s1 0.84 s2 s2 0.75 s4 s4 0.96 s5 s5 0.69 2 s4 s4 0.80 s5 s5 0.72 s1 s1 0.87 s4 s4 0.67 3 s2 s2 0.63 s3 s3 0.72 s2 s2 0.80 s1 s1 0.60 4 s3 s3 0.55 s1 s1 0.53 s3 s3 0.71 s3 s3 0.55 5 s5 s5 0.41 s4 s4 0.38 s5 s5 0.67 s2 s2 0.52 Paired buyer and seller agents b3b3 b1b1 b2b2 b4b4 s4s4 s1s1 s5s5 s2s2 0.96 0.84 0.75 0.69 buyerssellerssimilarity Lu Yang 10 Ranked Pairing

11. Special Case 1 Rank b 1 b 2 b 3 b 4 1 s1 s1 0.84 s2 s2 0.88 s4 s4 0.61 s5 s5 0.88 2 s4 s4 0.80 s5 s5 0.79 s1 s1 0.58 s4 s4 0.67 3 s2 s2 0.63 s3 s3 0.77 s2 s2 0.43 s1 s1 0.60 4 s3 s3 0.55 s1 s1 0.68 s3 s3 0.34 s3 s3 0.55 5 s5 s5 0.41 s4 s4 0.63 s5 s5 0.20 s2 s2 0.52 Lu Yang 11 Ranked Pairing

12. Special Case 2 Rank b 1 b 2 b 3 b 4 1 s3 s3 0.89 s1 s1 0.84 s3 s3 0.89 s3 s3 0.76 2 s4 s4 0.50 s3 s3 0.79 s5 s5 0.88 s4 s4 0.70 3 s2 s2 0.47 s5 s5 0.77 s1 s1 0.76 s2 s2 0.67 4 s1 s1 0.39 s4 s4 0.68 s4 s4 0.73 s1 s1 0.55 5 s5 s5 0.28 s2 s2 0.63 s2 s2 0.65 s5 s5 0.52 Lu Yang 12 Ranked Pairing

13. Focused Negotiation-An Example (I) Priority b 1 b 2 b 3 b 4 1s1s1 0.84s2s2 0.75s4s4 0.96s5s5 0.69 2s3s3 0.80s 12 0.74s 10 0.85s8s8 0.67 3s6s6 0.75s9s9 0.70s7s7 0.76s 11 0.64 Lu Yang Suppose that the total demand of capacity is: 125 MW Power availability Parameters price quality capacity Middle Load 0.2 0.1 0.3 0.4 Large …… 13

14. Focused Negotiation-An Example (II) Lu Yang total demand 125 MW 14 s1s1 s2s2 s4s4 s5s5 20 10 20 5 8 3 4 sellers capacity(MW) price($) ∑= 70 P1:P1: s6s6 s9s9 s7s7 s 11 5 5 15 10 6 3 12 18 sellers capacity(MW) price($) ∑=35 P3:P3: s3s3 s 12 s 10 s8s8 10 5 5 20 2 10 7 20 sellers capacity(MW) price($) ∑=40 P2:P2:

15. Focused Negotiation-An Example (III) Lu Yang Capacity (MW) Price ($/MWh) P9P9 Q6Q6 P6P6 Q9Q9 P7P7 Q7Q7 Clearing Price -Q-Q 3 6 12 110115120130125 15 s6s6 s9s9 s7s7 s 11 5 5 15 10 6 3 12 18 sellers capacity(MW) price($) ∑=35 P3:P3:

16. Conclusion Lu Yang 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 Tree similarity is the basis for subsequent ranked pairing and focused negotiation 16

18. “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.

20. 1999 Car Make Year Ford 0.3 0.7 2002 Car Make Year Ford 0.3 0.7 tree ttree t´ 1 0

21.  (A(s i )(w i + w' i )/2) vehicle autumn auto make 0.5 model year 0.3334 ford mini 1999 summer make model year 0.3334 0.3333 van 2000 free star e-series wagon montery free star 0.5 vehicle autumn auto make 0.5 model year 0.3334 ford van 1999 summer make model year 0.3334 0.3333 ford 2001 0.5 big ford big mini van bigmini e-series wagon free star truck tree t tree t´

22. t1t1 t2t2 High PowerPlant Middle 0.8 0. 2 High PowerPlant Middle 0.8 price 0. 2 High PowerPlant Middle 0. 2 price 0.8 capacity t3t3 price where n = the number of weight pairs