大規模マルチエージェントシミュレーションに基づく社会システムデザインの可能性
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- かずゆき はにうだ
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1 a) Exploring Potential for Social System Design Using Multi-Agent Simulations Hiromitsu HATTORI a), Shunsuke JUMI, and Yuu NAKAJIMA MASim: Multi-Agent Simulation MASim MASim MASim MASim 1. MASim: Multi-Agent Simulation [1] [2], [3] [4] College of Information Science and Engineering, Ritsumeikan University, Kusatsu-shi, Japan Rakuten Inc., Setagaya-ku, Tokyo, Japan Faculty of Science, Toho University, Funabashi-shi, Japan a) DOI: /transinfj.2016JDP7062 [5], [6] [7], [8] MASim MASim MASim EV: Electronic Vehicle PV: Photovoltaic MASim 180 D Vol. J100 D No. 2 pp c 2017
![PHV PV [10] PV PV [11]](/docs-images/95/123954862/images/2-1.jpg "PV EV (V2G:")
![Vehicle-to-Grid) [12]](/docs-images/95/123954862/images/2-2.jpg "EV EV V2G PV EV PV EV")
2 MASim EV PV 2030 [9] EV PHV PV [10] PV PV [11] PV EV (V2G: Vehicle-to-Grid) [12] EV EV V2G PV EV PV EV MASim EV PV EV OD PV EV EV EV PV 1) 2) PV 1 PV Fig. 1 Overview of city-wide PV-power distribution simulation. 181
3 2017/2 Vol. J100 D No. 2 1 MATSim [3], [13] [14], [15] MATSim e.g., EV e.g., PV EV EV MASim 1) 2) 3) C c i c i c i = {v i1,v i2,..., v ij} d = {v im v im c i,i =1,..., C,j =1,..., c i } d i A d d GA: Genetic Algorithm d 182
4 3 Fig. 3 I/O model of electricity for EV. 2 Fig. 2 I/O model of electricity for buildings EV PV EV PV EV EV PV PV 3 EV EV EV EV EV 3. 3 EV EV EV EV (EV 1. ) EV PV PV PV EV (1) 1 EV 2 EV 183
5 2017/2 Vol. J100 D No. 2 3 EV 4 EV 5 EV 6 PV 3. 4 EV PV PV PV PV 25% PV 2 PV PV PV (1:1:1) 1 3 PV EV PV PV 20% 30% 40% 80% 90% 4 PV 50m, 100m, 150m,, 1550m, 1600m 32 5 PV PV (2:1:1), (4:1:1), (1:2:1), (1:4:1), (1:1:2), (1:1:4), (3:2:1), (1:1:1) (2:1:1) (4:1:1) (1:2:1) (1:4:1) (1:1:2) (1:1:4) (3:2:1) (1:1:1) PV 1 PV 2 visionlist.pdf 184
6 PV MASim MASim 3. 5 GA 3. 4 (1) (2) (3) (6) GA (3), (5), (6) 8 (3bits) (4) 32 (5bits) 14bits 3bits, 5bits, 3bits,3bits {(3), (4), (5), (6)} = {1, 25, 4, 5} MASim GA 1 2 MASim 3 4 GA F F PV PV PV F F = C C P C : EV P : EV EV 1 PV P<C 0 <C P F C>C P F >1 F C P 1:1 5 MASim F 6 a F b c
7 2017/2 Vol. J100 D No km m/s (= 30km/h) EV EV 2000 OD 1 EV OD EV e.g., e.g., 2 PV EV [10], [16] 3. 2 PV PV 24 1 A 1 A 1 PV 5kWh EV 20kWh 50kWh 100kWh 3 1:3: EV 2. 2 EV 3 4 EV PV EV EV EV 186
8 3. 3 / EV EV / [10], [16] / 1.0kW/0.6kW 30kW/18kW 3.0kW 5 50kW EV 10 5kWh 3kWh 10 EV EV EV EV EV EV EV EV GA GA (DGA: Distributed Genetic Algorithm) [17] DGA [18] DGA A EV EV EV EV EV (EV : 9000) Fig. 4 Transition of fitness value (# of EV: 9000). 5 EV PHV : GA METI.pdf 5 (EV : 20000) Fig. 5 Transition of fitness value (# of EV: 20000). 187
9 2017/2 Vol. J100 D No. 2 3 / / : MWh EV : 9000 Table 3 Corresponding data of electricity to min/ mean/max of fitness (# of EV: 9000). 6 EV : 9000 Fig. 6 Transition of max. of fitness (# of EV: 9000). EV EV EV / / : MWh EV : Table 4 Corresponding data of electricity to min/ mean/max of fitness (# of EV: 20000). 7 EV : Fig. 7 Transition of max. of fitness (# of EV: 20000). 1 / / EV : 9000 Table 1 Comparison of min/mean/max of fitness (# of EV: 9000). PV (%) (m) (3:2:1) (1:4:1) (1:4:1) / / EV : Table 2 Comparison of min/mean/max of fitness (# of EV: 20000). PV (%) (m) (4:1:1) (4:1:1) (1:2:1) EV EV EV EV DGA EV 1:1 3 4 EV 9000 = = = = = %, 3.0% EV
10 = = , = = 218.5, = = %, 3.0% [19] % 2 50m MASim MASim 4. 3 EV EV % 5 Table 5 / / / EV : 9000 Comparison of min/medium/mean/max of fitness by exhaustive search (# of EV: 9000). (3) PV (%) (4) (m) (5) (6) (1:2:1) (3:2:1) (1:1:2) (1:1:2) / / / : MWh EV : 9000 Table 6 Corresponding data of electricity to min/medium/mean/max of fitness by exhaustive search (# of EV: 9000). EV EV EV
11 2017/2 Vol. J100 D No. 2 EV % g MASim 1 t 1 MASim p s i T ceiling(x) x x s/i T = g t (1) ceiling(p/i) ceiling(p/i) DGA 1 ceiling(p/i) MASim ceiling(p/i) MASim 1 ceiling(p/i) s/i t 1 ceiling(p/i) (1) EV t = 25 (minutes), p =12,g = 100, s =32,i =4 T 5.2 (days) p/i T = g t s T p g MASim 1 t s 1 MASim p t 3 s s [18] p p GA 5. s/i 190
12 4. 3 [20] 3. 4 MASim MASim [4], [21] [1] J. Epstein and R. Axtell, Growing Artificial Societies: Social Science from the Bottom Up, MIT Press, [2] H. Hattori, Y. Nakajima, and S. Yamane, Massive multiagent-based urban traffic simulation with finegrained behavior models, J. Advanced Computational Intelligence and Intelligent Informatics, vol.15, no.2, pp , [3] B. Raney and K. Nagel, Iterative route planning for large-scale modular transportation simulations, Future Generation Computer Systems, vol.20, no.7, pp , [4] P. Vytelingum, T.D. Voice, S.D. Ramchurn, A. Rogers, and N.R. Jennings, Agent-based microstorage management for the smart grid, Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS- 2010), pp.39 46, [5] T. Yamashita, S. Soeda, and I. Noda, Evacuation planning assist system with network model-based pedestrian simulator, Proc. 12th International Conference on Principles of Practice in Multi-Agent Systems (PRIMA-2009), pp , [6] vol.53, no.7, pp , [7] vol.7, pp.19 28, [8] D3 vol.71, no.5, pp , [9] 2014, / A.pdf [10] NEDO Nedo [11] B vol.126, no.10, pp , [12] W. Kempton and J. Tomic, Vehicle-to-grid power fundamentals: Calculating capacity and net revenue, J. Power Sources, vol.144, no.1, pp , [13] A. Stahel, F. Ciari, and K.W. Axhausen, Modeling impacts of weather conditions in agent-based transport microsimulations, Proc. 93rd Annual Meeting of the Transportation Research Board, pp.1 1, [14] vol.64, no.3, pp.38 44, [15] Y. Nakajima, S. Yamane, and H. Hattori, Multimodel based simulation platform for urban traffic simulation, Proc. 13th International Conference 191
13 2017/2 Vol. J100 D No. 2 on Principles of Practice in Multi-Agent Systems (PRIMA-2010), pp , [16] NEDO 2013, [17] J. Cohoon, S. Hegde, W. Martin, and D. Richards, Distributed genetic algorithms for the floorplan design problem, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., vol.10, no.4, pp , [18] : vol.43, pp , [19] 2013 Technical report [20] Y. Murase, T. Uchitane, and N. Ito, A tool for parameter-space explorations, Physics Procedia, vol.57, pp.73 76, [21] S. Ramchurn, P. Vytelingum, A. Rogers, and N. Jennings, Agent-based control for decentralised demand side management in the smart grid, Proc. 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2011), pp.5 12, EV EV kW 15km/h 7 20kWh EV 200km 1m 0.3Ws 1 15km 0.75kW 3600sec = 2700kWs(= 0.75kwh) 15km 0.3 = 4500kWs(= 1.25kWh) 9km/h EV 6 2. A 1 Table A 1 Parameters: Efficiency of Power Generation and Power Consumption. (%) (kw) 0:00 1: :00 2: :00 3: :00 4: :00 5: :00 6: :00 7: :00 8: :00 9: :00 10: :00 11: :00 12: :00 13: :00 14: :00 15: :00 16: :00 17: :00 18: :00 19: :00 20: :00 21: :00 22: :00 23: :00 24: Parameters A 2 DGA Table A 2 Parameters for DGA. Values Chromosome length 14 bits (= L) Population size 32 Number of islands 4 Max. number of generation 100 Selection method Tournament selection Tournament size 4 Crossover rate 1.0 Crossover method One-point crossover Mutation rate 0.08 (= 1/L) Mutation method Bit string mutation Migration interval 5 Migration rate 0.5 Migration topology Bi-Directional ring Emigrant method Tournament selection Immigrant method Random km/h 20km/h
14 PD ELSI DC ( ) 193
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