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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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, 525 0058 Japan Rakuten Inc., Setagaya-ku, Tokyo, 158 0094 Japan Faculty of Science, Toho University, Funabashi-shi, 274 8510 Japan a) E-mail: hatto@fc.ritsumei.ac.jp DOI:10.14923/transinfj.2016JDP7062 [5], [6] [7], [8] MASim MASim MASim EV: Electronic Vehicle PV: Photovoltaic MASim 180 D Vol. J100 D No. 2 pp. 180 193 c 2017

MASim 2. 2. 1 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 2. 2 1 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

2017/2 Vol. J100 D No. 2 1 MATSim [3], [13] [14], [15] MATSim e.g., EV e.g., PV EV EV 3. 3. 1 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

3 Fig. 3 I/O model of electricity for EV. 2 Fig. 2 I/O model of electricity for buildings. 3. 2 2 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

2017/2 Vol. J100 D No. 2 3 EV 4 EV 5 EV 6 PV 3. 4 EV PV 1 2 3 1 PV PV PV 25% PV 2 PV PV PV 1 2 3 (1:1:1) 1 3 PV EV PV PV 20% 30% 40% 80% 90% 4 PV 50m, 100m, 150m,, 1550m, 1600m 32 5 PV 2 140 20 30 40 70 80 6 PV 1 2 3 (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 http://www.pref.kyoto.jp/denkizidousya/documents/ visionlist.pdf 184

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 00111001100101 {(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 2 185

2017/2 Vol. J100 D No. 2 4. 4. 1 4. 1. 1 10km 7000 14000 3 8.33m/s (= 30km/h) 4. 1. 2 EV 9000 20000 2 4 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 1:3:1 4. 1. 3 3. 2 3. 3 EV 2. 2 EV 3 4 EV PV EV EV EV 186

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 4. 1. 4 GA GA (DGA: Distributed Genetic Algorithm) [17] DGA [18] DGA A 2 100 4. 2 EV 9000 4 6 1 3 EV 20000 5 7 2 4 4 5 EV 9000 20 75 EV 20000 10 6 7 4 5 EV 9000 4 (EV : 9000) Fig. 4 Transition of fitness value (# of EV: 9000). 5 EV PHV : http://www.chademo.com/wp/pdf/japan/2016ga/ 2016GA METI.pdf 5 (EV : 20000) Fig. 5 Transition of fitness value (# of EV: 20000). 187

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 0.08 2.26 1.97 EV 4.70 12.49 6.62 4.38 6.15 2.66 EV 3.64 3.66 3.64 93.30 93.89 94.29 85.44 84.23 84.65 4 / / : MWh EV : 20000 Table 4 Corresponding data of electricity to min/ mean/max of fitness (# of EV: 20000). 7 EV : 20000 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 (%) 20 70 90 (m) 50 100 50 30 70 80 (3:2:1) (1:4:1) (1:4:1) 1.016 1.221 1.426 2 / / EV : 20000 Table 2 Comparison of min/mean/max of fitness (# of EV: 20000). PV (%) 20 80 90 (m) 50 100 50 50 90 50 (4:1:1) (4:1:1) (1:2:1) 1.026 1.279 1.532 EV 20000 2 EV 0.31 5.98 4.45 0.31 5.98 4.45 EV 12.60 27.66 12.80 12.60 27.66 12.80 11.82 8.44 2.12 11.82 8.44 2.12 EV 8.20 8.25 8.21 206.95 210.06 209.82 187.48 184.34 185.73 DGA 1 2 6 3 4 1 2 EV 1:1 3 4 EV 9000 = 4.38+93.30 = 97.68 =6.15+93.89+100.04 =2.66 + 94.29 = 96.95 0.7%, 3.0% EV 20000 6 188

=11.82 + 206.95 = 218.77, = 8.44 + 210.06 = 218.5, = 2.12 + 209.82 = 211.94 3.1%, 3.0% [19] 2011 4% 2 50m MASim MASim 4. 3 EV 9000 5 6 5 6 5 EV 9000 1.426 2 16384 0.02% 5 Table 5 / / / EV : 9000 Comparison of min/medium/mean/max of fitness by exhaustive search (# of EV: 9000). (3) PV (%) 20 60 90 90 (4) (m) 50 1000 350 100 (5) 20 50 30 20 (6) (1:2:1) (3:2:1) (1:1:2) (1:1:2) 1.013 1.108 1.220 1.428 6 / / / : MWh EV : 9000 Table 6 Corresponding data of electricity to min/medium/mean/max of fitness by exhaustive search (# of EV: 9000). EV 0.06 6.15 6.05 2.73 EV 4.66 63.28 33.34 9.21 4.37 41.97 9.44 2.68 EV 3.632 3.89 3.74 3.66 93.33 92.79 95.99 94.35 85.47 79.93 81.18 84.35 189

2017/2 Vol. J100 D No. 2 EV 20000 9000 3% 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 20000 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

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, 1996. [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.233 239, 2011. [3] B. Raney and K. Nagel, Iterative route planning for large-scale modular transportation simulations, Future Generation Computer Systems, vol.20, no.7, pp.1101 1118, 2004. [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, 2010. [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.649 656, 2009. [6] vol.53, no.7, pp.1732 1744, 2012. [7] vol.7, pp.19 28, 2011. [8] D3 vol.71, no.5, pp.875 888, 2015. [9] 2014, 2014. http://www.meti.go.jp/press/2014/11/ 20141117003/20141117003-A.pdf [10] NEDO Nedo 2 2014. http://www.nedo.go.jp/content/100535728.pdf [11] B vol.126, no.10, pp.1003 1012, 2006. [12] W. Kempton and J. Tomic, Vehicle-to-grid power fundamentals: Calculating capacity and net revenue, J. Power Sources, vol.144, no.1, pp.268 279, 2005. [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, 2014. [14] vol.64, no.3, pp.38 44, 2010. [15] Y. Nakajima, S. Yamane, and H. Hattori, Multimodel based simulation platform for urban traffic simulation, Proc. 13th International Conference 191

2017/2 Vol. J100 D No. 2 on Principles of Practice in Multi-Agent Systems (PRIMA-2010), pp.228 241, 2010. [16] NEDO 2013, 2013. http://www.nedo.go.jp/content/100535728.pdf [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.483 491, 1991. [18] : vol.43, pp.199 217, 2002. [19] 2013 Technical report 2014. [20] Y. Murase, T. Uchitane, and N. Ito, A tool for parameter-space explorations, Physics Procedia, vol.57, pp.73 76, 2014. [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, 2011. 1. EV EV 1 0.75kW 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 0.0 0.60 1:00 2:00 0.0 0.52 2:00 3:00 0.0 0.44 3:00 4:00 0.0 0.42 4:00 5:00 0.0 0.40 5:00 6:00 0.0 0.42 6:00 7:00 0.06 0.58 7:00 8:00 0.03 0.96 8:00 9:00 0.47 1.00 9:00 10:00 0.64 0.88 10:00 11:00 0.73 0.84 11:00 12:00 0.77 0.76 12:00 13:00 0.75 0.84 13:00 14:00 0.70 0.80 14:00 15:00 0.56 0.72 15:00 16:00 0.41 0.78 16:00 17:00 0.23 0.84 17:00 18:00 0.06 1.10 18:00 19:00 0.0 1.20 19:00 20:00 0.0 1.40 20:00 21:00 0.0 1.32 21:00 22:00 0.0 1.36 22:00 23:00 0.0 1.18 23:00 24:00 0.0 0.94 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 7 http://www.mlit.go.jp/road/ir/ir-data/data/107.pdf 15km/h 20km/h 28 4 21 9 14 11 2 192

2004 2004 PD 2007 2014 ELSI 2015 2006 DC1 2009 ( ) 193

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