access icon free Optimal location of PEVCSs using MAS and ER approach

The location of public electric vehicle charging stations (PEVCSs) has a great influence on the operational efficiency of charging stations, charging behaviours of EVs and the power quality of grids. To optimise the PEVCS locations for plug-in electric taxis (PETs), this study proposes to utilise the multi-agent system (MAS) and evidential reasoning (ER) approach. First, an MAS simulation framework for PET operation is proposed to dynamically simulate the PETs’ daily operation and estimate the charging demands of PETs, where a variety of agents are built to simulate not only the operation related players but also the operational environments. To accelerate the convergence rate and provide better operational strategies for PETs, a multi-step learning is developed to make decisions for PET agents whether to find passengers or to charge under various situations. Moreover, a multi-objective model for optimising the location of PEVCSs is developed considering the benefits of PETs and the power grid. Finally, an ER approach is applied to determine the final optimal siting considering the uncertainties of the assessor's cognition. Simulation results have demonstrated that the proposed MAS simulation framework and ER approach can effectively optimise the PEVCS locations.

Inspec keywords: battery powered vehicles; learning (artificial intelligence); power engineering computing; power supply quality; power grids; inference mechanisms; electric vehicle charging; multi-agent systems

Other keywords: PET agents; ER; convergence rate; ER approach; charging behaviours; PET operation; public electric vehicle charging stations; power quality; multiagent system; MAS approach; evidential reasoning approach; plug-in electric t-axis; operational efficiency; optimal PEVCS location; MAS simulation framework; operational strategies; MAS; charging demand estimation; multistep Q(λ) learning

Subjects: Traffic engineering computing; Knowledge engineering techniques; Power engineering computing; Power supply quality and harmonics; Transportation

References

    1. 1)
      • 15. Xin, H., Yan, Z., Xu, S.L.: ‘Multi-agent system based coordinated charging strategy for electric vehicles’, Power Syst. Technol., 2015, 39, pp. 4854.
    2. 2)
      • 39. Paterakis, N.G., Mazza, A., Santos, S.F., et al: ‘Multi-objective reconfiguration of radial distribution systems using reliability indices’, IEEE Trans. Power Syst., 2016, 31, pp. 10481062.
    3. 3)
      • 33. Erdinc, O., Tascikaraoglu, A., Paterakis, N.G., et al: ‘Comprehensive optimization model for sizing and siting of DG units, EV charging stations and energy storage systems’, IEEE Trans. Smart Grid, 2017, 99, pp. 11.
    4. 4)
      • 3. Xu, X.H., Yao, L., Zeng, P., et al: ‘Architecture and performance analysis of a smart battery charging and swapping operation service network for electric vehicles in China’, J. Mod. Power Syst. Clean Energy, 2015, 3, pp. 259268.
    5. 5)
      • 21. Wu, Q.H, Liao, H.L.: ‘Function optimisation by learning automata’, Inf. Sci. (Ny), 2013, 220, pp. 379398.
    6. 6)
      • 13. Bae, S., Kwasinski, A.: ‘Spatial and temporal model of electric vehicle charging demand’, IEEE Trans. Smart Grid, 2012, 3, pp. 394403.
    7. 7)
      • 24. Shang, X, Li, Z, Ji, T, et al: ‘Online area load modeling in power systems using enhanced reinforcement learning’, Energies, 2017, 10, pp. 18391852.
    8. 8)
      • 18. Chaudhari, K.S., Kandasamy, N.K., Krishnan, A., et al: ‘Agent based aggregated behavior modelling for electric vehicle charging load’, IEEE Trans. Ind. Inf., 2018, p. 1-1, DOI: 10.1109/TII.2018.2823321.
    9. 9)
      • 28. Shahraki, N., Cai, H., Turkay, M., et al: ‘Optimal locations of electric public charging stations using real world vehicle travel patterns’, Transp. Res. D, 2015, 41, pp. 165176.
    10. 10)
      • 29. Jia, L., Hu, Z.C., Song, Y.H., et al: ‘Optimal siting and sizing of electric vehicle charging stations’. Proc. IEEE Electric Vehicle Conf., Greenville, SC, USA, March 2012, pp. 16.
    11. 11)
      • 27. Liu, J.: ‘Electric vehicle charging infrastructure assignment and power grid impacts assessment in Beijing’, Energy Policy, 2012, 51, pp. 544557.
    12. 12)
      • 1. Iversen, E.B., Morales, J.M., Madsen, H.: ‘Optimal charging of an electric vehicle using a Markov decision process’, Appl. Energy, 2014, 123, pp. 112.
    13. 13)
      • 8. Qian, K.J., Zhou, C.K., Allan, M., et al: ‘Modeling of load demand due to EV battery charging in distribution systems’, IEEE Trans. Power Syst., 2011, 26, pp. 802810.
    14. 14)
      • 43. Chen, T.D., Kockelman, K.M., Hanna, J.P.: ‘Operations of a shared, autonomous, electric vehicle fleet: implications of vehicle & charging infrastructure decisions’, Transp. Res. A, Policy Pract., 2016, 94, pp. 243254.
    15. 15)
      • 10. Shun, T., Liao, K.Y., Xiao, X.M., et al: ‘Charging demand for electric vehicle based on stochastic analysis of trip chain’, IET Gener. Transm. Distrib., 2016, 10, pp. 26892698.
    16. 16)
      • 30. Awasthi, A., Venkitusamy, K., Sanjeevikumar, P., et al: ‘Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm’, Energy, 2017, 133, pp. 7078.
    17. 17)
      • 37. Zhang, H., Hu, Z., Xu, Z., et al: ‘An integrated planning framework for different types of PEV charging facilities in urban area’, IEEE Trans. Smart Grid, 2017, 7, pp. 22732284.
    18. 18)
      • 14. Papadopoulos, P., Jenkins, N., Cipcigan, L.M., et al: ‘Coordination of the charging of electric vehicles using a multi-agent system’, IEEE Trans. Smart Grid, 2013, 4, pp. 18021809.
    19. 19)
      • 16. Cui, X.H., Liu, C., Kim, H.K., et al: ‘A multi agent-based framework for simulating household PHEV distribution and electric distribution network impact’, TRB Committees Transp. Energy, 2010, pp. 121.
    20. 20)
      • 11. Mu, Y.F., Wu, J.Z., Jenkins, N., et al: ‘A spatial–temporal model for grid impact analysis of plug-in electric vehicles’, Appl. Energy, 2014, 114, pp. 456465.
    21. 21)
      • 9. Yu, L., Zhao, T.Y., Chen, Q.F., et al: ‘Centralized bi-level spatial–temporal coordination charging strategy for area electric vehicles’, CSEE J. Power Energy Syst., 2015, 1, pp. 7483.
    22. 22)
      • 5. Yang, Z., Sun, L., Chen, J., et al: ‘Profit maximization for plug-in electric taxi with uncertain future electricity prices’, IEEE Trans. Power Syst., 2014, 29, pp. 30583068.
    23. 23)
      • 44. Mueller, K., Sgouridis, S.P.: ‘Simulation-based analysis of personal rapid transit systems: service and energy performance assessment of the Masdar City PRT case’, J. Adv. Transp., 2011, 45, pp. 252270.
    24. 24)
      • 47. Li, F., Liu, T.Q., Jiang, D.L.: ‘Distribution network reconfiguration with multi-objective based on improved immune algorithm’, Power Syst. Technol., 2011, 7, pp. 134138.
    25. 25)
      • 19. Sheppard, C.J.R, Harris, A., Gopal, A.R.: ‘Cost-effective siting of electric vehicle charging infrastructure with agent-based modeling’, IEEE Trans. Transp. Electrification, 2016, 2, pp. 174189.
    26. 26)
      • 20. Jiang, C.X., Jing, Z.X., Cui, X.R., et al: ‘Multiple agents and reinforcement learning for modelling charging loads of electric taxis’, Appl. Energy, 2018, 222, pp. 158168.
    27. 27)
      • 46. Ahuja, A., Das, S., Pahwa, A.: ‘An AIS-ACO hybrid approach for multi-objective distribution system reconfiguration’, IEEE Trans. Power Syst., 2008, 22, pp. 11011111.
    28. 28)
      • 42. Shafer, G.: ‘Mathematical theory of evidence’ (Princeton University Press, Princeton, NJ, USA, 1976).
    29. 29)
      • 2. Lam, A.Y.S., Leung, Y.W., Chu, X.: ‘Electric vehicle charging station placement: formulation, complexity, and solutions’, IEEE Trans. Smart Grid, 2017, 5, pp. 28462856.
    30. 30)
      • 45. Jing, P., Williams, R.J.: ‘Incremental multi-step Q-learning’, Mach. Learn., 1994, 22, pp. 226232.
    31. 31)
      • 35. Zhang, H., Moura, S., Hu, Z., et al: ‘PEV fast-charging station siting and sizing on coupled transportation and power networks’, IEEE Trans. Smart Grid, 2016, 99, p. 1.
    32. 32)
      • 40. Yang, J.B., Singh, M.G.: ‘An evidential reasoning approach for multiple-attribute decision making with uncertainty’, IEEE Trans. Syst. Man Cybern., 1994, 24, pp. 118.
    33. 33)
      • 26. Zhang, X.S., Yu, T., Yang, B., et al: ‘Approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems’, Energy Convers. Manage., 2015, 106, pp. 543556.
    34. 34)
      • 17. Karfopoulos, E.L., Hatziargyriou, N.D.: ‘A multi-agent system for controlled charging of a large population of electric vehicles’, IEEE Trans. Power Syst., 2013, 28, pp. 11961204.
    35. 35)
      • 41. Yang, J.B., Xu, D.L.: ‘On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty’, IEEE Trans. Syst. Man Cybern. A, Syst. Hum., 2002, 32, pp. 289304.
    36. 36)
      • 36. Guo, S., Zhao, H.: ‘Optimal site selection of electric vehicle charging station by using fuzzy TOPSIS based on sustainability perspective’, Appl. Energy, 2015, 158, pp. 390402.
    37. 37)
      • 22. Liao, H.L., Wu, Q.H., Li, Y.Z., et al: ‘Economic emission dispatching with variations of wind power and loads using multi-objective optimization by learning automata’, Energy Convers. Manage., 2014, 87, pp. 990999.
    38. 38)
      • 48. Electric vehicle news: ‘BYD e6 electric taxis hit roads in south China’, 2010. URL Available at http://www.electric-vehiclenews.com/2010/05/byd-e6-electric-taxis-hit-roads-in.html, accessed May 2010.
    39. 39)
      • 6. Du, J., Ouyang, M., Chen, J.: ‘Prospects for Chinese electric vehicle technologies in 2016–2020: ambition and rationality’, Energy, 2016, 120, pp. 584596.
    40. 40)
      • 12. Li, G., Zhang, X.P.: ‘Modeling of plug-in hybrid electric vehicle charging demand in probabilistic power flow calculations’, IEEE Trans. Smart Grid, 2012, 3, pp. 492499.
    41. 41)
      • 31. Shojaabadi, S., Abapour, S., Abapour, M., et al: ‘Optimal planning of plug-in hybrid electric vehicle charging station in distribution network considering demand response programs and uncertainties’, IET Gener. Transm. Distrib., 2016, 10, pp. 33303340.
    42. 42)
      • 49. China Southern Power Grid: ‘Guangzhou electricity price list’, 2017. URL Available at https://95598.guangzhou.csg.cn/help/wzcx.do, accessed August 2017.
    43. 43)
      • 4. Tian, Z., Jung, T., Wang, Y., et al: ‘Real-time charging station recommendation system for electric-vehicle taxis’, IEEE Trans. Intell. Transp. Syst., 2016, 17, pp. 30983109.
    44. 44)
      • 32. Khalkhali, K., Abapour, S., Moghaddas-Tafreshi, S.M., et al: ‘Application of data envelopment analysis theorem in plug-in hybrid electric vehicle charging station planning’, IET Gener. Transm. Distrib., 2015, 9, pp. 666676.
    45. 45)
      • 38. Abido, M.A., Ahmed, M.W.: ‘Multi-objective optimal power flow considering the system transient stability’, IET Gener. Transm. Distrib., 2016, 10, pp. 42134221.
    46. 46)
      • 23. Sutton, R.S., Barto, A.G.: ‘Reinforcement learning: an introduction’ (MIT Press, Cambridge, MA, USA, 1998, 2nd edn.).
    47. 47)
      • 7. Navigant Research Group: ‘More than 1.8 million plug-in electric vehicles will be sold in the largest 102 U.S. Cities from 2012 to 2020 Navigant Research’. 2013. URL Available at https://www.navigantresearch.com/newsroom/more-than-1-8-million-plug-in-electric-vehicles-will-be-sold-in-the-largest-102-u-s-cities-from-2012-to-2020, accessed January 2013.
    48. 48)
      • 25. Ye, D., Zhang, M., Sutanto, D.: ‘A hybrid multiagent framework with Q-learning for power grid systems restoration’, IEEE Trans. Power Syst., 2011, 26, pp. 24342441.
    49. 49)
      • 34. Wang, G., Xu, Z., Wen, F., et al: ‘Traffic-constrained multiobjective planning of electric-vehicle charging stations’, IEEE Trans. Power Deliv., 2013, 28, pp. 23632372.
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