Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Multi-agent modeling and analysis of EV users’ travel willingness based on an integrated causal/statistical/behavioral model
Author:
Affiliation:

1. Nanjing University of Science & Technology, Nanjing 210094, China 2. State Grid Electric Power Research Institute, Nanjing 210003, China 3. Queen’s University, Belfast, Northern Ireland, UK 4. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China 5. Chinese University of Hong Kong, Shenzhen 518100, China 6. Department of Electrical Engineering, Center for Electric Power and Energy, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark

Fund Project:

This work is supported by National Natural Science Foundation of China (No. 51407039), and State Grid Corporation Project “Analysis and function designs of correlations between the power system and its external information”.

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    Abstract:

    An electric vehicle (EV) centred ecosystem has not yet been formed, the existing limited statistic data are far from enough for the analysis of EV users’ travel and charge behaviors, which however tends to be affected by many certain and uncertain factors. An experimental economics (EE) based simulation method can be used to analyze the behaviors of key participants in a system. However, it is restricted by the system size, experimental site and the number of qualified human participants. Therefore, this method is hard to be adopted for the behavioral analysis of a large number of human participants. In this paper, a new method combining a questionnaire statistics and the EEbased simulation is proposed. The causal relationship is considered in the design of the questionnaires and data extraction, then a multi-agent modeling integration method is introduced in the EE-based simulation, which enables the integration of causal/statistical/behavioral models into the multi-agent framework to reflect the EV users’ travel willingness statistically. The generated multi-agents are used to replace human participants in the EE-based simulation in order to evaluate EV users’ travel demands in different scenarios, and compare the differences of simulated or measured travel behaviors between potential EV users and internal combustion engine (ICE) vehicle users.

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  • Received:
  • Revised:
  • Adopted:
  • Online: November 11,2018
  • Published: