1 Department of Electrical Engineering, Technical University of Denmark2 Center for Electric Power and Energy, Department of Electrical Engineering, Technical University of Denmark3 Electric power systems, Center for Electric Power and Energy, Department of Electrical Engineering, Technical University of Denmark4 State Grid Electric Power System Research Institute5 Nanjing University of Information Science and Technology6 State Grid Electric Power Research Institute7 Queen's University Belfast8 State Grid Shanghai Municipal Electric Power Company9 Zhejiang University10 Queen's University Belfast11 Zhejiang University
Traditional experimental economics methods often consume enormous resources of qualified human participants,and the inconsistence of a participant’s decisions among repeated trials prevents investigation from sensitivity analyses. The problem can be solved if computer agents are capable of generating similar behaviors as the given participants in experiments. An experimental economics based analysis method is presented to extract deep information from questionnaire data and emulate any number of participants.Taking the customers’ willingness to purchase electric vehicles(EVs) as an example, multi-layer correlation information is extracted from a limited number of questionnaires. Multiagents mimicking the inquired potential customers are modelled through matching the probabilistic distributions of their willingness embedded in the questionnaires. The authenticity of both the model and the algorithm is validated by comparing the agent-based Monte Carlo simulation results with the questionnaire-based deduction results. With the aid of agent models, the effects of minority agents with specific preferences on the results are also discussed.
Journal of Modern Power Systems and Clean Energy, 2015, Vol 3, Issue 2, p. 149-159
Behavioral analysis; Experimental economics; Human experimenters; Knowledge extraction; Multi- agents; EV purchase