401. Multi-agent simulation-based residential electricity pricing schemes design and user selection decision-making
- Author
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Shanlin Yang, Lanlan Li, Kaile Zhou, and Chen Wang
- Subjects
Atmospheric Science ,Operations research ,business.industry ,020209 energy ,Multi-agent system ,Electricity pricing ,02 engineering and technology ,computer.software_genre ,Demand response ,Intelligent agent ,Electric power system ,0202 electrical engineering, electronic engineering, information engineering ,Earth and Planetary Sciences (miscellaneous) ,Economics ,Electricity market ,Electric power ,Electricity ,Marketing ,business ,computer ,Water Science and Technology - Abstract
Multi-agent system employs the functions of communication, coordination and cooperation among intelligent agents to help people judge and analyze complex phenomena that cannot be directly observed, and it has become an important tool for solving large-scale complex problems. The problem of demand response (DR) in electric power system is difficult to be modeled due to the complicated environment and continuously evolving subjects. Multi-agent system can simulate the operation mechanism of electric power system, thus playing an important role in solving the DR problems. In this study, based on multi-agent simulation, we establish a multi-agent model of residential power market and propose a satisfaction function of residential users about electricity price. We focus on the interaction process among all the agents of power supply side, selling side and demand side and conduct simulation to obtain the selection and decision-making of residential users on different electricity pricing schemes. The results show that multi-agent system is beneficial to analyze, simulate and solve the DR problem in power market. Also, the satisfaction function of residential users on electricity price can support power selling enterprise to better understand the intention of residential users when choosing electricity pricing schemes and participating in DR program.
- Published
- 2017
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