Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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A Game Theory Based Interaction Strategy between Residential Users and an Electric Company

  • Wang, Jidong (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University) ;
  • Fang, Kaijie (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University) ;
  • Yang, Yuhao (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University) ;
  • Shi, Yingchen (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University) ;
  • Xu, Daoqiang (State Grid Jiangsu Electric Power Company) ;
  • Zhao, Shuangshuang (State Grid Jiangsu Electric Power Company)
  • Received : 2017.02.27
  • Accepted : 2017.07.18
  • Published : 2018.01.01
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Abstract

With the development of smart grid technology, it has become a hotspot to increase benefits of both residential users and electric power companies through demand response technology and interactive technology. In this paper, the game theory is introduced to the interaction between residential users and an electric company, making a mutually beneficial situation for the two. This paper solves the problem of electricity pricing and load shifting in the interactive behavior by building the game-theoretic process, proposing the interaction strategy and doing the optimization. In the simulation results, the residential users decrease their cost by 11% mainly through shifting the thermal loads and the electric company improves its benefits by 5.6% though electricity pricing. Simulation analysis verifies the validity of the proposed method and shows great revenue for the economy of both sides.

Keywords

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Fig. 1. The structure of a typical residential house

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Fig. 2. Process of the overall strategy

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Fig. 3. Power of the photovoltaic array

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Fig. 4. Active power of non-thermal loads

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Fig. 5. Demand of hot water

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Fig. 6. Active power of non-thermal loads before and afteroptimization

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Fig. 7. The state of the thermal loads before optimization

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Fig. 8. The state of the thermal loads after optimization

Table 1. The users’ and electric company’s objective functions values before and after optimization

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