• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2022, Vol. 43 ›› Issue (12): 47-55.doi: 10.12204/j.issn.1000-7229.2022.12.005

• Planning and Design • Previous Articles     Next Articles

Energy Internet-oriented Optimization Planning Method for Intelligent Sensing Equipment of Zero-Carbon Park

PAN Xiao1, ZHANG Mingli1, HAN Zhentao1, HU Jingwei1, LIU Jiaheng2, GE Leijiao2()   

  1. 1. State Grid Liaoning Electric Power Company Limited Economic Research Institute, Shenyang 110015, China
    2. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072,China
  • Received:2022-05-09 Online:2022-12-01 Published:2022-12-06
  • Contact: GE Leijiao E-mail:legendglj99@163.com
  • Supported by:
    State Grid Corporation of China Research Program(5400-202128572A-0-5-SF)


Energy internet-oriented zero-carbon park is dominated by new energy sources, bringing together a high percentage of renewable energy sources such as wind, biomass and solar energy, hydrogen generation, coal power and other forms of energy. However, there is little research on data state sensing of equipment in zero-carbon parks. In order to reasonably plan intelligent sensing devices for data collection and analysis in zero-carbon parks and ensure the reliable, safe, high-quality, low-carbon and economic operation of energy systems in zero-carbon parks, this paper proposes an energy Internet-oriented optimal planning method for intelligent sensing devices in zero-carbon parks. Firstly, the paper analyzes the requirements of state sensing devices in zero-carbon parks, formulates the principles of intelligent sensing device optimization planning, considers the investment cost, maintenance cost and failure cost, and proposes a mathematical model for intelligent sensing device optimization planning in zero-carbon parks. Secondly, in order to realize the accurate solution of the formulated mathematical model, the paper proposes a grey wolf and teaching-learning hybrid optimization (GWO-TLBO) algorithm. Finally, a practical case of a zero-carbon park is used as a simulation example to verify that the proposed intelligent sensing device optimization planning method for zero-carbon park can significantly reduce the life-cycle cost. The comparison experiments with existing intelligent algorithms show that the proposed GWO-TLBO has the highest solution accuracy.

Key words: zero-carbon park, intelligent sensing equipment, optimized planning, energy internet, grey wolf and teaching-learning hybrid optimization algorithm

CLC Number: