• 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 ›› 2019, Vol. 40 ›› Issue (3): 17-26.doi: 10.3969/j.issn.1000-7229.2019.03.003

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Regression Model for Dynamic Thermal Performance of the District Heating System and Combined Heat and Power Dispatch

LI Ping1,2, WANG Chunsheng3, MU Yongqiang3, WANG Haixia1, CHANG Youyi3, ZHANG Gengyu3, LIU Aimin2, LI Weidong1   

  1. 1. School of Electrical Engineering, Dalian University of Technology, Dalian 116024, Liaoning Province, China;2. State Grid Liaoning Electric Power Co., Ltd. Electric Power Research Institute, Shenyang 110006, China;3. State Grid Liaoning Electric Power Co., Ltd., Shenyang 110006, China
  • Online:2019-03-01
  • Supported by:
    This work is supported by National Natural Science Foundation of China (No. 51607021).

Abstract: Dynamic thermal performance of a district heating system can be utilized to improve the peak regulation capacity of combined heat and power units so as to enhance wind power integration in heating seasons. Modeling dynamic thermal performance with the physical model involves individual characteristics of each part inside the system and topological structure characteristics of pipelines network, resulting in that the combined heat and power dispatch model based on the physical model is complicated, which is difficult to be applied in large-scale power grid analysis. Focusing on the system overall performance, a reduced-form regression model is built to describe the dynamic thermal performance of the district heating system under the quality regulation mode by utilizing the auto-regressive distributed lag time series. The model connotation is analyzed and the modeling method is given. The combined heat and power dispatch model based on the regression model is built. Compared with the dispatch mode based on physical model, the dispatch mode based on the regression model can reduce variable number and calculation time, which has good simplification effect.

Key words: district heating system, dynamic thermal performance, regression model, combined heat and power dispatch, wind power integration

CLC Number: