An Optimal Operation Strategy of Regenerative Electric Heating Considering the Difference in User Thermal Comfort

Duojiao Guan, Zhongnan Feng, Li Song, Kun Hu, Zhenjia Li and Peng Ye ()
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Duojiao Guan: School of Electric Power, Shenyang Institute of Engineering, Shenyang 110136, China
Zhongnan Feng: State Grid Liaoning Electric Power Co., Ltd., Liaoyang Power Supply Company, Liaoyang 110006, China
Li Song: State Grid Liaoning Electric Power Co., Ltd., Liaoyang Power Supply Company, Liaoyang 110006, China
Kun Hu: State Grid Liaoning Electric Power Co., Ltd., Liaoyang Power Supply Company, Liaoyang 110006, China
Zhenjia Li: School of Electric Power, Shenyang Institute of Engineering, Shenyang 110136, China
Peng Ye: School of Electric Power, Shenyang Institute of Engineering, Shenyang 110136, China

Energies, 2023, vol. 16, issue 15, 1-20

Abstract: Regenerative electric heating has gradually become one of the main forms of winter heating with the promotion of “coal to electricity” project. By fully exploiting its regulating capacity, it can effectively achieve a win–win situation of “peak shaving and valley filling” on the grid side and “demand response” on the customer side. In order to meet the different heating demands of users, a regenerative electric heating optimization and control strategy is proposed, taking into account the difference in users’ thermal comfort. Firstly, the reasons for the difference in user thermal comfort are analyzed, and the differentiated preference factors are calculated based on the maximum likelihood estimation method to design differentiated heating schemes. Then, a dynamic optimization and control model for regenerative electric heating with comfort and economic evaluation indicators is established and solved by using quantum genetic algorithm. Finally, a numerical example is used for simulation analysis. The research results show that the strategy proposed in this paper can take into account the comfort of customers and the economy of peaking and low load shifting, so that the operation of regenerative electric heating can respond to the different needs of different customer groups, and realize flexible adjustment at any time of the day.

Keywords: regenerative electric heating; maximum likelihood estimation; difference in thermal comfort; quantum genetic algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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