Heating by electricity rather than coal is considered one effective way to reduce environmental problems. Thus, the electric heating load is growing rapidly, which may cause undesired problems in distribution grids be...Heating by electricity rather than coal is considered one effective way to reduce environmental problems. Thus, the electric heating load is growing rapidly, which may cause undesired problems in distribution grids because of the randomness and dispersed integration of the load. However, the electric heating load may also function as an energy storage system with optimal operational control. Therefore, the optimal modeling of electric heating load characteristics, considering its randomness, is important for grid planning and construction. In this study, the heating loads of distributed residential users in a certain area are modeled based on the Fanger thermal comfort equation and the predicted mean vote thermal comfort index calculation method. Different temperatures are considered while modeling the users' heating loads. The heat load demand curve is estimated according to the time-varying equation of interior temperature. A multi-objective optimization model for the electric heating load with heat energy storage is then studied considering the demand response(DR), which optimizes economy and the comfort index. A fuzzy decision method is proposed, considering the factors influencing DR behavior. Finally, the validity of the proposed model is verified by simulations. The results show that the proposed model performs better than the traditional method.展开更多
基金supported by the State Grid Science and Technology Project(No.52020118000M)
文摘Heating by electricity rather than coal is considered one effective way to reduce environmental problems. Thus, the electric heating load is growing rapidly, which may cause undesired problems in distribution grids because of the randomness and dispersed integration of the load. However, the electric heating load may also function as an energy storage system with optimal operational control. Therefore, the optimal modeling of electric heating load characteristics, considering its randomness, is important for grid planning and construction. In this study, the heating loads of distributed residential users in a certain area are modeled based on the Fanger thermal comfort equation and the predicted mean vote thermal comfort index calculation method. Different temperatures are considered while modeling the users' heating loads. The heat load demand curve is estimated according to the time-varying equation of interior temperature. A multi-objective optimization model for the electric heating load with heat energy storage is then studied considering the demand response(DR), which optimizes economy and the comfort index. A fuzzy decision method is proposed, considering the factors influencing DR behavior. Finally, the validity of the proposed model is verified by simulations. The results show that the proposed model performs better than the traditional method.
