Heating by electricity, replacing coal by electricity, is regarded as an effective way to solve the environment problem. Thus electric heating load is growing rapidly, which may result in some undesired problem to distribution grid due to its randomness and dispersed integration. However, electric heating load may be a kind of energy storage system by optimal control of its operation. So the optimal modeling of electric heating load characteristic considering its randomness is of important for grid planning and construction. In this paper, the heating loads of distributed residential users in a certain area are modeled based on Fanger Thermal Comfort Equation (FTCE) and the PMV thermal comfort index calculation method. Different temperatures are taken into consideration during modeling users’ heating loads. According to the time- varying equation of inside temperature, the heat load demand curve is estimated. And then a multi-objective optimization model for electric heating load with heat energy storage is studied considering demand response (DR), which optimizes economy and comfort index. A fuzzy decision method is proposed considering the influence factors of DR behavior. Finally, the validity of the proposed model is verified by simulations. The results show that the proposed model performances better than the traditional method.Heating by electricity, replacing coal by electricity, is regarded as an effective way to solve the environment problem. Thus electric heating load is growing rapidly, which may result in some undesired problem to distribution grid due to its randomness and dispersed integration. However, electric heating load may be a kind of energy storage system by optimal control of its operation. So the optimal modeling of electric heating load characteristic considering its randomness is of important for grid planning and construction. In this paper, the heating loads of distributed residential users in a certain area are modeled based on Fanger Thermal Comfort Equation (FTCE) and the PMV thermal comfort index calculation method. Different temperatures are taken into consideration during modeling users’ heating loads. According to the time- varying equation of inside temperature, the heat load demand curve is estimated. And then a multi-objective optimization model for electric heating load with heat energy storage ...