)1 Department of Electrical Engineering, College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; [EMAIL_ADDRESS] (F.-J.Z. ); [EMAIL_ADDRESS] (Q.-Y.S. )1 Department of Electrical Engineering, Northeastern University, Shenyang 110819, China; [EMAIL_ADDRESS] (F.-J.Z. ); [EMAIL_ADDRESS] (Q.-Y.S. )1 Department of Electrical Engineering, College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; 1870514@stu. power quality problems [2, 3]. Due to the intensification of environmental problems and the oversupply of electrical energy, electric thermal storage heating equipment, which holds the function of time shifting and storage of electrical energy, has been widely applied. In fact, the location and capacity of ETSHE is closely related to the characteristics of heat load. The characteristics of heat load mainly include the duration of heat load and the distribution of heat load within the day. There are various factors that affect load such as population, weather, and holidays, etc. The relationship between these factors and load is non-linear, which is even more difficult to describe and predict. The difference of energy use characteristics of thermal load brings new challenges to distribution network planning. For different thermal loads, the optimal configuration planning of ETSHE will also change accordingly, therefore, load portrait is the premise and foundation of optimal operation [4]. In light of the improvement of power quality, scholars at home and abroad have found that the goal of improving power quality can be achieved through the reasonable allocation of energy storage devices at corresponding nodes. However, traditional storage battery energy storage equipment, with poor peak-regulating capacity and high cost, cannot meet the needs of heat users. However, the storage of energy through the ETSHE can make good use of its time-shifting and storage characteristics to meet the multi-energy needs of users. However, the different access locations and capacity of the energy storage systems have a great influence on the power quality governance, so the choice of access location and the determination of capacity are problems that need to be solved urgently [5]. In [6], a constant capacity optimization model for energy storage equipment location was presented and solved by genetic algorithm. Meanwhile, the total cost was saved by adding the capacity correction link. In [7], a two-layer optimization method for multi-point distributed energy storage system location and fixed capacity in high photovoltaic penetration distribution networks was proposed. The authors in [6, 7] only considered traditional electric energy storage. In addition to the basic energy storage planning presented above, some have also studied the energy storage planning method considering the demand characteristics. Such as [8], the proposed polygeneration microgrids system includes trigeneration plants, renewables, district heating, thermal and electrical storage systems and flexible loads, building demand-response model and storage systems model. Thermal storage proved to potentially have a very relevant impact on the microgrid operation, and the analysis also showed the potential reduction of the operating costs that could be achieved by demand-response. Furthermore, the authors in [9, 10] realized the development of an energy management system (EMS), which was implemented and run at the smart polygeneration microgrid (SPM), that modified the existing EMS model and predictive control to take into account the existence of a thermal storage system with a suitable capacity. It is worth noting that the heat storage has been represented by resorting to a new battery-type model that is able to predict the transient behavior of the grid, according to the historical series of heat and electricity demand data during 2017. In such a way, it has been possible to calculate the optimal size of the thermal storage that maximizes the global saving.