# Sizing Hybrid Energy Storage Systems for Distributed Power Systems under Multi-Time Scales  
[PERSON]  
1 Department of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China; [EMAIL_ADDRESS] (H.L. ); [EMAIL_ADDRESS] (M.D. )1 Department of Power Consumption, China Electric Power Research Institute, Beijing 100192, China; [EMAIL_ADDRESS] (D.L. ); [EMAIL_ADDRESS] (Y.L. )1 Department of fundamental teaching, Changchun Architecture and Civilengeering College, Changchun 130022, China; [EMAIL_ADDRESS] 12  
In recent years, many models have been proposed to optimize the installation capacity of a distributed energy storage system. However, by selecting different simulation time scales and optimization criterion, the results of the optimal capacity are quite different. In [8; 9; 10; 11; 12], one hour is selected as the simulation time scale to configure the capacity of a distributed energy storage system. Among them, in [8], the capacity of a distributed energy storage system is configured based on the redundant energy generated by renewable energy generators. In addition, to meet the peak demands of an isolated island, [PERSON] et al [9] design the optimal energy storage capacity of PV systems according to system stability constraints. Similarly, system spinning reserve and the unit commitment problem are fully considered in [10] to estimate the ideal energy storage system capacity for both grid-connected and off-grid power systems. Moreover, [PERSON] et al. sized system battery capacity, aiming at reducing the average yearly energy costs of users who have participated in time-of-use (TOU) tariffs [11]. Finally, and crucially, as the mainstream method of configuring energy storage capacity, battery capacity is designed on the basis of the capability of shaving peak demands [12]. In summary, an hour-based energy storage system configuring is commonly focused on improving system economy and security, but it usually neglects the dynamic characteristic of renewable generation. To analyze the dynamic characteristic of renewable energy generation systems, one second is selected as the simulation cycle in [13] to configure the capacity of a flywheel battery hybrid energy storage system. However, [PERSON] et al. do not take system economy into account. Therefore, this paper is the first time that a battery and super-capacitor storage system is configured under multi-time scales while taking system economy, security, and the fluctuation of a renewable energy system into consideration. After translating distributed energy storage system capacity configuration problems into multi-objective optimization problems, many scholars have applied different algorithms to solve the proposed problems. [PERSON] et al [10; 14] use mixed-integer linear programming (MILP) to optimize the capacity and the operation strategy of energy storage systems for microgrids. Additionally, considering the efficiency and operation characteristics of the storage devices, dynamic programming is used in [15] to determine the optimal capacity of a vanadium redox battery (VRB). Moreover, some scholars use metaheuristic algorithms to find the optimal capacity of distributed system energy storage systems [16; 17]. The aforementioned papers have good performance on translating energy storage configuration problems into multi-objective optimization problems, generating a relatively high accuracy quantitative analysis, and revealing the best capacity and operation strategy of energy storage systems. However, these papers cannot directly point out the influence of different factors on the energy storage system configuration. Therefore, this paper firstly analyzes the key factors that can affect the installation capacity of distributed energy storage systems, and afterwards intuitively displays the key factors that can affect energy storage system installation capacity in a three-dimensional (3D) graph and carries out quantitative analysis.