## 1 Introduction
In addition, the performance of a DES needs to be evaluated from economic, environmental, energy efficiency, and other perspectives, thus the multi-objective optimization algorithm can improve the performance of a DES more comprehensively than single objective strategies. In the design phase of a DES, the operational strategy determines the operational mode of the DES equipment, thus it affects the selection and rated capacity of DES equipment. At present, a DES is usually optimized based on FEL and FTL, which has the advantages of reducing the complexity of DES optimization and operational management; however, this method is prone to cause unreasonable energy distributions and reduce energy efficiency [3; 19]. Therefore, various researchers improved the operational strategies. [PERSON] et al. [12] proposed the seasonal operational strategy (FSS), which could be seasonally adjusted by combining the cooling and heating ratio of the DES. [PERSON] et al. [20] proposed a hybrid thermal and power operational strategy that switched operational modes according to the monthly cooling and thermal power load demand ratio. [PERSON] et al. [21] proposed a novel operational strategy utilizing system flexibility to schedule energy dispatch, which reduced dependence on the power grid and the underused fuel consumption, as compared with traditional FEL and FTL strategies. Although the above studies on operational strategies improve the performance of DES, the cooling/heating or power load is still used as the unique constraint to determine the load rate of the equipment, resulting in a too high or too low equipment load rate. The load structure has an important influence on the optimization of a DES. Taking the data center as an example, its annual cooling/electricity ratio and heating/electricity ratio is highly stable and suitable for the energy supply of a DES [22; 23]. It not only improves the economic efficiency and the energy efficiency of the system, but also improves the security of the power supply and reduces the dependence on the power grid [22; 24]. However, in other scenarios, the load structure is often uncertain and fluctuates [25; 26]. Robust optimization is a leading technology with which to solve uncertain problems. [PERSON] et al. [27] described the uncertainties of energy and time as a nonlinear bilevel robust optimization model, transforming them into a single-stage mixed integer linear problem for optimization and obtaining the optimal electricity and heat prices for interactions with each distributed energy system. Furthermore, other researchers adopted other methods to study the load structure. [PERSON] et al. [18] constructed an optimization model to minimize the system cost under load discrete conditions, which is used to compare and analyze the annual cost under different load structure conditions. [PERSON] et al. [28] employed a chance-constrained method to address the load uncertainty and transform the uncertain optimization into deterministic optimization. Their results show that increasing the confidence level resulted in a capacity increase in the waste heat boiler in heating mode. [PERSON] et al. [29] proposed an optimal design method for the multi-energy complementary integrated energy system considering load uncertainties. Although these studies have optimized the DES considering the uncertainty of the load structure, the optimization of the DES that is based on the typical daily load in winter and summer ignores the fluctuation of the annual load of buildings, which leads to a deviation in optimization results from the optimal solution. In addition, the fluctuation studied by the extant load simulation method needs repeated simulations that involve constantly changing parameters, resulting in exorbitant calculation and strong randomness. Although researchers in relevant domains have realized that the fluctuation in the load has a negative impact on the performance of the DES, the existing methods are difficult to assess.