## 1 Introduction  
It is imperative to develop clean energy and energy conservation technology as a result of the scarcity of fossil fuels and the related environmental problems [1]. Along with the continuing growth of carbon dioxide emissions generated by the energy consumption of buildings, the distributed energy system (DES) has received much attention in terms of reducing energy consumption, increasing renewable energy utilization, and enhancing energy reliability. The DES is an energy supply system with many multiple synonymous names, such as the hybrid renewable energy system (HRES) [2], combined cooling heating and power (CCHP) [3], and the integrated energy system (IES) [4]. In this paper, the acronym DES is used. Compared with the separate energy system (SP), the DES can integrate a variety of clean energy sources, such as natural gas [5], solar energy [6], wind energy [7], biomass energy [4], and other energies to provide cooling, heating, and power for users. The advantages of the DES include financial savings, reduced emissions, improved system reliability, reduced pollution, and flexible operation [8, 9]. Therefore, DES is more and more widely used. The DES contains a variety of equipment, which simultaneously meet the needs of various energy loads, such as cooling, heating, and power [10; 11]. In addition, a variety of energy supply models and operational strategies can be used to improve performance [12; 13]. Therefore, astute design and optimization act to save costs, decrease fossil energy consumption, and reduce carbon emissions in the lifecycle of the building [11]. Common optimization algorithms include programming algorithms and intelligent algorithms. [PERSON] et al. [14] presented a multi-objective optimization strategy to balance the profit and energy costs of a chemical plant with the aim of best managing the production rates of various products to fulfill a sales plan organized to satisfy numerous client requests. [PERSON] et al. [15] established a DES that combined electric vehicles (EV) and hybrid energy storage (HES) to improve energy saving and emission reduction efficiency and reduce system investment operating costs. [PERSON] et al. [16] proposed a mixed integer optimization model by designing a continuation method for integer decision variables and a constraint strategy with double penalties to ensure that the optimal start/stop states and operating power level of the distributed units were accurately configured under the premise of completely consuming renewable energy. [PERSON] et al. [17] proposed a novel distributed energy system combining hybrid energy storage and system optimization configuration, simultaneously considering the operational strategy and a two-layer collaborative optimization method including energy efficiency, economy, and environmental protection. [PERSON] et al. [18] revolutionized the idealized demand-side distribution and built an optimization model for a regional DES under discrete conditions. The optimization of the DES, which took the comprehensive cost as the objective function, determined the number, site, capacity of the energy station, and the optimal pipe network layout path from the energy station to the load center. [PERSON] et al. developed an optimization tool for HRES based on a sequential linear programming (SLP) algorithm, which generates an operating plan over a specified time horizon of the setpoints of each device to meet all electrical and thermal load requirements with the minimum possible operating costs. The optimization of the DES may have a single extreme value or multiple extreme values owing to different constraints and assumptions. Programming algorithms have a higher accuracy and lower computational complexity when the optimization of the DES has a single extreme and clear constraints, but find it difficult to solve multi-extremum states, while intelligent algorithms are aimed at finding the global optimum of the multi-extremum problem as much as possible.