**2. Literature Review**

Conventional power plants are used at about 30% efficiency for fuel energy, with most of the remaining energy lost in the form of waste heat [27]. After more than a century of development, the CCHP system has been proven to be an efficient and energy-efficient device operation mode [28]. The advancement of technology has gradually changed the fuel source and energy circulation mode of CCHP systems. Nami et al. [29] established a model of the solar-assisted biomass energy CCHP system, and analyzed the key factors of system performance optimization. The results show that the energy provided by the system can meet the energy demand of household users. However, the system's technological maturity is low, and the complexity is too high. Moreover, the literature does not evaluate the economics of the system. Wei et al. [30] proposed a CCHP system based on proton exchange membrane fuel cells, optimized the system from the aspects of economy and environmental protection by using the improved mayfly optimizer, and obtained the system configuration that is more economical and environmentally friendly than the original algorithm. However, the system lacks clean energy and the optimization process does not consider the problem of environmental friendliness. Tonekaboni et al. [31] applied nanofluid and porous media to a solar collector and used the collector in a CCHP system to improve the energy absorption efficiency of the system. However, the literature lacks a discussion of the economics and environmental protection of the system. Li et al. [32] used the heat pump as the heat source of the CCHP system. Through the analysis of the primary energy saving rate, the carbon dioxide emission reduction rate, and the annual cost saving rate, the system was optimized and analyzed in practical application. However, this system lacks energy storage equipment, which will cause waste of energy. Nazari et al. [33] investigated the effect of electric boiler, hydro storage, and heat storage tank on the scheduling problem of conventional trigeneration system. The results indicated that the use of the model could improve the profits of the system. However, this system does not contain renewable energy, and the environmental protection of the system needs to be improved. Additionally, this system does not contain energy storage devices, and the lack of energy storage devices can cause energy waste. Previous studies have optimized the design of traditional CCHP systems from the perspective of system energy source, equipment material selection, and energy circulation mode. However, due to the difficulty of promoting and applying new materials, the high economic cost of installation, and the instability of new energy sources, the above-mentioned CCHP systems are still in the theoretical research stage.

The energy crisis and environmental issues have promoted the development of clean and renewable energy, and CCHP systems with clean and renewable energy have become a new trend in recent research [34]. Leonzio et al. [35] used software to design and simulate a trigeneration system powered by biogas. The results showed that the system has significant improvements in power generation and carbon dioxide emissions relative to conventional CCHP systems. However, the application scenarios of biogas power generation methods are limited to rural areas and other areas, and large-scale popularization applications cannot be carried out. Dong et al. [36] designed a CCHP system including wind power generation and photovoltaic power generation equipment as the coupling hub of the electric-gas system. Although this system is more economical than separated systems, the cost of wind turbines installation is still too high for the ordinary user, and the wind turbines are often far away from the user side, which is contrary to the installation concept of CCHP systems integrated on the user side. Zhang et al. [37] used a multi-objective particle swarm algorithm to optimize the CCHP system containing photovoltaic power generation, so that different buildings can achieve a balance between economy and environmental protection. Compared with wind power plants, photovoltaic power generation units can be better used in most scenarios, especially in areas where large equipment and power supply from the grid cannot be installed due to geographical constraints [38]. As a relatively mature clean power generation method, photovoltaic power generation is applied to the CCHP system model built in this study. Photovoltaic power generation equipment often needs to be used in conjunction with energy storage devices to alleviate the intermittent characteristics of power generation [39]. However, in previous studies, energy storage devices were generally configured by the user side of the CCHP system [40]. Considering factors such as installation costs and supporting management, the capacity configuration of the energy storage device is smaller, and it plays a less influential role in buffering and relieving the pressure on the grid. This study proposes that using the ESS operating mode, the operator can establish a large capacity of energy storage devices to serve the CCHP system. The CCHP system needs to pay only the service fee in exchange for the power usage and storage rights of the ESS.

The establishment of ESS increases the difficulty of optimizing the configuration of the CCHP system. Mathematical programming and intelligent optimization algorithms are two commonly used methods for optimizing the configuration. Compared to mathematical programming methods, the use of intelligent optimization algorithms does not require extensive mathematical proofs or changing nonlinear terms in the original model [41]. In the previous studies, GA and PSO have often been used in the optimal configuration and scheduling of the system [42,43]. However, the original algorithm has unstable performance and is easy to fall into local extremums, which has spawned scholars to improve the algorithm and produce new algorithms. Wang et al. [44] introduced the chaos and elite strategies into the original PSO algorithm, which improved the search range and search ability of the original algorithm. Cao et al. [2] replaced the random parameter β in the original owl search algorithm with a circular mapping based on the chaotic mechanism to improve the premature convergence problem of the original algorithm. Abualigah et al. [23] proposed the AO algorithm by simulating the process of finding, tracking, and capturing prey by the aquila. The AO algorithm has been applied to optimize scheduling, parameter tuning, and feature extraction, and has shown superior performance to other algorithms [45,46]. However, when the AO algorithm deals with the multi-variable and multidimensional optimal configuration model proposed in this study, there are still problems of slow convergence speed and low convergence accuracy similar to other algorithms. Therefore, this study proposes the IAO algorithm by improving the different stages of the original AO algorithm.
