*5.1. Optimization Results*

This study aims to improve the economy and environmental protection of the system by combining ESS. By arranging the energy flow of the CCHP system properly, it can reduce the pressure on the grid and the pollutant gas emissions. Meanwhile, ESS makes a profit by serving the CCHP system. The improvements to the original AO algorithm have improved its performance. The gray wolf optimizer (GWO), the whale optimization algorithm (WOA), and the original AO algorithm are selected as comparison algorithms to verify the performance of the IAO algorithm. Based on the different schemes, four algorithms are used to optimize the system, respectively. Section 3.3 provides a specific description of the schemes. The results are the average values of 50 runs based on the program. The convergence curves of the four algorithms are shown in Figures 5–7.

**Figure 5.** The convergence curves of Place 1 under different schemes.

**Figure 6.** The convergence curves of Place 2 under different schemes.

**Figure 7.** The convergence curves of Place 3 under different schemes.

The analysis of the convergence curves reveals the following conclusions.

(1) The convergence speed of the IAO algorithm is significantly improved compared to the original AO algorithm. At the iteration number of 100, the IAO algorithm can already converge to a smaller value. Moreover, the convergence speed of the IAO algorithm is faster compared with GWO and WOA.

(2) The convergence accuracy of the IAO algorithm is higher. When reaching the maximum number of iterations, the convergence value of the IAO algorithm is smaller than the convergence values of the other three compared algorithms.

(3) For the same place and optimization algorithm, the convergence values of the iterative curves based on different schemes have significant differences. Specifically, the convergence value of the iteration curve is smaller when the system is operating with scheme 1.

The above results show that the improvement to the AO algorithm is effective. The convergence speed and convergence accuracy of the IAO algorithm are better than the comparison algorithms. Meanwhile, the operation scheme proposed in this study has better economy and environmental protection.

Table 6 shows the objective function values and pollutant gas emissions for different operation schemes.


**Table 6.** Objective function values and pollutant gas emissions.

Note: *F* indicates the objective function value and *G* indicates the pollutant gas emission.

The data in Table 6 more intuitively show the effectiveness of the optimal configuration using the IAO algorithm. For different places and different schemes, the proposed algorithm can obtain the minimum objective function value. Therefore, the IAO algorithm has better search capability and stability than other algorithms. Meanwhile, the calculation shows

that the daily economic cost obtained by the IAO algorithm is lower when the systems of the three places are operating with scheme 1. Compared with the cost of scheme 2, the values decreased by 20.54%, 17.95%, and 25.46%, respectively. Compared with the cost when the systems of three places are operating with scheme 3, the values decreased by 37.88%, 33.88%, and 43.38%, respectively. The values for pollutant gas emissions decreased by 16.8%, 28.57%, 32.93% and 32.45%, 43.37%, 49.39%, respectively. The results indicate that the construction of ESS is beneficial to the operation of the CCHP system. Therefore, scheme 1 achieves both economic and environmental improvements for the user side of the CCHP system.

This study aims to take advantage of the scale of ESS to improve the performance of the CCHP system. At the same time, the ESS operator can be profitable. Table 7 shows the capacity configurations and daily investment costs of the energy storage batteries and the revenue of the operator.

**Table 7.** Energy storage configuration results.


Note: The cost is obtained by Equation (12) and the revenue is obtained by Equation (16).

From the data in Table 7, it can be found that compared to the CCHP system in scheme 2, which is configured with energy storage equipment alone, in scheme 1, ESS can be configured with larger capacity energy storage batteries. Compared with scheme 2, although the form of building ESS will increase the investment cost in energy storage equipment, the merchant still has room for profit. Therefore, the configuration of energy storage equipment proposed in this study is feasible. We consider the profitability of the merchant, and also take into account the economy and environmental protection of the CCHP system. A win-win situation can be achieved by operating ESS with multiple CCHP systems.
