# Period Cycle Optimization of Integrated Energy Systems with Long-Term Scheduling Consideration
## 3 Results and Discussion  
### Real-Time Scheduling Data  
The system will provide real-time scheduling data for cases, which differ from conventional scheduling data. It is based on long-term evaluated parameters and takes into account the power related to the total energy consumption, carbon emissions, and energy costs of each component due to their operating time. Therefore, short-term overall scheduling plans are not considered, and long-term operational scheduling optimization is employed. Within the operating time of the energy structure, an optimized operational plan for the energy structure over a long period and multiple cycles is created by displaying real-time loads of various energy components in the aforementioned real-time scheduling data and combining them with the attributes of capacity and power of the components.  
In this case study, the final energy demands of the energy structure are categorized into electricity, heating, and cooling requirements, with load curves for electricity, heating, and cooling being provided on an hourly basis. Figure 2 displays the real-time power of each energy component within one operating cycle for electric, heating, and cooling energy loads. By analyzing the variations in the long-term load rates of energy components, the energy utilization methods are continuously optimized based on the previous cycle's operation. This ensures the comprehensive optimization of the energy structure's generation, storage, and supply operation states in each operating cycle.  
Figure 3 presents the distribution of the load proportions in this case study across three optimization cycles. The first circle from the inside out represents the energy storage proportions for the three major storage components, namely electricity, heat, and cold. Moving outward, the proportions indicate the energy interaction ratios for each component under electrical, thermal, and refrigeration loads, respectively. A comparison of the three operational cycles reveals that the proportion of electricity storage is significantly higher  
\begin{table}
\begin{tabular}{c c} \hline
**Item (Unit)** & **Value** \\ \hline Runtime (h) & 8760 \\ Run optimized population size & 20 \\ Run optimization iterations & 25 \\ Mutation operator & 1.8 \\ Cross operator & 0.62 \\ \hline \end{tabular}
\end{table}
Table 4: System optimization parameters.  
Figure 2: Load profiles of various energy components within one operating cycle. ((**a**) represents the power load curve of each energy equipment in a period; (**b**) represents the heat load curve of each energy equipment in a period; (**c**) represents the cold load curve of each energy equipment in a period).  
than that of heat and refrigeration storage. There are two reasons for this: first, the efficiency of refrigeration and heat storage is much lower than that of electricity storage; second, by utilizing electricity storage, excess power can be transferred and stored during periods of low electricity prices and sold or used as a backup power source during periods of high electricity prices. To reduce energy and grid costs, the system improves energy dispatch by deliberately reducing refrigeration and heat storage while increasing electricity storage in this energy structure. As the case study progresses, the utilization of CHP and gas boilers gradually increases. This is primarily due to the implementation of time-of-use electricity pricing, where the increased use of natural gas replaces electricity from the high-priced grid, thereby reducing costs and emissions. As the overall efficiency of CHP is higher than that of gas boilers, the quantity of gas boilers used is smaller than that of CHP.