## 5 Example Analysis  
In this paper, the typical daily load data of a park are selected, and the structural model diagram of an electricity-gas integrated energy system shown in Figure 1 is used for simulation analysis, and the parameters of each piece of equipment are shown in Table 1 [49; 50; 51; 52]. The system's new energy generation and load forecast data are shown in Figure 2, using peak and valley tariffs as shown in Table 2. In this paper, a particle swarm algorithm is used to simulate and analyze the optimal dispatching model of an integrated electricity-gas energy system to minimize the daily operating cost of the system. From Figure 3, it can be seen that during the valley tariff period, the new energy output is not enough to provide the electric load required, so it is necessary to purchase power from the grid, and the battery is charged at this time so that it can be discharged during the peak tariff period to reduce the power purchased from the grid; during the peak tariff period, the new energy and gas turbine coordinate to supply power, and when the new energy output is insufficient, the battery starts to discharge to provide the rest of the required power together with the gas turbine. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters of Each Equipment** & **Value/Unit** \\ \hline Rated power of electric chillers & 200 kW \\ Rated power of the electric boiler & 150 kW \\ The power rating of P2G & 200 kW \\ Rated power of the micro gas turbine & 200 kW \\ The power rating of heat exchangers & 180 kW \\ The power rating of absorption chillers & 200 kW \\ The initial capacity of the battery & 200 kWh \\ Maximum battery state of charge & 0.9 \\ Minimum battery state of charge & 0.2 \\ Battery charging and discharging efficiency & 0.97 \\ The efficiency of electric chillers & 4 \\ The efficiency of electric boilers & 0.94 \\ The efficiency of P2G & 0.6 \\ Micro gas turbine efficiency & 0.32 \\ Heat exchanger efficiency & 0.9 \\ Unit maintenance cost of electric chillers & 0.01 yuan/kWh \\ Unit maintenance cost of electric boilers & 0.02 yuan/kWh \\ Unit maintenance cost of P2G & 0.12 yuan/kWh \\ Unit maintenance cost of micro gas turbines & 0.23 yuan/kWh \\ Heat exchanger unit maintenance cost & 0.025 yuan/kWh \\ Unit maintenance cost of absorption chillers & 0.025 yuan/kWh \\ Energy storage per day investment cost & 1000 yuan \\ \hline \hline \end{tabular}
\end{table} 
Table 1: System equipment parameters. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Project Category (Tariff)** & **Time Period (h)** & **Unit Price (yuan/kWh)** \\ \hline Peak & 8:00–11:00 & 0.886 \\  & 18:00–20:00 & 0.886 \\ Flat value & 11:00–18:00 & 0.559 \\ Valley value & 23:00–8:00 the next day & 0.223 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Peak and valley period electricity prices. From the thermal power optimization results in Figure 4, it can be seen that the heat load demand is high, and the electric boiler and heat exchanger are needed to provide heat energy at the same time. In the period of the flat tariff, the heat load demand is relatively small, and the heat energy is mainly provided by the gas turbine and the heat exchanger, and in the period of the valley tariff, the electric boiler is subject to the upper limit of output, and the heat exchanger is required to provide heat energy together. From the cold power optimization results in Figure 5, it can be seen that the energy required for the cold load is mainly provided by the electric chiller, which is because the electric chiller has lower operating costs than the gas turbine. When the waste heat generated by the gas turbine is not fully utilized by the heat exchanger, the absorption  
Figure 3: Electric power optimization diagram. Figure 2: New energy power generation and load day prediction. chiller uses the remaining waste heat to provide energy to the cold load at this time, avoiding the excess cost of the gas turbine due to heat dissipation.