## 3 Results and Discussion  
In this section, we provide further explanation and clarification of the ST-MPC control method proposed in this paper for solving the microgrid supply-demand balancing problem with constraints on battery and hydrogen storage capacities using the MATLAB R2024a simulation platform. This simulation example considers three components: the power generation side composed of renewable energy sources such as wind and photovoltaic, the control part consisting of a rolling optimization controller, and the demand side composed of loads. From [9; 10; 11; 12], the simulation will assume that during a short period of control time, the output of the energy supply side remains relatively stable, and the energy input demand on the demand side changes, thereby mobilizing the various microgrids in the power grid to supplement and store energy supply. The relevant parameters used in this simulation example are shown in Table 1.  
The relevant parameters satisfying the conditions in the article are set as follows: \(\tau/h=0.05\), \(H_{p}=10\), \(Q=[6,0;0,6]\), \(R=[1,0;0,1]\), \(K=2\), \(L_{J}=1.4\), \(L_{f}=1.2\), \(\alpha=0.85\). In the current simulation experiment, as shown in Figure 2, the power supply side can continuously provide stable energy within 24 h, while the power consumption by the load side during this period is as follows:  
\[P_{Load}(t)=5-3 sin(\frac{\tau t}{60}).\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Value** & **Variable** & **Value** \\ \hline \(\eta_{Bat}\) & 85\% & min\(\chi_{soc}\) & 30 \\ \(\eta_{H}\) & 90\% & max\(\chi_{soc}\) & 70 \\ \(C_{max}\) & 125 & min\(\chi_{loh}\) & 20 \\ \(V_{max}\) & 125 & max\(\chi_{loh}\) & 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the electrochemical-to-gas conversion devices. Figure 2: Power of the supply and demand sides. Based on the simulation results graph depicting the variation in battery capacity shown in Figure 3, it can be observed that within 24 h, the battery capacity remains within the constraints (i.e., \(30\leq\chi_{soc}\leq 70\)). The simulation results graph in Figure 4 illustrates the variation in the capacity of the hydrogen storage tank in an AC/DC hybrid microgrid based on ST-MPC. According to the simulation experiment results, it is evident that within 24 h, the hydrogen storage tank capacity remains within the prescribed constraints (i.e., \(20\leq\chi_{Loh}\leq 60\)), and there are no instances of exceeding the constraint boundaries. The situation described in Figure 5 is a simulation result graph illustrating the state changes in battery charging and discharging. From this graph, it can be observed that within 24 h, the battery charging and discharging behavior, after a brief adjustment by ST-MPC, effectively tracks the variations in power supply and demand (as shown in Figure 2). Figure 6 represents the simulation results of the energy consumption of the electrolyzer or the electricity generation by the fuel cell over a certain time period. From the information presented in the graph, it is evident that the charging and dis  
Figure 4: Hydrogen storage tank capacity variation. Figure 3: Battery capacity variation. electrolytezer, fuel cell, and battery are perfectly synchronized. Thus, the electrolyzer and fuel cell can effectively collaborate with the battery to achieve the conversion and storage of electricity and hydrogen, ultimately accomplishing the peak shaving and valley filling control objective of regulating supply-demand balance. Figure 7 illustrates the triggering time points under ST-MPC control. It can be clearly seen from the figure that under ST-MPC control, the system does not perform continuous control but only controls when the trigger control is needed, effectively saving control resources. Figure 8 is a comparison histogram of the number of ST-MPC triggers in this article and the number of MPC triggers in [22].