## 5 Example Analysis  
To simplify the model, the efficiency of the battery is regarded as 1. An example of industrial production processes in manufacturing devices is selected. It is assumed that this task includes 6 steps. In the production processes, Step 1 to Step 3 ought to be accomplished by order and Step 4 to Step 6 ought to be accomplished by order. That means before Step 2 starts, Step 1 should be accomplished already. Before Step 3 starts, Step 1 and Step 2 ought to be finished. Step 4, Step 5, and Step 6 are alike. The normal production work is divided into 24 time slots (each slot lasts for 1 h) and all the work ought to be carried out between 8:00 and 18:00. As is shown in Figure 4, industrial electricity tariff is in accordance with city industrial production patterns, including three sections: peak, valley, and plain. In Figure 4, the unit is \(\Psi\). It represents RMB Yuan. The distributed unit is made up of a 600 kW PV system and a 300 kW wind-power generating unit. A 2000 Ah/480 V lead-acid storage battery is equipped in this system also. The life loss of the system is limited to 1/1000. Figure 4: The price of electricity of a city. Figure 3: Flowchart of the PSO process. Production time of the original, the original loads and the output of the distributed power are shown in Figure 5. The original loads consist of task-continuous processes and uncontrollable load, which is shown by the bars. Specifically, the load bars are stacked in a time slot. Therefore, the original maximum load power (800 kW) also occurs in the tariff peak. According to the energy management model proposed in this paper, the optimized power curve of the energy storage device and the optimal process order are shown in Figure 6. Comparing Figure 6 with Figure 4, it can be found that the period between 14:00 and 17:00 is the advantageous period where the electricity price is low and the output of PV maintains a high level. Therefore, Processes 1, 2, 4 and 5 are shifted into this period. Because of the characteristic of the task-continuous load, Processes 3 and 6 are shifted behind. At the same time, the results arrange the energy storage device to discharge to support Processes 3 and 6. The curve of grid in Figure 6 demonstrates that the schedule reduces both the selling power and the buying power in this period. The reduction of selling power benefits the local consumption of the distributed generation and the reduction of buying power is in favor of saving users' bills. Before optimization, the user's revenue is 6.53 Yuan. However, after optimization, the user's revenue is 8.57 Yuan. Therefore, the amount of revenue increase is 2.04 Yuan. Figure 5: The stacked bars of the original loads and power curve of the distributed generation. Figure 6: The process order and the power curve of the energy storage device after optimization. A comparison of production processes and power allocation sequences before and after optimization shows that this model reduces the load during the high tariff period time and transfers the load to the time slots when distributed energy generation is more prominent. From the power exchange with the grid and the actual power of the battery, we can verify that this model improves the local consumption of the distributed generation. On the other hand, the stability of the power grid has been improved with less distributed energy sent back to the grid. At the same time, the model improves the power curve and effectively responds to the electric tariff signal. Specially, it is obvious that the former disordered working scheme of the task-continuous load is well reorganized and shifted. The optimal scheme of the task-continuous load shown in Figure 6 not only reduces the electricity bill but also makes the working process more compact and smooth. Due to the limitations of the time slot division and the user working time, the task-continuous load optimization becomes relatively concentrated. However, such planning has not uplifted peak loads, thus causing no extra pressure on the power grid.