# Research on Optimized Energy Scheduling of Rural Microgrid
## 6 Solution Procedure
### Scheduling Results and Analysis  
In this study, power generation and electricity consumption data with a time resolution of 30 min in a rural area in Shanghai, China, are used for simulation. Moreover, the dispatching scheme and optimal storage capacity of rural microgrid can be solved by using the solver LINGO, which is a software specially used for solving optimization problems. To simplify the calculation, several reasonable assumptions are made. First, users and the CES system cannot sell excess power to the grid. Second, there is no energy available in the CES system in the initial state. Third, the network loss and line loss during power transmission is negligible. Fourth, it is assumed that all rural users do not purchase distributed energy-storage devices, and they can meet their energy-storage requirements by ordering energy-storage services from CES system. Fifth, it is assumed that the charging and discharging characteristics of the CES battery are linear and independent of the internal characteristics of the battery. The parameters of CES service and power generation cost are as shown in Table 4.  
Users can periodically adjust the storage capacity ordered from CES according to the situation of power consumption and power generation. The size of storage capacity will affect the scheduling and management of energy. The total electricity cost is composed of grid electricity cost, storage cost and generation cost, which fluctuates with the change of the energy-storage capacity. Therefore, ordering the optimal CES storage capacity can further reduce the electricity costs of the microgrid. According to the predicted power consumption of all loads in a day, the optimal storage capacity and electricity costs suitable for that day are calculated, as shown in Figure 9. The fluctuations of each cost are not exactly same. It can be found that the grid electricity cost reaches the local optimal when the capacity is 44 kWh, but the total electricity cost is not the minimum at this time. The total cost \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Numerical Value** \\ \hline \(\lambda_{\alpha\beta}^{ddch}\) & 0.98 \\ \(\lambda_{\alpha\beta}^{ddch}\) & 0.98 \\ \(S_{b,max}\) & 0.9 \\ \(S_{b,min}\) & 0.2 \\ \(\alpha\) (RMB/kWh) & 0.469 \\ \(C_{wind}\) (RMB/kWh) & 0.5 \\ \(C_{pv}\) (RMB/kWh) & 0.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters of energy storage and power generation. Figure 8: Load forecasting results of rural microgrid: (**a**) residential load forecasting result; (**b**) agricultural load forecasting result; (**c**) total load forecasting result. continues to decline until the local optimum is achieved at 47 kWh, after which the search continues and the optimal capacity is finally determined to be 57 kWh, which is the total storage capacity that the microgrid should order from the CES system. The daily load consumption of the microgrid is different. Adjusting the energy-storage capacity to match the daily power consumption can help users improve energy utilization and thus reduce power costs. The above experiment verifies that the optimal energy-storage capacity can help users minimize the electricity cost of the scheduling scheme. In order to further verify that the rural microgrid architecture with CES proposed in this paper has better scheduling results, we designed the following two cases. Case 1: All users in the rural microgrid are equipped with distributed energy-storage equipment. The storage capacity is fixed and the total capacity of the equipment is 57 kWh. The forecast data, power generation data and load data are input into LINGO to solve the energy-dispatching scheme. Case 2: All users in the rural microgrid use cloud energy-storage equipment, and the total energy-storage capacity is adjusted to 57 kWh. The same forecast data, power generation data, and load data as in case 1 are input into LINGO to solve the energy-dispatching scheme.