# Research on Optimal Dispatching Strategy for Micro-Energy-Grid of Protected Agriculture
## 5 Optimal Dispatching of Micro-Energy-Grid
### Results  
The day-ahead optimal scheduling in this paper is a 0-1 mixed integer linear programming (MIP) problem. GAMS is an optimization software using CPLEX to solve the MIP problem. The scheduling step size is 1 h and the scheduling cycle is 24 h of a day. Figure 7 shows the result of optimal dispatch of electric power in five cases. The positive value represents the power provided by PV, CHP, grid and storage battery. The negative value represents the power consumed by time-shifting load, non-time-shifting load, ASHP power, surplus power grid and battery charging. Figure 8 shows the optimal dispatch result of thermal power in five cases. The positive value represents the provided thermal power, including waste heat recovery in CHP, heat generation by ASHP and heat release by phase change heat storage device. The negative value represents the consumption of thermal power, including heat load and heat absorption by phase change heat storage device. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case Number** & **Allowed Getting Profit from Grid** & **Time-Shifting or Not** & **Electricity Price** \\ \hline Case 1 & \(\surd\) & \(\times\) & TOD tariff \\ Case 2 & \(\surd\) & \(\surd\) & TOD tariff \\ Case 3 & \(\surd\) & \(\surd\) & Real-time tariff \\ Case 4 & \(\times\) & \(\times\) & TOD tariff \\ Case 5 & \(\times\) & \(\surd\) & TOD tariff \\ \hline \hline \end{tabular}
\end{table} 
Table 13: The specific setup for each case. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Gaseous Pollutants** & **Emission Coefficient (g/kWh)** & **Environmental Costs (Yuan/kWh)** \\ \hline NO & 0.619 & 8.475 \\ CO\({}_{2}\) & 184 & 0.028 \\ CO & 0.17 & 0.983 \\ SO\({}_{2}\) & 0.001 & 5.933 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Emission coefficients of gaseous pollutants and corresponding environmental costs. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** & **Parameters** & **Value** & **Parameters** & **Value** \\ \hline \(SOC_{\max}\) & 0.9 & \(SOH_{\max}\) & 0.9 & \(\eta_{CHP}\) & 0.3 & \(P_{CHPmax}\) & 60 kW \\ \(SOC_{\min}\) & 0.2 & \(SOH_{\min}\) & 0.1 & \(\eta_{l}\) & 0.02 & \(P_{AHPmax}\) & 600 kW \\ \(\eta_{sd}\) & 0.01 & \(\eta_{sh}\) & 0.02 & \(\eta_{AP}\) & 0.9 & \(H_{CHPmax}\) & 122 kW \\ \(\eta_{c}\) & 0.96 & \(\eta_{ch}\) & 0.98 & \(\eta_{COP,AHP}\) & 3.7 & \(H_{AHPmax}\) & 2220 kW \\ \(\eta_{d}\) & 0.96 & \(\eta_{dh}\) & 0.98 & \(C_{\text{cp,CHP}}\) & 0.03 Yuan/kW & \(P_{girdmax}\) & 500 kW \\ \(C_{b}\) & 40 kW & \(C_{h}\) & 50 kWh & \(C_{\text{cp,AP}}\) & 0.02 Yuan/kW & \(P_{girdmin}\) & \(-\)500 kW \\ \(P_{c\max}\) & 50 kW & \(H_{c\max}\) & 50 kW & \(C_{\text{cp,AHP}}\) & 0.02 Yuan/kW & \(C_{\text{cr,PV}}\) & 3.5 Yuan/kW \\ \(P_{d\max}\) & 70 kW & \(H_{d\max}\) & 50 kW & \(C_{\text{cp,PV}}\) & 0.03 Yuan/kW & & \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Equipment parameters in micro-energy-grid. Figure 8: The optimal dispatch result of thermal power in five cases. Figure 7: The result of optimal dispatch of electric power in five cases. Combining Figures 8 and 9, it can be concluded that:  
1. When daylight is sufficient, comparing Cases 3 and 4, we found that the power load is borne by PV whether or not it is permissible the sale of electricity to micro-energy-gird. In Case 3, another source of power load is CHP. However, in Case 4, the source of power load is purchasing from grid and electricity can be converted into heat. 2. The introduction of time-shifting loads can make the micro-energy-grid have the characteristics of load transfer. Comparing Cases 1 and 2, the time-shifting loads were transferred from high electricity price at 16:00 to low electricity price at 6:00, which made the load curve optimized. The input time schedule of time-shifting load is shown in Table 14. Cases 2 and 3 are selected to compare the load response to TOD tariff and real-time price, as shown in Figure 9. Case 2 uses the current TOD tariff and Case 3 calculates the dynamic real-time tariff.