# A Hybridized Flower Pollination Algorithm and Its Application on Microgrid Operations Planning
## 4 Application of the HSFPA for Microgrid Operations Planning
### Results Analysis and Discussion  
Because the microgrid model has been incorporated with more new energy sources, the parameters of the model space are growing exponentially, causing optimization scheduling as a nonlinear issue to become more complex and difficult to calculate. The traditional methods, e.g., mixed integer programming or gradient methods with quadric solutions, are highest for economic dispatching with a single component of the microgrid system [18; 19]. The traditional techniques are suffered from complex computation and massive executed computation times. This subsection verifies the suggested scheme HSFPA performance by analyzing according to the multi-objectives, Pareto optimal, and weighting selected points optimal for the issues of the microgrid operations planning. The multi-object HSFPA (MHSPFA) outcomes are compared with the popular multi-object methods, e.g., multi-object FPA (MOFPA) [54], multi-object SCA (MOSCA) [55], and multi-object evolution algorithm (MOEA/D) [56]. The setting parameters for the algorithms and environment for the microgrid system are conducted for the operations planning based on several scenarios of scheduling periods of the days, months, and years, e.g., the period day is divided into 24 h periods; 30 days/month, and 12 months/year. The selected point in Pareto optimality is used to conduct the weighting multi-object by applying the MHSPPA. The outcome results of the weighting MHSPFA are also compared with the other approaches in the literature, e.g., the MBGA [57], PSO [28], and FPA schemes. The parameters for the algorithms refer to Table 2, and the other setting parameters, e.g., for MEA/D: the \(P_{c}=0.3\), \(P_{m}=0.01\); for PSO: inertia factor \(\omega_{max}=0.95\), \(\omega_{min}=0.15\), learning factor \(c_{1}=c_{4}=1.687\), the maximum number of iterations of the algorithms is 1000, and the population size is 60. The microgrid system's setting environment is set, e.g., emission coefficient of various pollutants, treatment costs, and operations' prices. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Power Sources**} & \multicolumn{2}{c}{**kW-Power Capacity**} & \multicolumn{2}{c}{**Constraint of**} & \multicolumn{2}{c}{**Maintenance**} & \multicolumn{2}{c}{**Install Costs**} & \multicolumn{2}{c}{**Capacity**} & \multicolumn{2}{c}{**Service**} \\  & **Upper** & **Lower** & **Climb Rate** & **Costs (S/kW)** & **(S/kW)** & **Factors (\%)** & **Life/Year** \\ \hline ES & 61.87 & \(-\)61.500 & 0.0001 & 0.005 & 0.107 & 40 & 15 \\ \hline FC & 69.45 & 5.150 & 1.9600 & 0.107 & 2.258 & 37 & 20 \\ \hline MT & 73.92 & 18.450 & 12.3000 & 0.108 & 1.606 & 68 & 20 \\ \hline PV & 35.52 & 0.000 & 0.0012 & 0.012 & 5.180 & 36 & 18 \\ \hline WT & 39.69 & 0.000 & 0.0012 & 0.036 & 2.915 & 27 & 18 \\ \hline EX & 74.29 & \(-\)73.800 & 0.0012 & 0.001 & 0.0001 & 2 & 22 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The relevant parameters of each unit of the operation model of the power sources. Tables 5 and 6 illustrate the emission-related pollution remediation and emission coefficients and the units of the operation power sources' relevant operation price. Figure 6 displays the daily demanding grid voltage curves of the microgrid power loads. In the grid-connected state, the operation cost of microgrid generating electricity could be more expensive than the large grid's consumed electricity because of much large-scale consumption and convenience equipment. However, the microgrid effectively produces balance and service by supplying power to the major grid during peak load hours and peak load times to meet the load demands. The microgrid's battery storage is charged at the trough hours, but it provides the power to meet the peak load hour demand. The battery storage can also be charged when the system creates excess electric energy to ensure the system's continuous power supply. The generators of the miniature gas turbines, diesel engineering, and fuel cells in the microgrid operate at full power at peak demands.