# An AC/DC Distribution Network DG Planning Problem: A Genetic-Ant Colony Hybrid Algorithm Approach
## 6 Case Analysis
### Result Analysis  
(1) Consider Timing Characteristics  
By simulating the timing characteristics of DG and the load, this paper draws a plan that is more in line with the actual operation of the AC/DC distribution network. If the timing characteristics of DG and load are not considered, the output of DG in the system remains unchanged, and the output is based on the rated capacity. The two planning schemes considering the timing characteristics and disregarding the timing characteristics are shown in Tables 3 and 4. It can be seen from the above results that the algorithm avoids including the MT if the timing characteristics are not considered. This is because although the investment and maintenance cost of gas MT are low, the environmental cost is too high, and the environmental subsidies for PV and WG are very high, the overall cost of MT is higher than that of PV and WG. However, the fact that the gas turbine is not added is obviously contrary to the actual situation, because in fact, the PV and WG \begin{table}
\begin{tabular}{c c c} \hline \hline
**DG Type** & **Consider Timing Characteristics** & **Disregarding Timing Characteristics** \\ \hline Maintenance cost/10,000 yuan & 679.9 & 884.7 \\ Investment cost/10,000 yuan & 358.3 & 444.1 \\ Loss cost/10,000 yuan & 203.3 & 267 \\ Fuel cost/10,000 yuan & 1961 & 0 \\ Pollution compensation cost/10,000 yuan & 78.75 & 0 \\ Subsidy cost/10,000 yuan & \(-\)417.6 & \(-\)1322.8 \\ Total cost/10,000 yuan & 2863.7 & 273.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Costs of planning schemes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DG Type** &
\begin{tabular}{c} **Maintenance** \\ **10,000 Yuan(MW-h)** \\ \end{tabular} & **10,000 Yuan(MW-h)** & **10,000 Yuan(MW-h)** & **10,000 Yuan(MW-h)** \\ \hline MT & 0.01 & 48 & 0.06 & 0 \\ PV & 0.02 & 100 & 0 & 0.036 \\ WG & 0.03 & 63 & 0 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: DG cost. \begin{table}
\begin{tabular}{c c c} \hline \hline
**DG Type** &
\begin{tabular}{c} **Consider Timing** \\ **Characteristics** \\ \end{tabular} & **Disregarding Timing Characteristics** \\ \hline MT & 2(11), 6(15), 10(11), 14(4) & / \\ PV & 21(2), 24(3), 32(8) & 21(12), 24(14), 32(14) \\ WG & 22(5), 25(1) & 22(1), 25(7) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Planning schemes. outputs have strong volatility. If only these two types of DG are installed, there will be a certain period of time when the power supply does not meet the demand and this affects the reliable operation of the power grid. Therefore, it is necessary to consider the timing of DG. (2) Algorithm Comparison  
In order to verify the effectiveness of the GA-ACO algorithm, the genetic algorithm, ant colony algorithm and GA-ACO algorithm were used to optimize the DG access to the AC/DC distribution network. Since the results calculated by each of the algorithms in each iteration are different, this study performed 10 experiments on each of the three algorithms and took the average of the results. The final costs of the three algorithms are shown in Table 5 and the optimization curve for the three optimization algorithms is shown in Figure 10.  
From the table and the figure we can see that the genetic algorithm at the beginning of the optimization results is better than the ant colony algorithm, and the convergence speed is faster than the ant colony algorithm before the 20 th generation, because the genetic algorithm has a wide range of search capabilities at the beginning of the search. The ant colony algorithm takes a long time to accumulate pheromones at the beginning. In the 20 th generation of the iterations, the redundant iterations generated by the genetic algorithm in the later stage affect the convergence speed and fall into the local optimal solution. The convergence speed of the ant colony algorithm starts to accelerate, and the convergence is completed at about 40 generations. However, because the initial pheromone accumulation is not complete, the global optimal solution cannot be searched.