## 4 Simulation Results
### Ramp-Up Scheduling Result of the Units within 30 mins after the Economic Consideration  
Tables 3-5 present the UC results generated using the proposed algorithm, IPSO, and SA after the units had been selected using the APL method. The units should generate 1560 MW of power within 30 min. The rising power is defined as the incremental amount of baseload units and dispatch units at each time period (one- or five-minute). According to Table 3, a total of 12 units participated in the scheduling and generated 1561 MW of power, with a total cost of NT$ 40087 and computation time of 1.56 s. Table 4 shows the IPSO analysis results. A total of 13 units participated in the scheduling and generated 1561 MW of power, with a total cost of NT$ 40265 and computation time of 3.84 s. Table 5 shows the results obtained using SA. A total of 13 units participated in the scheduling and generated 1567 MW of power, with a total cost of NT$ 40359 and computation time of 4.45 s. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multicolumn{5}{c}{**Base-Load Units**} & \multicolumn{5}{c}{**Dispatch Units**} & **Rising** & **Cumulative** \\ \cline{2-10}  & **U7-8** & **U9-10** & **U11** & **U17** & **U2** & **U3-6** & **U16** & **Power** & **Rising** \\ \hline
0 & 160 & 279 & 230 & 206 & 358 & 315 & 340 & 0 & 0 \\
1 & 165 & 283 & 236 & 226 & 389 & 338 & 361 & 188 & 188 \\
2 & 170 & 287 & 242 & 246 & 419 & 362 & 361 & 170 & 358 \\
3 & 175 & 291 & 248 & 266 & 450 & 385 & 361 & 167 & 525 \\
4 & 180 & 295 & 254 & 278 & 480 & 408 & 361 & 158 & 683 \\
5 & 185 & 299 & 260 & 278 & 511 & 432 & 361 & 151 & 834 \\
6 & 190 & 303 & 266 & 278 & 542 & 455 & 361 & 147 & 981 \\
7 & 195 & 307 & 272 & 278 & 587 & 479 & 361 & 165 & 1146 \\
8 & 200 & 311 & 278 & 278 & 587 & 502 & 361 & 116 & 1262 \\
9 & 205 & 315 & 284 & 278 & 587 & 502 & 361 & 24 & 1286 \\
10 & 210 & 319 & 290 & 278 & 587 & 502 & 361 & 24 & 1310 \\
11–15 & 235 & 339 & 320 & 278 & 587 & 502 & 361 & 120 & 1430 \\
16–20 & 259 & 359 & 321 & 278 & 587 & 502 & 361 & 89 & 1519 \\
21–25 & 259 & 379 & 321 & 278 & 587 & 502 & 361 & 40 & 1559 \\
26–30 & 259 & 380 & 321 & 278 & 587 & 502 & 361 & 2 & 1561 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The unit commitment (UC) problem solved using the HA to increase power output by 1560 MW within 30 min. Figure 2: Ramp-up cost of the 20 natural gas combined-cycle units provided by Taiwan Power Company within 30 min. The results indicated that the proposed algorithm resulted in less computation time and required fewer units being started up and a lower scheduling cost. Thus, it was superior to the other two algorithms. The SA incurred the highest cost and required the most computation time. The comparison of the results of three algorithms in different renewable energy gap and time scenarios is shown in the Table 6. To verify the advantages of the proposed algorithm, three scenarios were used: 1720 MW power gap of renewable energy to be filled within 30 min, and 1560 and 1720 MW within 10 min. In these scenarios, HA also exhibited the highest performance (the detailed UC results to increase power output by 1560 MW within 10 min, shown in Table 7), followed by IPSO, and SA exhibited the lowest performance. Under the same scenario with the same three algorithms, when the PL method was used, HA still had higher performance than IPSO and SA did. This result indicated that fuel cost was lower in the ramp-up situation with a longer time (30 min) than in the emergency ramp-up situation with only a short time (10 min).