Jasrul Jamani JAMIAN1, Abdullah Asuhaimi MOHD ZIN1, Makmur SAINI1, Mohd Wazir MUSTAFA1, Hazlie MOKHLIS2
Universiti Teknologi Malaysia (1), University of Malaya (2)
A Novel TVA-REPSO Technique in Solving Generators Sizing Problems for South Sulawesi Network
This paper present a novel optimization method, Time Varying Acceleration – Rank Evolutionary Particle Swarm Optimization (TVAREPSO) in solving optimum generator sizing for minimising power losses in the transmission system of South Sulawesi, Indonesia. A comparison between the proposed method and three other methods was done in order to find the best method to optimize the generators’ output size. The results show that the TVA-REPSO algorithm can obtain the same performance as PSO but it only required shorter computing time and can converges faster than the original PSO.
W artykule przedstawiono matematyczną metodę rozwiązania zagadnienia znalezienia optymalnego rozmiaru generatora, w celu minimalizacji strat w elektroenergetycznym systemie przesyłowym Południowej Sulawesi w Indonezji. W algorytmie wykorzystano optymalizację roju cząstek ze zmiennym w czasie przyspieszeniem (ang. TVA-REPSO). Dokonano porównania z innymi metodami, pokazało, że opracowana metoda ma skuteczność podobną do klasycznej metody PSO, lecz krótszy czas obliczeń. (Nowoczesna technika TVA-REPSO w rozwiązaniu zagadnienia doboru rozmiarów generatora w sieci elektroenergetycznej Południowej Sulawesi).
Keywords: Generators’ optimal Output, Optimization Method, Power Loss Reduction, Voltage Stability
Results and Discussions
South Sulawesi electrical systems can generally be separated into 2 sub-systems which are the North and South Sub-System as shown in Fig. 5. North central section consists of a large generation centre with low operating costs (Bakaru, Sengkang and Suppa), whereas in the South Sub-Station, the thermal generating units with expensive cost operations is more dominant. This leads to the high power transfer from the north area to the south area in obtaining cheaper cost of generation, which can be proved by using the load flow analysis. From the results, at the normal operating conditions, the power transferred from the north area (bus 15) to the south area (bus 1) was 143.97 MW which is near to 37 percent from total generation capacity of all units at the north area (bus 17, bus 24 and bus 31). The high power transfer between these 2 regions causes the high losses existing in the system, even though the cost of generation is low.
Due to this problem, the government has planned to build a new power plant in the Barru area (bus 16) and the Jeneponto area (bus 27) which is located at the southern part of South Sulawesi’s electrical system (shaded generators). The total generations capacity at buses 16 and\ 27 are 100MW and 250 MW respectively. Even though the total capacity of both generators seems to be sufficient enough, it should be noted that both generators will not be operated at maximum capacity due to some limitations in power system such as power losses increment, stability of the network, voltage profile and others. The power losses of the network will increase and the system might collapse without the proper generator output configuration. Thus, in this study, 3 different well known optimization methods are used and compared with the proposed algorithm in determining the optimal generation output for reducing the The performance of these optimization methods will be evaluated in terms of their ability to obtain the optimal generator capacity, the total computation time required as well as the new power losses value in the system. Table I shows the performance of EP, AIS, PSO and TVA-REPSO algorithms in sizing the two new units of generator in the South Sulawesi transmission line system. By assuming the existing generators’ capacity is maintained, both new generators’ capacity will give an impact to the new power losses value in the network. From the results, it can be clearly seen that the ability of AIS and EP in finding the optimal size of generators and searching the minimum power losses is similar. By operating the generator at bus 16 at 84.141 MW and bus 27 at 53.172 MW, the power losses in the network is reduced from 23.4120MW to 10.5150MW which is nearly 55 percent of reduction. However, the AIS required only 25.61 seconds or 3 iterations to achieve the results while the EP required the maximum iteration (200) to obtain this value. Therefore, although the generators’ output given by these two optimization methods are similar, the AIS are superior then EP in terms of computational time.
On the other hand, the PSO and TVA-REPSO gave better results in finding the optimal size of generators
compared to EP and AIS. The optimal size of generators obtained using PSO is 76.3239MW for bus 16 and 68.4480MW for bus 27 while for TVA-REPSO, the generators’ optimal output are 76.3202MW and 68.4466MW for bus 16 and bus 27 respectively. By increasing the generator output at bus 27 and reducing the output at bus 16 as shown in Fig. 6, the power losses in the network, has reduced from 10.5151MW to 10.3864MW which is nearly to 1.3 percent power losses improvement. Same as in EP and AIS cases, the PSO and TVAREPSO also gave the similar performance in finding the generators’ optimal generators’ optimal output. However, the PSO algorithm needed until the maximum number of iteration before reaching the optimal value. Thus, the PSO will require a larger computing time in finding the generators’ optimal results. On the other hand, the TVAREPSO has obtained the optimal solution before the algorithm reaches the maximum iteration. From Table 1 results, the TVA-REPSO only required 68 seconds to gain the optimal value or 53 iteration compared to PSO that required 309.27 seconds. The computation time for PSO is higher than EP even though both methods run until themaximum iteration (200). It is due to the mutation process of PSO that required several steps (required to find Pbest, Gbest, vi+1) compared to the EP algorithm. Furthermore, the AIS gave the smallest computing time compared to other algorithms where it only needed 25.61 seconds to get the optimal value.
AcknowledgementsThe authors would like to thanks Universiti Teknologi Malaysia, The State Polytechnic of Ujung Pandang , PLN PERSERO of South Sulawesi Indonesia and Government of South Sulawesi Indonesia for providing the financial and technical support for the research.
Jasrul Jamani Jamian, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bharu, Johor, Malaysia. E-mail: firstname.lastname@example.org.
Abdullah Asuhaimi Mohd Zin, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bharu, Johor, Malaysia. E-mail: email@example.com.
Makmur Saini, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bharu, Johor, Malaysia. E-mail: firstname.lastname@example.org.
Mohd Wazir Mustafa, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bharu, Johor, Malaysia. E-mail: email@example.com.
Hazlie Mokhlis, Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia. E-mail: firstname.lastname@example.org.