Optimization of Operating Patterns Using Particle Swarm Optimization for a 3 × 93 MW Combined Cycle Power Plant

DOI: https://doi.org/10.33650/jeecom.v8i2.17058
Authors

(1) * Burhanuddin Burhanuddin   (Unissula University)  
        Indonesia
(2)  Muhamad Haddin   ()  
        Indonesia
(*) Corresponding Author

Abstract


Changes in electricity demand can result in frequent variations in generator loading patterns, which may increase natural gas consumption and power generation costs. Therefore, an optimal loading pattern is required to achieve efficient operation of generating units while maintaining the required power output. An appropriate load distribution among generating units is important to ensure that the available generating capacity can be utilized efficiently and economically. This study investigates the optimization of the operating pattern of a 3 × 93 MW Combined Cycle Power Plant (CCPP) at Tambak Lorok, Semarang, with the objective of improving gas fuel utilization and reducing power generation costs. The parameters considered include load pattern, gas fuel consumption, and generator output power. The Particle Swarm Optimization (PSO) method was applied to determine the optimal combination of generating-unit loading, and the optimization process was implemented using MATLAB R2021a. The proposed optimization approach is expected to provide a more efficient loading strategy that can support economical and reliable power plant operation. The results show that PSO can effectively optimize the loading pattern of the 3 × 93 MW Tambak Lorok CCPP. The average gas fuel consumption decreased from 1,061.78 MMBTU/h under the conventional operating pattern to 1,032.56 MMBTU/h after PSO optimization, resulting in a fuel saving of 29.22 MMBTU/h. Based on a natural gas price of Rp118,000/MMBTU, the optimization resulted in estimated operational cost savings of Rp3,448,032.75/h, equivalent to approximately Rp82,752,786/day and Rp29.79 billion/year. These results demonstrate that PSO can improve the economic efficiency of CCPP operation by optimizing the distribution of load among generating units. Thus, the application of PSO has potential as a decision-support approach for improving fuel efficiency and reducing operational costs in CCPP operation.

Keywords

elektro



Full Text: PDF



References


PT PLN Indonesia Power UBP Semarang, Manual Book PLTGU Tambak Lorok. Semarang, Indonesia: PT PLN Indonesia Power UBP Semarang, 2024.

D. A. Satrio, R. Firmansyah, P. D. Widayaka, and S. A. Alamsyah, “Analysis of the effect of load changes on frequency in electric power systems,” Scientica: Scientific Journal of Science and Technology, vol. 4, no. 1, pp. 502–517, 2026.

M. A. M. Hamzah, S. Sofyan, A. R. Sultan, and M. I. Bachtiar, “Optimization of slack bus placement during peak load by considering the voltage profile of the generation bus in the SULSELRABAR electricity system,” Jurnal Teknologi Elekterika, vol. 22, no. 1, pp. 21–27, 2025, doi: 10.31963/ elekterika. V 22i1.5425.

M. Barkah, B. Suprianto, and U. T. Kartini, “Optimization of seal steam turbine pressure control on CCPP boiler system using Fuzzy-PID auto-tuning method,” INAJEEE (Indonesian Journal of Electrical and Electronics Engineering), vol. 9, no. 2, pp. 48–55, 2026, doi: 10.26740/inajeee. v9n2. P 48-55.

M. B. N. A. Muhammad, “Analysis of PLTS as distributed generation (DG) to reduce losses and improve voltage in Peugout feeders,” 2025.

H. S. Mukminin, T. Tumiran, and A. N. Widiastuti, “Machine learning-based health index prediction for medium voltage induction motors in power plants,” in Proc. 2025 International Future Energy Electronics Conference (IFEEC), pp. 410–415, 2025, doi: 10.1109/IFEEC65025.2025.11301575.

M. F. Waldi, “Simulation of load shedding scheme using under frequency relay (UFR) in DIgSILENT PowerFactory based substation,” Doctoral dissertation, Malikussaleh University, 2026.

P. Ahmadi, I. Fakhari, and M. A. Rosen, “A comprehensive approach for tri-objective optimization of a novel advanced energy system with gas turbine prime mover, ejector cooling system and multi-effect desalination,” Energy, vol. 254, p. 124352, 2022, doi: 10.1016/j.energy.2022.124352.

M. Colakoglu and A. Durmayaz, “Multiobjective optimization of a novel solar tower-based gas turbine-driven multi-generation plant with energy, exergy, economic, and environmental impact analysis,” Journal of Energy Resources Technology, vol. 144, no. 5, p. 051302, 2022, doi: 10.1115/1.4051805.

H. Nurdin, I. Nawawi, A. Wuryanto, D. Yuliandari, and H. Sugiarto, “Prediction of student payment delays to mitigate leave risk using SVM PSO optimization,” JASIEK (Journal of Science, Information, Electronics and Computer Applications), vol. 7, no. 1, pp. 1–11, 2025.

K. D. N. Cahyo and M. F. Dwidanasaputra, “Optimization of node placement in WSN with adaptive PSO for energy efficiency and optimal coverage,” Karapan Network Journal: Journal of Computer Technology and Mobile Ad Hoc Network, vol. 1, no. 1, 2025.

A. A. B. Zebua, “Analysis of the impact of clogging by sea butterflies on the thermal efficiency of the LNG regasification system in FSRU Jawa Satu,” Doctoral dissertation, STIP, 2025.

A. D. Rana, J. B. Darji, and M. Pandya, “Backward/forward sweep load flow algorithm for radial distribution systems,” International Journal of Scientific Research and Development, vol. 2, no. 1, pp. 398–400, 2014.

S. Kawambwa, R. Mwifunyi, D. Mnyanghwalo, N. Hamisi, E. Kalinga, and N. Mvungi, “An improved backward/forward sweep power flow method based on network tree depth for radial distribution systems,” Journal of Electrical Systems and Information Technology, vol. 8, no. 1, 2021, doi: 10.1186/s43067-021-00031-0.

J. C. Bansal, P. K. Singh, M. Saraswat, A. Verma, and others, “Inertia weight strategies in particle swarm optimization,” in Proc. 3rd World Congress on Nature and Biologically Inspired Computing (NaBIC), Salamanca, Spain, 2011, doi: 10.1109/NaBIC.2011.6089659.

A. Biswas, A. V. Lakra, S. Kumar, and A. Singh, “An improved random inertia weighted particle swarm optimization,” in Proc. 2013 International Symposium on Computational and Business Intelligence (ISCBI), pp. 96–99, 2013, doi: 10.1109/ISCBI.2013.27.

A. Nurfanani, H. B. Tambunan, I. A. Aditya, O. Oktavianty, W. Kusumaningdyah, R. A. Sari, W. Azlia, and A. R. Dewi, “Particle swarm optimization for hydrogen refueling station location problem to minimize emissions,” in AIP Conference Proceedings, vol. 3380, no. 1, p. 070003, 2026, doi: 10.1063/5.0313179.

H. Nurohmah, A. S. C. Buana, R. Rukslin, M. Ali, and M. R. Djalal, “Economic generation optimization based on whale optimization algorithm in multi-machine system,” FORTECH Journal, vol. 6, no. 2, pp. 70–79, 2025.

A. D. Wardhana, T. Sukmadi, and M. A. Riyadi, “Optimisasi penjadwalan ekonomis PLTG pada PLTGU Tambak Lorok dengan menggunakan metode Cuckoo Search Algorithm,” Transient: Jurnal Ilmiah Teknik Elektro, vol. 6, no. 3, pp. 276–284, 2017, doi: 10.14710/ transient.v6i3.276-284.

J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. IEEE International Conference on Neural Networks (ICNN), vol. 4, pp. 1942–1948, 1995, doi: 10.1109/ICNN.1995.488968.


Dimensions, PlumX, and Google Scholar Metrics

10.33650/jeecom.v8i2.17058


Refbacks

  • There are currently no refbacks.


Copyright (c) 2026 burhanuddin burhan burhanuddin, Muhamad Haddin

 
This work is licensed under a Creative Commons Attribution License (CC BY-SA 4.0)

Journal of Electrical Engineering and Computer (JEECOM)
Published by LP3M Nurul Jadid University, Indonesia, Probolinggo, East Java, Indonesia.