Optimasi Biaya Operasional PLTGU Priok Menggunakan Model Unit Commitment berbasis Mixed Integer Linear Programming

Fani Nur Hidayah; I Nyoman Pujawan; Raihan Muhammad

doi:10.33506/mt.v12i2.5656

Authors

Fani Nur Hidayah Institut Teknologi Sepuluh November, Perusahaan Listrik Negara
I Nyoman Pujawan Institut Teknologi Sepuluh November
Raihan Muhammad Perusahaan Listrik Negara

DOI:

https://doi.org/10.33506/mt.v12i2.5656

Keywords:

Combined cycle; Mixed Integer Linear Programming; Operational cost optimization; Priok combined cycle power plant; Unit commitment

Abstract

Priok Combined cycle Power Plant (CCPP) is one of the strategic generation units supplying electricity demand in the Greater Jakarta area, operating with a multi‑block combined‑cycle configuration consisting of gas turbines and steam turbines. In current practice, unit selection is largely based on unit availability, which may lead to operational inefficiencies, particularly during partial‑load periods and transitions to peak demand. This study aims to develop a unit commitment model based on Mixed Integer Linear Programming (MILP) that accurately represents the real operational characteristics of Priok CCPP and produces a reliable and cost‑efficient operating schedule. The proposed unit commitment model explicitly incorporates key technical constraints, including power balance, minimum and maximum output limits, minimum up and down times, ramp rate limits, gas turbine–steam turbine operational coupling, and natural gas supply limitations. The MILP formulation is solved using the LINGO optimization software. The optimization results indicate that the generation schedule consistently follows system load variations, where high‑efficiency units are selectively committed during low‑load periods, while additional units are activated in a controlled manner during peak demand. Furthermore, the combined‑cycle operational logic at the block level is well captured, ensuring that steam turbines operate only when the corresponding gas turbines meet the technical requirements. The optimal solution achieved a minimum operating cost of IDR 26,165,246,971, which is lower than the actual operating cost of IDR 29,701,429,532 at Priok CCPP. The results demonstrate that the proposed MILP-based unit commitment model is capable of enhancing both the economic efficiency and operational reliability of Priok CCPP, resulting in an 11.91% reduction in operating costs compared to the actual operating practice.

References

Agarwal, A., & Pal, K. (2021). Optimization of Unit Commitment Problem Using Genetic Algorithm. International Journal of System Dynamics Applications, 10(3), 21–37. https://doi.org/10.4018/ijsda.2021070102

Allen J. Wood, B. F. W. G. B. S. (2014). Power Generation, Operation and Control (3rd ed.). John Wiley & Sons.

Carrión, M., & Arroyo, J. M. (2006). A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem. IEEE Transactions on Power Systems, 21(3), 1371–1378. https://doi.org/10.1109/TPWRS.2006.876672

Chen, Y., Pan, F., Qiu, F., Xavier, A. S., Zheng, T., Marwali, M., Knueven, B., Guan, Y., Luh, P. B., Wu, L., Yan, B., Bragin, M. A., Zhong, H., Giacomoni, A., Baldick, R., Gisin, B., Gu, Q., Philbrick, R., & Li, F. (2023). Security-Constrained Unit Commitment for Electricity Market: Modeling, Solution Methods, and Future Challenges. IEEE Transactions on Power Systems, 38(5), 4668–4681. https://doi.org/10.1109/TPWRS.2022.3213001

Feng, Z. kai, Niu, W. jing, Wang, W. chuan, Zhou, J. zhong, & Cheng, C. tian. (2019). A mixed integer linear programming model for unit commitment of thermal plants with peak shaving operation aspect in regional power grid lack of flexible hydropower energy. Energy, 175, 618–629. https://doi.org/10.1016/j.energy.2019.03.117

Glover, J. D., Overbye, T. J., Sarma, M. S. (2017). POWER SYSTEM ANALYSIS & DESIGN SIXTH EDITION. www.cengage.com/highered

González-Sierra, M., & Wogrin, S. (2024). Self-Unit Commitment of Combined-Cycle Units with Real Operational Constraints. Energies, 17(1). https://doi.org/10.3390/en17010051

Kirschen, D. Sadi., & Strbac, Goran. (2004). Fundamentals of Power System Economics. Wiley.

Morales-España, G., Latorre, J. M., & Ramos, A. (2013). Tight and compact MILP formulation for the thermal unit commitment problem. IEEE Transactions on Power Systems, 28(4), 4897–4908. https://doi.org/10.1109/TPWRS.2013.2251373

Nemati, M., Braun, M., & Tenbohlen, S. (2018). Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming. Applied Energy, 210, 944–963. https://doi.org/10.1016/j.apenergy.2017.07.007

Nguyen-Duc, T., Hoang-Tuan, L., Ta-Xuan, H., Do-Van, L., & Takano, H. (2022). A Mixed-Integer Programming Approach for Unit Commitment in Micro-Grid with Incentive-Based Demand Response and Battery Energy Storage System. Energies, 15(19). https://doi.org/10.3390/en15197192

Nicolosi, F. F., Alberizzi, J. C., Caligiuri, C., & Renzi, M. (2021). Unit commitment optimization of a micro-grid with a MILP algorithm: Role of the emissions, bio-fuels and power generation technology. Energy Reports, 7, 8639–8651. https://doi.org/10.1016/j.egyr.2021.04.020

Ozoemena, O. E., & Adamu, A. (2022). Optimal unit-commitment generation scheduling using genetic algorithm: A case study of a 10-generator power system network. Nigerian Journal of Technology, 41(2), 302–310. https://doi.org/10.4314/njt.v41i2.12

Padhy, N. P. (2004). Unit commitment - A bibliographical survey. IEEE Transactions on Power Systems, 19(2), 1196–1205. https://doi.org/10.1109/TPWRS.2003.821611

Tziovani, L., Asprou, M., Ciornei, I., Kolios, P., Hadjidemetriou, L., Lazari, A., Tapakis, R., & Timotheou, S. (2023). Long-Term Unit Commitment with Combined-Cycle Units. 2023 IEEE Belgrade PowerTech, 1–6. https://doi.org/10.1109/PowerTech55446.2023.10202920