Optimization of the Biomass Supply Chain for Co-Firing at Adipala Power Plant to Improve Cost Efficiency
Keywords:
Biomass Cofiring, linear programming, Cost Efficiency, adipala power plantAbstract
The utilization of biomass as a renewable energy source is becoming increasingly important to reduce dependence on fossil fuels and decrease carbon emissions. However, the efficient distribution of biomass in the co-firing system at the Adipala Steam Power Plant (PLTU) faces significant challenges related to high logistics costs. This study aims to optimize the distribution costs of biomass in the co-firing system at PLTU Adipala using Linear Programming (LP) methods. The primary objective of this research is to determine the optimal amount of biomass needed to meet the energy requirements of the power plant with efficient distribution costs, as well as to identify supply chain strategies that can enhance cost efficiency. The method employed is a Linear Programming optimization model that considers factors such as transportation costs, supply capacity, and energy needs. The results indicate that the application of LP can reduce logistics costs by up to 15% and improve the efficiency of biomass distribution. These findings make a significant contribution to enhancing the efficiency of biomass supply chain management at PLTU Adipala and can serve as a reference for the development of renewable energy policies in Indonesia. In conclusion, the application of LP in biomass supply chain management can provide efficient and sustainable solutions while promoting the reduction of carbon emissions in the energy sector.
Downloads
References
Atashbar, N. Z., Labadie, N., & Prins, C. (2016). Modeling and optimization of biomass supply chains: A review and a critical look. IFAC-PapersOnLine, 49(12). https://doi.org/10.1016/j.ifacol.2016.07.742.
Ba, B. H., Prins, C., & Prodhon, C. (2016). Models for optimization and performance evaluation of biomass supply chains: An Operations Research perspective. Renewable Energy, 87. https://doi.org/10.1016/j.renene.2015.07.045.
Dreves, H. (2022). Growing Plants, Power, and Partnerships through Agrivoltaics. National Renewable Energy Laboratory (NREL).
Lo, S. L. Y., How, B. S., Leong, W. D., Teng, S. Y., Rhamdhani, M. A., & Sunarso, J. (2021). Techno-economic analysis for biomass supply chain: A state-of-the-art review. In Renewable and Sustainable Energy Reviews (Vol. 135). https://doi.org/10.1016/j.rser.2020.110164.
Nunes, L. J. R., & Silva, S. (2023). Optimization of the Residual Biomass Supply Chain: Process Characterization and Cost Analysis. Logistics, 7(3). https://doi.org/10.3390/logistics7030048.
Parikka, M. (2004). Global biomass fuel resources. Biomass and Bioenergy, 27(6). https://doi.org/10.1016/j.biombioe.2003.07.005
Renewable Energy Agency, I. (2021). Renewable Energy Statistics 2021. In Renewable Energy Statistic 2021 (Vol. 56, Issue December 2021).
Rentizelas, A. A., Tatsiopoulos, I. P., & Tolis, A. (2009). An optimization model for multi-biomass tri-generation energy supply. Biomass and Bioenergy, 33(2). https://doi.org/10.1016/j.biombioe.2008.05.008
Sun, O., & Fan, N. (2020). A Review on Optimization Methods for Biomass Supply Chain: Models and Algorithms, Sustainable Issues, and Challenges and Opportunities. In Process Integration and Optimization for Sustainability (Vol. 4, Issue 3). https://doi.org/10.1007/s41660-020-00108-9.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 ITB Graduate School Conference
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
