Optimizing Potential Supply Chain of Biomass Agricultural Waste for Co-firing of Coal Power Plant Using MCDA, GIS, and Linear Programming in the Java and Sumatra Islands, Indonesia - Ahmudi, Ali; Hudaya, Chairul; Garniwa, Iwa; Amraini, Said Zul; Sugiyono, Agus; Semedi, Jarot Mulyo; Sidqi, M. Ahsin; Daulay, Andini Dwi Khairunnisa; Yumnaristya, Syefiara Hania (Heca Sentra Analitika, 2025)
The development of renewable energy is a key priority for the Indonesian government and many other nations. Utilizing biomass as a co-firing fuel in coal-fired power plants (PLTUs) offers a viable pathway to meet renewable energy targets in the electricity sector. Co-firing technology involves substituting coal with biomass at specific ratios while maintaining the operational quality and efficiency of the power plants. Indonesia plans to implement a co-firing program in 114 PLTUs, with a combined capacity of 18.1 GW, requiring approximately 9 million tons of biomass annually. This study aims to develop a biomass supply chain model for co-firing, focusing on transportation cost optimization. Geographic Information Systems (GIS), Multi-Criteria Decision Analysis (MCDA), and Linear Programming are employed to map biomass potential from agricultural waste, identify optimal storage and factory locations, calculate the shortest distances to PLTUs, and design an efficient supply chain. Key biomass sources considered include agricultural waste from rice, corn, cassava, palm oil, coconut, sugarcane, and rubber. The study concentrates on co-firing in the Java and Sumatra regions, which house 14 and 12 PLTUs, respectively. Assuming a 5% biomass mix, the total annual bio-pellet demand is estimated at 3.34 million tons. By contrast, the annual production capacity of bio-pellets is calculated to be 143.58 million tons, indicating a surplus supply. Optimization results confirm that the available biomass supply can adequately meet the co-firing requirements for PLTUs in Java and Sumatra. The study also identifies optimal locations for storage facilities and bio-pellet factories near PLTU sites, enhancing supply chain efficiency. By integrating data on biomass potential, storage, factory, and PLTU locations, this research facilitates the design of an effective and efficient biomass supply chain, contributing to the broader goal of renewable energy development.