Desenvolvimento de software de otimização baseado em programação linear para redução de custos relativos a cadeia de suprimentos de biomassa para geração de energia elétrica - Biuk, Lucas Henrique (2024)
The search for renewable energy is driven by the need to mitigate the effects of climate change, reduce dependence on fossil fuels, and create a more sustainable energy system. Renewable sources - such as solar, wind, hydroelectric, biomass, and geothermal - offer viable alternatives because they are inexhaustible and have a low environmental impact. Investments in research, innovation, and infrastructure are crucial for expanding the use of these energy sources, driving the transition toward a cleaner and more resilient future. The goal was to develop an algorithm capable of optimizing the costs related to the supply chain in a thermoelectric plant’s energy generation using a mixture of biomasses, making it a more efficient and commercially attractive option. Based on the biomass supply chain, logistical, transport, and storage data for inputs and their respective limitations, intrinsic characteristics of the thermoelectric plant, and the physicochemical characteristics of the biomasses to be used - namely elephant grass, forest residues, and palm residues - were gathered. The data obtained were shaped into parameters of a linear programming problem, and along with the mathematical modeling, the constraints, decision variables, and objective function of the problem were defined. The algorithm was developed in Python, with primary support from the Pyomo library, which structures linear programming problems, and the GNU Linear Programming Kit (GLPK), a solver seeking an optimal solution to the problem. The algorithm was also converted into executable software on the Windows operating system. The developed software produced the expected results, including the quantities of each biomass to be purchased, processed, stored, and burned in each considered period, as well as the operating costs of the thermoelectric plant in the case study.