Biomass is a renewable energy source that is easy to find in agricultural countries and can be quickly implemented by co-combusting CFPP in an effort to reduce GHG emissions. However, the integrated optimization of the blending process involving different coal ranks and
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Biomass is a renewable energy source that is easy to find in agricultural countries and can be quickly implemented by co-combusting CFPP in an effort to reduce GHG emissions. However, the integrated optimization of the blending process involving different coal ranks and biomass synergizing has yet to be achieved in order to meet the quality requirements of a number of CFPPs. This study offers an optimization approach for synergizing blending biomass in several coal-fired power plants (CFPPs). The objective is to reduce fuel costs and carbon dioxide emissions by taking into account CFPP's fuel quality requirements as well as constraints on CFPP demand, source supply capacity, and transportation alternatives. The optimization model used is mixed integer linear programming (MILP), which leverages OR-Tools in Google Colab to provide optimal solutions for the allocation of coal and biomass, whereas in the mathematical model, the amount of biomass that can be mixed into coal is limited in the range of 5% to 10%. Case studies conducted on 17 sources of coal, 1 biomass production facility, 3 alternative transportation capacities, and 4 CFPPs show that blending biomass with coal can reduce fuel costs by 2.77% and carbon dioxide emissions by 9.99% when compared to business as usual. This model offers a practical solution to reduce costs while simultaneously tackling climate change in accordance with the objectives outlined in the Paris Agreement
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