Multi-objective optimization model for regional renewable biomass supported electricity generation in rural regions

Abstract

Greenhouse gas (GHG) emissions from fossil fuel-generated electricity can be avoided by utilizing biomass (wood waste) available in rural communities. This study developed a two-layer supply chain network model comprising feedstock supply sites and candidate power plant locations. The multi-objective mathematical model considers various decisions such as power plant locations, allocation of suppliers to power plants, biomass harvesting, storage, and transportation options in the supply chain network. The goal of this formulation is to minimize the overall system cost and GHG emissions in each process of the entire network. A case study is presented in which a direct-fired power plant converts wood waste to electricity for the Grenada County, Mississippi. A Pareto solution was obtained for the case study using a Lexicographic augmented ∊-constraint algorithm. The solution with no limits on GHG emissions facilitates a higher power plant capacity, 25 MW with lower system costs and satisfies 32.1% of the total electricity demand of the case study area. The solution with the highest GHG emission restrictions (5.19 Kiloton (kt)) reduces the power plant capacity to 10 MW, which satisfies only 10.2% of the total electricity demand with higher overall system costs due to the increase in the purchase of electricity from external sources as penalty cost. Finally, a sensitivity analysis was performed by varying the total feedstock supply quantity, total electricity demand, and conversion rate of the power plant. The results show that 10.2–41.5% (base case- 40% increase in conversion rate) of the total electricity can be satisfied by the bioenergy facility taking into account the solution which facilitates bioenergy facility. In conclusion, by proper selection of power plant location and plant capacity based on the supply chain network, wood waste-based electricity generation can reduce both GHG emissions and total operational costs. This model can be applied to any region to determine the ideal location of a bioenergy facility.