Disjunctive fuzzy optimisation of electricity generation by biomass power producers in a feed‐in tariff programme

Abstract

The global drive to achieve the climate change mitigation targets set during the Paris Agreement and COP26 necessitates sustainable electricity generation from renewable energy sources. However, renewable energy systems face economic challenges due to unattractive feed-in-tariffs (FiT). This paper presents a mathematical programming model to optimise the participation and electricity generation by biomass power plants in a FiT programme. A disjunctive fuzzy optimisation approach is adopted in this work due to the presence of several biomass power plants, thus resulting in a multi-objective optimisation.The newly developed mixed-integer linear programming (MILP) model maximises the overall satisfaction extent of biomass power plants in power generation. The optimisation model ensures the satisfaction of all biomass power plants' technical and economic constraints. A base case study of seven power plants utilising various palm biomass is used to demonstrate the disjunctive fuzzy optimisation approach for the MILP model. This is followed by a scenario investigating the impact of demand variation. The optimisation of the case studies favours the utilisation of empty fruit bunch (EFB), palm oil mill effluent (POME), and palm mesocarp fibre (PMF) for sustainable electricity generation. Palm kernel shell (PKS) is the least preferred palm biomass due to its lowest net availability of useful energy from biomass combustion. This work provides a decision tool for the selection of biomass type during electricity generation. The MILP model in this work may be readily extended for any number of power plants, handling a variety of biomass feedstocks.