Multi-Objective Optimization of Biomass Conversion Technologies by Using Evolutionary Algorithm and Mixed Integer Linear Programming (MILP)
The design and operation of energy systems are key issues for matching the energy supply and consumption. Moreover, in the present context of finding ways to decrease CO2 emission, poly-generation technologies, together with the integration of renewable energy resources, have a high potential for CO2 emission reduction. An optimisation model and systematic procedure to select, size and operate a poly-generation plant are developed and presented in this paper. In the optimisation model the integration of biomass resources is mainly investigated. Several options for integrating biomass in the energy system, namely back pressure steam turbine, biomass ranking cycle (BRC), biomass integrated gasification gas engine (BIGGE), biomass integrated gasification gas turbine, production of synthetic natural gas (SNG) and biomass integrated gasification combined cycle (BIGCC), are investigated in this paper. The goal is to minimize costs and CO2 emission simultaneously with a multi-objective evolutionary algorithm (QMOO) and a Mixed Integer Linear Programming (MILP). Finally the proposed model demonstrated by means of a case study. The results shown the simultaneous production of electricity and heat with biomass and natural gas are reliable upon the established assumptions. Besides, higher primary energy savings and CO2 emission reduction are obtained through the gradual increase of renewable energy sources compare to the natural gas usage.