Integration of next-generation biofuel production in the Swedish forest industry – A geographically explicit approach

•Geographically explicit study of biofuel production integrated in forest industry.•Bottom-up studies of integrated production included in national energy system model.•Six examples of different next-generation biofuel routes considered.•Cost of biomass and plant capital cost generally dominate the total biofuel cost.•Plants with high production and/or low biomass transportation cost are favoured. The geographic locations of biofuel production facilities should be strategically chosen in order to minimise the total cost of using biofuels. Proximity to biomass resources, possibilities for integration, and distance to biofuel users are aspects that need to be considered. In this paper, the geographically explicit optimisation model BeWhere Sweden was used to investigate the future production of next-generation biofuels from forest biomass in Sweden. A focus was placed on the integration of biofuel production with the existing forest industry, as well as on how different parameters affect biofuel production costs, the choice of technologies and biofuels, and the localisation of new biofuel plants. Six examples of different biofuel routes were considered. A methodology was developed considering detailed, site-specific conditions for potential host industries. The results show that the cost of biomass and the biofuel plant capital cost generally dominate the biofuel cost, but the cost for biomass transportation and biofuel distribution can also have a significant impact. DME produced via black liquor gasification (naturally integrated with chemical pulp mills) and SNG produced via solid biomass gasification (mainly integrated with sawmills), dominate the solutions. The distribution of these technology cases varies depending on a number of parameters, including criteria for sizing biofuel plants, the electricity price, the biofuel distribution cost and the cost of biomass, and is sensitive to changes in these parameters. Generally, plants with low specific investment costs (i.e., high biofuel production) and/or plants with low specific biomass transportation costs occur most frequently in the solutions. Because these properties often vary significantly among biofuel production facilities at different host industry sites of the same type, the results show the advantage of including site-specific data in this type of model.