A spatially explicit techno‐economic assessment of green biorefinery concepts

Abstract

Biorefi neries are seen as a key component for the transition toward a resource-effi cient, lowcarbon bio-economy. The green biorefi nery (GBR) concept is promoted in several countries to create business and employment opportunities in rural areas. It aims at providing alternative utilization pathways for surplus grassland areas by producing bioenergy, biomaterials, livestock feed, and organic acids. We have developed a spatially explicit, mixed integer programming model that maximizes total producer surpluses of GBR supply chains subject to resource endowments by selecting optimal plant locations and sizes. The model is applied to assess the economic viability of three GBR concepts compared to biogas plants. Impacts of uncertain model input parameters on model outputs are analyzed by Monte-Carlo simulations and regression analysis. The model results reveal that green biorefi neries could utilize signifi cantly more biomass than biogas plants, leading to higher regional feedstock prices. Furthermore, the Monte-Carlo simulation results demonstrate that the economic viability of green biorefi neries mainly depends on the selected process layout and the prices of the main products. Under favorable market conditions GBRs are economically viable even without policy support measures with average profi ts between €15 and €115 t -1 feedstock input.

Spatiotemporal modelling for integrated spatial and energy planning

“…Since the dispatch from biomass-based energy generation plants is controllable, research emphasis has been conferred to the optimal location and sizing of these energy generation plants based on economic factors and the proximity to biomass sources. The definition of these optimal locations relies mainly on the spatial (and not the temporal) availability of biomass and has been largely performed with mathematical programming and agent-based modelling approaches [51][52][53][54][55].…”

Biogas between renewable energy and bio-economy policies—opportunities and constraints resulting from a dual role

“…Production of green gas as transportation fuel has to comply with sustainability regulations defined in the European RED, 2 while bio-based products are not yet subject to comparable regulations. Some biomaterial applications can be realized despite the unfavourable financial situation, but this is expected to result in a competition over resources that increase feedstock prices, which in turn strongly influence the economic viability of applications [42]. Both energy and material applications furthermore face different types of regulations, e.g.…”

A critical analysis of thermochemical cellulosic biorefinery capital cost estimates

“…Uncertainty analyses of cellulosic thermochemical biorefineries in the open literature employ both triangular and normal distributions to model TCI uncertainty. In the case of triangular distributions, the deterministic point estimate TCI calculated by the TEA serves as the most likely value while the minimum and maximum values are derived from the expected accuracy range of the capital cost estimation methodology (generally ±30%), resulting in distributions that are both symmetrical and unbiased.…”

“…Two different approaches have been employed in the case of normal distributions. In the first approach the mean and standard deviation are calculated based on an assumed ±20% accuracy range for a deterministic point estimate TCI value . In the second approach the mean and standard deviation are calculated based on the minimum and maximum TCI/TPEC values reported in the literature for similar pathways …”

“…In the fi rst approach the mean and standard deviation are calculated based on an assumed ±20% accuracy range for a deterministic point estimate TCI value. 54 In the second approach the mean and standard deviation are calculated based on the minimum and maximum TCI/TPEC values reported in the literature for similar pathways. 26 Th e use of both the symmetrical triangular and normal distributions makes the assumption that TCI uncertainty is neither biased nor skewed, with the most likely and mean TCI values, respectively, equaling the deterministic point estimate calculated by the underlying TEA model and equal weightings on either side.…”