BVCM: a comprehensive and flexible toolkit for whole system biomass value chain analysis and optimisation – mathematical formulation

Abstract

Abstract This paper presents the novel MILP formulation of the Biomass Value Chain Model (BVCM), a comprehensive and flexible optimisation toolkit that models a large number of bioenergy system pathways. The model accounts for the economic and environmental impacts associated with the end-to-end elements of a pathway: crop production, conversion technologies, transport, storage, local purchase, import (from abroad), sale and disposal of resources, as well as CO2 sequestration by CCS technologies and forestry. It supports decision-making around optimal use of land, biomass resources and technologies with respect to different objectives, scenarios and constraints. Objectives include minimising cost, maximising profit, minimising GHG emissions, maximising energy/exergy production or any combination of these. These objectives are combined with a number of scenarios (such as including different CO2 prices, different technology and climate scenarios, import scenarios, waste cost scenarios), different credits (e.g. by-product and end-product, CCS and forestry carbon sequestration) and a number of constraints such as minimum levels of energy production and maximum environmental impacts. The toolkit includes an extensive database of different biomass technologies including pretreatment, densification, liquid and gaseous fuel production, heat and power generation (separately or combined, biodedicated or co-fired), waste-to-energy conversion and carbon capture and sequestration. A large number of resources are considered including a variety of bio-resources (e.g. energy crops such as Miscanthus and SRC willow, arable crops such as winter wheat, sugar beet and oilseed rape and short and long rotation forestry), intermediates, products, by-products and wastes. The BVCM is a spatio-temporal model: currently it is configured for the UK using 157 square cells of length 50 km and the planning horizon is from the 2010s to the 2050s, with seasonal variations considered. The framework is data-driven so the model can be easily extended: for example adding new resources, technologies, transport modes, etc. or changing the time horizon and the location to another country is only a matter of changing the data. Results of example UK case studies are presented to demonstrate the functionality of the model.