(186s) A Multi-Objective MILP Model for Planning, Design and Operation of Biomass Supply Chains – Capturing the Trade-Offs within the Food-Energy-Water-Environment Nexus

Biomass is a strategic resource for the production of fuels, energy, chemicals and other valuable materials. Demands for biomass are increasing due to many governments subsidising its use to meet renewable energy and GHG emissions targets. These growing demands can cause greater competition with food production and have profound effects on the environment and ecosystems. Producing biomass requires large areas of land, water for irrigation and often fertilisers and pesticides, which can leach into bodies of water. Large and rapid changes in land use (especially deforestation) degrade biodiversity. Effective management of biomass supply chains is crucial to ensure its production and utilisation are sustainable, do not contribute to global warming and preserve the delicate balance in the food-energy-water-environment nexus.

A spatio-temporal multi-objective optimisation model, based on mixed integer programming, was developed for biomass value chains that can maximise synergies and minimise conflicts within the food-energy-water-environment nexus. The model considers a planning horizon, from 2010 to 2050, in order to model the investment in and retirement of technologies and land use changes, as well as seasonal time steps in order to capture the seasonal production and storage of biomass. The model accounts for spatial dependencies of the problem at a 50 km level in order to model the variation of biomass yield potentials across different locations, suitable siting of biomass plantations and processing facilities, transport of biomass and products etc. Land use is modelled using GIS and different constraints were developed, according to the suitability of the land for cultivation and harvesting and social and environmental criteria, in order to limit the use of land for bioenergy. Different types of biomass are modelled including food crops, energy crops and forestry resources. Different technologies for the production of different types of biofuel, as well heat and electricity, are modelled at different scales. The model optimises a large number of interdependent decisions such as: what biomass to grow, where and when to grow them; what energy services/products to generate (e.g. heat, biofuels, electricity, chemicals); what processing technologies to use, where to locate them, when to invest in them; centralised or distributed processing; whether or not to densify/pre-process biomass before transportation; how to transport biomass and distribute products to customers; and how to manage inventory (as the production of most types of biomass is seasonal and not available all year round), in order to obtain the greatest benefit with the lowest impact on the nexus. In this conference, the MILP model will be presented along with the efficient and robust value chains that are synergistic with the nexus, identified using the model.

References:

[1] S. Samsatli, N.J. Samsatli (2018). A multi-objective MILP model for the design and operation of future integrated multi-vector energy networks capturing detailed spatio-temporal dependencies. Applied Energy. DOI: 10.1016/j.apenergy.2017.09.055.

[2] S. Samsatli, N.J. Samsatli, N. Shah (2015). BVCM: a comprehensive and flexible toolkit for whole-system biomass value chain analysis and optimisation - mathematical formulation. Applied Energy, 147, pp. 131-160.

[3] S.M. Jarvis, S. Samsatli (2018). Technologies and infrastructures underpinning future CO2 value chains: a comprehensive review and comparative analysis. Renewable & Sustainable Energy Reviews, 85, 46-68.

[4] S. Samsatli, N.J. Samsatli (2015). A general spatio-temporal model of energy systems with a detailed account of transport and storage. Computers and Chemical Engineering, 80, 155-176.