Model Course

Description

From 2030 onwards, biofuels of the second generation could make a substantial contribution to satisfy the demand for climate-neutral mobility. While battery electric vehicles are expected to decarbonize the personal mobility, other parts like long haul goods transport and aviation will still need fuels as energy density of batteries is too low for these applications.
In this study, we design an example supply chain to satisfy the future demand for biofuels (2030 to 2050) and assess if this approach is feasible concerning biomass availability and cost. Your task is to model the supply chain for a range of European countries. Furthermore, you may assume that you can access all available biomass, focus however should also be that it does not compete with food production. This favours e.g. agricultural waste, miscanthus, switchgrass or sawdust.
For each region, you can assume that you have a demand for the years 2030 to 2050. All demand has to be satisfied. The demand for fuel as well as the availability of biofuel is given in the same unit (PJ). We assume that we can convert all biomasses with the same conversion factor.
You should determine the most advantageous regions for establishing production facilities. Each facility involves an initial investment cost and can operate for a specified period. Once built, there are annual operational costs. The construction and operational costs are provided for a typical facility. After 10 years, you can expand the production by adding new facilities to existing ones. However, existing facilities cannot be demolished.  (Note: for simplicity reason, we will not discount investments, calculate net present values, etc.)
The transportation between the location of the biomass and the facilities is done by truck. The same is true for the transport between the facility and the regions with the demand. You can assume linear costs for the transportation, relating to distance traveled and tonne transported. Each biomass, as well as the biofuel, has a heating value that gives a weight that is attributed to a PJ of that biomass/biofuel. 

There is already a model available, however this does not consider uncertain biomass availability or uncertain demand.

You are given the following scenarios:

Scenarios with Probabilities

Scenario	Definition	Probability
0	ENS_Low, All biomasses	10%
1	ENS_Low, No forestry residues	25%
2	ENS_Low, No forestry, no agriculture and landscape residues	15%
3	ENS_Mid, All biomasses	10%
4	ENS_Mid, No forestry residues	25%
5	ENS_Mid, No forestry, no agriculture and landscape residues	15%

ENS_Low, stands for generally low availability and ENS_Mid for medium availability. High availability scenarios are available but highly unlikely and therefore not considered.

Besides the biomass also the demand is considered uncertain.

Demand with Probabilities

Scenario	Definition	Probability
0	EU Ref 2016 100%	50%
1	EU Ref 2016 150%	50%

The following course will guide you through extending an already available model to incorporate these scenarios and optimize these supply chain under uncertainty.