I have an optimization model for power-to-methanol process using combined investment and dispatch optimization mode. There is a fixed CO2 point source, electricity price data as inputs. I also have a excess sink for CO2 so if there isn’t enough hydrogen available or its expensive to produce, it can be dumped into this sink.
Now the issue is regarding assigning variable costs for this Excess CO2 sink. When I assign very high cost (i.e variable_costs=1000), the model behaves differently to when I assign some lower costs (i.e variable_costs=10) and has an impact on the cost of production calculated at the end and also difference in operation hours (since the PEM electrolyzer can also operate in part-load so the no. of full load and part-load hours are different). There are investment and variable costs attached to hydrogen and methanol production as well.
Can someone explain why is this happening?
Best regards,
Nouman
thanks for reaching out. From my perspective, the described behaviour makes sense: If the CO2 price is high, methanol will be produced just to avoid these costs and even if it was not profitable to produce it else. If you do not want that, you might want to set a source that is not fixed but just has an upper bound (Flow(nominal_value=value, max=series) instead of fix=series).
Thanks for the response. I implemented this suggestion but in most of the cases, methanol is not produced. Most probably because cost of selling methanol is too low (I put negative variable costs for methanol sink as an indicator of selling price). When I increase the selling price to a random large number then it starts producing methanol. So is it with just trial & error that I get a proper value or something else can be tried?
In other case, if I go with CO2 excess sink with a price, how should the price be determined make sure the result is optimal and fair?
Under the boundary conditions you define, you find that producing methanol is not cost-optimal. That’s a result already. Now, you can scan through the different parameters (like CO2 price, revenue for selling green methanol, etc.) and search under which conditions this will change. It might help to set a minimum value for the methanol production and check how the objective value will change to find the additional costs you need to compensate.