David Torres Sanchez

Additionally, as this seems pretty tricky, you may want to consider posting your question to https://or.stackexchange.com/ to have a few more eyes on this.Cheers, David

Hi Amanda,Thanks for double-checking my logic. Indeed, you are right.It would be good to (somehow) find out which of these flow variables have to be positive in order to satisfy demand, but since y...

Hi Amanda, Sorry for the delay and thanks for the link to the paper.It seems that making capacity continuous is harder. Could you maybe try a discrete approach?  I guess it also makes sense to maxi...

Hi Amanda,Thanks for the updated code. This is interesting, I think we need to find a reformulation as the number of unexplored nodes in that log doesn't inspire much confidence in actually finding...

Hi Amanda,I'm not sure what you mean. Your example allows flow in both directions.Could you provide a working example?With values for \(\texttt{capacities}\), \(\texttt{e}\), and \(\texttt{demand}\...

Also posted here: https://or.stackexchange.com/q/8872/4752

Hi Alessandra,Sorry for the delay, both of these settings are carried along.Setting the MIP gap to 0.001 means that the optimization will stop when 0.1% optimality is reached.In the logs you are sh...

Is it because it is terminating at the first iteration with \(\texttt{primobj}\) and \(\texttt{dualobj}\) equal to 0?I have checked a fair number of models (the ones in the Gurobi examples data dir...

Hi Sebastian,Unfortunately, I think PyPSA uses Pyomo internally, which creates a default environment for you, so you cannot use the method you described.Try saving the license file to be accessible...

I did a preliminary check, relaxing the binary vars to real in [0,1], and rounding the solution. For 20 iterations (I'm solving this problem repeatedly), the constraints held, but then they failed...