Description
Within every eGo run, a number of MV grids are simulated. The problem is, that it can be assumed that (also in the long-term) some of these MV grid simulations fail. These failures can have a number of reasons, e.g. convergence, faulty data allocation, etc...
The question is, how these faulty grids should be considered in the eGo calculations.
I just implemented a function in eGo that returns the relative number of successfully calculated MV grids, also including the weighting of each grid (thus, a faulty grid with a high weighting is more significant). Currently, the resulting grid expansion costs are divided by this relative number of successful grids in order to "extrapolate" the costs over the faulty grids. However, this is a fairly simple solution and better results could be obtained with other, more specific solutions.
One idea from the project meeting was to choose other MV grids from the same cluster and use those as the representative grid instead if the original grid fails. Of course, this entails many more problems and is only applicable for the clustering, but not if all grids are chosen.
I think, I will leave the current solution (relative number of successful grids) for now, until new suggestions are made, or any decisions are taken...