Dear all,

I realized that in **oemof**, so far, it is not possible to optimize the capacity of a transformer using the `Investment`

class and simultaneously set a minimum load, such as 30% using the `NonConvex`

class.

I want to create such a model for a diesel genset which is the non-renewable part of a mini-grid power generation system. The renewable part consists of PV cells, storage (battery), and dc/ac inverter.

As we all know, diesel gensets cannot operate under a certain load (normally around 20-30%). But, since I cannot use the `Investment`

and `NonConvex`

classes simultaneously, first, I must obtain the nominal capacity of the diesel genset using only the `Investment`

class; then, in a second optimization problem, I must assume a fixed capacity for the diesel genset and use the `NonConvex`

class to implement the minimum load and the on/off status for this component.

This approach is not accurate because, according to the results of the first optimization (capacity optimization), the diesel genset operates only a few hours in full-load, but most of the time, it operates below the minimum load for this component, which is practically not feasible. Moreover, when I run the second optimization problem, the annuity associated with the diesel genset will not be part of the objective function, and the results will not represent the *real* optimal results.

So, to make the long story short, I know that the usage of the `Investment`

class is not compatible with the `NonConvex`

class in oemof so far, but I would like to know if it is possible to create a new transformer and use these two classes together? Or is there any other way to do it in oemof?

As you might know, in some other optimization platforms, such as **gurobipy**, this is possible, but since oemof offers a variety of other options besides the optimizer, I would like to create the model in oemof. I would appreciate it if someone with good oemof knowledge could help me with this issue. Thanks!

Bests,

Saeed