Hi everyone,

Let’s assume a simple co-optimized expansion and generation problem with 13 nodes: 1 for expansion and 12 generations corresponding to 12 months. There is uncertainty during generation, e.g., monthly water inflows.

Initially, since I wanted to use the optimal generation policy found by SDDP.jl for future years as well, I added this constraint:

water level at the beginning of node 2 = water level at the end of node 13.

Then, I noticed an end-horizon effect in my solution. It means that the policy was optimal for the one-year problem, but it seems sub-optimal for a longer period.

Then, to resolve this end-of-horizon effect, I modeled the problem as an infinite horizon problem by eliminating the mentioned constraint and adding:

```
SDDP.add_edge(graph, 13 => 2, 0.9)
```

to the policy graph.

Now, how can I compare the generation policies derived from infinite/finite horizon models for a two-year simulation (while the training is for a year)?

Note that for the second year, there shouldn’t be any expansion node; so, for the finite horizon approach, duplicating the simulation process would not work.