We initially decided to select 3-4 days, in which there were varying levels of scarcity. I did this initially by inspecting the residual load (RL) duration curve. However, after fixing many bugs (mostly data input issues), I realised that the RL is not a good indicator of scarcity, since scarcity occurred due to congestion, unit commitment and reserve requirements as well as high RL (see Table 2 here, where inclusion of operating reserves network leads to load shedding).
At that point I was choosing days which led to load shedding for a full year economic dispatch model. While it led to the results shown in the above pdf, I noticed more bugs, and when I solve the full year model now I don't get any load shedding at all (again, because I need UC + operating reserves to trigger load shedding, and including both in a full year model would be computationally expensive). So I went back to selecting based on the RL, but I have the same problem that the "most scarce day" doesn't display any scarcity at all (see #11, where I realised that .
That there is no scarcity is problematic mostly for me, since I want to look at the tradeoff between reserve shedding and load shedding. If there's no load shedding, there's no tradeoff. For interacting with ULiege, it's perhaps less problematic, because in any case my schedule will likely have operational security issues anyway, and their main goal is to train their machine learning algorithm.
Solutions:
Ignore it, just send Efthymios the new selection of days and don't worry about it.
Doesn't resolve anything really.
Add day 309 to the list of days to analyse, so that I have at least one day where this tradeoff is evident.
No justification for doing this, which was the whole point of using the RL in the first place (it's transparent).
Select a number of days with highest RL, e.g. the first 5, instead of just 1.
No real guarantee that this will fix the issue.
Adds an unnecessary amount of additional days
Solve the full year model again, this time with a load multiplier of 1.5 to ensure load shedding, and select days based on that.
Seems the most logical option.
So I'm going with the last option. Hopefully it leads to load shedding...
Brief description of issue:
We initially decided to select 3-4 days, in which there were varying levels of scarcity. I did this initially by inspecting the residual load (RL) duration curve. However, after fixing many bugs (mostly data input issues), I realised that the RL is not a good indicator of scarcity, since scarcity occurred due to congestion, unit commitment and reserve requirements as well as high RL (see Table 2 here, where inclusion of operating reserves network leads to load shedding).
At that point I was choosing days which led to load shedding for a full year economic dispatch model. While it led to the results shown in the above pdf, I noticed more bugs, and when I solve the full year model now I don't get any load shedding at all (again, because I need UC + operating reserves to trigger load shedding, and including both in a full year model would be computationally expensive). So I went back to selecting based on the RL, but I have the same problem that the "most scarce day" doesn't display any scarcity at all (see #11, where I realised that .
That there is no scarcity is problematic mostly for me, since I want to look at the tradeoff between reserve shedding and load shedding. If there's no load shedding, there's no tradeoff. For interacting with ULiege, it's perhaps less problematic, because in any case my schedule will likely have operational security issues anyway, and their main goal is to train their machine learning algorithm.
Solutions:
So I'm going with the last option. Hopefully it leads to load shedding...