Ok, despite already having explained to you earlier the partial load efficiency functionality quite in detail, I will try to explain it once again, focusing on the process alkELSRMOBDECEN in 2050 as you requested.

Let's first look at the operational and capacity results of this process (using my results, which may slightly differ from yours). The installed capacity of the process in 2050 is 42.059 PJ = 1334 MW. The following table shows the maximum and minimum load, the online capacity and the load fraction of the minimum load in each season.

As you can see, in the seasons S10, S15, S17 and S19 the maximum load is 42.059 PJ, i.e. the full capacity is on-line and that full capacity is also fully utilized in some timeslices within those four seasons. In other seasons, the maximum load is less than the installed capacity, which means that it is reasonable to have part or all of the installed capacity off-line. In seasons, S02, S06 and S13 the full capacity is off-line, and the load is thus zero in those seasons. But in all other seasons, we can see that the minimum load is exactly 59.8% of the on-line capacity. One can thus immediately conclude that it is either not possible or it is too costly to operate the technology at loads below 59.8%.

To see why the load is always either zero or ≥59.8%, we can check how the partial load efficiencies have been modelled for this process. According to your model files, ACT_MINLD=0.4, meaning that the minimum stable load is 40%, ACT_LOSSPL(FX)=0.99, meaning that the increase in specific fuel consumption is 99% at the minimum stable load, and ACT_LOSSPL(UP)=0.33, meaning that the efficiency losses start at the load of 0.4+0.33×(1−0.4)=59.8%. Consequently, we can now see that it would be possible to operate the technology at loads as low as 40%, but because the efficiency losses are so high, it would hardly ever be economically justified to operate it below 59.8% (unless the savings in variable O&M costs would be large enough to compensate for the efficiency loss). This can be clearly seen in the Figure below, illustrating the efficiency and input energy consumption as a function of the load level.

As you can see, the efficiency losses are so high that it would consume more input energy to produce at any lower load level than 59.8%, and thus it would not make much sense to operate at load levels below 59.8%. Thus, your partial load efficiency modeling appears to work quite effectively, as the model avoids the partial load efficiency losses by operating the technology at levels where they do not occur.

Of course, it might be more reasonable to model partial load efficiencies that would in some cases also be realized. The Figure below shows an example where fuel consumption under partial load efficiency losses always stays under the fuel consumption at any higher loads. In such cases the model might indeed choose to operate the technology at lower loads, even going down to the minimum stable load.

Let's first look at the operational and capacity results of this process (using my results, which may slightly differ from yours). The installed capacity of the process in 2050 is 42.059 PJ = 1334 MW. The following table shows the maximum and minimum load, the online capacity and the load fraction of the minimum load in each season.

As you can see, in the seasons S10, S15, S17 and S19 the maximum load is 42.059 PJ, i.e. the full capacity is on-line and that full capacity is also fully utilized in some timeslices within those four seasons. In other seasons, the maximum load is less than the installed capacity, which means that it is reasonable to have part or all of the installed capacity off-line. In seasons, S02, S06 and S13 the full capacity is off-line, and the load is thus zero in those seasons. But in all other seasons, we can see that the minimum load is exactly 59.8% of the on-line capacity. One can thus immediately conclude that it is either not possible or it is too costly to operate the technology at loads below 59.8%.

To see why the load is always either zero or ≥59.8%, we can check how the partial load efficiencies have been modelled for this process. According to your model files, ACT_MINLD=0.4, meaning that the minimum stable load is 40%, ACT_LOSSPL(FX)=0.99, meaning that the increase in specific fuel consumption is 99% at the minimum stable load, and ACT_LOSSPL(UP)=0.33, meaning that the efficiency losses start at the load of 0.4+0.33×(1−0.4)=59.8%. Consequently, we can now see that it would be possible to operate the technology at loads as low as 40%, but because the efficiency losses are so high, it would hardly ever be economically justified to operate it below 59.8% (unless the savings in variable O&M costs would be large enough to compensate for the efficiency loss). This can be clearly seen in the Figure below, illustrating the efficiency and input energy consumption as a function of the load level.

As you can see, the efficiency losses are so high that it would consume more input energy to produce at any lower load level than 59.8%, and thus it would not make much sense to operate at load levels below 59.8%. Thus, your partial load efficiency modeling appears to work quite effectively, as the model avoids the partial load efficiency losses by operating the technology at levels where they do not occur.

Of course, it might be more reasonable to model partial load efficiencies that would in some cases also be realized. The Figure below shows an example where fuel consumption under partial load efficiency losses always stays under the fuel consumption at any higher loads. In such cases the model might indeed choose to operate the technology at lower loads, even going down to the minimum stable load.