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In this scenario, the following flex options are set:
Batteries in households : Local forecasting algorithm
Batteries in electric vehicles: Forecasting residual load
Large-scale batteries: Forecasting residual load
Load shifting in metal industry: 35% WTA 850
Load shifting in other industry: 25% WTA 500
Load shifting in metal industry: 20% WTA 2000
Load shifting in central ICT industry: 55% WTA 75
In a lot of hours, the price is set to 2000.
This is remarkable, since according to the merit order, the technology Hydro (mountain) should generate more than enough energy in this scenario, with a marginal cost of 2.49 EUR/MWh.
Now, there are 2 ways in which a price profile with a price of 2.49 occurs:
Setting the Batteries in households to no Forecasting algorithm
Shutting off the Load shifting in industry.
For some reason, the forecasting algorithm (both the system and the local algorithm) takes over the price of the highest flex option (in this case, the load shifting in industry of the metal sector).
In the following scenario : https://energytransitionmodel.com/saved_scenarios/18545
We see a very spiky price profile:
In this scenario, the following flex options are set:
In a lot of hours, the price is set to 2000.
This is remarkable, since according to the merit order, the technology Hydro (mountain) should generate more than enough energy in this scenario, with a marginal cost of 2.49 EUR/MWh.
Now, there are 2 ways in which a price profile with a price of 2.49 occurs:
For some reason, the forecasting algorithm (both the system and the local algorithm) takes over the price of the highest flex option (in this case, the load shifting in industry of the metal sector).
This relates to part 4 of issue #1403.
@louispt1 @noracato Would be great if one of you could have a look some time.
Notifying @mabijkerk .
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