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Implement Feedback to Renewable Potential Validation Notebook #25
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In the section "Objectives":
a. Compute Technical Installable Potential (define what the Technical Installable Potential is, explain how it is computed in pypsa-earth, and explain how it can be obtained). b. Validate Technical Installable Potential (compare the results obtained in c. with external sources). c. Compute Capacity Factors (define what the capacity factor is, explain how it is computed in pypsa-earth and explain how it can be obtained from it). d. Validate Capacity Factors (compare the results obtained in a. with external sources). e. Create Maps |
In the section "Generate profiles (if not already available)" After the last sentence, add what are the options for {technology}. Something like "The keyword {technology}" should be one of solar or onwind." (Note that offwind-ac, or offwind-dc is not an option for Zambia, althought it can be for other regions). |
In the section "Preparation":
The paths should be made relative to the pypsa-earth repository. See examples in other notebooks. We might need to specify what is the "default" structure of the repository pypsa-zm-data within pypsa-earth. This is done in the pypsa-kz-data repository for instance. |
In the section "Technical Resource Energy Potential":
Is it possible that the technical constraints account for the "Unsuitable Areas"? Otherwise, the unsuitable areas don't seem to enter the formula. It could also be that the starting point is "Suitable Areas" instead of "Total Available Land". |
In the section "Technical Installable Potential in GW":
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In the section "Capacity Factor Validation for Solar and Wind":
The reason is that the percentage of time that solar or wind generate at nameplate capacity (or rated capacity) is very close to zero. They almost always produce somewhere in between 0 and nameplate capacity. Note that you used the second definition in the formula.
A capacity factor of 30% means that it would generate 30% of the maximum possible generation, not that it will be generating 30% of the time at maximum output, nor that "supporting" power plants will be needed 70% of the time. Re-shape the example.
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In the section "Plotting solar and wind Capacity Factors":
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In section "Renewable Ninja Data": Very well presented! The argument is followed clearly, and the conclusion that the pypsa-earth capacity factors are good is clearly stated and justified.
In detail, "the size of the system" is irrelevant to the capacity factor, as the capacity factor is a unitless ratio of production. In other words, having 1 MW of solar panels or 1GW of solar doesn't change it. "The location of the system and the weather conditions" are already included in the capacity factor computations.
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In section "Generating maps that illustrate the potential for wind and solar energy in different part of zambia":
ValueError Traceback (most recent call last) c:\Users\alsov\Documents\code\asolavi\pypsa-earth\zambia_renewable_potential_validation.ipynb Cell 34 line 4 File c:\Users\alsov\anaconda3\envs\pypsa-earth\lib\site-packages\xarray\plot\accessor.py:430, in DataArrayPlotAccessor.imshow(self, *args, **kwargs) File _ckdtree.pyx:795, in scipy.spatial._ckdtree.cKDTree.query() ValueError: 'x' must be finite, check for nan or inf values |
The objective of this issue is to implement the feedback to the Renewable Potential Validation Notebook. Find feedback classified by section in the comments below.
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