Fix loader and examples

[nick-harder]

Description

• check if availability_df containt values >1 and warn user if so
• fix these values automatically by normalizing them
• also check in the power plant itself when calculating max_power if availability is larger than max_power
• restructure examples for easier understanding
• pack all feature example to example_01 and all learning to example_02
• add a full year DRL scenario from the fit paper
• all year examples are now as example_03

Checklist

Please check all applicable items:

• Code changes are sufficiently documented (docstrings, inline comments, doc folder updates)
• New unit tests added for new features or bug fixes
• Existing tests pass with the changes
• Reinforcement learning examples are operational (for DRL-related changes)
• Code tested with both local and Docker databases
• Code follows project style guidelines and best practices
• Changes are backwards compatible, or deprecation notices added
• New dependencies added to pyproject.toml
• A note for the release notes doc/release_notes.rst of the upcoming release is included
• Consent to release this PR's code under the GNU Affero General Public License v3.0

[codecov]

Codecov Report

Attention: Patch coverage is 28.57143% with 15 lines in your changes missing coverage. Please review.

Project coverage is 77.40%. Comparing base (8b7d076) to head (219eb77).

Files	Patch %	Lines
assume/common/utils.py	9.09%	10 Missing ⚠️
assume/scenario/loader_csv.py	33.33%	4 Missing ⚠️
assume/units/powerplant.py	75.00%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #399      +/-   ##
==========================================
- Coverage   77.59%   77.40%   -0.19%     
==========================================
  Files          47       47              
  Lines        5797     5816      +19     
==========================================
+ Hits         4498     4502       +4     
- Misses       1299     1314      +15     

Flag	Coverage Δ
pytest	77.40% <28.57%> (-0.19%)	⬇️

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[maurerle]

I have some small remarks :)

And I updated the docs to have proper footnotes

all issues have been adressed