From b4be66c3f7356fd7f2a23f791fa2af1b76f8435e Mon Sep 17 00:00:00 2001 From: BeneharoRB Date: Thu, 13 Jun 2024 11:37:17 +0200 Subject: [PATCH] Delete outdated part in documentation --- docs/source/grid_generation/index.rst | 1 - .../main_workflow/main_workflow.rst | 91 ------------------- docs/source/grid_generation/usage/usage.rst | 5 +- 3 files changed, 1 insertion(+), 96 deletions(-) delete mode 100644 docs/source/grid_generation/main_workflow/main_workflow.rst diff --git a/docs/source/grid_generation/index.rst b/docs/source/grid_generation/index.rst index 95b0ca0..4be19e7 100644 --- a/docs/source/grid_generation/index.rst +++ b/docs/source/grid_generation/index.rst @@ -17,7 +17,6 @@ Contents .. toctree:: :maxdepth: 2 - main_workflow/main_workflow usage/usage explanation/overview explanation/resources diff --git a/docs/source/grid_generation/main_workflow/main_workflow.rst b/docs/source/grid_generation/main_workflow/main_workflow.rst deleted file mode 100644 index de7f4ed..0000000 --- a/docs/source/grid_generation/main_workflow/main_workflow.rst +++ /dev/null @@ -1,91 +0,0 @@ -Main workflow -************* - -Main workflow of the model -=========================== - -#. The research scope identification is done by either manually setting the plz code in grid generator scripts or - an automatic search according to the administrative name of the district. - -#. Run grid generator script, and the process will be proceeded to: - - * extract correlated buildings, roads and transformers involved in the selected area; - * estimate the buildings' peak load and remove too large consumers (connected directly to medium-voltage grid) - * connect the buildings and transformers to the roads and analyse the network topology, remove isolated components; - * according to edge-distance matrix, assign transformers with corresponding neighboring buildings, regarding cable - length limit and capacity limit; - * the remaining unsupplied buildings are subdivided into local distribution grids by hierarchical clustering, with - timely simultaneous peak load validation to determine proper cluster sizes; - * the optimal positions of manually grouped distribution grids are determined by minimal power-distance algorithm, - aiming to minimize the network voltage band, energy losses on conductors and with shorter total cable length; - -#. At the end of GridGeneration.py process, the basic nodal elements of all the local distribution grids have been - determined. The installation of cables are determined in Cable_installation.py with support of pandapower; - -#. The timely power flow calculation shall afterwards be conducted with random assignment of household load profiles, - the default validation period is one year from 00:00:00 01.01.2019 - 24:00:00 31.12.2019 with time step of 15 - minutes, where users can freely shorten or prolong the period or increase the time step by modifying default - parameters and run power_flow_calculation.py - -#. In branch result_analysis presents the plot_result.py where according to pandapower result from step 3, the grid - generation result will be analysed to multiple perspectives including: - - * some general overviews of total numbers of transformers, loads, cable length, etc.; - * numerical statistics of each size of transformers; - * spatial distribution of transformers; - * load estimation of household; - * spatial detailed picture of a single distribution grid (picked by random index); - -users can by commenting or uncommenting corresponding codes in plot_result.py to select the required plots. - -Main workflow of the model (depricated) -======================================= -1. The research scope identification is done by either manually setting the plz code in GridGeneration.py main script or - an automatic search according to the administrative name of the district. - -2. Run GridGeneration.py script, and the process will be proceeded to: - - - extract correlated buildings, roads and transformers involved in the selected area - - estimate the buildings' peak load and remove too large consumers (connected directly to medium-voltage grid) - - connect the buildings and transformers to the roads and analyse the network topology, remove isolated components - - according to edge-distance matrix, assign transformers with corresponding neighbouring buildings, regarding cable - length limit and capacity limit - - the remaining unsupplied buildings are subdivided into local distribution grids by hierarchical clustering, with - timely simultaneous peak load validation to determine proper cluster sizes - - the optimal positions of manually grouped distribution grids are determined by a minimal power-distance algorithm, - aiming to minimize the network voltage band, energy losses on conductors and total cable length - -3. At the end of GridGeneration.py process, the basic nodal elements of all the local distribution grids have been - determined. The installation of cables are determined in Cable_installation.py with support of pandapower; - -4. The timely power flow calculation shall afterward be conducted with random assignment of household load profiles, the - default validation period is one year from 00:00:00 01.01.2019 - 24:00:00 31.12.2019 with time step of 15 minutes, - where users can freely shorten or prolong the period or increase the time step by modifying defaulte parameters and - run power_flow_calculation.py - -5. Some example vizualizations with an overview on total numbers and statistics of transformers, loads, cable length, - etc. on one side and a detialed spatial picture of chosen distribution grids on the other side are in development and - will be provided in a jupyter notebook in the examples directory. - -Software preparation -===================== -The main script runs in Python, in addition you would need: - -1. PostgreSQL: default database; - -Output data -============ -1. A folder of all local distribution grid results will be created (if you toggle the default - SAVE_GRID_FOLDER in config, it is default False so the results only get saved to the database) - as .csv files, named as 'kcid{a}bcid{b}' (a,b are - variables), such files can be easily read in python or input to pandapower; - -2. A folder of all statistical analysis will be created, which the plotting process is based on; - -3. Detailed building, road and transformer records will be saves in '_result' table in SQL; - -4. All the graphics will also be saved as .png in a separate folder; - -*5. There are some auxiliary tables that result_analysis.py would use but not correlated with final results, you can -either delete them and the process will generate them again, or keep them to save some computational effort for next -run. diff --git a/docs/source/grid_generation/usage/usage.rst b/docs/source/grid_generation/usage/usage.rst index 05d29ff..488c1bf 100644 --- a/docs/source/grid_generation/usage/usage.rst +++ b/docs/source/grid_generation/usage/usage.rst @@ -46,7 +46,4 @@ In the examples notebooks you will learn more about: * the objects / elements the LV grids are made up of * the pandapower networks that are used to store the LV grids * graph representation of the networks -* and parameter visualisation options - -[QGIS]: https://www.qgis.org/de/site/forusers/download.html -Refer to the Jupyter notebooks and other analysis tools? \ No newline at end of file +* and parameter visualisation options \ No newline at end of file