Enrich an existing grid container by EVs and EVCS

CHANGELOG.md

@@ -11,6 +11,7 @@ All notable changes to this project will be documented in this file.
 - Using `NBVAL` as validation for jupyter notebooks [#351](https://github.com/ie3-institute/pypsdm/issues/351) 
 - Switch to `nb-clean` for removing metadata from jupyter notebooks [#365](https://github.com/ie3-institute/pypsdm/issues/365)
 - Add example for Line Results to documentation [#341](https://github.com/ie3-institute/pypsdm/issues/341)
+- Enrich an existing grid container by EVs and EVCS [#355](https://github.com/ie3-institute/pypsdm/issues/355)
 
 ### Changed
 - Move `NBVAL` to dev dependencies [#374](https://github.com/ie3-institute/pypsdm/issues/374)

nbs/enrich_grid_with_ev_and_evcs.ipynb

@@ -0,0 +1,162 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Some jupyter notebook magic to reload modules automatically when they change\n",
+    "# not necessary for this specific notebook but useful in general\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "# Gives you high resolution images within the notebook\n",
+    "%config InlineBackend.figure_format = 'retina'"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "from definitions import ROOT_DIR\n",
+    "import os\n",
+    "from pypsdm import GridContainer\n",
+    "\n",
+    "# The PSDM specific input models can be imported from the pypsdm.models.input and\n",
+    "# pypsdm.models.result. The `GridWithResults` container is located in pypsdm.models.gwr\n",
+    "from pypsdm.models.gwr import GridWithResults\n",
+    "\n",
+    "grid_path = os.path.join(ROOT_DIR, \"tests\", \"resources\", \"simple_grid\", \"input\")\n",
+    "# IO data models in general have a from_csv method to parse psdm files\n",
+    "gwr = GridContainer.from_csv(grid_path)\n",
+    "\n",
+    "# Output directory\n",
+    "target_grid_path = os.path.join(ROOT_DIR, \"output\", \"enriched_grid\")\n",
+    "if not os.path.exists(target_grid_path):\n",
+    "    os.makedirs(target_grid_path)"
+   ],
+   "id": "8a6d151463bebeff"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "from pypsdm.io.utils import delete_all_files_in_directory\n",
+    "\n",
+    "delete_all_files_in_directory(target_grid_path)"
+   ],
+   "id": "ece5d5686f9eda0c"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "# input parameter, so far we only support to sample p_rated of the EVs in a normal distribution.\n",
+    "# EVs will have a storage size of 120.0 kWh and a consumption of 0.18 kWh/km each\n",
+    "# The EVCS will have a charging power of 22.0 kW and two charging points.\n",
+    "ev_p_rated_mean = 22.0\n",
+    "ev_p_rated_sigma = 0.0\n",
+    "ev_p_rated_min = 22.0\n",
+    "ev_p_rated_max = 22.0\n",
+    "ev_p_rated_params = [ev_p_rated_mean, ev_p_rated_sigma, ev_p_rated_min, ev_p_rated_max]\n",
+    "evcs_v2g = False"
+   ],
+   "id": "dfd54ffa547aa7a6"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "# identify nodes to which should be connected. For example, customer nodes most probably have an existing load. So we're looking for nodes in lv grids with an existing load\n",
+    "lv_nodes = gwr.nodes.data[gwr.nodes.data[\"v_rated\"] <= 1.0]\n",
+    "household_nodes = lv_nodes[lv_nodes.index.isin(gwr.loads.node)]\n",
+    "household_nodes.head()"
+   ],
+   "id": "5e5143be50696313"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "from pypsdm.models.input.create.enrich_grid import enrich_grid_by_evs_and_evcs\n",
+    "from pypsdm.models.input.create.participants import create_electric_vehicles\n",
+    "from pypsdm.models.input.create.participants import create_ev_charging_stations\n",
+    "\n",
+    "# get dictionaries for the ev and evcs as well as DataFrames for POIs and POI Mapping\n",
+    "ev_dict, evcs_dict, df_poi, df_poi_map = enrich_grid_by_evs_and_evcs(\n",
+    "    nodes=household_nodes,\n",
+    "    ev_p_rated_params=ev_p_rated_params,\n",
+    "    evcs_v2g=evcs_v2g,\n",
+    "    controlling_em=False,\n",
+    ")\n",
+    "\n",
+    "# with these the EVs and EVCSs can be created\n",
+    "evs = create_electric_vehicles(ev_dict)\n",
+    "evcs = create_ev_charging_stations(evcs_dict)"
+   ],
+   "id": "6e0ba8c8222b49cf"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "from pypsdm.models.input.container.grid import GridContainer\n",
+    "from pypsdm.models.input.create.enrich_grid import add_pois_of_enriched_evcs\n",
+    "\n",
+    "# we now can create an updated SystemParticipantsContainer and an updated node_participants_map\n",
+    "updated_participants = gwr.participants.copy(evs=evs, evcs=evcs)\n",
+    "node_participants = updated_participants.build_node_participants_map(gwr.raw_grid.nodes)\n",
+    "node_participants_map = updated_participants.build_node_participants_map(\n",
+    "    gwr.raw_grid.nodes\n",
+    ")\n",
+    "\n",
+    "# with this we can build our updated grid containing the updated participants.\n",
+    "updated_grid = GridContainer(\n",
+    "    gwr.raw_grid, updated_participants, gwr.primary_data, node_participants_map\n",
+    ")\n",
+    "\n",
+    "# Finally, we can write the updated grid as well as the updated POI and POI Mapping data to our output path\n",
+    "updated_grid.to_csv(target_grid_path, include_primary_data=True)\n",
+    "add_pois_of_enriched_evcs(df_poi, df_poi_map, target_grid_path)"
+   ],
+   "id": "adfcb8863d6ca0b3"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

pypsdm/io/utils.py

@@ -1,8 +1,11 @@
 import os
+import shutil
 from datetime import datetime
 from enum import Enum
 from pathlib import Path
+from random import normalvariate
 from typing import Optional, Union
+from uuid import UUID
 
 import pandas as pd
 from pandas import DataFrame
@@ -64,6 +67,21 @@ def read_csv(
         return pd.read_csv(full_path, delimiter=delimiter, quotechar='"')
 
 
+def delete_all_files_in_directory(directory_path):
+    if os.path.exists(directory_path) and os.path.isdir(directory_path):
+        for filename in os.listdir(directory_path):
+            file_path = os.path.join(directory_path, filename)
+            try:
+                if os.path.isfile(file_path) or os.path.islink(file_path):
+                    os.unlink(file_path)
+                elif os.path.isdir(file_path):
+                    shutil.rmtree(file_path)
+            except Exception as e:
+                print(f"Failed to delete {file_path}. Reason: {e}")
+    else:
+        print(f"Directory {directory_path} does not exist or is not a directory.")
+
+
 def to_date_time(zoned_date_time: str) -> datetime:
     """
     Converts zoned date time string with format: "yyyy-MM-dd'T'HH:mm:ss[.S[S][S]]'Z'"
@@ -164,3 +182,16 @@ def bool_converter(maybe_bool):
         return maybe_bool.lower() == "true"
     else:
         raise ValueError("Cannot convert to bool: " + str(maybe_bool))
+
+
+def normaldistribution(dist_params):
+    normal_variant = normalvariate(dist_params[0], dist_params[1])
+    return max(min(normal_variant, dist_params[3]), dist_params[2])
+
+
+def is_valid_uuid(uuid_string):
+    try:
+        uuid_obj = UUID(uuid_string)
+        return str(uuid_obj) == uuid_string
+    except ValueError:
+        return False