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dynawaltz example
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Nicolas PIERRE committed Dec 14, 2022
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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pypowsybl dynawaltz simulation"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"import pypowsybl as pp\n",
"import pypowsybl.dynamic as dyn\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"network = pp.network.load(\"./dynawaltz/IEEE14.iidm\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"You can add dynamic mappings with dataframes like this :"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df_alpha_beta = pd.DataFrame.from_dict({\"static_id\": [network.get_loads().loc[l].name for l in network.get_loads().index],\n",
" \"parameter_set_id\": [\"LAB\" for l in network.get_loads().index]})\n",
"df_GSTWPR = pd.DataFrame.from_dict({\"static_id\": [network.get_generators().loc[l].name for l in network.get_generators().index],\n",
" \"parameter_set_id\": [\"GSTWPR\" for l in network.get_generators().index]})\n",
"df_omega_ref = pd.DataFrame.from_dict({\"generator_id\": [network.get_generators().loc[l].name for l in network.get_generators().index]})"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Don't forget to index the dataframe on the network element id column"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"model_mapping = dyn.ModelMapping()\n",
"model_mapping.add_all_dynamic_mappings(dyn.DynamicMappingType.ALPHA_BETA_LOAD, df_alpha_beta.set_index(\"static_id\"))\n",
"model_mapping.add_all_dynamic_mappings(dyn.DynamicMappingType.GENERATOR_SYNCHRONOUS_THREE_WINDINGS_PROPORTIONAL_REGULATIONS, df_GSTWPR.set_index(\"static_id\"))\n",
"model_mapping.add_all_dynamic_mappings(dyn.DynamicMappingType.OMEGA_REF, df_omega_ref.set_index(\"generator_id\"))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Adding events"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"events = dyn.EventMapping()\n",
"events.add_event(\"EQD\", dyn.EventType.BRANCH_DISCONNECTION, \"_BUS____1-BUS____5-1_AC\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Adding curves, you can batch curves creation for a given id"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"timeseries = dyn.CurveMapping()\n",
"timeseries.add_curves(\"_LOAD___2_EC\", [\"load_PPu\", \"load_QPu\"])"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Running the simulation (will load the default config file in ~/.itools folder)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"sim = dyn.Simulation()\n",
"res = sim.run(network, model_mapping, events, timeseries, 0, 30)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Display data of the run:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>_LOAD___2_EC_load_QPu</th>\n",
" <th>_LOAD___2_EC_load_PPu</th>\n",
" </tr>\n",
" <tr>\n",
" <th>timestamp</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.126992</td>\n",
" <td>0.216992</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.126992</td>\n",
" <td>0.216992</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.126992</td>\n",
" <td>0.216992</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.126992</td>\n",
" <td>0.216992</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.126992</td>\n",
" <td>0.216992</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9900</th>\n",
" <td>0.125207</td>\n",
" <td>0.215157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12710</th>\n",
" <td>0.125208</td>\n",
" <td>0.215157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18331</th>\n",
" <td>0.125208</td>\n",
" <td>0.215158</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28331</th>\n",
" <td>0.125208</td>\n",
" <td>0.215158</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30000</th>\n",
" <td>0.125208</td>\n",
" <td>0.215158</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>252 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" _LOAD___2_EC_load_QPu _LOAD___2_EC_load_PPu\n",
"timestamp \n",
"0 0.126992 0.216992\n",
"0 0.126992 0.216992\n",
"0 0.126992 0.216992\n",
"0 0.126992 0.216992\n",
"0 0.126992 0.216992\n",
"... ... ...\n",
"9900 0.125207 0.215157\n",
"12710 0.125208 0.215157\n",
"18331 0.125208 0.215158\n",
"28331 0.125208 0.215158\n",
"30000 0.125208 0.215158\n",
"\n",
"[252 rows x 2 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res.status()\n",
"res.curves()"
]
}
],
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