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Nicolas PIERRE
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dynawaltz example
Signed-off-by: Nicolas PIERRE <nicolas.pierre@artelys.com>
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‎dynawaltz.ipynb

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