Feat: script for generating pv module coefficients

.gitignore

@@ -0,0 +1,10 @@
+.ipynb_checkpoints
+.pytest_cache
+.venv
+*.xlsx
+result.csv
+*.zip
+.idea
+*.log
+*.pyc
+

README.md

@@ -1,2 +1,12 @@
 # pvmodules
 Code to generate parameters for PV module models
+
+
+## Develop
+Install dependencies:`poetry install`
+
+Run tests: `pytest test`
+
+
+## Run PV parameter generation
+`python pvmodules/cec_pv_modules.py <CEC-dataset-path>.xlsx <output-path>.csv <number of processes>`

convert-cec-inverters-to-sam.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "c015bfe7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from PySAM.InvCecCg import new as InvCecCg\n",
+    "from itertools import product\n",
+    "from pvlib.inverter import fit_sandia"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a6c92a2e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inverter_columns = ['Name', 'Vac', 'Pso', 'Paco', 'Pdco', 'Vdco', 'C0', 'C1', 'C2', 'C3',\n",
+    "       'Pnt', 'Vdcmax', 'Idcmax', 'Mppt_low', 'Mppt_high', 'CEC_Date',\n",
+    "       'CEC_Type', 'CEC_hybrid']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1e1b9bcf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cec_inverters = pd.read_excel('/home/allen/Downloads/Grid_Support_Inverter_List_Full_Data_ADA.xlsm',\n",
+    "                              engine='openpyxl', skiprows=[*range(14), 15], usecols=range(60))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8ae10c60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cec_inverters['Name'] = cec_inverters.apply(lambda row: f\"{row['Manufacturer Name']} {row['Model Number1']}\", axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "5489c7ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inverter = cec_inverters.loc[cec_inverters['Model Number1'].str.contains('PV4400')].squeeze()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "25dd3ca2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "voltages = [k for k in cec_inverters.columns if k.startswith('Voltage ')]\n",
+    "\n",
+    "powers = [k for k in cec_inverters.columns if k.startswith('Power Level')]\n",
+    "\n",
+    "efficiencies = [k for k in cec_inverters.columns if k.startswith('Efficiency @V') and '6' not in k]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "fcaaa574",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "curve_keys = [[*v_p, e] for v_p, e in zip(product(voltages, powers), efficiencies)]\n",
+    "curve_keys = [[p, v, e] for v, p, e in curve_keys]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "7a9d8d40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_curve_pvlib(inverter: pd.Series):\n",
+    "    curve = pd.DataFrame(columns=['Pac', 'Vdc', 'Eff'], data=[[inverter[k] for k in row] for row in curve_keys])\n",
+    "\n",
+    "    curve['Eff'] *= 1e-2\n",
+    "\n",
+    "    curve['Pdc'] = curve['Pac'] / curve['Eff']\n",
+    "    curve['Vlvl'] = curve['Vdc'].replace(\n",
+    "        curve['Vdc'].min(), 'Vmin').replace(\n",
+    "        curve['Vdc'].median(), 'Vnom').replace(\n",
+    "        curve['Vdc'].max(), 'Vmax')\n",
+    "    return curve\n",
+    "    \n",
+    "    \n",
+    "\n",
+    "def get_params_pvlib(inverter: pd.Series):\n",
+    "    curve = get_curve(inverter)\n",
+    "    return fit_sandia(\n",
+    "        ac_power=curve['Pac'] * 1e3,\n",
+    "        dc_power=curve['Pdc'] * 1e3,\n",
+    "        dc_voltage=curve['Vdc'],\n",
+    "        dc_voltage_level=curve['Vlvl'],\n",
+    "        p_ac_0=inverter['Maximum Continuous Output Power at Unity Power Factor'] * 1e3,\n",
+    "        p_nt=inverter['Night Tare Loss'],\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "0ce11459",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_params_sam(inverter: pd.Series):\n",
+    "    curve = [[inverter[k] for k in row] for row in curve_keys]\n",
+    "    solver = InvCecCg()\n",
+    "\n",
+    "    solver.Common.assign({\n",
+    "        'inv_cec_cg_paco': inverter['Maximum Continuous Output Power at Unity Power Factor'] * 1e3,\n",
+    "        'inv_cec_cg_sample_power_units': 1,\n",
+    "        'inv_cec_cg_test_samples': curve\n",
+    "    })\n",
+    "\n",
+    "    solver.execute()\n",
+    "    return {\n",
+    "        k: v\n",
+    "        for k, v in solver.export()['Outputs'].items()\n",
+    "        if k in {'Pdco', 'Pso', 'Vdco', 'c0', 'c1', 'c2', 'c3'}\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "776e4d41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cec_row_to_sam_row(cec_row: pd.Series, get_params) -> pd.Series:\n",
+    "    params = get_params(cec_row)\n",
+    "    return pd.Series({\n",
+    "        'Name': f\"{cec_row['Manufacturer Name']} {cec_row['Model Number1']}\",\n",
+    "        'Vac': cec_row['Nominal Voltage'],\n",
+    "        'Vdco': cec_row['Voltage Nominal'],\n",
+    "        'Vdcmax': cec_row['Voltage Maximum'],\n",
+    "        'Pnt': cec_row['Night Tare Loss'],\n",
+    "        'Paco': cec_row['Maximum Continuous Output Power at Unity Power Factor'] * 1e3,\n",
+    "        'Pdco': params['Pdco'],\n",
+    "        'Pso': params['Pso'],\n",
+    "        'C0': params['c0'],\n",
+    "        'C1': params['c1'],\n",
+    "        'C2': params['c2'],\n",
+    "        'C3': params['c3'],\n",
+    "        'Idcmax': params['Pdco'] / params['Vdco'],\n",
+    "        'Mppt_low': cec_row['Voltage Minimum'],\n",
+    "        'Mppt_high': cec_row['Voltage Maximum'],\n",
+    "        'CEC_Date': inverter['Grid Support Listing Date'].date().isoformat(),  # TODO: or latest update\n",
+    "        'CEC_Type': 'Grid Support',\n",
+    "        'CEC_hybrid': 'N'\n",
+    "    })[inverter_columns]\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "2d2105c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sam_inverter = cec_row_to_sam_row(inverter, get_params_sam)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "543b32d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sam_inverter.to_frame().transpose().to_csv('production/nrel_sam/CEC_Inverters.csv', mode='a', header=False, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7fec9c48",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}