Test and notebook changes for fixed vs floating osw

debt_fraction_calculator/debt_fraction_dev.ipynb

@@ -0,0 +1,370 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Development notebook for debt fraction calculator. Use this to test code changes without re-running the scrape.\n",
+    "\n",
+    "debt_fraction_calc.py has a few more comments and polish"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import PySAM.Singleowner as singleowner\n",
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "sys.path.insert(0, os.path.dirname(os.getcwd()))\n",
+    "from lcoe_calculator.process_all import ProcessAll\n",
+    "from lcoe_calculator.extractor import Extractor\n",
+    "\n",
+    "from lcoe_calculator.tech_processors import FixedOffShoreWindProc, FloatingOffShoreWindProc, LandBasedWindProc, DistributedWindProc,\\\n",
+    "    UtilityPvProc, CommPvProc, ResPvProc, UtilityPvPlusBatteryProc,\\\n",
+    "    CspProc, GeothermalProc, HydropowerProc, PumpedStorageHydroProc,\\\n",
+    "    NuclearProc, BiopowerProc\n",
+    "\n",
+    "from lcoe_calculator.macrs import MACRS_6, MACRS_16, MACRS_21"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Data master version on sharepoint - empty string if you haven't renamed the file\n",
+    "version_string = \"_v1.46\"\n",
+    "\n",
+    "# Path to data master spreadsheet\n",
+    "data_master_filename = \"path/to/2024-ATB-Data_Workbook.xlsx\"\n",
+    "\n",
+    "techs = [UtilityPvPlusBatteryProc\n",
+    "            ]\n",
+    "\n",
+    "\n",
+    "CRP_CHOICES = [20]\n",
+    "scraper = ProcessAll(data_master_filename, techs=techs)\n",
+    "scraper.process()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_itc, df_ptc = Extractor.get_tax_credits_sheet(data_master_filename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(set(scraper.data.Parameter))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fuel_filename = 'fuel_prices.csv'\n",
+    "df_fuel = pd.read_csv(fuel_filename).set_index(\"Price\")\n",
+    "df_fuel.columns = df_fuel.columns.astype(int)\n",
+    "print(df_fuel)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_debt_fraction(input_vals, debug=False):\n",
+    "    model = singleowner.default(\"GenericSystemSingleOwner\")\n",
+    "\n",
+    "    analysis_period = 20\n",
+    "    ac_capacity = 1000 # kW\n",
+    "    capacity_factor = input_vals[\"CF\"]\n",
+    "    gen = [capacity_factor * ac_capacity] * 8760 # Distribute evenly throughout the year\n",
+    "\n",
+    "    capex = input_vals[\"OCC\"]\n",
+    "    con_fin_costs = input_vals[\"CFC\"]\n",
+    "    initial_investment = capex * ac_capacity\n",
+    "    con_fin_total = con_fin_costs * ac_capacity\n",
+    "    o_and_m = input_vals[\"Fixed O&M\"]\n",
+    "    v_o_and_m = input_vals[\"Variable O&M\"]\n",
+    "    dscr = input_vals[\"DSCR\"]\n",
+    "\n",
+    "    ## Set these here so we can adjust below\n",
+    "    irr_target = input_vals[\"IRR\"] \n",
+    "    tax_federal = input_vals[\"Tax Rate (Federal and State)\"] * 100\n",
+    "    tax_state = 0\n",
+    "    inflation = input_vals[\"Inflation Rate\"] * 100\n",
+    "\n",
+    "    degradation = 0.0 # ATB presents average capactity factors\n",
+    "\n",
+    "    model.value(\"analysis_period\", analysis_period)\n",
+    "    model.value(\"flip_target_year\", analysis_period)\n",
+    "    model.value(\"gen\", gen)\n",
+    "    model.value(\"system_pre_curtailment_kwac\", gen)\n",
+    "    model.value(\"system_capacity\", ac_capacity)\n",
+    "    model.value(\"cp_system_nameplate\", ac_capacity / 1000)\n",
+    "    model.value(\"total_installed_cost\", initial_investment)\n",
+    "\n",
+    "    ## Single Owner should apply the O&M cost to each year, so no need to multiply by analysis period?\n",
+    "    model.value(\"om_capacity\", [o_and_m] ) \n",
+    "    model.value(\"om_production\", [v_o_and_m])\n",
+    "    if 'Fuel' in input_vals:\n",
+    "        model.value(\"om_fuel_cost\", [input_vals['Fuel']])\n",
+    "    if 'Heat Rate' in input_vals:\n",
+    "        model.value(\"system_heat_rate\", input_vals['Heat Rate'])\n",
+    "\n",
+    "    model.value(\"degradation\", [degradation]) # Specify length 1 so degradation is applied each year. An array of 0.7 len(analysis_period) assumes degradation the first year, but not afterwards\n",
+    "    model.value(\"system_use_lifetime_output\", 0) # Do degradation in the financial model\n",
+    "\n",
+    "    model.value(\"debt_option\", 1) # Use DSCR\n",
+    "    model.value(\"dscr\", dscr)\n",
+    "    # model.value(\"debt_percent\", 51.9)\n",
+    "    model.value(\"inflation_rate\", inflation)\n",
+    "    model.value(\"term_int_rate\", input_vals['Interest Rate Nominal'] * 100)\n",
+    "    model.value(\"term_tenor\", 18)\n",
+    "    model.value(\"real_discount_rate\", input_vals['Calculated Rate of Return on Equity Real'] * 100) ## \"real equity rate\" (also get this from data?)\n",
+    "    model.value(\"flip_target_percent\", irr_target) ## \"nominal equity rate\"\n",
+    "    model.value(\"ppa_escalation\", 0.0)\n",
+    "\n",
+    "    model.value(\"federal_tax_rate\", [tax_federal])\n",
+    "    model.value(\"state_tax_rate\", [tax_state])\n",
+    "\n",
+    "    # This group is included in fixed O&M\n",
+    "    model.value(\"insurance_rate\", 0)\n",
+    "    model.value(\"property_tax_rate\", 0)\n",
+    "    model.value(\"prop_tax_cost_assessed_percent\", 0)\n",
+    "\n",
+    "    model.value(\"reserves_interest\", 0)\n",
+    "    model.value(\"salvage_percentage\", 0)\n",
+    "    model.value(\"months_receivables_reserve\", 0)\n",
+    "    model.value(\"months_working_reserve\", 0)\n",
+    "    model.value(\"dscr_reserve_months\", 0)\n",
+    "    model.value(\"equip1_reserve_cost\", 0)\n",
+    "    model.value(\"equip2_reserve_cost\", 0)\n",
+    "    model.value(\"equip3_reserve_cost\", 0)\n",
+    "    model.value(\"cost_debt_closing\", 0)\n",
+    "    model.value(\"cost_debt_fee\", 0)\n",
+    "    model.value(\"loan_moratorium\", 0)\n",
+    "    model.value(\"construction_financing_cost\", con_fin_total)\n",
+    "    model.value(\"itc_fed_percent\", [input_vals[\"ITC\"] * 100])\n",
+    "    model.value('itc_fed_percent_maxvalue', [1e38])\n",
+    "    model.value(\"itc_sta_amount\", [0])\n",
+    "    model.value(\"ptc_fed_amount\", [input_vals[\"PTC\"] / 1000]) # Convert $/MWh to $/kWh\n",
+    "\n",
+    "    if input_vals[\"MACRS\"] == MACRS_6:\n",
+    "        model.value(\"depr_alloc_macrs_5_percent\", 100)\n",
+    "        model.value(\"depr_itc_fed_macrs_5\", 1)\n",
+    "        model.value(\"depr_itc_sta_macrs_5\", 1)\n",
+    "        model.value(\"depr_alloc_macrs_15_percent\", 0)\n",
+    "        model.value(\"depr_alloc_sl_20_percent\", 0)\n",
+    "    elif input_vals[\"MACRS\"] == MACRS_16:\n",
+    "        model.value(\"depr_alloc_macrs_5_percent\", 0)\n",
+    "        model.value(\"depr_alloc_macrs_15_percent\", 100)\n",
+    "        model.value(\"depr_itc_fed_macrs_15\", 1)\n",
+    "        model.value(\"depr_itc_sta_macrs_15\", 1)\n",
+    "        model.value(\"depr_alloc_sl_20_percent\", 0)\n",
+    "    elif input_vals[\"MACRS\"] == MACRS_21:\n",
+    "        model.value(\"depr_alloc_macrs_5_percent\", 0)\n",
+    "        model.value(\"depr_alloc_macrs_15_percent\", 0)\n",
+    "        model.value(\"depr_alloc_sl_20_percent\", 100)\n",
+    "        model.value(\"depr_itc_fed_sl_20\", 1)\n",
+    "        model.value(\"depr_itc_fed_sl_20\", 1)\n",
+    "    model.value(\"depr_alloc_custom_percent\", 0)\n",
+    "    model.value(\"depr_alloc_sl_5_percent\", 0)\n",
+    "    model.value(\"depr_alloc_sl_15_percent\", 0)\n",
+    "    model.value(\"depr_alloc_sl_39_percent\", 0)\n",
+    "    model.value(\"depr_bonus_fed\", 0)\n",
+    "    model.value(\"depr_bonus_sta\", 0)\n",
+    "    model.value(\"depr_bonus_fed_macrs_5\", 0)\n",
+    "    model.value(\"depr_bonus_sta_macrs_5\", 0)\n",
+    "    model.value(\"depr_bonus_fed_macrs_15\", 0)\n",
+    "    model.value(\"depr_bonus_sta_macrs_15\", 0)\n",
+    "\n",
+    "    model.value(\"depr_fedbas_method\", 0)\n",
+    "    model.value(\"depr_stabas_method\", 0)\n",
+    "\n",
+    "    model.value(\"ppa_soln_mode\", 0)\n",
+    "    model.value(\"payment_option\", 0)\n",
+    "\n",
+    "    model.value('en_electricity_rates', 1 )\n",
+    "\n",
+    "    model.execute()\n",
+    "\n",
+    "    if debug:\n",
+    "        print(\"LCOE: \" + str(model.Outputs.lcoe_real)) #Cents / kWh - multiply by 10 to get $ / MWh\n",
+    "        print(\"NPV: \" + str(model.Outputs.project_return_aftertax_npv))\n",
+    "        print()\n",
+    "        print(\"IRR in target year: \" + str(model.Outputs.flip_target_irr))\n",
+    "        print(\"IRR at end of project: \" + str(model.Outputs.analysis_period_irr))\n",
+    "        print(\"O&M: \" + str(model.Outputs.cf_om_capacity_expense))\n",
+    "        print(\"PPA price: \" + str(model.Outputs.cf_ppa_price))\n",
+    "        print(\"Debt Principal: \" + str(model.Outputs.cf_debt_payment_principal))\n",
+    "        print(\"Debt Interest: \" + str(model.Outputs.cf_debt_payment_interest))\n",
+    "        print(\"Depreciation: \" + str(model.Outputs.cf_feddepr_total))\n",
+    "        print(\"Production: \" + str(model.Outputs.cf_energy_net))\n",
+    "        print(\"Tax \" + str(model.Outputs.cf_fedtax))\n",
+    "        print(\"ITC \" + str(model.Outputs.itc_total_fed))\n",
+    "        print(\"PTC \" + str(model.Outputs.cf_ptc_fed))\n",
+    "        print(\"Debt fraction \" + str(model.Outputs.debt_fraction))\n",
+    "\n",
+    "    return model.Outputs.debt_fraction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = scraper.data\n",
+    "print(d.columns)\n",
+    "print(set(d.Parameter))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fin_cases = ['Market','R&D']\n",
+    "\n",
+    "d = scraper.data\n",
+    "debt_frac_dict = {}\n",
+    "\n",
+    "years = range(2021, 2051)\n",
+    "print (years)\n",
+    "cols = [\"Technology\", \"Case\", *years]\n",
+    "\n",
+    "for tech in techs:\n",
+    "    for fin_case in fin_cases:\n",
+    "        print(\"Fin case \" , fin_case)\n",
+    "        debt_fracs = [tech.tech_name, fin_case]\n",
+    "        for year in years:\n",
+    "            input_vals = d[(d.DisplayName == tech.default_tech_detail) & (d.Case == fin_case) & (d.Scenario == 'Moderate') & (d.CRPYears == 20) & \n",
+    "                ((d.Parameter == 'Fixed O&M') | (d.Parameter == 'Variable O&M') |(d.Parameter == 'OCC') | (d.Parameter == 'CFC') | (d.Parameter == 'CF')\n",
+    "                | (d.Parameter == 'Heat Rate') | (d.Parameter == 'Fuel'))]\n",
+    "            input_vals = input_vals.set_index('Parameter')[year].to_dict()\n",
+    "\n",
+    "            gen_vals = d[(d.Technology == tech.tech_name) & (d.CRPYears == 20) & (d.Case == fin_case) & \n",
+    "                ((d.Parameter == 'Inflation Rate') | (d.Parameter == 'Tax Rate (Federal and State)') | (d.Parameter == 'Calculated Rate of Return on Equity Real') | (d.Parameter == 'Interest Rate Nominal'))]\n",
+    "            gen_vals = gen_vals.set_index('Parameter')[year].to_dict()\n",
+    "\n",
+    "            if tech.has_itc and tech.has_ptc and fin_case == 'Market':\n",
+    "                input_vals[\"PTC\"] = df_ptc.loc[tech.sheet_name][year]\n",
+    "                input_vals[\"ITC\"] = df_itc.loc[tech.sheet_name][year]\n",
+    "            else:\n",
+    "                input_vals[\"PTC\"] = 0\n",
+    "                input_vals[\"ITC\"] = 0\n",
+    "            input_vals[\"DSCR\"] = tech.dscr\n",
+    "            input_vals[\"IRR\"] = tech.irr_target\n",
+    "            if isinstance(tech.depreciation_schedule, list):\n",
+    "                input_vals[\"MACRS\"] = tech.depreciation_schedule\n",
+    "            elif isinstance(tech.depreciation_schedule, dict):\n",
+    "                input_vals[\"MACRS\"] = tech.depreciation_schedule[year]\n",
+    "            input_vals.update(gen_vals)\n",
+    "            debt_frac = calculate_debt_fraction(input_vals)\n",
+    "\n",
+    "            debt_fracs.append(debt_frac / 100.0)\n",
+    "        \n",
+    "        debt_frac_dict[tech.tech_name + fin_case] = debt_fracs\n",
+    "\n",
+    "df_df = pd.DataFrame.from_dict(debt_frac_dict, orient='index', columns=cols)\n",
+    "df_df.to_csv(\"2023_debt_fractions.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_fuel.at['Natural Gas ($/MMBtu)', year]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "if tech == NaturalGasProc:\n",
+    "    input_vals['Fuel'] = df_fuel.at['Natural Gas ($/MMBtu)', year]\n",
+    "if tech == CoalProc:\n",
+    "    input_vals['Fuel'] = df_fuel.at['Coal ($/MMBtu)', year]\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(input_vals)\n",
+    "\n",
+    "print(debt_frac)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "atb_datamaster_etl",
+   "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": "3.11.5"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "7453c7a7c4c6aacca0bd3e48c10e9f590f02fc03a145dba291554d026d9242d0"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}