REopt_API_CHPbranch_PostAndPoll_derate_cf.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# REopt CHPbeta testing by calling API locally or staging server"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from results_poller import results_plots, reo_optimize, reo_optimize_chpstaging, reo_optimize_localhost\n",
    "from API_REopt_results import read_scenario_data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "import json\n",
    "import requests\n",
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets\n",
    "# For REopt Desktop importing results\n",
    "from REopt_results import REopt_TempResults"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load a .pickle file to get previous API response and scenario data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_directory = 'saved_results_6/'\n",
    "pickle_file = 'DEN_MT-PV_base_hospital'\n",
    "with open(save_directory + pickle_file + '.pickle', 'rb') as handle:\n",
    "    api_response_and_scenario_data = pickle.load(handle)\n",
    "base_api_response = api_response_and_scenario_data['API_response']\n",
    "base_scenario_data = api_response_and_scenario_data['scenario_data']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Load another pickle file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_directory = 'saved_results_6/'\n",
    "pickle_file =  'NYC_MT-PV_base_hospital'\n",
    "with open(save_directory + pickle_file + '.pickle', 'rb') as handle:\n",
    "    api_response_and_scenario_data = pickle.load(handle)\n",
    "api_response = api_response_and_scenario_data['API_response']\n",
    "scenario_data = api_response_and_scenario_data['scenario_data']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scenario Inputs (Post), if wanting to do a new API call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#loads_filepath = \"CHP_SanFran_Loads.csv\"\n",
    "#loads_all_dict = pd.read_csv(loads_filepath)\n",
    "#boiler_fuel_load_mmbtu = list(loads_all_dict['Heat+HW [MMBtu]'])\n",
    "#electric_load_kw = list(loads_all_dict['Elec Load Minus Chilling [kW]'])\n",
    "#chiller_electric_load_kw = list(loads_all_dict['Cooling [kW]'])\n",
    "\n",
    "location = 'DEN'\n",
    "\n",
    "if location == 'SF':\n",
    "    ambient_temperature = list(pd.read_csv(\"SanFran_Tamb_degF.csv\", squeeze=True, header = None))\n",
    "    lat = 37.78\n",
    "    long = -122.45\n",
    "    elevation = 0\n",
    "    nat_gas_price = 7.9 #9.6 commercial\n",
    "elif location == 'PHX':\n",
    "    ambient_temperature = list(pd.read_csv(\"PHX_Tamb_degF.csv\", squeeze=True, header = None))\n",
    "    lat = 33.45\n",
    "    long = -111.983\n",
    "    elevation = 1106\n",
    "    nat_gas_price = 3 # 3 industrial # 6.64 comm (Feb 2020)\n",
    "elif location == 'CHI':\n",
    "    ambient_temperature = list(pd.read_csv(\"CHI_Tamb_degF.csv\", squeeze=True, header = None))\n",
    "    lat = 41.983\n",
    "    long = -87.917\n",
    "    elevation = 659\n",
    "    nat_gas_price = 4.16 #6.7 commercial\n",
    "elif location == 'NYC':\n",
    "    ambient_temperature = list(pd.read_csv(\"NYC_Tamb_degF.csv\", squeeze=True, header = None))\n",
    "    lat = 40.65\n",
    "    long = -73.8\n",
    "    elevation = 16\n",
    "    nat_gas_price = 6.15\n",
    "elif location == 'DEN':\n",
    "    ambient_temperature = list(pd.read_csv(\"Golden_Tamb_degF.csv\", squeeze=True, header = None))\n",
    "    lat = 39.742\n",
    "    long = -105.179\n",
    "    elevation = 6000\n",
    "    nat_gas_price = 5.7\n",
    "elif location == 'LA':\n",
    "    ambient_temperature = list(pd.read_csv(\"LA_Tamb_degF.csv\", squeeze=True, header = None))\n",
    "    lat = 33.933\n",
    "    long = -118.4\n",
    "    elevation = 32\n",
    "    nat_gas_price = 7.9 #9.6 commercial # 7.9 ind\n",
    "    \n",
    "#nat_gas_price = 4\n",
    "\n",
    "post_1 = {\"Scenario\": {\n",
    "    \"timeout_seconds\": 500,\n",
    "    \"optimality_tolerance_techs\": 0.05,\n",
    "    \"use_decomposition_model\": False,\n",
    "    \"Site\": {\n",
    "    \"latitude\": lat, \"longitude\": long,\n",
    "    \"outdoor_air_temp_degF\": ambient_temperature,\n",
    "    \"elevation_ft\": elevation,  \n",
    "    #\"Financial\": {\n",
    "    #    \"om_cost_escalation_pct\": 0.01,\n",
    "    #    \"escalation_pct\": 0.006,\n",
    "    #    \"offtaker_discount_pct\": 0.07,\n",
    "    #    \"analysis_years\": 25,\n",
    "    #    \"offtaker_tax_pct\": 0.0\n",
    "    #},   \n",
    "    \"LoadProfile\": {\n",
    "        \"doe_reference_name\": \"Hospital\",\n",
    "        #\"doe_reference_name\": \"LargeHotel\",\n",
    "        #\"loads_kw\": electric_load_kw\n",
    "    },\n",
    "    \"LoadProfileBoilerFuel\": {\n",
    "        \"doe_reference_name\": \"Hospital\",\n",
    "        #\"doe_reference_name\": \"LargeHotel\",\n",
    "        #\"annual_mmbtu\": 12000,\n",
    "        #\"loads_mmbtu_per_hour\": boiler_fuel_load_mmbtu,\n",
    "    },\n",
    "    \"Boiler\": {\n",
    "        \"existing_boiler_production_type_steam_or_hw\": \"hot_water\",\n",
    "        \"boiler_efficiency\": 0.8\n",
    "    },\n",
    "    \"LoadProfileChillerElectric\": {\n",
    "        \"doe_reference_name\": \"Hospital\",\n",
    "        #\"doe_reference_name\": \"LargeHotel\",\n",
    "        #\"loads_kw\": chiller_electric_load_kw\n",
    "    },\n",
    "#     \"ElectricChiller\": {\n",
    "#         \"chiller_cop\": 3.5\n",
    "#     },\n",
    "    \"ElectricTariff\": {\n",
    "        #\"urdb_label\": '5e1676e95457a3f87673e3b0', # San Fran PG&E\n",
    "        #\"urdb_label\": '5cef0a415457a33576f60fe2', # San Fran PG&E - E-19 Medium General Demand TOU Secondary - Comm. - Bundle\n",
    "        #\"urdb_label\": '5e1678075457a3d35f73e3b0', # San Fran PG&E - E-20 Maximum demand of 1000KW or more Option R (Secondary) - Comm. - Bundle\n",
    "        #\"urdb_label\": '5e611fec5457a3665e019406', # Denver Public Service Co. of CO - \n",
    "        \"urdb_label\": '5a6b921b5457a3c650e4a644', # Denver Public Service Co. of CO - Secondary general low load factor\n",
    "        #\"urdb_label\": '5cd200395457a3c62754e9d3', # NYC Edison - General Large TOD Service (NYC)\n",
    "        #\"urdb_label\": '5cd2f6935457a3f73d54e9d4', # NYC Edison - Multi dwelleings voluntary TOD sercive\n",
    "        #\"urdb_label\": '5caf93355457a35c5f7780e3', # AZ Public Service Co. - Large General Service TOU (E-32 L) Primary\n",
    "        #\"urdb_label\": '5caf94185457a326617780e4', # AZ Public Service Co. - Large General Service TOU (E-32 L) Transmission\n",
    "        #\"urdb_label\": '5cc76c2a5457a34a0ee327e3', # CHI Commmon. Edison - BES Very large Load Secondary - Comm - Delivery\n",
    "        #\"urdb_label\": '5ed698d45457a3a805dd15ae', # LA Southern California Edison Co - Time-Of-Use - General Service - Large: TOU-8, Option D (Under 2 kV)\n",
    "        #\"urdb_label\": '5d9aba5d5457a3875061b9f1', # LA Southern California Edison Co - Time-Of-Use - General Service - Large: TOU-8, Option R (under 2kV)\n",
    "        #\"urdb_label\": '5e109a4f5457a3e73efa5c5d', # LA Southern California Edison Co - Time-Of-Use - General Service - Large: TOU-8, Option B (under 2kV)\n",
    "        #\"net_metering_limit_kw\": 1e6,\n",
    "        #\"wholesale_rate_us_dollars_per_kwh\": 0\n",
    "    },\n",
    "    \"FuelTariff\": {\n",
    "        \"existing_boiler_fuel_type\": \"natural_gas\",\n",
    "        \"boiler_fuel_blended_annual_rates_us_dollars_per_mmbtu\": nat_gas_price,\n",
    "        #\"boiler_fuel_blended_annual_rates_us_dollars_per_mmbtu\": 11.0, # SF (given)\n",
    "        #\"boiler_fuel_blended_annual_rates_us_dollars_per_mmbtu\": 7.9, # SF (March 2020) \n",
    "        \"chp_fuel_type\": \"natural_gas\",\n",
    "        \"chp_fuel_blended_annual_rates_us_dollars_per_mmbtu\": nat_gas_price\n",
    "        #\"chp_fuel_blended_annual_rates_us_dollars_per_mmbtu\": 11.0 # SF (given)\n",
    "        #\"chp_fuel_blended_annual_rates_us_dollars_per_mmbtu\": 7.9 # SF (March 2020)\n",
    "    },\n",
    "    \"CHP\": {\n",
    "        \"prime_mover\": \"micro_turbine\",\n",
    "        \"min_kw\": 30,\n",
    "        #\"min_allowable_kw\": 30,\n",
    "        \"max_kw\": 1000,\n",
    "        \"use_default_derate\": True, #True = consider weather, False = ISO only\n",
    "        \"max_derate_factor\": 1,\n",
    "        \"derate_start_temp_degF\": 72,\n",
    "        \"derate_slope_pct_per_degF\": 0.005,\n",
    "        \"elec_effic_full_load\": 0.30,\n",
    "        \"elec_effic_half_load\": 0.271,\n",
    "        \"thermal_effic_full_load\": 0.365,\n",
    "        \"thermal_effic_half_load\": 0.351,\n",
    "        # use private api defaults\n",
    "        \"installed_cost_us_dollars_per_kw\": 3200,\n",
    "        \"om_cost_us_dollars_per_kw\": 60,\n",
    "        \"om_cost_us_dollars_per_kwh\": 0.003,\n",
    "      },\n",
    "#    \"ColdTES\": \n",
    "#        \"min_gal\": 0,\n",
    "#        \"max_gal\": 100000,\n",
    "#         \"installed_cost_us_dollars_per_gal\": 3,\n",
    "#         \"thermal_decay_rate_fraction\": 0.004,\n",
    "#         \"om_cost_us_dollars_per_gal\": 0,\n",
    "#         \"internal_efficiency_pct\": 0.97,\n",
    "#     },\n",
    "#     \"HotTES\": {\n",
    "#         \"min_gal\": 2000,\n",
    "#         \"max_gal\": 2000,\n",
    "#         \"installed_cost_us_dollars_per_gal\": 3,\n",
    "#         \"thermal_decay_rate_fraction\": 0.004,\n",
    "#         \"om_cost_us_dollars_per_gal\": 0,\n",
    "#         \"internal_efficiency_pct\": 0.97,\n",
    "#     },\n",
    "#    \"AbsorptionChiller\": {\n",
    "#         \"min_ton\": 0,\n",
    "#         \"max_ton\": 1000,\n",
    "#         \"chiller_cop\": 0.7,\n",
    "#         \"installed_cost_us_dollars_per_ton\": 500,\n",
    "#         \"om_cost_us_dollars_per_ton\": 0.5,\n",
    "#    },\n",
    "     \"PV\": {\n",
    "        \"min_kw\": 30,\n",
    "        \"max_kw\": 2000,\n",
    "        #\"installed_cost_us_dollars_per_kw\": 1500.0,\n",
    "        #\"om_cost_us_dollars_per_kw\": 16,\n",
    "     },\n",
    "    \"Storage\": {\n",
    "        \"min_kwh\": 0,\n",
    "        \"max_kwh\": 0,\n",
    "        \"min_kw\": 0,\n",
    "        \"max_kw\": 0,\n",
    "    }\n",
    "}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the post to a .json file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert python dictionary post into json and save to a .json file\n",
    "with open('min_inputs_chp_only_SanFran_hospital.json', 'w') as fp:\n",
    "    json.dump(post_1, fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Or load in a saved .json file for the post"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load a json into a python dictionary\n",
    "with open('POST_chp_sanfran_hospital.json', 'r') as fp:\n",
    "    post_1 = json.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "post_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Post and poll API to get a new result, if not loading in from .pickle file\n",
    "## ...This may take a while!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Post 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response OK from http://localhost:8000/v1/job/?format=json&api_key=uaz52dr5KTT5Qs5U72rS70hw3IYeHVEyAaDUFQvo.\n",
      "Polling http://localhost:8000/v1/job/e61bee32-3c0d-4221-bd55-214ccd4e6fed/results/?api_key=uaz52dr5KTT5Qs5U72rS70hw3IYeHVEyAaDUFQvo for results with interval of 10s...\n"
     ]
    }
   ],
   "source": [
    "#api_response = reo_optimize_localhost(post_1)\n",
    "api_response = reo_optimize_localhost(post_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'optimal'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api_response[\"outputs\"][\"Scenario\"][\"status\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "940.8932517933254\n",
      "1680.4569\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "optimal\n",
      "118.58959197998047\n",
      "10058109.0\n",
      "18666763.0\n",
      "8608654.0\n",
      "8608654.0\n"
     ]
    }
   ],
   "source": [
    "#base_size_kw = base_api_response['outputs']['Scenario']['Site']['CHP']['size_kw']\n",
    "#print(base_size_kw)\n",
    "\n",
    "\n",
    "size_kw = api_response['outputs']['Scenario']['Site']['CHP']['size_kw']\n",
    "print(size_kw)\n",
    "size_pv_kw = api_response['outputs']['Scenario']['Site']['PV']['size_kw']\n",
    "print(size_pv_kw)\n",
    "size_bess_kw = api_response['outputs']['Scenario']['Site']['Storage']['size_kw']\n",
    "print(size_bess_kw)\n",
    "size_bess_kwh = api_response['outputs']['Scenario']['Site']['Storage']['size_kwh']\n",
    "print(size_bess_kwh)\n",
    "size_hotTES_gal = api_response['outputs']['Scenario']['Site']['HotTES']['size_gal']\n",
    "print(size_hotTES_gal)\n",
    "size_ChillerAbs_ton = api_response['outputs']['Scenario']['Site']['AbsorptionChiller']['size_ton']\n",
    "print(size_ChillerAbs_ton)\n",
    "\n",
    "optimality = api_response['outputs']['Scenario']['status']\n",
    "print(optimality)\n",
    "time = api_response['outputs']['Scenario']['Profile']['reopt_seconds']\n",
    "print(time)\n",
    "\n",
    "\n",
    "#financial \n",
    "lcc = api_response['outputs']['Scenario']['Site']['Financial']['lcc_us_dollars']\n",
    "print(lcc)\n",
    "lcc_bau = api_response['outputs']['Scenario']['Site']['Financial']['lcc_bau_us_dollars']\n",
    "print(lcc_bau)\n",
    "lcc_savings = lcc_bau - lcc\n",
    "print(lcc_savings)\n",
    "npv = api_response['outputs']['Scenario']['Site']['Financial']['npv_us_dollars']\n",
    "print(npv)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dispatch code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Processing for one scenario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import linregress\n",
    "chp_elec_eff_full_load = api_response['inputs']['Scenario']['Site']['CHP']['elec_effic_full_load']\n",
    "chp_elec_eff_half_load = api_response['inputs']['Scenario']['Site']['CHP']['elec_effic_half_load']\n",
    "chp_therm_eff_full_load = api_response['inputs']['Scenario']['Site']['CHP']['thermal_effic_full_load']\n",
    "chp_therm_eff_half_load = api_response['inputs']['Scenario']['Site']['CHP']['thermal_effic_half_load']\n",
    "chp_size_kwe = api_response['outputs']['Scenario']['Site']['CHP']['size_kw']\n",
    "CHP_fuel_burn_full_load = chp_size_kwe / chp_elec_eff_full_load * 3412/1e6 #[MMBtu/hr]\n",
    "CHP_fuel_burn_half_load = 0.5*chp_size_kwe / chp_elec_eff_half_load * 3412/1e6\n",
    "slope, intercept, rvalue, pvalue, stderr = linregress([0.5*chp_size_kwe, chp_size_kwe], [CHP_fuel_burn_half_load, CHP_fuel_burn_full_load])\n",
    "CHP_elec_slope = slope\n",
    "CHP_elec_intercept = intercept\n",
    "CHP_max_heat_full_load = CHP_fuel_burn_full_load * chp_therm_eff_full_load #[MMBtu/hr]\n",
    "CHP_max_heat_half_load = CHP_fuel_burn_half_load * chp_therm_eff_half_load \n",
    "slope_tp, intercept_tp, rvalue, pvalue, stderr = linregress([0.5*chp_size_kwe, chp_size_kwe], [CHP_max_heat_half_load, CHP_max_heat_full_load])\n",
    "CHP_therm_slope = slope_tp\n",
    "CHP_therm_intercept = intercept_tp\n",
    "print(CHP_elec_slope)\n",
    "print(CHP_elec_intercept)\n",
    "print(CHP_therm_slope)\n",
    "print(CHP_therm_intercept)\n",
    "\n",
    "\n",
    "load_year = str(api_response['inputs']['Scenario']['Site']['LoadProfile']['year'])\n",
    "dti = pd.date_range( load_year +'-01-01', periods=8760, freq='H')\n",
    "bau_grid_load_less_chiller_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']\n",
    "                                               ['LoadProfile']['year_one_electric_load_series_kw'], index = dti)\n",
    "bau_boiler_load_fuel_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']\n",
    "                                         ['LoadProfileBoilerFuel']['year_one_boiler_fuel_load_series_mmbtu_per_hr_bau'], index = dti)\n",
    "bau_chiller_load_elec_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']\n",
    "                                          ['LoadProfileChillerElectric']['year_one_chiller_electric_load_series_kw'], index = dti)\n",
    "boiler_eff = api_response['inputs']['Scenario']['Site']['Boiler']['boiler_efficiency']\n",
    "\n",
    "bau_grid_load_hrly = bau_grid_load_less_chiller_hrly + bau_chiller_load_elec_hrly\n",
    "bau_grid_load_peak = np.max(bau_grid_load_hrly)\n",
    "bau_grid_load_mean = np.mean(bau_grid_load_hrly)\n",
    "bau_grid_load_total = np.sum(bau_grid_load_hrly)\n",
    "bau_therm_load_peak = np.max(bau_boiler_load_fuel_hrly)*boiler_eff #converted units of fuel to units of heat\n",
    "bau_therm_load_mean = np.mean(bau_boiler_load_fuel_hrly)*boiler_eff\n",
    "bau_therm_load_total = np.sum(bau_boiler_load_fuel_hrly)*boiler_eff\n",
    "chp_therm_to_elec_full_load = chp_therm_eff_full_load/chp_elec_eff_full_load\n",
    "\n",
    "bau_therm_load_min = np.min(bau_boiler_load_fuel_hrly)*boiler_eff\n",
    "bau_grid_load_min = np.min(bau_grid_load_hrly)\n",
    "bau_chiller_thermal_load_max = (np.max(bau_chiller_load_elec_hrly) * 3.5) * 0.2843451361\n",
    "bau_chiller_thermal_load_mean = (np.mean(bau_chiller_load_elec_hrly) * 3.5) * 0.2843451361\n",
    "bau_chiller_thermal_load_min = (np.min(bau_chiller_load_elec_hrly) * 3.5) * 0.2843451361\n",
    "print(\"Heating min load =\", '%.2f' %bau_therm_load_min, \"MMBtu\")\n",
    "print(\"Elec min load =\", '%.2f' %bau_grid_load_min, \"kW\")\n",
    "print(\"Cooling max load =\", '%.2f' %bau_chiller_thermal_load_max, \"ton\")\n",
    "print(\"Cooling mean load =\", '%.2f' %bau_chiller_thermal_load_mean, \"ton\")\n",
    "print(\"Cooling min load =\", '%.2f' %bau_chiller_thermal_load_min, \"ton\")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "chp_elec_total_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                   ['year_one_electric_production_series_kw'], index = dti) #total CHP elec production (hourly)\n",
    "chp_elec_to_load_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                   ['year_one_to_load_series_kw'], index = dti) #CHP elec produciton to load (hourly)\n",
    "chp_elec_to_grid_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                   ['year_one_to_grid_series_kw'], index = dti) #CHP elec production to grid (hourly)\n",
    "\n",
    "grid_load_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']\n",
    "                                               ['LoadProfile']['year_one_electric_load_series_kw'], index = dti)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#-----chris-calc electric efficiency----\n",
    "slope_ee, intercept_ee, rvalue, pvalue, stderr = linregress([0.5*chp_size_kwe, chp_size_kwe], [100*chp_elec_eff_half_load, 100*chp_elec_eff_full_load])\n",
    "print(\"Electric Efficiency Slope = \", '%.4f' %slope_ee)\n",
    "print(\"Electric Efficiency Intercept = \", '%.1f' %intercept_ee)        \n",
    "year_one_elec_eff_series_percent = slope_ee*np.array(chp_elec_total_hrly.values)+ intercept_ee\n",
    "\n",
    "# fuel burn\n",
    "slope_fb, intercept_fb, rvalue, pvalue, stderr = linregress([0.5*chp_size_kwe, chp_size_kwe], [CHP_fuel_burn_half_load, CHP_fuel_burn_full_load])\n",
    "corr = 1 # 1 = MT corr , 2 = ICE corr, else = base\n",
    "cf_fb = np.ones(8760)\n",
    "t_ref = 77  # [deg F]\n",
    "if corr == 1:\n",
    "    T_ISO = 59\n",
    "    for i in range(8760):\n",
    "        cf_fb[i] = 0.9529*np.exp(0.0008249*ambient_temperature[i]) - (7.496*(10**(-6)))*np.exp(0.06726*ambient_temperature[i])\n",
    "if corr == 2:\n",
    "    T_ISO = 77\n",
    "    for i in range(8760):\n",
    "        if ambient_temperature[i] <= t_ref:\n",
    "                 cf_fb[i] = 1 + 0.0003*(ambient_temperature[i] - t_ref)\n",
    "        else:\n",
    "                 cf_fb[i] = 1\n",
    "else:\n",
    "    T_ISO = 59\n",
    "    \n",
    "chp_elec_total_hrly_a = np.array(chp_elec_total_hrly)\n",
    "year_one_fuel_burn_series = np.ones(8760)\n",
    "year_one_elec_eff_series_percent_corr = np.ones(8760)\n",
    "for i in range(8760):\n",
    "\tyear_one_fuel_burn_series[i] = cf_fb[i]*(slope_fb*chp_elec_total_hrly_a[i] + intercept_fb)\n",
    "for i in range(8760):    \n",
    "    year_one_elec_eff_series_percent_corr[i] = 100*(chp_elec_total_hrly_a[i] / (year_one_fuel_burn_series[i]* (1e6/3412)))    \n",
    "\n",
    "\n",
    "#thermal production\n",
    "year_one_thermal_prod_series_corr = np.ones(8760)\n",
    "cf_tp = np.ones(8760)\n",
    "if corr == 1:\n",
    "    for i in range(8760):\n",
    "        cf_tp[i] = 0.005852*ambient_temperature[i] + 0.6615\n",
    "if corr == 2:\n",
    "    for i in range(8760):\n",
    "        if ambient_temperature[i] <= t_ref:\n",
    "                 cf_tp[i] = 1 + 0.001*(ambient_temperature[i] - t_ref)\n",
    "        else:\n",
    "                 cf_tp[i] = 1\n",
    "for i in range(8760):\n",
    "\tyear_one_thermal_prod_series_corr[i] = cf_tp[i]*(slope_tp*chp_elec_total_hrly_a[i] + intercept_tp)                \n",
    "                                                                                               \n",
    "\n",
    "hours = np.linspace(1,8760,8760)\n",
    "s = 8000 #start hour mark\n",
    "e = 8050 #end hour mark\n",
    "plt.figure()\n",
    "plt.plot(hours[s:e], year_one_elec_eff_series_percent[s:e], color='green') #linestyle='none',marker='o') \n",
    "#------end----\n",
    "\n",
    "electric_demand = np.array(api_response['outputs']['Scenario']['Site']['LoadProfile']['year_one_electric_load_series_kw'])\n",
    "chp_power = np.array(api_response['outputs']['Scenario']['Site']['CHP']['year_one_electric_production_series_kw'])\n",
    "chp_power_to_load = np.array(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                   ['year_one_to_load_series_kw'])\n",
    "chp_power_to_grid = np.array(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                   ['year_one_to_grid_series_kw'])\n",
    "one = np.zeros(8760)\n",
    "\n",
    "save = 2 # 1 = save , 2 = do not save\n",
    "mode = 1 #CHP+PV = 1 , CHP = 2\n",
    "if mode == 1:\n",
    "#-----chris-plot electric load profiles----\n",
    "    pv_elec_total_hrly = np.array(api_response['outputs']['Scenario']['Site']['PV']\n",
    "                                   ['year_one_power_production_series_kw']) #PV hourly total elec production\n",
    "    pv_elec_to_load_hrly = np.array(api_response['outputs']['Scenario']['Site']['PV']\n",
    "                                   ['year_one_to_load_series_kw']) #PV hourly elec production to load\n",
    "    pv_elec_to_grid_hrly = np.array(api_response['outputs']['Scenario']['Site']['PV']\n",
    "                                   ['year_one_to_grid_series_kw']) #PV hourly elec production to grid\n",
    "    grid_elec_to_load_hrly = electric_demand - chp_power_to_load - pv_elec_to_load_hrly\n",
    "    pv_plus_chp_to_load_hrly = chp_power_to_load + pv_elec_to_load_hrly\n",
    "\n",
    "#show load profile and distribution amongst DG techs\n",
    "    tolerance = 0.01 #sometimes tech power output just barely exceeds the demand and messes up plotting\n",
    "    plt.figure() \n",
    "    line1, = plt.plot(hours[s:e], electric_demand[s:e],'k', label = \"Demand\")\n",
    "    #line2, = plt.plot(hours[s:e], chp_power_to_load[s:e], 'b',  label = \"CHP\")\n",
    "    #line3, = plt.plot(hours[s:e], pv_plus_chp_to_load_hrly[s:e], 'r', label = \"PV\")\n",
    "    #line4, = plt.plot(hours[s:e], grid_elec_to_load_hrly[s:e], 'r', label = \"Grid\")\n",
    "    fill3 = plt.fill_between(hours[s:e], pv_plus_chp_to_load_hrly[s:e], electric_demand[s:e], \\\n",
    "                     where=   electric_demand[s:e] + tolerance >= pv_plus_chp_to_load_hrly[s:e] , facecolor = 'green', alpha = 0.5, label = 'Grid' )\n",
    "    fill2 = plt.fill_between(hours[s:e], pv_plus_chp_to_load_hrly[s:e], chp_power_to_load[s:e], \\\n",
    "                     where=   pv_plus_chp_to_load_hrly[s:e] >= chp_power_to_load[s:e] , facecolor = 'red', alpha = 0.5, label = 'PV' )\n",
    "    fill1 = plt.fill_between(hours[s:e], chp_power_to_load[s:e], one[s:e], \\\n",
    "                     where=  chp_power_to_load[s:e] >= one[s:e] , facecolor = 'blue', alpha = 0.5, label = 'CHP' )\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Electric Power [kWe]')\n",
    "    plt.legend(handles = [fill1, fill2, fill3, line1])\n",
    "    if save == 1:\n",
    "        plt.savefig('figures/CHI/CHI_ICE-PV_corr_e_profile_summer.png', dpi = 300)\n",
    "#show DG power flow to grid versus load (ignore battery)\n",
    "    plt.figure() \n",
    "    line1, = plt.plot(hours[s:e], chp_elec_to_load_hrly[s:e], 'b',  label = \"CHP to load\")\n",
    "    line2, = plt.plot(hours[s:e], chp_elec_to_grid_hrly[s:e], 'b--',  label = \"CHP to grid\")\n",
    "    line3, = plt.plot(hours[s:e], pv_elec_to_load_hrly[s:e], 'g', label = \"PV to load\")\n",
    "    line4, = plt.plot(hours[s:e], pv_elec_to_grid_hrly[s:e], 'g--', label = \"PV to grid\")\n",
    "    plt.legend(handles = [line1, line2, line3, line4])\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Electric Power [kWe]')\n",
    "    \n",
    "    #calculate CHP distributions\n",
    "    chp_power_to_load_plus_grid = chp_power_to_load + chp_power_to_grid\n",
    "    plt.figure() \n",
    "    #line1, = plt.plot(hours[s:e], chp_power_to_load[s:e], 'b',  label = \"CHP to load\")\n",
    "    #line2, = plt.plot(hours[s:e], chp_power_to_load_plus_grid[s:e], 'g',  label = \"CHP to grid\")\n",
    "    #line3, = plt.plot(hours[s:e], chp_elec_to_bess_hrly[s:e], 'b',  label = \"CHP to BESS\")\n",
    "    line4, = plt.plot(hours[s:e], chp_power[s:e], 'k',  label = \"Total CHP\", linewidth=2)\n",
    "    \n",
    "    fill1 = plt.fill_between(hours[s:e], chp_power_to_load[s:e], one[s:e], \\\n",
    "                     where=  chp_power_to_load[s:e] >= one[s:e] , facecolor = 'blue', alpha = 0.5, label = 'CHP to Load' )\n",
    "    fill2 = plt.fill_between(hours[s:e], chp_power_to_load[s:e], chp_power[s:e], \\\n",
    "                     where=  chp_power[s:e] >= chp_power_to_load[s:e] , facecolor = 'green', alpha = 0.5, label = 'CHP to Grid' )\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Electric Power [kWe]')\n",
    "    plt.legend(handles = [fill1, fill2, line4])\n",
    "    #SAVE\n",
    "    #plt.savefig('figures_5/SF/SF_ICE-PV-BESS_base_CHPprofile_summer.png', dpi = 300)\n",
    "    \n",
    "    \n",
    "    #show PV power distribution\n",
    "    pv_elec_to_load_plus_grid_hrly = pv_elec_to_load_hrly + pv_elec_to_grid_hrly\n",
    "    plt.figure()\n",
    "    #line1, = plt.plot(hours[s:e], pv_elec_to_load_hrly[s:e], 'b', label = \"PV to load\")\n",
    "    #line2, = plt.plot(hours[s:e], pv_elec_to_load_plus_grid_hrly[s:e], 'g', label = \"PV to Grid\")\n",
    "    #line3, = plt.plot(hours[s:e], pv_elec_to_battery_hrly[s:e], 'g--', label = \"PV to BESS\")\n",
    "    line4, = plt.plot(hours[s:e], pv_elec_total_hrly[s:e], 'k',  label = \"Total PV\", linewidth=2)\n",
    "    fill1 = plt.fill_between(hours[s:e], pv_elec_to_load_hrly[s:e], one[s:e], \\\n",
    "                     where=  pv_elec_to_load_hrly[s:e] >= one[s:e] , facecolor = 'blue', alpha = 0.5, label = 'PV to Load' )\n",
    "    fill2 = plt.fill_between(hours[s:e], pv_elec_to_load_hrly[s:e], pv_elec_total_hrly[s:e], \\\n",
    "                     where=  pv_elec_total_hrly[s:e] >= pv_elec_to_load_hrly[s:e] , facecolor = 'green', alpha = 0.5, label = 'PV to Grid' )\n",
    "\n",
    "    plt.legend(handles = [fill1, fill2, line4])\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Electric Power [kWe]')\n",
    "else:\n",
    "    grid_elec_to_load_hrly = electric_demand - chp_power\n",
    "    #show load profile and distribution amongst DG techs\n",
    "    plt.figure() \n",
    "    line1, = plt.plot(hours[s:e], electric_demand[s:e],'k', label = \"Demand\")\n",
    "    line2, = plt.plot(hours[s:e], chp_power[s:e], 'b',  label = \"CHP\")\n",
    "    #line3, = plt.plot(hours[s:e], grid_elec_to_load_hrly[s:e], 'y', label = \"Grid\")\n",
    "    fill2 = plt.fill_between(hours[s:e], chp_power[s:e], electric_demand[s:e], \\\n",
    "                     where=   electric_demand[s:e] >= chp_power[s:e] , facecolor = 'green', alpha = 0.5, label = 'Grid' )\n",
    "    fill1 = plt.fill_between(hours[s:e], chp_power[s:e], one[s:e], \\\n",
    "                     where=  chp_power[s:e] >= one[s:e] , facecolor = 'blue', alpha = 0.5, label = 'CHP' )\n",
    "    plt.legend(handles = [fill1, fill2, line2])\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Electric Power [kWe]')\n",
    "    #grid export?\n",
    "    plt.figure() \n",
    "    line1, = plt.plot(hours[s:e], chp_elec_to_load_hrly[s:e], 'b',  label = \"CHP to load\")\n",
    "    line2, = plt.plot(hours[s:e], chp_elec_to_grid_hrly[s:e], 'b--',  label = \"CHP to grid\")\n",
    "    plt.legend(handles = [line1, line2])\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Electric Power [kWe]')\n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "chp_therm_to_load_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                      ['year_one_thermal_to_load_series_mmbtu_per_hour'], index = dti)\n",
    "chp_therm_to_TES_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                     ['year_one_thermal_to_tes_series_mmbtu_per_hour'], index = dti)\n",
    "chp_therm_total_hrly = chp_therm_to_load_hrly + chp_therm_to_TES_hrly\n",
    "chp_fuel_annual = api_response['outputs']['Scenario']['Site']['CHP']['year_one_fuel_used_mmbtu']\n",
    "chp_elec_effic = np.sum(chp_elec_total_hrly)/(chp_fuel_annual * 1E6/3412.0)\n",
    "chp_total_effic = (np.sum(chp_elec_total_hrly) + np.sum(chp_therm_total_hrly)* 1E6/3412.0)/(chp_fuel_annual * 1E6/3412.0)\n",
    "chp_elec_cf = np.sum(chp_elec_total_hrly)/(chp_size_kwe*8760)\n",
    "chp_avg_load_when_on = chp_elec_total_hrly[chp_elec_total_hrly>1].mean()\n",
    "boiler_fuel_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['Boiler']\n",
    "                                ['year_one_boiler_fuel_consumption_series_mmbtu_per_hr'], index = dti)\n",
    "boiler_fuel_annual = boiler_fuel_hrly.resample('Y').sum()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#-----chris-plot thermal load profiles----\n",
    "boiler_therm_total_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['Boiler']\n",
    "                                     ['year_one_boiler_thermal_production_series_mmbtu_per_hr'], index = dti)\n",
    "boiler_fuel_hrly_bau = pd.DataFrame(api_response['outputs']['Scenario']['Site']['LoadProfileBoilerFuel']\n",
    "                                     ['year_one_boiler_fuel_load_series_mmbtu_per_hr_bau'], index = dti)\n",
    "therm_load_hrly = boiler_fuel_hrly_bau*boiler_eff\n",
    "\n",
    "chp_tp = np.array(api_response['outputs']['Scenario']['Site']['CHP']['year_one_thermal_to_load_series_mmbtu_per_hour'])\n",
    "boiler_tp = np.array(api_response['outputs']['Scenario']['Site']['Boiler']['year_one_boiler_thermal_production_series_mmbtu_per_hr'])\n",
    "#thermal_demand = np.array(api_response['outputs']['Scenario']['Site']['LoadProfileBoilerFuel']\n",
    "#                          ['year_one_boiler_fuel_load_series_mmbtu_per_hr_bau'])/boiler_eff\n",
    "thermal_demand = chp_tp+boiler_tp\n",
    "\n",
    "#print(np.transpose(chp_therm_total_hrly.values[s:e])\n",
    "#HotTES_to_load_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['HotTES']\n",
    "#                                     ['year_one_thermal_from_hot_tes_series_mmbtu_per_hr'], index = dti)\n",
    "plt.figure()\n",
    "line1, = plt.plot(hours[s:e], chp_tp[s:e], 'b',  label = \"CHP\")\n",
    "#line2, = plt.plot(hours[s:e], boiler_tp[s:e], 'r',  label = \"Boiler\")\n",
    "#line3, = plt.plot(hours[s:e], HotTES_to_load_hrly[s:e], 'y',  label = \"TES\")\n",
    "line4, = plt.plot(hours[s:e], thermal_demand[s:e], 'k',  label = \"Demand\") #really\"Total\"\n",
    "\n",
    "fill2 = plt.fill_between(hours[s:e], chp_tp[s:e],thermal_demand[s:e], \\\n",
    "                     where=   thermal_demand[s:e] >= chp_tp[s:e] , facecolor = 'green', alpha = 0.5, label = 'Boiler' )\n",
    "fill1 = plt.fill_between(hours[s:e], chp_tp[s:e], one[s:e], \\\n",
    "                     where=  chp_tp[s:e] >= one[s:e] , facecolor = 'blue', alpha = 0.5, label = 'CHP' )\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Thermal Production [MMBtu/hr]')\n",
    "plt.legend(handles = [fill1, fill2, line4])\n",
    "if save == 1:\n",
    "    plt.savefig('figures/CHI/CHI_ICE-PV_corr_tp_profile_summer.png', dpi = 300)\n",
    "\n",
    "\n",
    "\n",
    "#check CF calcs\n",
    "plt.figure() \n",
    "line2, = plt.plot(hours[s:e], chp_therm_to_load_hrly[s:e], 'g',  label = \"Dispatched\") #see if sizes are different\n",
    "line3, = plt.plot(hours[s:e], CHP_max_heat_full_load*np.ones(e-s), 'b',  label = \"Rated\")\n",
    "line4, = plt.plot(hours[s:e], year_one_thermal_prod_series_corr[s:e], 'r',  label = \"Produced\")\n",
    "plt.legend(handles = [line3, line2, line4])\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Thermal Production [MMBtu/hr]')\n",
    "#plt.ylim([0, 1])\n",
    "#plt.savefig('figures/CHI/CHI_ICE-PV_corr_tp_profile.png', dpi = 300)\n",
    "\n",
    "plt.figure() \n",
    "line2, = plt.plot(hours[s:e], T_ISO*np.ones(e-s), 'b',  label = \"ISO\") #see if sizes are different\n",
    "line3, = plt.plot(hours[s:e], ambient_temperature[s:e], 'g',  label = \"TMY3\")\n",
    "plt.legend(handles = [line2, line3])\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Ambient Temperature [F]')\n",
    "#plt.savefig('SF_MT_Tamb.png', dpi = 300)\n",
    "plt.figure() \n",
    "line2, = plt.plot(hours[s:e], chp_size_kwe*np.ones(e-s), 'b',  label = \"Rated\") #see if sizes are different\n",
    "line3, = plt.plot(hours[s:e], chp_elec_to_load_hrly[s:e], 'g',  label = \"Dispatched\")\n",
    "plt.legend(handles = [line2, line3])\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Electric Power [kWe]')\n",
    "#plt.savefig('figures/PHX/PHX_ICE-PV_corr_e_profile.png', dpi = 300)\n",
    "plt.figure() \n",
    "line2, = plt.plot(hours[s:e], chp_elec_eff_full_load*100*np.ones(e-s), 'b',  label = \"Rated\") #see uf sizes are different\n",
    "line3, = plt.plot(hours[s:e], year_one_elec_eff_series_percent_corr[s:e], 'g',  label = \"Dispatched\")\n",
    "plt.legend(handles = [line2, line3])\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Electric Efficiency [%]')\n",
    "#plt.ylim([36,38])\n",
    "#plt.savefig('figures/CHI/CHI_ICE-PV_corr_ee_profile.png', dpi = 300)\n",
    "\n",
    "plt.rcParams['font.family'] = 'sans-serif'\n",
    "plt.rcParams['font.sans-serif'] = ['Times New Roman']\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#chp vs boiler fuel consumption\n",
    "year_one_chp_fuel = api_response['outputs']['Scenario']['Site']['CHP']['year_one_fuel_used_mmbtu']\n",
    "print(\"Annual CHP Fuel Use =\", '%.3f' %year_one_chp_fuel)\n",
    "year_one_boiler_fuel = api_response['outputs']['Scenario']['Site']['Boiler']['year_one_boiler_fuel_consumption_mmbtu']\n",
    "print(\"Annual Boiler Fuel Use =\", '%.3f' %year_one_boiler_fuel)\n",
    "\n",
    "\n",
    "\n",
    "#chp_annual_runhrs = np.count_nonzero(chp_elec_total_hourly)\n",
    "chp_annual_runhrs = np.sum(chp_elec_total_hrly>1) #alternative to np.count_nonzero to eliminate very small numbers. Sums the number of TRUE\n",
    "#chp_annual_runhrs = chp_elec_total_hourly[chp_elec_total_hourly>1].count() #alternative to np.count_nonzero to eliminate very small numbers\n",
    "chp_elec_cf_when_on = chp_avg_load_when_on/chp_size_kwe\n",
    "chp_annual_heatinghrs = np.sum(chp_therm_total_hrly>0.01) #alternative to np.count_nonzero to eliminate very small numbers\n",
    "chp_capacity_elec_to_load_peak = chp_size_kwe/bau_grid_load_peak\n",
    "chp_capacity_elec_to_load_mean = chp_size_kwe/bau_grid_load_mean\n",
    "chp_capacity_therm_to_load_peak = chp_size_kwe*chp_therm_to_elec_full_load/(bau_therm_load_peak*1e6/3412)\n",
    "chp_capacity_therm_to_load_mean = chp_size_kwe*chp_therm_to_elec_full_load/(bau_therm_load_mean*1e6/3412)\n",
    "\n",
    "load_hours_less_than_chp_capacity = np.sum(bau_grid_load_hrly < chp_size_kwe)\n",
    "\n",
    "#find base load of bau_grid_load_hrly. Using value with 85% exceedance as described in ASHRAE 'Combined Heat and Power Design Guide' \n",
    "base_load_site_elec = bau_grid_load_hrly.sort_values(0, ascending = False).iloc[int(8760*.85)-1]\n",
    "base_load_site_therm = bau_boiler_load_fuel_hrly.sort_values(0, ascending = False).iloc[int(8760*.85)-1]/boiler_eff\n",
    "chp_capacity_elec_to_base_load = chp_size_kwe/base_load_site_elec\n",
    "chp_capacity_therm_to_base_load = chp_size_kwe*chp_therm_to_elec_full_load/(base_load_site_therm*1e6/3412)\n",
    "chp_gen_elec_to_load = chp_elec_total_hrly.resample('Y').sum()/bau_grid_load_hrly.resample('Y').sum()\n",
    "chp_gen_therm_to_load = chp_therm_total_hrly.resample('Y').sum()/(bau_boiler_load_fuel_hrly.resample('Y').sum()*boiler_eff)\n",
    "#net_fuel_consumption_change = \n",
    "\n",
    "print(\"Annual CHP Electric Efficiency =\", '%.3f' %chp_elec_effic)\n",
    "print(\"Annual CHP Total Efficiency =\", '%.3f' %chp_total_effic)\n",
    "print(\"Annual CHP Elec Capacity Factor =\", '%.2f' %chp_elec_cf)\n",
    "print(\"Annual CHP Elec Capacity Factor when running =\", '%.2f' %chp_elec_cf_when_on)\n",
    "print(\"Annual CHP run hours =\", '%.0f' %chp_annual_runhrs, \"(fraction\",'%.2f'%(chp_annual_runhrs/8760), \")\")\n",
    "print(\"Annual CHP hours of useful heat =\", '%.0f' %chp_annual_heatinghrs, \"(fraction\",'%.2f'%(chp_annual_heatinghrs/8760), \")\")\n",
    "print(\"Annual CHP mean power output when on =\", '%.0f' %chp_avg_load_when_on, \"kW\")\n",
    "print(\"Elec peak load =\", '%.0f' %bau_grid_load_peak, \"kW\")\n",
    "print(\"Elec mean load =\", '%.0f' %bau_grid_load_mean, \"kW\")\n",
    "print(\"Elec base load =\", '%.0f' %base_load_site_elec, \"kW (85% exceedance value)\")\n",
    "print(\"Heating peak load =\", '%.2f' %bau_therm_load_peak, \"MMBtu\")\n",
    "print(\"Heating mean load =\", '%.2f' %bau_therm_load_mean, \"MMBtu\")\n",
    "print(\"Heating base load =\", '%.2f' %base_load_site_therm, \"MMBtu (85% exceedance value)\")\n",
    "print(\"Ratio of CHP elec capacity to peak elec load =\", '%.3f' %chp_capacity_elec_to_load_peak)\n",
    "print(\"Ratio of CHP elec capacity to mean elec load =\", '%.3f' %chp_capacity_elec_to_load_mean)\n",
    "print(\"Ratio of CHP elec capacity to base elec load =\", '%.2f' %chp_capacity_elec_to_base_load)\n",
    "print(\"Ratio of CHP therm capacity to peak heat load =\", '%.3f' %chp_capacity_therm_to_load_peak)\n",
    "print(\"Ratio of CHP therm capacity to mean heat load =\", '%.3f' %chp_capacity_therm_to_load_mean)\n",
    "print(\"Ratio of CHP therm capacity to base heat load =\", '%.3f' %chp_capacity_therm_to_base_load)\n",
    "print(\"Useful heat recovery ratio =\", '%.3f' )\n",
    "print(\"Annual fraction of site elec load served by CHP =\", '%.3f' %chp_gen_elec_to_load.iloc[0][0])\n",
    "print(\"Annual fraction of site thermal load served by CHP =\", '%.3f' %chp_gen_therm_to_load.iloc[0][0])\n",
    "\n",
    "#checks #there is a tolerance where tech power is +0.001 above electric demand\n",
    "#print(electric_demand[5095:5098])\n",
    "#print(grid_elec_to_load_hrly[5095:5098])\n",
    "#print(pv_plus_chp_to_load_hrly[5095:5098])\n",
    "#print(electric_demand[5095:5098] - (pv_plus_chp_to_load_hrly[5095:5098]+grid_elec_to_load_hrly[5095:5098]))\n",
    "#print(electric_demand[5095:5098] - pv_plus_chp_to_load_hrly[5095:5098])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# # thermal grade calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Processing to compare 2 scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import linregress\n",
    "chp_elec_eff_full_load = api_response['inputs']['Scenario']['Site']['CHP']['elec_effic_full_load']\n",
    "chp_elec_eff_half_load = api_response['inputs']['Scenario']['Site']['CHP']['elec_effic_half_load']\n",
    "chp_therm_eff_full_load = api_response['inputs']['Scenario']['Site']['CHP']['thermal_effic_full_load']\n",
    "chp_therm_eff_half_load = api_response['inputs']['Scenario']['Site']['CHP']['thermal_effic_half_load']\n",
    "chp_size_kwe = api_response['outputs']['Scenario']['Site']['CHP']['size_kw']\n",
    "CHP_fuel_burn_full_load = chp_size_kwe / chp_elec_eff_full_load * 3412/1e6 #[MMBtu/hr]\n",
    "CHP_fuel_burn_half_load = 0.5*chp_size_kwe / chp_elec_eff_half_load * 3412/1e6\n",
    "slope_fb, intercept_fb, rvalue, pvalue, stderr = linregress([0.5*chp_size_kwe, chp_size_kwe], [CHP_fuel_burn_half_load, CHP_fuel_burn_full_load])\n",
    "CHP_elec_slope = slope_fb\n",
    "CHP_elec_intercept = intercept_fb\n",
    "CHP_max_heat_full_load = CHP_fuel_burn_full_load * chp_therm_eff_full_load #[MMBtu/hr]\n",
    "CHP_max_heat_half_load = CHP_fuel_burn_half_load * chp_therm_eff_half_load \n",
    "slope_tp, intercept_tp, rvalue, pvalue, stderr = linregress([0.5*chp_size_kwe, chp_size_kwe], [CHP_max_heat_half_load, CHP_max_heat_full_load])\n",
    "CHP_therm_slope = slope_tp\n",
    "CHP_therm_intercept = intercept_tp\n",
    "print(CHP_elec_slope)\n",
    "print(CHP_elec_intercept)\n",
    "print(CHP_therm_slope)\n",
    "print(CHP_therm_intercept)\n",
    "\n",
    "\n",
    "load_year = str(api_response['inputs']['Scenario']['Site']['LoadProfile']['year'])\n",
    "dti = pd.date_range( load_year +'-01-01', periods=8760, freq='H')\n",
    "bau_grid_load_less_chiller_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']\n",
    "                                               ['LoadProfile']['year_one_electric_load_series_kw'], index = dti)\n",
    "bau_boiler_load_fuel_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']\n",
    "                                         ['LoadProfileBoilerFuel']['year_one_boiler_fuel_load_series_mmbtu_per_hr_bau'], index = dti)\n",
    "bau_chiller_load_elec_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']\n",
    "                                          ['LoadProfileChillerElectric']['year_one_chiller_electric_load_series_kw'], index = dti)\n",
    "boiler_eff = api_response['inputs']['Scenario']['Site']['Boiler']['boiler_efficiency']\n",
    "\n",
    "bau_grid_load_hrly = bau_grid_load_less_chiller_hrly + bau_chiller_load_elec_hrly\n",
    "bau_grid_load_peak = np.max(bau_grid_load_hrly)\n",
    "bau_grid_load_mean = np.mean(bau_grid_load_hrly)\n",
    "bau_grid_load_total = np.sum(bau_grid_load_hrly)\n",
    "bau_therm_load_peak = np.max(bau_boiler_load_fuel_hrly)*boiler_eff #converted units of fuel to units of heat\n",
    "bau_therm_load_mean = np.mean(bau_boiler_load_fuel_hrly)*boiler_eff\n",
    "bau_therm_load_total = np.sum(bau_boiler_load_fuel_hrly)*boiler_eff\n",
    "chp_therm_to_elec_full_load = chp_therm_eff_full_load/chp_elec_eff_full_load\n",
    "\n",
    "\n",
    "chp_elec_total_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                   ['year_one_electric_production_series_kw'], index = dti) #total CHP elec production (hourly)\n",
    "chp_elec_to_load_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                   ['year_one_to_load_series_kw'], index = dti) #CHP elec produciton to load (hourly)\n",
    "chp_elec_to_grid_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                   ['year_one_to_grid_series_kw'], index = dti) #CHP elec production to grid (hourly)\n",
    "\n",
    "\n",
    "\n",
    "#-----chris-calc electric efficiency----\n",
    "slope_ee, intercept_ee, rvalue, pvalue, stderr = linregress([0.5*chp_size_kwe, chp_size_kwe], [100*chp_elec_eff_half_load, 100*chp_elec_eff_full_load])\n",
    "print(\"Electric Efficiency Slope = \", '%.4f' %slope_ee)\n",
    "print(\"Electric Efficiency Intercept = \", '%.1f' %intercept_ee)        \n",
    "year_one_elec_eff_series_percent = slope_ee*np.array(chp_elec_total_hrly.values)+ intercept_ee\n",
    "\n",
    "# fuel burn\n",
    "slope_fb, intercept_fb, rvalue, pvalue, stderr = linregress([0.5*chp_size_kwe, chp_size_kwe], [CHP_fuel_burn_half_load, CHP_fuel_burn_full_load])\n",
    "corr = 1 # 1 = MT corr , 2 = ICE corr, else = base\n",
    "cf_fb = np.ones(8760)\n",
    "t_ref = 77  # [deg F]\n",
    "if corr == 1:\n",
    "    T_ISO = 59\n",
    "    for i in range(8760):\n",
    "        cf_fb[i] = 0.9529*np.exp(0.0008249*ambient_temperature[i]) - (7.496*(10**(-6)))*np.exp(0.06726*ambient_temperature[i])\n",
    "if corr == 2:\n",
    "    T_ISO = 77\n",
    "    for i in range(8760):\n",
    "        if ambient_temperature[i] <= t_ref:\n",
    "                 cf_fb[i] = 1 + 0.0003*(ambient_temperature[i] - t_ref)\n",
    "        else:\n",
    "                 cf_fb[i] = 1\n",
    "else:\n",
    "    T_ISO = 59\n",
    "    \n",
    "chp_elec_total_hrly_a = np.array(chp_elec_total_hrly)\n",
    "year_one_fuel_burn_series = np.ones(8760)\n",
    "year_one_elec_eff_series_percent_corr = np.ones(8760)\n",
    "for i in range(8760):\n",
    "\tyear_one_fuel_burn_series[i] = cf_fb[i]*(slope_fb*chp_elec_total_hrly_a[i] + intercept_fb)\n",
    "for i in range(8760):    \n",
    "    year_one_elec_eff_series_percent_corr[i] = 100*(chp_elec_total_hrly_a[i] / (year_one_fuel_burn_series[i]* (1e6/3412)))    \n",
    "\n",
    "\n",
    "#thermal production\n",
    "year_one_thermal_prod_series_corr = np.ones(8760)\n",
    "cf_tp = np.ones(8760)\n",
    "if corr == 1:\n",
    "    for i in range(8760):\n",
    "        cf_tp[i] = 0.005852*ambient_temperature[i] + 0.6615\n",
    "if corr == 2:\n",
    "    for i in range(8760):\n",
    "        if ambient_temperature[i] <= t_ref:\n",
    "                 cf_tp[i] = 1 + 0.001*(ambient_temperature[i] - t_ref)\n",
    "        else:\n",
    "                 cf_tp[i] = 1\n",
    "for i in range(8760):\n",
    "\tyear_one_thermal_prod_series_corr[i] = cf_tp[i]*(slope_tp*chp_elec_total_hrly_a[i] + intercept_tp)  \n",
    "\n",
    "\n",
    "#base electric efficiency\n",
    "base_chp_elec_total_hrly = pd.DataFrame(base_api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                   ['year_one_electric_production_series_kw'], index = dti)\n",
    "base_chp_elec_eff_full_load = base_api_response['inputs']['Scenario']['Site']['CHP']['elec_effic_full_load']\n",
    "base_chp_elec_eff_half_load = base_api_response['inputs']['Scenario']['Site']['CHP']['elec_effic_half_load']\n",
    "base_chp_size_kwe = base_api_response['outputs']['Scenario']['Site']['CHP']['size_kw']\n",
    "base_slope_ee, base_intercept_ee, base_rvalue, base_pvalue, base_stderr = linregress([0.5*base_chp_size_kwe, base_chp_size_kwe], [100*base_chp_elec_eff_half_load, 100*base_chp_elec_eff_full_load])\n",
    "print(\"Electric Efficiency Slope = \", '%.4f' %slope_ee)\n",
    "print(\"Electric Efficiency Intercept = \", '%.1f' %intercept_ee)\n",
    "base_year_one_elec_eff_series_percent = base_slope_ee*base_chp_elec_total_hrly.values+ base_intercept_ee\n",
    "\n",
    "\n",
    "\n",
    "hours = np.linspace(1,8760,8760)\n",
    "s = 4000 #start hour mark\n",
    "e = 4100 #end hour mark\n",
    "#------end----\n",
    "\n",
    "electric_demand = np.array(api_response['outputs']['Scenario']['Site']['LoadProfile']['year_one_electric_load_series_kw'])\n",
    "chp_power = np.array(api_response['outputs']['Scenario']['Site']['CHP']['year_one_electric_production_series_kw'])\n",
    "chp_power_to_load = np.array(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                   ['year_one_to_load_series_kw'])\n",
    "one = np.zeros(8760)\n",
    "\n",
    "mode = 1 #CHP+PV = 1 , CHP = 2, CHP+PV+BESS = 3\n",
    "if mode == 1:\n",
    "#-----chris-plot electric load profiles----\n",
    "    pv_elec_total_hrly = np.array(api_response['outputs']['Scenario']['Site']['PV']\n",
    "                                   ['year_one_power_production_series_kw']) #PV hourly total elec production\n",
    "    pv_elec_to_load_hrly = np.array(api_response['outputs']['Scenario']['Site']['PV']\n",
    "                                   ['year_one_to_load_series_kw']) #PV hourly elec production to load\n",
    "    pv_elec_to_grid_hrly = np.array(api_response['outputs']['Scenario']['Site']['PV']\n",
    "                                   ['year_one_to_grid_series_kw']) #PV hourly elec production to grid\n",
    "    grid_elec_to_load_hrly = electric_demand - chp_power_to_load - pv_elec_to_load_hrly\n",
    "    pv_plus_chp_to_load_hrly = chp_power_to_load + pv_elec_to_load_hrly\n",
    "\n",
    "#show load profile and distribution amongst DG techs\n",
    "    plt.figure() \n",
    "    line1, = plt.plot(hours[s:e], electric_demand[s:e],'k', label = \"Total\")\n",
    "    #line2, = plt.plot(hours[s:e], chp_power_to_load[s:e], 'b',  label = \"CHP\")\n",
    "    #line3, = plt.plot(hours[s:e], pv_plus_chp_to_load_hrly[s:e], 'r', label = \"PV\")\n",
    "    #line4, = plt.plot(hours[s:e], grid_elec_to_load_hrly[s:e], 'r', label = \"Grid\")\n",
    "    #plt.legend(handles = [line1, line2, line3])\n",
    "    plt.fill_between(hours[s:e], pv_plus_chp_to_load_hrly[s:e], electric_demand[s:e], \\\n",
    "                     where=   electric_demand[s:e] + 0.01 >= pv_plus_chp_to_load_hrly[s:e] , facecolor = 'green', alpha = 0.5 )\n",
    "    plt.fill_between(hours[s:e], pv_plus_chp_to_load_hrly[s:e], chp_power_to_load[s:e], \\\n",
    "                     where=   pv_plus_chp_to_load_hrly[s:e] >= chp_power_to_load[s:e] , facecolor = 'red', alpha = 0.5 )\n",
    "    plt.fill_between(hours[s:e], chp_power_to_load[s:e], one[s:e], \\\n",
    "                     where=  chp_power_to_load[s:e] >= one[s:e] , facecolor = 'blue', alpha = 0.5 )\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Electric Power [kWe]')\n",
    "#show DG power flow to grid versus load (ignore battery)\n",
    "    plt.figure() \n",
    "    line1, = plt.plot(hours[s:e], chp_elec_to_load_hrly[s:e], 'b',  label = \"CHP to load\")\n",
    "    line2, = plt.plot(hours[s:e], chp_elec_to_grid_hrly[s:e], 'b--',  label = \"CHP to grid\")\n",
    "    line3, = plt.plot(hours[s:e], pv_elec_to_load_hrly[s:e], 'g', label = \"PV to load\")\n",
    "    line4, = plt.plot(hours[s:e], pv_elec_to_grid_hrly[s:e], 'g--', label = \"PV to grid\")\n",
    "    plt.legend(handles = [line1, line2, line3, line4])\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Electric Power [kWe]')\n",
    "#-----end-----\n",
    "elif mode == 2:\n",
    "    grid_elec_to_load_hrly = electric_demand - chp_power\n",
    "    #show load profile and distribution amongst DG techs\n",
    "    plt.figure() \n",
    "    line1, = plt.plot(hours[s:e], electric_demand[s:e],'k', label = \"Demand\")\n",
    "    line2, = plt.plot(hours[s:e], chp_power[s:e], 'b',  label = \"CHP\")\n",
    "    #line3, = plt.plot(hours[s:e], grid_elec_to_load_hrly[s:e], 'y', label = \"Grid\")\n",
    "    plt.fill_between(hours[s:e], chp_power[s:e], electric_demand[s:e], \\\n",
    "                     where=   electric_demand[s:e] >= chp_power[s:e] , facecolor = 'green', alpha = 0.5 )\n",
    "    plt.fill_between(hours[s:e], chp_power[s:e], one[s:e], \\\n",
    "                     where=  chp_power[s:e] >= one[s:e] , facecolor = 'blue', alpha = 0.5 )\n",
    "    plt.legend(handles = [line1, line2])\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Electric Power [kWe]')\n",
    "    #grid export?\n",
    "    plt.figure() \n",
    "    line1, = plt.plot(hours[s:e], chp_elec_to_load_hrly[s:e], 'b',  label = \"CHP to load\")\n",
    "    line2, = plt.plot(hours[s:e], chp_elec_to_grid_hrly[s:e], 'b--',  label = \"CHP to grid\")\n",
    "    plt.legend(handles = [line1, line2])\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Electric Power [kWe]')\n",
    "    \n",
    "    \n",
    "\n",
    "    \n",
    "#--------compare electric profile------\n",
    "base_chp_elec_to_load_hrly = np.array(base_api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                   ['year_one_to_load_series_kw']) #CHP elec produciton to load (hourly)\n",
    "#base_grid_elec_to_load_hrly = bau_grid_load_hrly - base_chp_elec_to_load_hrly\n",
    "base_pv_elec_to_load_hrly = np.array(base_api_response['outputs']['Scenario']['Site']['PV']\n",
    "                                   ['year_one_to_load_series_kw'])\n",
    "base_chp_plus_pv = base_chp_elec_to_load_hrly + base_pv_elec_to_load_hrly\n",
    "\n",
    "#show load profile and distribution amongst DG techs\n",
    "plt.figure() \n",
    "line1, = plt.plot(hours[s:e], base_chp_elec_to_load_hrly[s:e],'r', label = \"Base\")\n",
    "line2, = plt.plot(hours[s:e], chp_elec_to_load_hrly[s:e], 'b',  label = \"Corr\") #see uf sizes are different\n",
    "plt.legend(handles = [line1, line2])\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Electric Power [kWe]')\n",
    "#electric efficiency comparison\n",
    "plt.figure() \n",
    "line2, = plt.plot(hours[s:e], base_year_one_elec_eff_series_percent[s:e], 'b',  label = \"Base\") #see uf sizes are different\n",
    "line3, = plt.plot(hours[s:e], year_one_elec_eff_series_percent_corr[s:e], 'g',  label = \"Corr\")\n",
    "plt.legend(handles = [line2, line3])\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Electric Efficiency [%]')\n",
    "#compare electric profile\n",
    "plt.figure() \n",
    "line1, = plt.plot(hours[s:e], electric_demand[s:e],'k', label = \"Demand\") \n",
    "line2, = plt.plot(hours[s:e], chp_power_to_load[s:e], 'b',  label = \"CHP\")\n",
    "line3, = plt.plot(hours[s:e], pv_plus_chp_to_load_hrly[s:e], 'r', label = \"PV\")\n",
    "#line4, = plt.plot(hours[s:e], grid_elec_to_load_hrly[s:e], 'r', label = \"Grid\")\n",
    "line5, = plt.plot(hours[s:e], base_chp_elec_to_load_hrly[s:e], 'b--', label = 'CHP base')\n",
    "line6, = plt.plot(hours[s:e], base_chp_plus_pv[s:e], 'r--', label = 'PV base')\n",
    "fill3 = plt.fill_between(hours[s:e], pv_plus_chp_to_load_hrly[s:e], electric_demand[s:e], \\\n",
    "                    where=   electric_demand[s:e] +0.01 >= pv_plus_chp_to_load_hrly[s:e] , facecolor = 'green', alpha = 0.5 )\n",
    "fill2 = plt.fill_between(hours[s:e], pv_plus_chp_to_load_hrly[s:e], chp_power_to_load[s:e], \\\n",
    "                     where=   pv_plus_chp_to_load_hrly[s:e] >= chp_power_to_load[s:e] , facecolor = 'red', alpha = 0.5 )  \n",
    "fill1 = plt.fill_between(hours[s:e], chp_power_to_load[s:e], one[s:e], \\\n",
    "                     where=  chp_power_to_load[s:e] >= one[s:e] , facecolor = 'blue', alpha = 0.5 )\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Electric Power [kWe]')\n",
    "plt.legend(handles = [line2, line5, line3, line6, line1])\n",
    "#plt.savefig('figures/CHI/CHI_ICE-PV_compare_e_profile.png', dpi = 300)\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    " #compare elec dispatch profile with battery\n",
    "if mode == 3:\n",
    "    #base\n",
    "    base_chp_elec_to_load_hrly = np.array(base_api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                   ['year_one_to_load_series_kw']) \n",
    "    base_pv_elec_to_load_hrly = np.array(base_api_response['outputs']['Scenario']['Site']['PV']\n",
    "                                   ['year_one_to_load_series_kw'])\n",
    "    base_bess_elec_to_load_hrly = bess_elec_to_load_hrly = np.array(api_response['outputs']['Scenario']['Site']['Storage']\n",
    "                                   ['year_one_to_load_series_kw'])\n",
    "    base_chp_plus_pv = base_chp_elec_to_load_hrly + base_pv_elec_to_load_hrly\n",
    "    base_chp_plus_pv_plus_bess = base_chp_elec_to_load_hrly + base_pv_elec_to_load_hrly + base_bess_elec_to_load_hrly\n",
    "    #PV\n",
    "    pv_elec_total_hrly = np.array(api_response['outputs']['Scenario']['Site']['PV']\n",
    "                                   ['year_one_power_production_series_kw']) #PV hourly total elec production\n",
    "    pv_elec_to_load_hrly = np.array(api_response['outputs']['Scenario']['Site']['PV']\n",
    "                                   ['year_one_to_load_series_kw']) #PV hourly elec production to load\n",
    "    pv_elec_to_grid_hrly = np.array(api_response['outputs']['Scenario']['Site']['PV']\n",
    "                                   ['year_one_to_grid_series_kw']) #PV hourly elec production to grid\n",
    "    #BESS\n",
    "    bess_SOC_hrly = np.array(api_response['outputs']['Scenario']['Site']['Storage']\n",
    "                                   ['year_one_soc_series_pct']) #bess state of charge\n",
    "    bess_elec_to_load_hrly = np.array(api_response['outputs']['Scenario']['Site']['Storage']\n",
    "                                   ['year_one_to_load_series_kw']) #bess hourly elec production to load\n",
    "    bess_elec_to_grid_hrly = np.array(api_response['outputs']['Scenario']['Site']['Storage']\n",
    "                                   ['year_one_to_grid_series_kw']) #bess hourly elec production to grid\n",
    "    print(bess_elec_to_grid_hrly)\n",
    "    #Grid\n",
    "    grid_elec_to_load_hrly = electric_demand - chp_power_to_load - pv_elec_to_load_hrly - bess_elec_to_load_hrly\n",
    "    pv_plus_chp_to_load_hrly = chp_power_to_load + pv_elec_to_load_hrly\n",
    "    pv_plus_chp_plus_bess_to_load_hrly = chp_power_to_load + pv_elec_to_load_hrly + bess_elec_to_load_hrly\n",
    "    \n",
    "    #ELEC Dispatch\n",
    "    plt.figure() \n",
    "    line1, = plt.plot(hours[s:e], electric_demand[s:e],'k', label = \"Demand\", linewidth=2)\n",
    "    line2, = plt.plot(hours[s:e], base_chp_elec_to_load_hrly[s:e], 'b--',  label = \"CHP base\", linewidth = 1)\n",
    "    line3, = plt.plot(hours[s:e], base_chp_plus_pv[s:e], 'r--', label = \"PV base\", linewidth =1)\n",
    "    line4, = plt.plot(hours[s:e], base_chp_plus_pv_plus_bess[s:e], 'y--', label = \"BESS\")\n",
    "    fill3 = plt.fill_between(hours[s:e], pv_plus_chp_to_load_hrly[s:e], pv_plus_chp_plus_bess_to_load_hrly[s:e], \\\n",
    "                     where=   pv_plus_chp_plus_bess_to_load_hrly[s:e] >= pv_plus_chp_to_load_hrly[s:e] , facecolor = 'yellow', alpha = 0.5, label = 'BESS' )\n",
    "    fill4 = plt.fill_between(hours[s:e], pv_plus_chp_plus_bess_to_load_hrly[s:e], electric_demand[s:e], \\\n",
    "                     where=   electric_demand[s:e] +0.01 >= pv_plus_chp_plus_bess_to_load_hrly[s:e] , facecolor = 'green', alpha = 0.5, label = 'Grid' )\n",
    "    fill2 = plt.fill_between(hours[s:e], pv_plus_chp_to_load_hrly[s:e], chp_power_to_load[s:e], \\\n",
    "                     where=   pv_plus_chp_to_load_hrly[s:e] >= chp_power_to_load[s:e] , facecolor = 'red', alpha = 0.5, label = 'PV' )\n",
    "    fill1 = plt.fill_between(hours[s:e], chp_power_to_load[s:e], one[s:e], \\\n",
    "                     where=  chp_power_to_load[s:e] >= one[s:e] , facecolor = 'blue', alpha = 0.5, label = 'CHP' )\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Electric Power [kWe]')\n",
    "    plt.legend(handles = [line1, line2, line3, line4])\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "chp_therm_to_load_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                      ['year_one_thermal_to_load_series_mmbtu_per_hour'], index = dti)\n",
    "chp_therm_to_TES_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                     ['year_one_thermal_to_tes_series_mmbtu_per_hour'], index = dti)\n",
    "chp_therm_total_hrly = chp_therm_to_load_hrly + chp_therm_to_TES_hrly\n",
    "chp_fuel_annual = api_response['outputs']['Scenario']['Site']['CHP']['year_one_fuel_used_mmbtu']\n",
    "chp_elec_effic = np.sum(chp_elec_total_hrly)/(chp_fuel_annual * 1E6/3412.0)\n",
    "chp_total_effic = (np.sum(chp_elec_total_hrly) + np.sum(chp_therm_total_hrly)* 1E6/3412.0)/(chp_fuel_annual * 1E6/3412.0)\n",
    "chp_elec_cf = np.sum(chp_elec_total_hrly)/(chp_size_kwe*8760)\n",
    "chp_avg_load_when_on = chp_elec_total_hrly[chp_elec_total_hrly>1].mean()\n",
    "boiler_fuel_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['Boiler']\n",
    "                                ['year_one_boiler_fuel_consumption_series_mmbtu_per_hr'], index = dti)\n",
    "boiler_fuel_annual = boiler_fuel_hrly.resample('Y').sum()\n",
    "\n",
    "#-----chris-plot thermal load profiles----\n",
    "boiler_therm_total_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['Boiler']\n",
    "                                     ['year_one_boiler_thermal_production_series_mmbtu_per_hr'], index = dti)\n",
    "boiler_fuel_hrly_bau = pd.DataFrame(api_response['outputs']['Scenario']['Site']['LoadProfileBoilerFuel']\n",
    "                                     ['year_one_boiler_fuel_load_series_mmbtu_per_hr_bau'], index = dti)\n",
    "therm_load_hrly = boiler_fuel_hrly_bau*boiler_eff\n",
    "\n",
    "\n",
    "chp_tp = np.array(api_response['outputs']['Scenario']['Site']['CHP']['year_one_thermal_to_load_series_mmbtu_per_hour'])\n",
    "base_chp_tp = np.array(base_api_response['outputs']['Scenario']['Site']['CHP']['year_one_thermal_to_load_series_mmbtu_per_hour'])\n",
    "boiler_tp = np.array(api_response['outputs']['Scenario']['Site']['Boiler']['year_one_boiler_thermal_production_series_mmbtu_per_hr'])\n",
    "#thermal_demand = np.array(api_response['outputs']['Scenario']['Site']['LoadProfileBoilerFuel']\n",
    "#                          ['year_one_boiler_fuel_load_series_mmbtu_per_hr_bau'])/boiler_eff\n",
    "thermal_demand = chp_tp+boiler_tp\n",
    "\n",
    "plt.figure()\n",
    "line1, = plt.plot(hours[s:e], chp_tp[s:e], 'b',  label = \"CHP\")\n",
    "#line2, = plt.plot(hours[s:e], boiler_tp[s:e], 'r',  label = \"Boiler\")\n",
    "#line3, = plt.plot(hours[s:e], HotTES_to_load_hrly[s:e], 'y',  label = \"TES\")\n",
    "line4, = plt.plot(hours[s:e], thermal_demand[s:e], 'k',  label = \"Demand\")\n",
    "line5, = plt.plot(hours[s:e], base_chp_tp[s:e], 'b--',  label = \"CHP base\")\n",
    "plt.legend(handles = [line1, line5, line4])\n",
    "plt.fill_between(hours[s:e], chp_tp[s:e],thermal_demand[s:e], \\\n",
    "                     where=   thermal_demand[s:e] >= chp_tp[s:e] , facecolor = 'green', alpha = 0.5 )\n",
    "plt.fill_between(hours[s:e], chp_tp[s:e], one[s:e], \\\n",
    "                     where=  chp_tp[s:e] >= one[s:e] , facecolor = 'blue', alpha = 0.5 )\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Thermal Production [MMBtu/hr]')\n",
    "#plt.savefig('figures/CHI/CHI_ICE-PV_compare_tp_profile.png', dpi = 300)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#------compare thermal profiles-------\n",
    "base_chp_therm_to_load_hrly = pd.DataFrame(base_api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                   ['year_one_thermal_to_load_series_mmbtu_per_hour'], index = dti) #CHP elec produciton to load (hourly)\n",
    "#show load profile and distribution amongst DG techs\n",
    "plt.figure() \n",
    "line1, = plt.plot(hours[s:e], base_chp_therm_to_load_hrly[s:e],'r', label = \"Base\")\n",
    "line2, = plt.plot(hours[s:e], chp_therm_to_load_hrly[s:e], 'b',  label = \"Corr\") #see uf sizes are different\n",
    "line3, = plt.plot(hours[s:e], CHP_max_heat_full_load*np.ones(e-s), 'b--',  label = \"Rated (Corr)\")\n",
    "plt.legend(handles = [line1, line2, line3])\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Thermal Production [MMBtu/hr]')\n",
    "plt.rcParams['font.family'] = 'sans-serif'\n",
    "plt.rcParams['font.sans-serif'] = ['Times New Roman']\n",
    "\n",
    "\n",
    "plt.figure() \n",
    "line2, = plt.plot(hours[s:e], T_ISO*np.ones(e-s), 'b',  label = \"ISO\") #see if sizes are different\n",
    "line3, = plt.plot(hours[s:e], ambient_temperature[s:e], 'g',  label = \"TMY3\")\n",
    "plt.legend(handles = [line2, line3])\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Ambient Temperature [F]')\n",
    "\n",
    "\n",
    "#chp vs boiler fuel consumption\n",
    "base_year_one_chp_fuel = base_api_response['outputs']['Scenario']['Site']['CHP']['year_one_fuel_used_mmbtu']\n",
    "year_one_chp_fuel = api_response['outputs']['Scenario']['Site']['CHP']['year_one_fuel_used_mmbtu']\n",
    "print(\"Annual CHP Fuel Use (Base) =\", '%.3f' %base_year_one_chp_fuel)\n",
    "print(\"Annual CHP Fuel Use (Corr) =\", '%.3f' %year_one_chp_fuel)\n",
    "base_year_one_boiler_fuel = base_api_response['outputs']['Scenario']['Site']['Boiler']['year_one_boiler_fuel_consumption_mmbtu']\n",
    "year_one_boiler_fuel = api_response['outputs']['Scenario']['Site']['Boiler']['year_one_boiler_fuel_consumption_mmbtu']\n",
    "print(\"Annual Boiler Fuel Use (Base) =\", '%.3f' %base_year_one_boiler_fuel)\n",
    "print(\"Annual Boiler Fuel Use (Corr) =\", '%.3f' %year_one_boiler_fuel)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Look at Staging Server runs\n",
    "run_id = \"85023f4f-a149-4df8-80cc-35301600691c\" #\"28e6404d-b977-47b9-95c8-43eb74a0f2fc\"\n",
    "API_KEY = 'uaz52dr5KTT5Qs5U72rS70hw3IYeHVEyAaDUFQvo'\n",
    "root_url = 'https://chpbeta1-reopt-stage-api.its.nrel.gov'\n",
    "results_url = root_url + '/v1/job/<run_uuid>/results/?api_key=' + API_KEY\n",
    "url = results_url.replace('<run_uuid>', run_id)\n",
    "#url = \"https://chp-reopt-stage.its.nrel.gov/tool/results/28e6404d-b977-47b9-95c8-43eb74a0f2fc\"\n",
    "response = requests.get(url=url)\n",
    "resp_dict = json.loads(response.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SAVE data to pickle file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_directory = 'saved_results_6/'\n",
    "pickle_file = 'NYC_MT-PV_base-corr_hospital_gap1'\n",
    "with open(save_directory + pickle_file + '.pickle', 'wb') as handle:\n",
    "    data = {'API_response': api_response, 'scenario_data': post_1}\n",
    "    pickle.dump(data, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data process scenario with BESS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import linregress\n",
    "chp_elec_eff_full_load = api_response['inputs']['Scenario']['Site']['CHP']['elec_effic_full_load']\n",
    "chp_elec_eff_half_load = api_response['inputs']['Scenario']['Site']['CHP']['elec_effic_half_load']\n",
    "chp_therm_eff_full_load = api_response['inputs']['Scenario']['Site']['CHP']['thermal_effic_full_load']\n",
    "chp_therm_eff_half_load = api_response['inputs']['Scenario']['Site']['CHP']['thermal_effic_half_load']\n",
    "chp_size_kwe = api_response['outputs']['Scenario']['Site']['CHP']['size_kw']\n",
    "CHP_fuel_burn_full_load = chp_size_kwe / chp_elec_eff_full_load * 3412/1e6 #[MMBtu/hr]\n",
    "CHP_fuel_burn_half_load = 0.5*chp_size_kwe / chp_elec_eff_half_load * 3412/1e6\n",
    "slope, intercept, rvalue, pvalue, stderr = linregress([0.5*chp_size_kwe, chp_size_kwe], [CHP_fuel_burn_half_load, CHP_fuel_burn_full_load])\n",
    "CHP_elec_slope = slope\n",
    "CHP_elec_intercept = intercept\n",
    "CHP_max_heat_full_load = CHP_fuel_burn_full_load * chp_therm_eff_full_load #[MMBtu/hr]\n",
    "CHP_max_heat_half_load = CHP_fuel_burn_half_load * chp_therm_eff_half_load \n",
    "slope_tp, intercept_tp, rvalue, pvalue, stderr = linregress([0.5*chp_size_kwe, chp_size_kwe], [CHP_max_heat_half_load, CHP_max_heat_full_load])\n",
    "CHP_therm_slope = slope_tp\n",
    "CHP_therm_intercept = intercept_tp\n",
    "print(CHP_elec_slope)\n",
    "print(CHP_elec_intercept)\n",
    "print(CHP_therm_slope)\n",
    "print(CHP_therm_intercept)\n",
    "\n",
    "\n",
    "load_year = str(api_response['inputs']['Scenario']['Site']['LoadProfile']['year'])\n",
    "dti = pd.date_range( load_year +'-01-01', periods=8760, freq='H')\n",
    "bau_grid_load_less_chiller_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']\n",
    "                                               ['LoadProfile']['year_one_electric_load_series_kw'], index = dti)\n",
    "bau_boiler_load_fuel_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']\n",
    "                                         ['LoadProfileBoilerFuel']['year_one_boiler_fuel_load_series_mmbtu_per_hr_bau'], index = dti)\n",
    "bau_chiller_load_elec_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']\n",
    "                                          ['LoadProfileChillerElectric']['year_one_chiller_electric_load_series_kw'], index = dti)\n",
    "boiler_eff = api_response['inputs']['Scenario']['Site']['Boiler']['boiler_efficiency']\n",
    "\n",
    "bau_grid_load_hrly = bau_grid_load_less_chiller_hrly + bau_chiller_load_elec_hrly\n",
    "bau_grid_load_peak = np.max(bau_grid_load_hrly)\n",
    "bau_grid_load_mean = np.mean(bau_grid_load_hrly)\n",
    "bau_grid_load_total = np.sum(bau_grid_load_hrly)\n",
    "bau_therm_load_peak = np.max(bau_boiler_load_fuel_hrly)*boiler_eff #converted units of fuel to units of heat\n",
    "bau_therm_load_mean = np.mean(bau_boiler_load_fuel_hrly)*boiler_eff\n",
    "bau_therm_load_total = np.sum(bau_boiler_load_fuel_hrly)*boiler_eff\n",
    "chp_therm_to_elec_full_load = chp_therm_eff_full_load/chp_elec_eff_full_load\n",
    "\n",
    "\n",
    "chp_elec_total_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                   ['year_one_electric_production_series_kw'], index = dti) #total CHP elec production (hourly)\n",
    "chp_elec_to_load_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                   ['year_one_to_load_series_kw'], index = dti) #CHP elec produciton to load (hourly)\n",
    "chp_elec_to_grid_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                   ['year_one_to_grid_series_kw'], index = dti) #CHP elec production to grid (hourly)\n",
    "\n",
    "grid_load_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']\n",
    "                                               ['LoadProfile']['year_one_electric_load_series_kw'], index = dti)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#-----chris-calc electric efficiency----\n",
    "slope_ee, intercept_ee, rvalue, pvalue, stderr = linregress([0.5*chp_size_kwe, chp_size_kwe], [100*chp_elec_eff_half_load, 100*chp_elec_eff_full_load])\n",
    "print(\"Electric Efficiency Slope = \", '%.4f' %slope_ee)\n",
    "print(\"Electric Efficiency Intercept = \", '%.1f' %intercept_ee)        \n",
    "year_one_elec_eff_series_percent = slope_ee*np.array(chp_elec_total_hrly.values)+ intercept_ee\n",
    "\n",
    "# fuel burn\n",
    "slope_fb, intercept_fb, rvalue, pvalue, stderr = linregress([0.5*chp_size_kwe, chp_size_kwe], [CHP_fuel_burn_half_load, CHP_fuel_burn_full_load])\n",
    "corr = 1 # 1 = MT corr , 2 = ICE corr, else = base\n",
    "cf_fb = np.ones(8760)\n",
    "t_ref = 77  # [deg F]\n",
    "if corr == 1:\n",
    "    T_ISO = 59\n",
    "    for i in range(8760):\n",
    "        cf_fb[i] = 0.9529*np.exp(0.0008249*ambient_temperature[i]) - (7.496*(10**(-6)))*np.exp(0.06726*ambient_temperature[i])\n",
    "if corr == 2:\n",
    "    T_ISO = 77\n",
    "    for i in range(8760):\n",
    "        if ambient_temperature[i] <= t_ref:\n",
    "                 cf_fb[i] = 1 + 0.0003*(ambient_temperature[i] - t_ref)\n",
    "        else:\n",
    "                 cf_fb[i] = 1\n",
    "else:\n",
    "    T_ISO = 59\n",
    "    \n",
    "chp_elec_total_hrly_a = np.array(chp_elec_total_hrly)\n",
    "year_one_fuel_burn_series = np.ones(8760)\n",
    "year_one_elec_eff_series_percent_corr = np.ones(8760)\n",
    "for i in range(8760):\n",
    "\tyear_one_fuel_burn_series[i] = cf_fb[i]*(slope_fb*chp_elec_total_hrly_a[i] + intercept_fb)\n",
    "for i in range(8760):    \n",
    "    year_one_elec_eff_series_percent_corr[i] = 100*(chp_elec_total_hrly_a[i] / (year_one_fuel_burn_series[i]* (1e6/3412)))    \n",
    "\n",
    "\n",
    "#thermal production\n",
    "year_one_thermal_prod_series_corr = np.ones(8760)\n",
    "cf_tp = np.ones(8760)\n",
    "if corr == 1:\n",
    "    for i in range(8760):\n",
    "        cf_tp[i] = 0.005852*ambient_temperature[i] + 0.6615\n",
    "if corr == 2:\n",
    "    for i in range(8760):\n",
    "        if ambient_temperature[i] <= t_ref:\n",
    "                 cf_tp[i] = 1 + 0.001*(ambient_temperature[i] - t_ref)\n",
    "        else:\n",
    "                 cf_tp[i] = 1\n",
    "for i in range(8760):\n",
    "\tyear_one_thermal_prod_series_corr[i] = cf_tp[i]*(slope_tp*chp_elec_total_hrly_a[i] + intercept_tp)                \n",
    "                                                                                               \n",
    "\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "hours = np.linspace(1,8760,8760)\n",
    "s = 8000 #start hour mark\n",
    "e = 8100 #end hour mark\n",
    "plt.figure()\n",
    "plt.plot(hours[s:e], year_one_elec_eff_series_percent[s:e], color='green') #linestyle='none',marker='o') \n",
    "#------end----\n",
    "\n",
    "electric_demand = np.array(api_response['outputs']['Scenario']['Site']['LoadProfile']['year_one_electric_load_series_kw'])\n",
    "chp_power = np.array(api_response['outputs']['Scenario']['Site']['CHP']['year_one_electric_production_series_kw'])\n",
    "chp_power_to_load = np.array(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                   ['year_one_to_load_series_kw'])\n",
    "chp_power_to_grid = np.array(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                   ['year_one_to_grid_series_kw'])\n",
    "one = np.zeros(8760)\n",
    "\n",
    "save = 2 # 1 = save , 2 = do not save\n",
    "mode = 3 #CHP+PV = 1 , CHP = 2, CHP+PV+BES = 3\n",
    "if mode == 1:\n",
    "#-----chris-plot electric load profiles----\n",
    "    pv_elec_total_hrly = np.array(api_response['outputs']['Scenario']['Site']['PV']\n",
    "                                   ['year_one_power_production_series_kw']) #PV hourly total elec production\n",
    "    pv_elec_to_load_hrly = np.array(api_response['outputs']['Scenario']['Site']['PV']\n",
    "                                   ['year_one_to_load_series_kw']) #PV hourly elec production to load\n",
    "    pv_elec_to_grid_hrly = np.array(api_response['outputs']['Scenario']['Site']['PV']\n",
    "                                   ['year_one_to_grid_series_kw']) #PV hourly elec production to grid\n",
    "    grid_elec_to_load_hrly = electric_demand - chp_power_to_load - pv_elec_to_load_hrly\n",
    "    pv_plus_chp_to_load_hrly = chp_power_to_load + pv_elec_to_load_hrly\n",
    "\n",
    "#show load profile and distribution amongst DG techs\n",
    "    plt.figure() \n",
    "    line1, = plt.plot(hours[s:e], electric_demand[s:e],'k', label = \"Demand\")\n",
    "    line2, = plt.plot(hours[s:e], chp_power_to_load[s:e], 'b',  label = \"CHP\")\n",
    "    line3, = plt.plot(hours[s:e], pv_plus_chp_to_load_hrly[s:e], 'r', label = \"PV\")\n",
    "    #line4, = plt.plot(hours[s:e], grid_elec_to_load_hrly[s:e], 'r', label = \"Grid\")\n",
    "    fill3 = plt.fill_between(hours[s:e], pv_plus_chp_to_load_hrly[s:e], electric_demand[s:e], \\\n",
    "                     where=   electric_demand[s:e] >= pv_plus_chp_to_load_hrly[s:e] , facecolor = 'green', alpha = 0.5, label = 'Grid' )\n",
    "    fill2 = plt.fill_between(hours[s:e], pv_plus_chp_to_load_hrly[s:e], chp_power_to_load[s:e], \\\n",
    "                     where=   pv_plus_chp_to_load_hrly[s:e] >= chp_power_to_load[s:e] , facecolor = 'red', alpha = 0.5, label = 'PV' )\n",
    "    fill1 = plt.fill_between(hours[s:e], chp_power_to_load[s:e], one[s:e], \\\n",
    "                     where=  chp_power_to_load[s:e] >= one[s:e] , facecolor = 'blue', alpha = 0.5, label = 'CHP' )\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Electric Power [kWe]')\n",
    "    plt.legend(handles = [fill1, fill2, fill3, line1])\n",
    "    if save == 1:\n",
    "        plt.savefig('figures/CHI/CHI_ICE-PV_corr_e_profile_summer.png', dpi = 300)\n",
    "#show DG power flow to grid versus load (ignore battery)\n",
    "    plt.figure() \n",
    "    line1, = plt.plot(hours[s:e], chp_elec_to_load_hrly[s:e], 'b',  label = \"CHP to load\")\n",
    "    line2, = plt.plot(hours[s:e], chp_elec_to_grid_hrly[s:e], 'b--',  label = \"CHP to grid\")\n",
    "    line3, = plt.plot(hours[s:e], pv_elec_to_load_hrly[s:e], 'g', label = \"PV to load\")\n",
    "    line4, = plt.plot(hours[s:e], pv_elec_to_grid_hrly[s:e], 'g--', label = \"PV to grid\")\n",
    "    plt.legend(handles = [line1, line2, line3, line4])\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Electric Power [kWe]')\n",
    "    \n",
    "elif mode == 2:\n",
    "    grid_elec_to_load_hrly = electric_demand - chp_power\n",
    "    #show load profile and distribution amongst DG techs\n",
    "    plt.figure() \n",
    "    line1, = plt.plot(hours[s:e], electric_demand[s:e],'k', label = \"Demand\")\n",
    "    line2, = plt.plot(hours[s:e], chp_power[s:e], 'b',  label = \"CHP\")\n",
    "    #line3, = plt.plot(hours[s:e], grid_elec_to_load_hrly[s:e], 'y', label = \"Grid\")\n",
    "    fill2 = plt.fill_between(hours[s:e], chp_power[s:e], electric_demand[s:e], \\\n",
    "                     where=   electric_demand[s:e] >= chp_power[s:e] , facecolor = 'green', alpha = 0.5, label = 'Grid' )\n",
    "    fill1 = plt.fill_between(hours[s:e], chp_power[s:e], one[s:e], \\\n",
    "                     where=  chp_power[s:e] >= one[s:e] , facecolor = 'blue', alpha = 0.5, label = 'CHP' )\n",
    "    plt.legend(handles = [fill1, fill2, line2])\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Electric Power [kWe]')\n",
    "    #grid export?\n",
    "    plt.figure() \n",
    "    line1, = plt.plot(hours[s:e], chp_elec_to_load_hrly[s:e], 'b',  label = \"CHP to load\")\n",
    "    line2, = plt.plot(hours[s:e], chp_elec_to_grid_hrly[s:e], 'b--',  label = \"CHP to grid\")\n",
    "    plt.legend(handles = [line1, line2])\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Electric Power [kWe]')\n",
    "\n",
    "elif mode == 3:\n",
    "    #PV\n",
    "    pv_elec_total_hrly = np.array(api_response['outputs']['Scenario']['Site']['PV']\n",
    "                                   ['year_one_power_production_series_kw']) #PV hourly total elec production\n",
    "    pv_elec_to_load_hrly = np.array(api_response['outputs']['Scenario']['Site']['PV']\n",
    "                                   ['year_one_to_load_series_kw']) #PV hourly elec production to load\n",
    "    pv_elec_to_grid_hrly = np.array(api_response['outputs']['Scenario']['Site']['PV']\n",
    "                                   ['year_one_to_grid_series_kw']) #PV hourly elec production to grid\n",
    "    #BESS\n",
    "    bess_SOC_hrly = np.array(api_response['outputs']['Scenario']['Site']['Storage']\n",
    "                                   ['year_one_soc_series_pct']) #bess state of charge\n",
    "    bess_elec_to_load_hrly = np.array(api_response['outputs']['Scenario']['Site']['Storage']\n",
    "                                   ['year_one_to_load_series_kw']) #bess hourly elec production to load\n",
    "    bess_elec_to_grid_hrly = np.array(api_response['outputs']['Scenario']['Site']['Storage']\n",
    "                                   ['year_one_to_grid_series_kw']) #bess hourly elec production to grid\n",
    "    print(bess_elec_to_grid_hrly)\n",
    "    #Grid\n",
    "    grid_elec_to_load_hrly = electric_demand - chp_power_to_load - pv_elec_to_load_hrly - bess_elec_to_load_hrly\n",
    "    pv_plus_chp_to_load_hrly = chp_power_to_load + pv_elec_to_load_hrly\n",
    "    pv_plus_chp_plus_bess_to_load_hrly = chp_power_to_load + pv_elec_to_load_hrly + bess_elec_to_load_hrly\n",
    "    \n",
    "    #ELEC Dispatch\n",
    "    plt.figure() \n",
    "    line1, = plt.plot(hours[s:e], electric_demand[s:e],'k', label = \"Demand\", linewidth=2)\n",
    "    #line2, = plt.plot(hours[s:e], chp_power_to_load[s:e], 'b',  label = \"CHP\", linewidth = 1)\n",
    "    #line3, = plt.plot(hours[s:e], pv_plus_chp_to_load_hrly[s:e], 'r', label = \"PV\", linewidth =1)\n",
    "    #line4, = plt.plot(hours[s:e], pv_plus_chp_plus_bess_to_load_hrly[s:e], 'y', label = \"BESS\")\n",
    "    fill3 = plt.fill_between(hours[s:e], pv_plus_chp_to_load_hrly[s:e], pv_plus_chp_plus_bess_to_load_hrly[s:e], \\\n",
    "                     where=   pv_plus_chp_plus_bess_to_load_hrly[s:e] >= pv_plus_chp_to_load_hrly[s:e] , facecolor = 'yellow', alpha = 0.5, label = 'BESS' )\n",
    "    fill4 = plt.fill_between(hours[s:e], pv_plus_chp_plus_bess_to_load_hrly[s:e], electric_demand[s:e], \\\n",
    "                     where=   electric_demand[s:e] +0.01 >= pv_plus_chp_plus_bess_to_load_hrly[s:e] , facecolor = 'green', alpha = 0.5, label = 'Grid' )\n",
    "    fill2 = plt.fill_between(hours[s:e], pv_plus_chp_to_load_hrly[s:e], chp_power_to_load[s:e], \\\n",
    "                     where=   pv_plus_chp_to_load_hrly[s:e] >= chp_power_to_load[s:e] , facecolor = 'red', alpha = 0.5, label = 'PV' )\n",
    "    fill1 = plt.fill_between(hours[s:e], chp_power_to_load[s:e], one[s:e], \\\n",
    "                     where=  chp_power_to_load[s:e] >= one[s:e] , facecolor = 'blue', alpha = 0.5, label = 'CHP' )\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Electric Power [kWe]')\n",
    "    plt.legend(handles = [fill1, fill2, fill3, fill4, line1])\n",
    "    #SAVE\n",
    "    #plt.savefig('figures_5/SF/SF_ICE-PV-BESS-ABS_base2_ELECprofile_summer.png', dpi = 300)\n",
    "    if save == 1:\n",
    "        plt.savefig('figures/CHI/CHI_ICE-PV_corr_e_profile_summer.png', dpi = 300)  \n",
    "\n",
    "        \n",
    "    #calculate CHP distributions\n",
    "    chp_power_to_battery = np.array(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                   ['year_one_to_battery_series_kw']) #PV hourly elec production to grid\n",
    "    chp_power_to_load_plus_grid = chp_power_to_load + chp_power_to_grid\n",
    "    plt.figure() \n",
    "    #line1, = plt.plot(hours[s:e], chp_power_to_load[s:e], 'b',  label = \"CHP to load\")\n",
    "    #line2, = plt.plot(hours[s:e], chp_power_to_load_plus_grid[s:e], 'g',  label = \"CHP to grid\")\n",
    "    #line3, = plt.plot(hours[s:e], chp_elec_to_bess_hrly[s:e], 'b',  label = \"CHP to BESS\")\n",
    "    line4, = plt.plot(hours[s:e], chp_power[s:e], 'k',  label = \"Total CHP\", linewidth=2)\n",
    "    \n",
    "    fill1 = plt.fill_between(hours[s:e], chp_power_to_load[s:e], one[s:e], \\\n",
    "                     where=  chp_power_to_load[s:e] >= one[s:e] , facecolor = 'blue', alpha = 0.5, label = 'CHP to Load' )\n",
    "    fill2 = plt.fill_between(hours[s:e], chp_power_to_load[s:e], chp_power_to_load_plus_grid[s:e], \\\n",
    "                     where=  chp_power_to_load_plus_grid[s:e] >= chp_power_to_load[s:e] , facecolor = 'green', alpha = 0.5, label = 'CHP to Grid' )\n",
    "    fill3 = plt.fill_between(hours[s:e], chp_power_to_load_plus_grid[s:e], chp_power[s:e], \\\n",
    "                     where=  chp_power[s:e] >= chp_power_to_load_plus_grid[s:e] , facecolor = 'yellow', alpha = 0.75, label = 'CHP to BESS' )\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Electric Power [kWe]')\n",
    "    plt.legend(handles = [fill1, fill2, fill3, line4])\n",
    "    #SAVE\n",
    "    #plt.savefig('figures_5/SF/SF_ICE-PV-BESS_base_CHPprofile_summer.png', dpi = 300)\n",
    "    \n",
    "    \n",
    "    #show PV power distribution\n",
    "    pv_elec_to_battery_hrly = np.array(api_response['outputs']['Scenario']['Site']['PV']\n",
    "                                   ['year_one_to_battery_series_kw']) #PV hourly elec production to grid\n",
    "    pv_elec_to_load_plus_grid_hrly = pv_elec_to_load_hrly + pv_elec_to_grid_hrly\n",
    "    plt.figure()\n",
    "    #line1, = plt.plot(hours[s:e], pv_elec_to_load_hrly[s:e], 'b', label = \"PV to load\")\n",
    "    #line2, = plt.plot(hours[s:e], pv_elec_to_load_plus_grid_hrly[s:e], 'g', label = \"PV to Grid\")\n",
    "    #line3, = plt.plot(hours[s:e], pv_elec_to_battery_hrly[s:e], 'g--', label = \"PV to BESS\")\n",
    "    line4, = plt.plot(hours[s:e], pv_elec_total_hrly[s:e], 'k',  label = \"Total PV\", linewidth=2)\n",
    "    fill1 = plt.fill_between(hours[s:e], pv_elec_to_load_hrly[s:e], one[s:e], \\\n",
    "                     where=  pv_elec_to_load_hrly[s:e] >= one[s:e] , facecolor = 'blue', alpha = 0.5, label = 'PV to Load' )\n",
    "    fill2 = plt.fill_between(hours[s:e], pv_elec_to_load_hrly[s:e], pv_elec_to_load_plus_grid_hrly[s:e], \\\n",
    "                     where=  pv_elec_to_load_plus_grid_hrly[s:e] >= pv_elec_to_load_hrly[s:e] , facecolor = 'green', alpha = 0.5, label = 'PV to Grid' )\n",
    "    fill3 = plt.fill_between(hours[s:e], pv_elec_to_load_plus_grid_hrly[s:e], pv_elec_total_hrly[s:e], \\\n",
    "                     where=  pv_elec_total_hrly[s:e] >= pv_elec_to_load_plus_grid_hrly[s:e] , facecolor = 'yellow', alpha = 0.75, label = 'PV to BESS' )\n",
    "\n",
    "    plt.legend(handles = [fill1, fill2, fill3, line4])\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Electric Power [kWe]')\n",
    "    #SAVE\n",
    "    #plt.savefig('figures_5/SF/SF_ICE-PV-BESS_base_PVprofile_summer.png', dpi = 300)\n",
    "    \n",
    "    \n",
    "    ##show BESS power distribution\n",
    "    #line3, = plt.plot(hours[s:e], bess_elec_to_load_hrly[s:e], 'r', label = \"BESS to load\")\n",
    "    ##line6, = plt.plot(hours[s:e], bess_elec_to_grid_hrly[s:e], 'r--', label = \"BESS to grid\")\n",
    "    #plt.legend(handles = [line1, line2, line3, line4, line5])\n",
    "    #plt.xlabel('Hours')\n",
    "    #plt.ylabel('Electric Power [kWe]')\n",
    "    \n",
    "    \n",
    "    #BESS state of charge\n",
    "    plt.figure()\n",
    "    line1, =  plt.plot(hours[s:e], 100*bess_SOC_hrly[s:e], 'r', label = \"BESS\")\n",
    "    plt.legend(handles = [line1])\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Battery State of Charge [%]')\n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    #THERMAL DISPATCH\n",
    "chp_therm_to_load_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                      ['year_one_thermal_to_load_series_mmbtu_per_hour'], index = dti)\n",
    "chp_therm_to_TES_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                     ['year_one_thermal_to_tes_series_mmbtu_per_hour'], index = dti)\n",
    "chp_therm_total_hrly = chp_therm_to_load_hrly + chp_therm_to_TES_hrly\n",
    "chp_fuel_annual = api_response['outputs']['Scenario']['Site']['CHP']['year_one_fuel_used_mmbtu']\n",
    "chp_elec_effic = np.sum(chp_elec_total_hrly)/(chp_fuel_annual * 1E6/3412.0)\n",
    "chp_total_effic = (np.sum(chp_elec_total_hrly) + np.sum(chp_therm_total_hrly)* 1E6/3412.0)/(chp_fuel_annual * 1E6/3412.0)\n",
    "chp_elec_cf = np.sum(chp_elec_total_hrly)/(chp_size_kwe*8760)\n",
    "chp_avg_load_when_on = chp_elec_total_hrly[chp_elec_total_hrly>1].mean()\n",
    "boiler_fuel_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['Boiler']\n",
    "                                ['year_one_boiler_fuel_consumption_series_mmbtu_per_hr'], index = dti)\n",
    "boiler_fuel_annual = boiler_fuel_hrly.resample('Y').sum()\n",
    "\n",
    "\n",
    "\n",
    "#-----chris-plot thermal load profiles----\n",
    "boiler_therm_total_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['Boiler']\n",
    "                                     ['year_one_boiler_thermal_production_series_mmbtu_per_hr'], index = dti)\n",
    "boiler_fuel_hrly_bau = pd.DataFrame(api_response['outputs']['Scenario']['Site']['LoadProfileBoilerFuel']\n",
    "                                     ['year_one_boiler_fuel_load_series_mmbtu_per_hr_bau'], index = dti)\n",
    "therm_load_hrly = boiler_fuel_hrly_bau*boiler_eff\n",
    "\n",
    "\n",
    "mode_therm = 1 # 1 = no HOT TES, 2 = HOT TES\n",
    "if mode_therm == 1:\n",
    "    chp_tp = np.array(api_response['outputs']['Scenario']['Site']['CHP']['year_one_thermal_to_load_series_mmbtu_per_hour'])\n",
    "    boiler_tp = np.array(api_response['outputs']['Scenario']['Site']['Boiler']['year_one_boiler_thermal_production_series_mmbtu_per_hr'])\n",
    "    #thermal_demand = np.array(api_response['outputs']['Scenario']['Site']['LoadProfileBoilerFuel']\n",
    "    #                          ['year_one_boiler_fuel_load_series_mmbtu_per_hr_bau'])/boiler_eff\n",
    "    thermal_demand = chp_tp+boiler_tp\n",
    "\n",
    "    plt.figure()\n",
    "    line1, = plt.plot(hours[s:e], chp_tp[s:e], 'b',  label = \"CHP\")\n",
    "    #line2, = plt.plot(hours[s:e], boiler_tp[s:e], 'r',  label = \"Boiler\")\n",
    "    #line3, = plt.plot(hours[s:e], HotTES_to_load_hrly[s:e], 'y',  label = \"TES\")\n",
    "    line4, = plt.plot(hours[s:e], thermal_demand[s:e], 'k',  label = \"Demand\") #really\"Total\"\n",
    "\n",
    "    fill2 = plt.fill_between(hours[s:e], chp_tp[s:e],thermal_demand[s:e], \\\n",
    "                     where=   thermal_demand[s:e] >= chp_tp[s:e] , facecolor = 'green', alpha = 0.5, label = 'Boiler' )\n",
    "    fill1 = plt.fill_between(hours[s:e], chp_tp[s:e], one[s:e], \\\n",
    "                     where=  chp_tp[s:e] >= one[s:e] , facecolor = 'blue', alpha = 0.5, label = 'CHP' )\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Thermal Production [MMBtu/hr]')\n",
    "    plt.legend(handles = [fill1, fill2, line4])\n",
    "    #SAVE\n",
    "    #plt.savefig('figures_5/SF/SF_ICE-PV-BESS-ABS_base2_HEATprofile_summer.png', dpi = 300)\n",
    "    if save == 1:\n",
    "        plt.savefig('figures/CHI/CHI_ICE-PV_corr_tp_profile_summer.png', dpi = 300)\n",
    "else:\n",
    "    chp_tp = np.array(api_response['outputs']['Scenario']['Site']['CHP']['year_one_thermal_to_load_series_mmbtu_per_hour'])\n",
    "    chp_to_HotTES = np.array(api_response['outputs']['Scenario']['Site']['CHP']['year_one_thermal_to_tes_series_mmbtu_per_hour'])\n",
    "    boiler_tp = np.array(api_response['outputs']['Scenario']['Site']['Boiler']['year_one_boiler_thermal_production_series_mmbtu_per_hr'])\n",
    "    HotTES_to_load_hrly = np.array(api_response['outputs']['Scenario']['Site']['HotTES']\n",
    "                                     ['year_one_thermal_from_hot_tes_series_mmbtu_per_hr'])\n",
    "    thermal_demand = chp_tp + boiler_tp + HotTES_to_load_hrly\n",
    "    chp_plus_tes_tp = chp_tp + HotTES_to_load_hrly\n",
    "    \n",
    "    #THERMAL LOAD DISTRIBUTION\n",
    "    plt.figure()\n",
    "    #line1, = plt.plot(hours[s:e], chp_tp[s:e], 'b',  label = \"CHP\")\n",
    "    #line2, = plt.plot(hours[s:e], boiler_tp[s:e], 'r',  label = \"Boiler\")\n",
    "    #line3, = plt.plot(hours[s:e], HotTES_to_load_hrly[s:e], 'y',  label = \"TES\")\n",
    "    line4, = plt.plot(hours[s:e], thermal_demand[s:e], 'k',  label = \"Demand\", linewidth = 2) #really\"Total\"\n",
    "    fill1 = plt.fill_between(hours[s:e], chp_tp[s:e], one[s:e], \\\n",
    "                     where=  chp_tp[s:e] >= one[s:e] , facecolor = 'blue', alpha = 0.5, label = 'CHP' )\n",
    "    fill2 = plt.fill_between(hours[s:e], chp_plus_tes_tp[s:e], chp_tp[s:e], \\\n",
    "                     where=   chp_plus_tes_tp[s:e] >= chp_tp[s:e] , facecolor = 'yellow', alpha = 0.5, label = 'TES' )\n",
    "    fill3 = plt.fill_between(hours[s:e], chp_plus_tes_tp[s:e], thermal_demand[s:e], \\\n",
    "                     where=   thermal_demand[s:e] >= chp_plus_tes_tp[s:e] , facecolor = 'green', alpha = 0.5, label = 'Boiler' )\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Thermal Production [MMBtu/hr]')\n",
    "    plt.legend(handles = [fill1, fill2, fill3, line4])\n",
    "    #SAVE\n",
    "    #plt.savefig('figures_5/SF/SF_ICE-PV-BESS-HOTTES_base_HEATprofile_summer.png', dpi = 300)\n",
    "    \n",
    "    #CHP THERMAL DISTRIBUTION\n",
    "    plt.figure()\n",
    "    line4, = plt.plot(hours[s:e], chp_tp[s:e]+chp_to_HotTES[s:e], 'k',  label = \"Total CHP\", linewidth = 2) #really\"Total\"\n",
    "    fill1 = plt.fill_between(hours[s:e], chp_tp[s:e], one[s:e], \\\n",
    "                     where=  chp_tp[s:e] >= one[s:e] , facecolor = 'blue', alpha = 0.5, label = 'CHP to Load' )\n",
    "    fill2 = plt.fill_between(hours[s:e], chp_tp[s:e]+chp_to_HotTES[s:e], chp_tp[s:e], \\\n",
    "                     where=   chp_tp[s:e]+chp_to_HotTES[s:e] >= chp_tp[s:e] , facecolor = 'yellow', alpha = 0.5, label = 'CHP to TES' )\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Thermal Production [MMBtu/hr]')\n",
    "    plt.legend(handles = [fill1, fill2, line4])\n",
    "    #SAVE\n",
    "    #plt.savefig('figures_5/SF/SF_ICE-PV-BESS-HOTTES_base_CHPHEATprofile_summer.png', dpi = 300)\n",
    "    \n",
    "    #HOT TES SOC\n",
    "    HotTES_SOC_pct = np.array(api_response['outputs']['Scenario']['Site']['HotTES']\n",
    "                                     ['year_one_hot_tes_soc_series_pct'])\n",
    "    plt.figure()\n",
    "    line1, = plt.plot(hours[s:e], 100*HotTES_SOC_pct[s:e], 'k',  label = \"Demand\", linewidth = 2)\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Hot TES State of Charge [%]')\n",
    "    plt.legend(handles = [line1])\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "#check CF calcs\n",
    "#no TES\n",
    "plt.figure() \n",
    "line2, = plt.plot(hours[s:e], chp_therm_to_load_hrly[s:e], 'g',  label = \"Dispatched\") #see if sizes are different\n",
    "line3, = plt.plot(hours[s:e], CHP_max_heat_full_load*np.ones(e-s), 'b',  label = \"Rated\")\n",
    "line4, = plt.plot(hours[s:e], year_one_thermal_prod_series_corr[s:e], 'r',  label = \"Produced\")\n",
    "plt.legend(handles = [line3, line2, line4])\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Thermal Production [MMBtu/hr]')\n",
    "#plt.ylim([0, 1])\n",
    "#plt.savefig('figures/CHI/CHI_ICE-PV_corr_tp_profile.png', dpi = 300)\n",
    "\n",
    "plt.figure() \n",
    "line2, = plt.plot(hours[s:e], T_ISO*np.ones(e-s), 'b',  label = \"ISO\") #see if sizes are different\n",
    "line3, = plt.plot(hours[s:e], ambient_temperature[s:e], 'g',  label = \"TMY3\")\n",
    "plt.legend(handles = [line2, line3])\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Ambient Temperature [F]')\n",
    "#plt.savefig('SF_MT_Tamb.png', dpi = 300)\n",
    "plt.figure() \n",
    "line2, = plt.plot(hours[s:e], chp_size_kwe*np.ones(e-s), 'b',  label = \"Rated\") #see if sizes are different\n",
    "line3, = plt.plot(hours[s:e], chp_elec_to_load_hrly[s:e], 'g',  label = \"Dispatched\")\n",
    "plt.legend(handles = [line2, line3])\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Electric Power [kWe]')\n",
    "#plt.savefig('figures/PHX/PHX_ICE-PV_corr_e_profile.png', dpi = 300)\n",
    "plt.figure() \n",
    "line2, = plt.plot(hours[s:e], chp_elec_eff_full_load*100*np.ones(e-s), 'b',  label = \"Rated\") #see if sizes are different\n",
    "line3, = plt.plot(hours[s:e], year_one_elec_eff_series_percent_corr[s:e], 'g',  label = \"Dispatched\")\n",
    "plt.legend(handles = [line2, line3])\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Electric Efficiency [%]')\n",
    "#plt.ylim([36,38])\n",
    "#plt.savefig('figures/CHI/CHI_ICE-PV_corr_ee_profile.png', dpi = 300)\n",
    "\n",
    "plt.rcParams['font.family'] = 'sans-serif'\n",
    "plt.rcParams['font.sans-serif'] = ['Times New Roman']\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "mode_abs_cooling = 1 # 1 = no abs chiller , 2 = abs chiller\n",
    "if mode_abs_cooling == 2:\n",
    "    cooling_load_elec_hrly_bau = np.array(api_response['outputs']['Scenario']['Site']['LoadProfileChillerElectric']['year_one_chiller_electric_load_series_kw_bau'])\n",
    "    cooling_load_elec_hrly = np.array(api_response['outputs']['Scenario']['Site']['LoadProfileChillerElectric']['year_one_chiller_electric_load_series_kw'])\n",
    "    cooling_load_therm_hrly = np.array(api_response['outputs']['Scenario']['Site']['LoadProfileChillerElectric']['year_one_chiller_thermal_load_series_ton'])\n",
    "    ChillerElec_thermal_to_load_hrly = np.array(api_response['outputs']['Scenario']['Site']['ElectricChiller']['year_one_electric_chiller_thermal_to_load_series_ton'])\n",
    "    ChillerAbs_thermal_to_load_hrly = np.array(api_response['outputs']['Scenario']['Site']['AbsorptionChiller']['year_one_absorp_chl_thermal_to_load_series_ton'])\n",
    "    ChillerAbs_thermal_consumption_hrly = np.array(api_response['outputs']['Scenario']['Site']['AbsorptionChiller']['year_one_absorp_chl_thermal_consumption_series_mmbtu_per_hr'])\n",
    "    ChillerElec_plus_ChillerAbs_thermal_to_load_hrly = ChillerElec_thermal_to_load_hrly + ChillerAbs_thermal_to_load_hrly\n",
    "    print(ChillerElec_thermal_to_load_hrly)\n",
    "    print(ChillerElec_thermal_to_load_hrly)\n",
    "    print(ChillerAbs_thermal_to_load_hrly)\n",
    "    \n",
    "    plt.figure()\n",
    "    #line1, = plt.plot(hours[s:e], cooling_load_elec_hrly_bau[s:e]/3.5, 'k', label='Demand', linewidth = 2)\n",
    "    #line2, = plt.plot(hours[s:e], ChillerElec_thermal_to_load_hrly[s:e], label = 'Elec Chiller')\n",
    "    #line3, = plt.plot(hours[s:e], ChillerAbs_thermal_to_load_hrly[s:e], label = 'Abs Chiller')\n",
    "    line4, = plt.plot(hours[s:e], ChillerElec_plus_ChillerAbs_thermal_to_load_hrly[s:e], 'k', label='Demand', linewidth = 2)\n",
    "    #fill1, = plt.fill_between(hours[s:e], ChillerElec_thermal_to_load_hrly[s:e], one[s:e], where= ChillerElec_thermal_to_load_hrly[s:e] >= one[s:e], facecolor = 'green', alpha = 0.5, label = 'Elec. Chiller to Load')\n",
    "    #fill2, = plt.fill_between(hours[s:e], ChillerElec_plus_ChillerAbs_thermal_to_load_hrly[s:e], ChillerElec_thermal_to_load_hrly[s:e], where=   ChillerElec_plus_ChillerAbs_thermal_to_load_hrly[s:e] >= ChillerElec_thermal_to_load_hrly[s:e] , facecolor = 'blue', alpha = 0.5, label = 'Abs. Chiller to TES' )\n",
    "    #fill2, = plt.fill_between(hours[s:e], ChillerElec_plus_ChillerAbs_thermal_to_load_hrly[s:e], ChillerElec_thermal_to_load_hrly[s:e], \\\n",
    "    #                 where=   ChillerElec_plus_ChillerAbs_thermal_to_load_hrly[s:e] >= ChillerElec_thermal_to_load_hrly[s:e] , facecolor = 'blue', alpha = 0.5, label = 'Abs. Chiller to TES' )\n",
    "    fill2 = plt.fill_between(hours[s:e], ChillerElec_plus_ChillerAbs_thermal_to_load_hrly[s:e], ChillerElec_thermal_to_load_hrly[s:e], \\\n",
    "                     where=   ChillerElec_plus_ChillerAbs_thermal_to_load_hrly[s:e] >= ChillerElec_thermal_to_load_hrly[s:e], facecolor = 'blue', alpha = 0.5, label = 'Abs. Chiller' )\n",
    "\n",
    "    fill3 = plt.fill_between(hours[s:e], ChillerElec_thermal_to_load_hrly[s:e], one[s:e], \\\n",
    "                     where=   ChillerElec_thermal_to_load_hrly[s:e] >= one[s:e], facecolor = 'green', alpha = 0.5, label = 'Elec. Chiller' )\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Cooling Production [Tons]')\n",
    "    plt.legend(handles = [fill2, fill3, line4])\n",
    "    #SAVE\n",
    "    #plt.savefig('figures_5/SF/SF_ICE-PV-BESS-ABS_base2_COOLprofile_summer.png', dpi = 300)\n",
    "    \n",
    "    s=3000\n",
    "    e=4000\n",
    "    plt.figure()\n",
    "    line3, = plt.plot(hours[s:e], ChillerAbs_thermal_to_load_hrly[s:e], label = 'Abs Chiller')\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Cooling Production [Tons]')\n",
    "    plt.legend(handles = [line3])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "#chp vs boiler fuel consumption\n",
    "year_one_chp_fuel = api_response['outputs']['Scenario']['Site']['CHP']['year_one_fuel_used_mmbtu']\n",
    "print(\"Annual CHP Fuel Use =\", '%.3f' %year_one_chp_fuel)\n",
    "year_one_boiler_fuel = api_response['outputs']['Scenario']['Site']['Boiler']['year_one_boiler_fuel_consumption_mmbtu']\n",
    "print(\"Annual Boiler Fuel Use =\", '%.3f' %year_one_boiler_fuel)\n",
    "\n",
    "#chp_annual_runhrs = np.count_nonzero(chp_elec_total_hourly)\n",
    "chp_annual_runhrs = np.sum(chp_elec_total_hrly>1) #alternative to np.count_nonzero to eliminate very small numbers. Sums the number of TRUE\n",
    "#chp_annual_runhrs = chp_elec_total_hourly[chp_elec_total_hourly>1].count() #alternative to np.count_nonzero to eliminate very small numbers\n",
    "chp_elec_cf_when_on = chp_avg_load_when_on/chp_size_kwe\n",
    "chp_annual_heatinghrs = np.sum(chp_therm_total_hrly>0.01) #alternative to np.count_nonzero to eliminate very small numbers\n",
    "chp_capacity_elec_to_load_peak = chp_size_kwe/bau_grid_load_peak\n",
    "chp_capacity_elec_to_load_mean = chp_size_kwe/bau_grid_load_mean\n",
    "chp_capacity_therm_to_load_peak = chp_size_kwe*chp_therm_to_elec_full_load/(bau_therm_load_peak*1e6/3412)\n",
    "chp_capacity_therm_to_load_mean = chp_size_kwe*chp_therm_to_elec_full_load/(bau_therm_load_mean*1e6/3412)\n",
    "\n",
    "load_hours_less_than_chp_capacity = np.sum(bau_grid_load_hrly < chp_size_kwe)\n",
    "\n",
    "#find base load of bau_grid_load_hrly. Using value with 85% exceedance as described in ASHRAE 'Combined Heat and Power Design Guide' \n",
    "base_load_site_elec = bau_grid_load_hrly.sort_values(0, ascending = False).iloc[int(8760*.85)-1]\n",
    "base_load_site_therm = bau_boiler_load_fuel_hrly.sort_values(0, ascending = False).iloc[int(8760*.85)-1]/boiler_eff\n",
    "chp_capacity_elec_to_base_load = chp_size_kwe/base_load_site_elec\n",
    "chp_capacity_therm_to_base_load = chp_size_kwe*chp_therm_to_elec_full_load/(base_load_site_therm*1e6/3412)\n",
    "chp_gen_elec_to_load = chp_elec_total_hrly.resample('Y').sum()/bau_grid_load_hrly.resample('Y').sum()\n",
    "chp_gen_therm_to_load = chp_therm_total_hrly.resample('Y').sum()/(bau_boiler_load_fuel_hrly.resample('Y').sum()*boiler_eff)\n",
    "#net_fuel_consumption_change = \n",
    "\n",
    "print(\"Annual CHP Electric Efficiency =\", '%.3f' %chp_elec_effic)\n",
    "print(\"Annual CHP Total Efficiency =\", '%.3f' %chp_total_effic)\n",
    "print(\"Annual CHP Elec Capacity Factor =\", '%.2f' %chp_elec_cf)\n",
    "print(\"Annual CHP Elec Capacity Factor when running =\", '%.2f' %chp_elec_cf_when_on)\n",
    "print(\"Annual CHP run hours =\", '%.0f' %chp_annual_runhrs, \"(fraction\",'%.2f'%(chp_annual_runhrs/8760), \")\")\n",
    "print(\"Annual CHP hours of useful heat =\", '%.0f' %chp_annual_heatinghrs, \"(fraction\",'%.2f'%(chp_annual_heatinghrs/8760), \")\")\n",
    "print(\"Annual CHP mean power output when on =\", '%.0f' %chp_avg_load_when_on, \"kW\")\n",
    "print(\"Elec peak load =\", '%.0f' %bau_grid_load_peak, \"kW\")\n",
    "print(\"Elec mean load =\", '%.0f' %bau_grid_load_mean, \"kW\")\n",
    "print(\"Elec base load =\", '%.0f' %base_load_site_elec, \"kW (85% exceedance value)\")\n",
    "print(\"Heating peak load =\", '%.2f' %bau_therm_load_peak, \"MMBtu\")\n",
    "print(\"Heating mean load =\", '%.2f' %bau_therm_load_mean, \"MMBtu\")\n",
    "print(\"Heating base load =\", '%.2f' %base_load_site_therm, \"MMBtu (85% exceedance value)\")\n",
    "print(\"Ratio of CHP elec capacity to peak elec load =\", '%.3f' %chp_capacity_elec_to_load_peak)\n",
    "print(\"Ratio of CHP elec capacity to mean elec load =\", '%.3f' %chp_capacity_elec_to_load_mean)\n",
    "print(\"Ratio of CHP elec capacity to base elec load =\", '%.2f' %chp_capacity_elec_to_base_load)\n",
    "print(\"Ratio of CHP therm capacity to peak heat load =\", '%.3f' %chp_capacity_therm_to_load_peak)\n",
    "print(\"Ratio of CHP therm capacity to mean heat load =\", '%.3f' %chp_capacity_therm_to_load_mean)\n",
    "print(\"Ratio of CHP therm capacity to base heat load =\", '%.3f' %chp_capacity_therm_to_base_load)\n",
    "print(\"Useful heat recovery ratio =\", '%.3f' )\n",
    "print(\"Annual fraction of site elec load served by CHP =\", '%.3f' %chp_gen_elec_to_load.iloc[0][0])\n",
    "print(\"Annual fraction of site thermal load served by CHP =\", '%.3f' %chp_gen_therm_to_load.iloc[0][0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Post 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "api_response = reo_optimize_localhost(post_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Start looking at the response/results data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "api_response['messages']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(post_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "api_response['outputs']['Scenario']['Site']['CHP']['year_one_thermal_to_load_series_mmbtu_per_hour']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save API response and scenario data into a .pickle file for later use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_directory = 'saved_results/'\n",
    "pickle_file = 'pickle_file_test_1'\n",
    "with open(save_directory + pickle_file + '.pickle', 'wb') as handle:\n",
    "    data = {'API_response': api_response, 'scenario_data': scenario_data}\n",
    "    pickle.dump(data, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract relevant data and assign object names to them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CHP_max_heat_full_load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pull CHP parameter inputs and do intermediate calcs for performance checks\n",
    "from scipy.stats import linregress\n",
    "chp_elec_eff_full_load = api_response['inputs']['Scenario']['Site']['CHP']['elec_effic_full_load']\n",
    "chp_elec_eff_half_load = api_response['inputs']['Scenario']['Site']['CHP']['elec_effic_half_load']\n",
    "chp_therm_eff_full_load = api_response['inputs']['Scenario']['Site']['CHP']['thermal_effic_full_load']\n",
    "chp_therm_eff_half_load = api_response['inputs']['Scenario']['Site']['CHP']['thermal_effic_half_load']\n",
    "chp_size_kwe = api_response['outputs']['Scenario']['Site']['CHP']['size_kw']\n",
    "CHP_fuel_burn_full_load = chp_size_kwe / chp_elec_eff_full_load * 3412/1e6 #[MMBtu/hr]\n",
    "CHP_fuel_burn_half_load = 0.5*chp_size_kwe / chp_elec_eff_half_load * 3412/1e6\n",
    "slope, intercept, rvalue, pvalue, stderr = linregress([0.5*chp_size_kwe, chp_size_kwe], [CHP_fuel_burn_half_load, CHP_fuel_burn_full_load])\n",
    "CHP_elec_slope = slope\n",
    "CHP_elec_intercept = intercept\n",
    "CHP_max_heat_full_load = CHP_fuel_burn_full_load * chp_therm_eff_full_load #[MMBtu/hr]\n",
    "CHP_max_heat_half_load = CHP_fuel_burn_half_load * chp_therm_eff_half_load \n",
    "slope, intercept, rvalue, pvalue, stderr = linregress([0.5*chp_size_kwe, chp_size_kwe], [CHP_max_heat_half_load, CHP_max_heat_full_load])\n",
    "CHP_therm_slope = slope\n",
    "CHP_therm_intercept = intercept\n",
    "print(CHP_elec_slope)\n",
    "print(CHP_elec_intercept)\n",
    "print(CHP_therm_slope)\n",
    "print(CHP_therm_intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Olis\n",
    "load_year = str(api_response['inputs']['Scenario']['Site']['LoadProfile']['year'])\n",
    "dti = pd.date_range( load_year +'-01-01', periods=8760, freq='H')\n",
    "bau_grid_load_less_chiller_hrly = pd.DataFrame(api_response['inputs']['Scenario']['Site']\n",
    "                                               ['LoadProfile']['loads_kw'], index = dti)\n",
    "bau_boiler_load_fuel_hrly = pd.DataFrame(api_response['inputs']['Scenario']['Site']\n",
    "                                         ['LoadProfileBoilerFuel']['loads_mmbtu_per_hour'], index = dti)\n",
    "bau_chiller_load_elec_hrly = pd.DataFrame(api_response['inputs']['Scenario']['Site']\n",
    "                                          ['LoadProfileChillerElectric']['loads_kw'], index = dti)\n",
    "boiler_eff = api_response['inputs']['Scenario']['Site']['Boiler']['boiler_efficiency']\n",
    "\n",
    "bau_grid_load_hrly = bau_grid_load_less_chiller_hrly + bau_chiller_load_elec_hrly\n",
    "bau_grid_load_peak = np.max(bau_grid_load_hrly)\n",
    "bau_grid_load_mean = np.mean(bau_grid_load_hrly)\n",
    "bau_grid_load_total = np.sum(bau_grid_load_hrly)\n",
    "bau_therm_load_peak = np.max(bau_boiler_load_fuel_hrly)*boiler_eff #converted units of fuel to units of heat\n",
    "bau_therm_load_mean = np.mean(bau_boiler_load_fuel_hrly)*boiler_eff\n",
    "bau_therm_load_total = np.sum(bau_boiler_load_fuel_hrly)*boiler_eff\n",
    "chp_therm_to_elec_full_load = chp_therm_eff_full_load/chp_elec_eff_full_load\n",
    "\n",
    "\n",
    "chp_elec_total_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                   ['year_one_electric_production_series_kw'], index = dti)\n",
    "chp_therm_to_load_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                      ['year_one_thermal_to_load_series_mmbtu_per_hour'], index = dti)\n",
    "chp_therm_to_TES_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['CHP']\n",
    "                                     ['year_one_thermal_to_tes_series_mmbtu_per_hour'], index = dti)\n",
    "chp_therm_total_hrly = chp_therm_to_load_hrly + chp_therm_to_TES_hrly\n",
    "chp_fuel_annual = api_response['outputs']['Scenario']['Site']['CHP']['year_one_fuel_used_mmbtu']\n",
    "chp_elec_effic = np.sum(chp_elec_total_hrly)/(chp_fuel_annual * 1E6/3412.0)\n",
    "chp_total_effic = (np.sum(chp_elec_total_hrly) + np.sum(chp_therm_total_hrly)* 1E6/3412.0)/(chp_fuel_annual * 1E6/3412.0)\n",
    "chp_elec_cf = np.sum(chp_elec_total_hrly)/(chp_size_kwe*8760)\n",
    "chp_avg_load_when_on = chp_elec_total_hrly[chp_elec_total_hrly>1].mean()\n",
    "boiler_fuel_hrly = pd.DataFrame(api_response['outputs']['Scenario']['Site']['Boiler']\n",
    "                                ['year_one_boiler_fuel_consumption_series_mmbtu_per_hr'], index = dti)\n",
    "boiler_fuel_annual = boiler_fuel_hrly.resample('Y').sum()\n",
    "\n",
    "#chp_annual_runhrs = np.count_nonzero(chp_elec_total_hourly)\n",
    "chp_annual_runhrs = np.sum(chp_elec_total_hrly>1) #alternative to np.count_nonzero to eliminate very small numbers. Sums the number of TRUE\n",
    "#chp_annual_runhrs = chp_elec_total_hourly[chp_elec_total_hourly>1].count() #alternative to np.count_nonzero to eliminate very small numbers\n",
    "chp_elec_cf_when_on = chp_avg_load_when_on/chp_size_kwe\n",
    "chp_annual_heatinghrs = np.sum(chp_therm_total_hrly>0.01) #alternative to np.count_nonzero to eliminate very small numbers\n",
    "chp_capacity_elec_to_load_peak = chp_size_kwe/bau_grid_load_peak\n",
    "chp_capacity_elec_to_load_mean = chp_size_kwe/bau_grid_load_mean\n",
    "chp_capacity_therm_to_load_peak = chp_size_kwe*chp_therm_to_elec_full_load/(bau_therm_load_peak*1e6/3412)\n",
    "chp_capacity_therm_to_load_mean = chp_size_kwe*chp_therm_to_elec_full_load/(bau_therm_load_mean*1e6/3412)\n",
    "\n",
    "load_hours_less_than_chp_capacity = np.sum(bau_grid_load_hrly < chp_size_kwe)\n",
    "\n",
    "#find base load of bau_grid_load_hrly. Using value with 85% exceedance as described in ASHRAE 'Combined Heat and Power Design Guide' \n",
    "base_load_site_elec = bau_grid_load_hrly.sort_values(0, ascending = False).iloc[int(8760*.85)-1]\n",
    "base_load_site_therm = bau_boiler_load_fuel_hrly.sort_values(0, ascending = False).iloc[int(8760*.85)-1]/boiler_eff\n",
    "chp_capacity_elec_to_base_load = chp_size_kwe/base_load_site_elec\n",
    "chp_capacity_therm_to_base_load = chp_size_kwe*chp_therm_to_elec_full_load/(base_load_site_therm*1e6/3412)\n",
    "chp_gen_elec_to_load = chp_elec_total_hrly.resample('Y').sum()/bau_grid_load_hrly.resample('Y').sum()\n",
    "chp_gen_therm_to_load = chp_therm_total_hrly.resample('Y').sum()/(bau_boiler_load_fuel_hrly.resample('Y').sum()*boiler_eff)\n",
    "#net_fuel_consumption_change = \n",
    "\n",
    "print(\"Annual CHP Electric Efficiency =\", '%.3f' %chp_elec_effic)\n",
    "print(\"Annual CHP Total Efficiency =\", '%.3f' %chp_total_effic)\n",
    "print(\"Annual CHP Elec Capacity Factor =\", '%.2f' %chp_elec_cf)\n",
    "print(\"Annual CHP Elec Capacity Factor when running =\", '%.2f' %chp_elec_cf_when_on)\n",
    "print(\"Annual CHP run hours =\", '%.0f' %chp_annual_runhrs, \"(fraction\",'%.2f'%(chp_annual_runhrs/8760), \")\")\n",
    "print(\"Annual CHP hours of useful heat =\", '%.0f' %chp_annual_heatinghrs, \"(fraction\",'%.2f'%(chp_annual_heatinghrs/8760), \")\")\n",
    "print(\"Annual CHP mean power output when on =\", '%.0f' %chp_avg_load_when_on, \"kW\")\n",
    "print(\"Elec peak load =\", '%.0f' %bau_grid_load_peak, \"kW\")\n",
    "print(\"Elec mean load =\", '%.0f' %bau_grid_load_mean, \"kW\")\n",
    "print(\"Elec base load =\", '%.0f' %base_load_site_elec, \"kW (85% exceedance value)\")\n",
    "print(\"Heating peak load =\", '%.2f' %bau_therm_load_peak, \"MMBtu\")\n",
    "print(\"Heating mean load =\", '%.2f' %bau_therm_load_mean, \"MMBtu\")\n",
    "print(\"Heating base load =\", '%.2f' %base_load_site_therm, \"MMBtu (85% exceedance value)\")\n",
    "print(\"Ratio of CHP elec capacity to peak elec load =\", '%.3f' %chp_capacity_elec_to_load_peak)\n",
    "print(\"Ratio of CHP elec capacity to mean elec load =\", '%.3f' %chp_capacity_elec_to_load_mean)\n",
    "print(\"Ratio of CHP elec capacity to base elec load =\", '%.2f' %chp_capacity_elec_to_base_load)\n",
    "print(\"Ratio of CHP therm capacity to peak heat load =\", '%.3f' %chp_capacity_therm_to_load_peak)\n",
    "print(\"Ratio of CHP therm capacity to mean heat load =\", '%.3f' %chp_capacity_therm_to_load_mean)\n",
    "print(\"Ratio of CHP therm capacity to base heat load =\", '%.3f' %chp_capacity_therm_to_base_load)\n",
    "print(\"Useful heat recovery ratio =\", '%.3f' )\n",
    "print(\"Annual fraction of site elec load served by CHP =\", '%.3f' %chp_gen_elec_to_load.iloc[0][0])\n",
    "print(\"Annual fraction of site thermal load served by CHP =\", '%.3f' %chp_gen_therm_to_load.iloc[0][0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chp_gen_elec_to_load_monthly = chp_elec_total_hrly.resample('M').sum()/bau_grid_load_hrly.resample('M').sum()\n",
    "chp_gen_therm_to_load_monthly = chp_therm_to_load_hrly.resample('M').sum()/(bau_boiler_load_fuel_hrly.resample('M').sum()*boiler_eff)\n",
    "blankdata = []*12\n",
    "dfmonthly = pd.DataFrame(blankdata)\n",
    "dfmonthly = pd.concat([chp_gen_elec_to_load_monthly, chp_gen_therm_to_load_monthly], axis = 1)\n",
    "dfmonthly.columns = ['CHP Elec fraction', 'CHP Heat fraction']\n",
    "print(dfmonthly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check for excess heat to heating load in every hour\n",
    "heat_balance = boiler_eff*bau_boiler_load_fuel_hrly-chp_therm_to_load_hrly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TimeSteps = 8760\n",
    "# Names of techs could have \"NM\" appended, depending on the analysis\n",
    "GridName = 'UTIL1'\n",
    "BoilerName = 'BOILER'\n",
    "PVName = 'PV'\n",
    "CHPName = 'CHP'\n",
    "ElecChlName = 'ELECCHL'\n",
    "AbsChlName = 'ABSORPCHL'\n",
    "NumLoads = 5  # 1R, 1W, 1X, 1S, T\n",
    "\n",
    "# Create a nice way to chain filters by using this custom function and Pandas mask method\n",
    "def mask(df, key, value):\n",
    "    return df[df[key] == value]\n",
    "pd.DataFrame.mask = mask\n",
    "\n",
    "# Electric load and supply\n",
    "try:\n",
    "    grid_num = scenario_data['Inputs/']['constant_' + run_uuid + '.dat'].mask('Tech', GridName).index.item()\n",
    "    bau_grid_load_elec_hourly_kwe = scenario_data['Inputs/']['Load8760_' + run_uuid + '.dat'].astype('float64', errors='ignore')\n",
    "    bau_grid_load_elec_total_kwhe = scenario_data['Inputs/']['LoadSize_' + run_uuid + '.dat'].astype('float64', errors='ignore')\n",
    "    \n",
    "    elec_price_hourly = scenario_data['Outputs_bau/']['energy_cost.txt'].astype('float64', errors='ignore')\n",
    "    demand_charge_hourly = scenario_data['Outputs_bau/']['demand_cost.txt'].astype('float64', errors='ignore')\n",
    "    grid_hourly_kwe = scenario_data['Outputs/']['GridToLoad.csv'].astype('float64', errors='ignore')\n",
    "    total_grid_kwhe = np.sum(grid_hourly_kwe)\n",
    "except:\n",
    "    print(\"No electric load or supply in this analysis\")\n",
    "\n",
    "# Boiler fuel load and supply\n",
    "try:\n",
    "    # Units for hot-thermal and fuel power are mmbtu/hr and energy is mmbtu - did not append units to names for brevity\n",
    "    boiler_num = scenario_data['Inputs/']['constant_' + run_uuid + '.dat'].mask('Tech', BoilerName).index.item()\n",
    "    boiler_effic = scenario_data['Inputs/']['Utility/']['FuelBurnRate.dat'].astype('float64', errors='ignore')['BoilerEfficiency'][0]\n",
    "    bau_boiler_load_fuel_hourly = scenario_data['Inputs/']['Load8760BoilerFuel_' + run_uuid + '.dat'].astype('float64', errors='ignore')\n",
    "    bau_boiler_load_fuel_total = scenario_data['Inputs/']['LoadSizeBoilerFuel_' + run_uuid + '.dat'].astype('float64', errors='ignore')\n",
    "    boiler_fuel_rate_hourly = scenario_data['Inputs/']['Utility/']['FuelCost.dat']['FuelRate'].iloc[boiler_num* \\\n",
    "                                                                                TimeSteps:(boiler_num + 1)*TimeSteps]\n",
    "    boiler_fuel_hourly = scenario_data['Outputs/']['FuelToBoiler.csv'].astype('float64', errors='ignore')\n",
    "    boiler_thermal_hourly = scenario_data['Outputs/']['BoilerThermal.csv'].astype('float64', errors='ignore')\n",
    "    total_boiler_fuel = np.sum(boiler_fuel_hourly)\n",
    "except:\n",
    "    print(\"No hot thermal/fuel load or boiler supply in this analysis\")\n",
    "    \n",
    "# CHP electric and hot thermal production\n",
    "# TODO look for files containing certain strings to assign only relevant variables because few/no irrelevant ones will be writting from Xpress\n",
    "try:\n",
    "    # Units for hot-thermal and fuel power are mmbtu/hr and energy is mmbtu - did not append units to names for brevity\n",
    "    chp_num = scenario_data['Inputs/']['constant_' + run_uuid + '.dat'].mask('Tech', CHPName).index.item()\n",
    "    chp_size_kwe = scenario_data['Outputs/']['REopt_results.json']['chp_kw']\n",
    "    chp_fuel_rate_hourly = scenario_data['Inputs/']['Utility/']['FuelCost.dat']['FuelRate'].iloc[chp_num* \\\n",
    "                                                                        TimeSteps:(chp_num + 1)*TimeSteps]\n",
    "    chp_elec_to_load_hourly_kwe = scenario_data['Outputs/']['CHPtoLoad.csv'].astype('float64', errors='ignore')\n",
    "    chp_elec_to_grid_hourly_kwe = scenario_data['Outputs/']['CHPtoGrid.csv'].astype('float64', errors='ignore') # This is 1W + 1R\n",
    "    chp_elec_total_hourly = scenario_data['Outputs/']['CHPElectricProduction.csv'].astype('float64', errors='ignore')\n",
    "    # Calculate thermal production total, which may be above Thermal-to-Load because of curtailment/above-load\n",
    "    chp_thermalprod_slope = scenario_data['Inputs/']['CHPPerformance_' + run_uuid + '.dat'].astype('float64', errors='ignore')['CHPThermalProdSlope'][0]\n",
    "    chp_thermalprod_intercept = scenario_data['Inputs/']['CHPPerformance_' + run_uuid + '.dat'].astype('float64', errors='ignore')['CHPThermalProdIntercept'][0]\n",
    "    chp_thermal_to_load_hourly = scenario_data['Outputs/']['CHPThermaltoLoad.csv'].astype('float64', errors='ignore')\n",
    "    chp_thermal_max_production_calculated_hourly = chp_thermalprod_intercept * chp_size_kwe * (chp_elec_total_hourly > 0) + \\\n",
    "                                                    chp_thermalprod_slope * chp_elec_total_hourly\n",
    "    # TBD Calculate fuel used by timestep, manually because not by timestep in Xpress\n",
    "    chp_fuelburnrate_slope = scenario_data['Inputs/']['Utility/']['FuelBurnRate.dat'].astype('float64', errors='ignore')[chp_num*NumLoads:(chp_num+1)*NumLoads].iloc[0,:]['FuelBurnRateM']\n",
    "    chp_fuelburnrate_intercept = scenario_data['Inputs/']['Utility/']['FuelBurnRate.dat'].astype('float64', errors='ignore')[chp_num*NumLoads:(chp_num+1)*NumLoads].iloc[0,:]['FuelBurnRateB']\n",
    "    chp_fuelburned = chp_fuelburnrate_intercept * chp_size_kwe  * (chp_elec_total_hourly > 0) + \\\n",
    "                                                    chp_fuelburnrate_slope * chp_elec_total_hourly\n",
    "except:\n",
    "    print(\"No CHP (chosen) in this analysis\")    \n",
    "    \n",
    "# TODO: PV, cold electric load, absorption chiller, electric chiller, battery, hot TES, cold TES\n",
    "\n",
    "# Some useful things for indexing on techs in the .dat files\n",
    "#df = df.replace(np.nan, 0, regex=True)  # For replacing NaN with 0, needed for some variables\n",
    "#PV_num = scenario_data['Inputs/']['constant_' + run_uuid + '.dat'].mask('Tech', PVName).index.item()\n",
    "#ElecChl_num = scenario_data['Inputs/']['constant_' + run_uuid + '.dat'].mask('Tech', ElecChlName).index.item()\n",
    "#AbsChl_num = scenario_data['Inputs/']['constant_' + run_uuid + '.dat'].mask('Tech', AbsChlName).index.item()\n",
    "    \n",
    "#dispatch_CHP_Elec_To_Batt = scenario_data['Outputs/']['CHPtoBatt.csv'].astype('float64', errors='ignore')\n",
    "\n",
    "# LEGACY FROM DESKTOP DATA ANALYSIS\n",
    "# Extract storage dispatch\n",
    "#storage_charge = scenario_data['ElecToStore.csv'].copy() * -1\n",
    "#storage_discharge = scenario_data['ElecFromStore.csv'].copy()\n",
    "#storage_dispatch = storage_charge + storage_discharge\n",
    "#EtaStor = -1 * np.sum(storage_discharge.values) / np.sum(storage_charge.values)\n",
    "    \n",
    "# Extract storage dispatch\n",
    "#storage_charge = scenario_data['ElecToStore.csv'].copy() * -1\n",
    "#storage_discharge = scenario_data['ElecFromStore.csv'].copy()\n",
    "#storage_dispatch = storage_charge + storage_discharge\n",
    "#SOC = scenario_data['StoredEnergy.csv'].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculations and printing results for validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Total CHP Electric Production (kWh) = \", '%.2f' %np.sum(chp_elec_total_hrly.values))\n",
    "print(\"Total CHP Thermal Production (MMBtu) = \", '%.2f' %np.sum(chp_thermal_to_load_hrly.values))\n",
    "print(\"Total CHP Fuel Used (MMBtu) = \", '%.2f' %np.sum(chp_fuelburned.values))\n",
    "chp_elec_effic = np.sum(chp_elec_total_hourly.values)/(np.sum(chp_fuelburned.values) * 1E6/3412.0)\n",
    "chp_full_thermal_effic = np.sum(chp_thermal_max_production_calculated_hourly.values) / np.sum(chp_fuelburned.values)\n",
    "chp_actual_thermal_effic = np.sum(chp_thermal_to_load_hourly.values) / np.sum(chp_fuelburned.values)\n",
    "print(\"Average CHP Electric Efficiency = \", '%.3f' %chp_elec_effic)\n",
    "print(\"Average Potential (all thermal used) CHP Thermal Efficiency = \", '%.3f' %chp_full_thermal_effic)\n",
    "print(\"Average Actual CHP Thermal Efficiency = \", '%.3f' %chp_actual_thermal_effic)\n",
    "chp_total_effic = np.sum(chp_elec_total_hourly.values + chp_thermal_to_load_hourly.values * 1E6/3412.0)/(np.sum(chp_fuelburned.values) * 1E6/3412.0)\n",
    "print(\"Average CHP Elec + Thermal Efficiency = \", '%.3f' %chp_total_effic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "plt.plot(chp_elec_total_hrly.values, markersize=1, linestyle='', marker='o', color='b') #.values turns it into an numpy array\n",
    "plt.plot()\n",
    "#plt.plot(bau_chiller_load_elec_hourly.values)\n",
    "#plt.plot(bau_grid_load)\n",
    "#plt.xlim(0,200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load in REopt Desktop results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_directory = r'C:\\Bill\\REopt\\Output\\TempResults'\n",
    "results_dict, filepath_list = REopt_TempResults(file_directory)\n",
    "# Check the high level results\n",
    "results_dict['REOresults.csv']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract relevant data from Desktop REopt to compare with API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PVName = 'PVNM'\n",
    "CHPName = 'CHPNM'\n",
    "\n",
    "# Extract baseline load data\n",
    "load_hourly_baseline = results_dict['Load.csv'].copy()\n",
    "try:\n",
    "    load_elec_hourly_baseline = load_hourly_baseline.iloc[:, 0]\n",
    "    load_fuel_hourly_baseline = load_hourly_baseline.iloc[:, 1]  # I had to add fuel (e.g. \"2R\") load to Load.csv in Xpress\n",
    "    load_elec_total_baseline = np.sum(load_elec_hourly_baseline)\n",
    "    load_fuel_total_baseline = np.sum(load_fuel_hourly_baseline)\n",
    "    elec_price_hourly = results_dict['ElecRateHourly.csv'].copy().iloc[:, 0]\n",
    "    demand_charge_hourly = results_dict['ElecRateHourly.csv'].copy().iloc[:, 1]\n",
    "except:\n",
    "    print(\"Either no elec or thermal load in this analysis\")\n",
    "\n",
    "# Extract dispatch results data\n",
    "dispatch_hourly = results_dict['DispatchHourly.csv'].copy()\n",
    "# Using the Pandas 'mask', as defined above, filter to get specific data\n",
    "try:\n",
    "    dispatch_elec_hourly_CHP_1R = dispatch_hourly.mask('Tech', CHPName).mask('FuelBin', '1R')\n",
    "    dispatch_elec_hourly_CHP_1W = dispatch_hourly.mask('Tech', CHPName).mask('FuelBin', '1W')\n",
    "    dispatch_elec_hourly_CHP_1X = dispatch_hourly.mask('Tech', CHPName).mask('FuelBin', '1X')\n",
    "    dispatch_elec_hourly_CHP_1S = dispatch_hourly.mask('Tech', CHPName).mask('FuelBin', '1S')\n",
    "    dispatch_heat_hourly_CHP = dispatch_hourly.mask('Tech', CHPName).mask('FuelBin', '2R')\n",
    "    FuelUsed_hourly_CHP = results_dict['FuelUsed.csv'].copy().iloc[:, 2]\n",
    "    dispatch_elec_total_CHP = np.sum(dispatch_elec_hourly_CHP_1R['PF*dvRP']+dispatch_elec_hourly_CHP_1W['PF*dvRP'])\n",
    "    dispatch_heat_total_CHP = np.sum(dispatch_heat_hourly_CHP['PF*dvRP'])\n",
    "except:\n",
    "    print(\"No CHPNM (chosen) in this analysis\")\n",
    "\n",
    "try:\n",
    "    dispatch_elec_hourly_Util1 = dispatch_hourly.mask('Tech', 'UTIL1').mask('FuelBin', '1R')\n",
    "    dispatch_heat_hourly_Util2 = dispatch_hourly.mask('Tech', 'UTIL2').mask('FuelBin', '2R')\n",
    "    dispatch_elec_total_Util1 = np.sum(dispatch_elec_hourly_Util1['PF*dvRP'])\n",
    "    dispatch_heat_total_Util2 = np.sum(dispatch_heat_hourly_Util2['PF*dvRP'])\n",
    "except:\n",
    "    print(\"Either UTIL1 or UTIL2 not used in this analysis\")\n",
    "\n",
    "try:\n",
    "    dispatch_elec_hourly_PV_1R = dispatch_hourly.mask('Tech', PVName).mask('FuelBin', '1R')\n",
    "    dispatch_elec_hourly_PV_1W = dispatch_hourly.mask('Tech', PVName).mask('FuelBin', '1W')\n",
    "    dispatch_elec_hourly_PV_1X = dispatch_hourly.mask('Tech', PVName).mask('FuelBin', '1X')\n",
    "    dispatch_elec_hourly_PV_1S = dispatch_hourly.mask('Tech', PVName).mask('FuelBin', '1S')\n",
    "except:\n",
    "    print(\"No PV in this analysis\")\n",
    "    \n",
    "# Extract storage dispatch\n",
    "storage_charge = results_dict['ElecToStore.csv'].copy() * -1\n",
    "storage_discharge = results_dict['ElecFromStore.csv'].copy()\n",
    "storage_dispatch = storage_charge + storage_discharge\n",
    "EtaStor = -1 * np.sum(storage_discharge.values) / np.sum(storage_charge.values)\n",
    "    \n",
    "# Extract storage dispatch\n",
    "storage_charge = results_dict['ElecToStore.csv'].copy() * -1\n",
    "storage_discharge = results_dict['ElecFromStore.csv'].copy()\n",
    "storage_dispatch = storage_charge + storage_discharge\n",
    "SOC = results_dict['StoredEnergy.csv'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ==============================================================="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example from Josiah for how to setup batch API calls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing DOE Reference Buildings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "doe_list = ['RetailStore', 'StripMall', 'Warehouse', 'Supermarket']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "api_response_dict = dict()\n",
    "\n",
    "for name in doe_list:\n",
    "    newpost = post\n",
    "    newpost['Scenario']['Site'][\"LoadProfile\"][\"doe_reference_name\"] = name\n",
    "    \n",
    "    print('Beginning Scenario:', name)\n",
    "    api_response = reo_optimize_localhost(newpost)\n",
    "    api_response_dict[name] = api_response\n",
    "    \n",
    "    if api_response['outputs']['Scenario']['status'] != 'optimal':\n",
    "        print('non-optimial solution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_dict['RetailStore']['outputs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_plots(results_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Try to loop through cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SF, PHX, CHI, NYC, DEN\n",
    "location = ['SF', 'PHX', 'CHI', 'NYC', 'DEN']\n",
    "lat = [37.78, 33.45, 41.983, 40.65, 39.742]\n",
    "long = [-122.45, -111.983, -87.917, -73.8, -105.179]\n",
    "elevation = [0, 1106, 659, 16, 6000]\n",
    "T_SF = list(pd.read_csv(\"SanFran_Tamb_degF.csv\", squeeze=True, header = None))\n",
    "T_PHX = list(pd.read_csv(\"PHX_Tamb_degF.csv\", squeeze=True, header = None)) \n",
    "T_CHI = list(pd.read_csv(\"CHI_Tamb_degF.csv\", squeeze=True, header = None))\n",
    "T_NYC = list(pd.read_csv(\"NYC_Tamb_degF.csv\", squeeze=True, header = None))\n",
    "T_DEN = list(pd.read_csv(\"Golden_Tamb_degF.csv\", squeeze=True, header = None))\n",
    "T_amb = [T_SF, T_PHX, T_CHI, T_NYC, T_DEN]\n",
    "\n",
    "#------if forcing a size------\n",
    "    #largehotel - sf elec rate - sf commercial ng price - ICE-PV\n",
    "#CHP_base_size = [274.9, 323.4, 366.4, 367.3, 352] \n",
    "#PV_base_size = [193.3, 495.7, 161, 228.2, 156.7]\n",
    "    #hospital - sf elec rate - sf industrial ng price - ICE-PV\n",
    "#CHP_base_size = [740.7, 824.2, 1004.2, 1014, 945.6] \n",
    "#PV_base_size = [1822.8, 1950.9, 1338.8, 1358.7, 1318.6]\n",
    "    #hospital - sf elec rate - sf industrial ng price - MT-PV-BESS\n",
    "#CHP_base_size = [334.1, 291.2, 430, 407.6, 380.7] \n",
    "#PV_base_size = [2283.3, 2878.3, 2475.4, 2764.2, 2637.2]\n",
    "#BESS_base_power = [340.8, 530.5, 379.9, 376.9, 441.2]\n",
    "#BESS_base_capacity = [1353.1, 1621.3, 1262, 1295.9, 1670.3]\n",
    "    #largehotel - sf elec rate - sf industrial ng price - MT-PV-BESS\n",
    "CHP_base_size = [129.2, 72, 188, 187.9, 190.8] \n",
    "PV_base_size = [404.9, 978.9, 448.8, 518.7, 429.6]\n",
    "BESS_base_power = [182.6, 284.5, 168.7, 161.8, 172.6]\n",
    "BESS_base_capacity = [617.7, 1410.9, 696.9, 729.1, 803.3]\n",
    "\n",
    "\n",
    "api_response_dict = dict()\n",
    "\n",
    "mode_force = 2  # 1 = fix size   # 2 = do not fix size\n",
    "mode_battery = 2 # 1 = fix battery size # 2 = do not fix battery size\n",
    "for i in range(5):\n",
    "    newpost = post_1\n",
    "    newpost['Scenario']['Site'][\"latitude\"] = lat[i]\n",
    "    newpost['Scenario']['Site'][\"longitude\"] = long[i]\n",
    "    newpost['Scenario']['Site'][\"outdoor_air_temp_degF\"] = T_amb[i]\n",
    "    newpost['Scenario']['Site'][\"elevation_ft\"] = elevation[i]\n",
    "    \n",
    "    #if forcing size\n",
    "    if mode_force == 1:\n",
    "        newpost['Scenario']['Site']['CHP']['min_kw'] = CHP_base_size[i]\n",
    "        newpost['Scenario']['Site']['CHP']['max_kw'] = CHP_base_size[i]\n",
    "        newpost['Scenario']['Site']['PV']['min_kw'] = PV_base_size[i]\n",
    "        newpost['Scenario']['Site']['PV']['max_kw'] = PV_base_size[i]\n",
    "        if mode_battery == 1:\n",
    "            newpost['Scenario']['Site']['Storage']['min_kw'] = BESS_base_power[i]\n",
    "            newpost['Scenario']['Site']['Storage']['max_kw'] = BESS_base_power[i]\n",
    "            newpost['Scenario']['Site']['Storage']['min_kwh'] = BESS_base_capacity[i]\n",
    "            newpost['Scenario']['Site']['Storage']['max_kwh'] = BESS_base_capacity[i]\n",
    "        \n",
    "    \n",
    "    #print('Beginning Scenario:', name)\n",
    "    api_response = reo_optimize_localhost(newpost)\n",
    "    api_response_dict[i] = api_response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_directory = 'saved_results_3/'\n",
    "pickle_file = 'DEN_MT-PV-BESS_corr'\n",
    "with open(save_directory + pickle_file + '.pickle', 'wb') as handle:\n",
    "    data = {'API_response': api_response_dict[4], 'scenario_data': post_1} #post data will be off for each case, because each post is within the loop\n",
    "    pickle.dump(data, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=3\n",
    "print(api_response_dict[x]['outputs']['Scenario']['Site']['CHP']['size_kw'])\n",
    "print(api_response_dict[x]['outputs']['Scenario']['Site']['PV']['size_kw'])\n",
    "print(api_response_dict[x]['outputs']['Scenario']['Site']['Storage']['size_kw'])\n",
    "print(api_response_dict[x]['outputs']['Scenario']['Site']['Storage']['size_kwh'])\n",
    "\n",
    "print(api_response_dict[x]['outputs']['Scenario']['Site']['Financial']['lcc_us_dollars'])\n",
    "print(api_response_dict[x]['outputs']['Scenario']['Site']['Financial']['lcc_bau_us_dollars'])\n",
    "print(api_response_dict[x]['outputs']['Scenario']['Site']['Financial']['npv_us_dollars'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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