From 1bdb09cdf00fd0d299dc5c3ab65fa463075abfef Mon Sep 17 00:00:00 2001 From: Byron Pullutasig <115118857+bpulluta@users.noreply.github.com> Date: Wed, 24 Jul 2024 10:09:25 -0600 Subject: [PATCH] added example script for creating custom table --- reoptjl/IEDO_custom_table.ipynb | 429 + ..._1fb7e69a-d75b-42a6-b818-eea2f824b57d.json | 140650 +++++++++++++++ ..._788961e5-6575-4b7d-80d0-095c569e47f3.json | 123094 +++++++++++++ ..._97be70d2-f73e-4d73-85da-44168ab7124a.json | 79255 ++++++++ ..._f6610331-810a-42dd-bac7-9eba9799c6ed.json | 123094 +++++++++++++ reoptjl/results.json | 107 + reoptjl/transposed_results.csv | 34 + 7 files changed, 466663 insertions(+) create mode 100644 reoptjl/IEDO_custom_table.ipynb create mode 100644 reoptjl/outputs/response_1fb7e69a-d75b-42a6-b818-eea2f824b57d.json create mode 100644 reoptjl/outputs/response_788961e5-6575-4b7d-80d0-095c569e47f3.json create mode 100644 reoptjl/outputs/response_97be70d2-f73e-4d73-85da-44168ab7124a.json create mode 100644 reoptjl/outputs/response_f6610331-810a-42dd-bac7-9eba9799c6ed.json create mode 100644 reoptjl/results.json create mode 100644 reoptjl/transposed_results.csv diff --git a/reoptjl/IEDO_custom_table.ipynb b/reoptjl/IEDO_custom_table.ipynb new file mode 100644 index 000000000..689eb8794 --- /dev/null +++ b/reoptjl/IEDO_custom_table.ipynb @@ -0,0 +1,429 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import json\n", + "import requests\n", + "import time\n", + "import os\n", + "import uuid\n", + "from collections import defaultdict\n", + "import re\n", + "\n", + "API_KEY = \"gYV8t7d6c5naotp67meIJyJRi6DksKv0VfPSQzEa\" # Replace with your API key\n", + "\n", + "# following is not necessary but silences warnings:\n", + "# InsecureRequestWarning: Unverified HTTPS request is being made to host 'developer.nrel.gov'. Adding certificate verification is strongly advised.\n", + "import urllib3\n", + "urllib3.disable_warnings()\n", + "\n", + "inputs_path = os.path.join(\".\", 'inputs')\n", + "outputs_path = os.path.join(\".\", 'outputs')\n", + "\n", + "# Define the post data for both scenarios\n", + "post_1 = {\n", + " \"Settings\": {\n", + " \"run_bau\": True\n", + " },\n", + " \"Site\": {\n", + " \"longitude\": -118.1164613,\n", + " \"latitude\": 34.5794343\n", + " },\n", + " \"PV\": {\n", + " \"array_type\": 0\n", + " },\n", + " \"ElectricLoad\": {\n", + " \"doe_reference_name\": \"Hospital\",\n", + " \"annual_kwh\": 100000.0,\n", + " \"year\": 2017\n", + " },\n", + " \"ElectricTariff\": {\n", + " \"urdb_label\": \"5ed6c1a15457a3367add15ae\"\n", + " },\n", + " \"Financial\": {\n", + " \"elec_cost_escalation_rate_fraction\": 0.026,\n", + " \"owner_discount_rate_fraction\": 0.081,\n", + " \"analysis_years\": 20,\n", + " \"offtaker_tax_rate_fraction\": 0.4,\n", + " \"owner_tax_rate_fraction\": 0.4,\n", + " \"om_cost_escalation_rate_fraction\": 0.025\n", + " }\n", + "}\n", + "\n", + "post_2 = {\n", + " \"Settings\": {\n", + " \"run_bau\": True\n", + " },\n", + " \"Site\": {\n", + " \"longitude\": -118.1164613,\n", + " \"latitude\": 34.5794343\n", + " },\n", + " \"PV\": {\n", + " \"array_type\": 0\n", + " },\n", + " \"ElectricLoad\": {\n", + " \"doe_reference_name\": \"RetailStore\",\n", + " \"annual_kwh\": 100000.0,\n", + " \"year\": 2017\n", + " },\n", + " \"ElectricStorage\":{\n", + " },\n", + " \"ElectricTariff\": {\n", + " \"urdb_label\": \"5ed6c1a15457a3367add15ae\"\n", + " },\n", + " \"Financial\": {\n", + " \"elec_cost_escalation_rate_fraction\": 0.026,\n", + " \"owner_discount_rate_fraction\": 0.081,\n", + " \"analysis_years\": 20,\n", + " \"offtaker_tax_rate_fraction\": 0.4,\n", + " \"owner_tax_rate_fraction\": 0.4,\n", + " \"om_cost_escalation_rate_fraction\": 0.025\n", + " }\n", + "}\n", + "\n", + "# Save the post data to json files\n", + "with open(os.path.join(inputs_path, \"post_1.json\"), 'w') as fp:\n", + " json.dump(post_1, fp)\n", + "\n", + "with open(os.path.join(inputs_path, \"post_2.json\"), 'w') as fp:\n", + " json.dump(post_2, fp)\n", + "\n", + "# Function to POST inputs to the API\n", + "def post_inputs_to_api(post_data, api_key, api_url):\n", + " headers = {'x-api-key': api_key}\n", + " response = requests.post(f\"{api_url}/job/\", headers=headers, json=post_data, verify=False)\n", + " response.raise_for_status()\n", + " return response.json()[\"run_uuid\"]\n", + "\n", + "# Function to poll for results\n", + "def poll_for_results(run_uuid, api_key, api_url, interval=5):\n", + " headers = {'x-api-key': api_key}\n", + " while True:\n", + " response = requests.get(f\"{api_url}/job/{run_uuid}/results/\", headers=headers, verify=False)\n", + " response.raise_for_status()\n", + " result = response.json()\n", + " if result[\"status\"] != \"Optimizing...\":\n", + " return result\n", + " time.sleep(interval)\n", + "\n", + "# Define the API key and URL\n", + "root_url = \"https://developer.nrel.gov/api/reopt/stable\"\n", + "\n", + "# Post the inputs to the API and get the run_uuid for both scenarios\n", + "run_uuid_1 = post_inputs_to_api(post_1, API_KEY, root_url)\n", + "print(f\"Run UUID for Scenario 1: {run_uuid_1}\")\n", + "\n", + "run_uuid_2 = post_inputs_to_api(post_2, API_KEY, root_url)\n", + "print(f\"Run UUID for Scenario 2: {run_uuid_2}\")\n", + "\n", + "# Poll for results for both scenarios\n", + "results_1 = poll_for_results(run_uuid_1, API_KEY, root_url)\n", + "print(\"Results retrieved successfully for Scenario 1.\")\n", + "\n", + "results_2 = poll_for_results(run_uuid_2, API_KEY, root_url)\n", + "print(\"Results retrieved successfully for Scenario 2.\")\n", + "\n", + "# Save the results to JSON files\n", + "outputs_file_name_1 = f\"response_{run_uuid_1}.json\"\n", + "with open(os.path.join(outputs_path,outputs_file_name_1), 'w') as fp:\n", + " json.dump(results_1, fp, indent=4)\n", + "print(f\"Results saved to {outputs_file_name_1}\")\n", + "\n", + "outputs_file_name_2 = f\"response_{run_uuid_2}.json\"\n", + "with open(os.path.join(outputs_path,outputs_file_name_2), 'w') as fp:\n", + " json.dump(results_2, fp, indent=4)\n", + "print(f\"Results saved to {outputs_file_name_2}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_with_suffix(df, key, suffix, default_val=0):\n", + " if not key.endswith(\"_bau\"):\n", + " key = f\"{key}{suffix}\"\n", + " return df.get(key, default_val)\n", + "\n", + "def flatten_dict(d, parent_key='', sep='.'):\n", + " items = []\n", + " for k, v in d.items():\n", + " new_key = f\"{parent_key}{sep}{k}\" if parent_key else k\n", + " if isinstance(v, dict):\n", + " items.extend(flatten_dict(v, new_key, sep=sep).items())\n", + " else:\n", + " items.append((new_key, v))\n", + " return dict(items)\n", + "\n", + "def generate_data_dict(config, df_gen, suffix):\n", + " data_dict = defaultdict(list)\n", + " for var_key, col_name in config:\n", + " if callable(var_key):\n", + " val = var_key(df_gen)\n", + " else:\n", + " val = get_with_suffix(df_gen, var_key, suffix, \"-\")\n", + " data_dict[col_name].append(val)\n", + " return data_dict\n", + "\n", + "def clean_data_dict(data_dict):\n", + " for key, value_array in data_dict.items():\n", + " new_value_array = [\"-\" if v in [0, float(\"nan\"), \"NaN\", \"0\", \"0.0\", \"$0.0\", -0, \"-0\", \"-0.0\", \"-$0.0\", None] else v for v in value_array]\n", + " data_dict[key] = new_value_array\n", + " return data_dict\n", + "\n", + "def remove_empty_data_columns(df):\n", + " # Remove columns where all the entries are \"-\"\n", + " df_cleaned = df.loc[:, (df != '-').any(axis=0)]\n", + " return df_cleaned\n", + "\n", + "def get_REopt_data(data_f, scenario_name, config, cur_gen_size=0):\n", + " scenario_name_str = str(scenario_name)\n", + " suffix = \"_bau\" if re.search(r\"(?i)\\bBAU\\b\", scenario_name_str) else \"\"\n", + " \n", + " df_gen = flatten_dict(data_f)\n", + " data_dict = generate_data_dict(config, df_gen, suffix)\n", + " data_dict = clean_data_dict(data_dict)\n", + " data_dict[\"Scenario\"] = [scenario_name_str]\n", + "\n", + " col_order = [\"Scenario\"] + [col_name for _, col_name in config]\n", + " df_res = pd.DataFrame(data_dict)\n", + " df_res = df_res[col_order]\n", + "\n", + " return df_res\n", + "\n", + "def get_bau_values(mock_scenarios, config):\n", + " bau_values = {col_name: None for _, col_name in config}\n", + " for scenario in mock_scenarios:\n", + " df_gen = flatten_dict(scenario[\"outputs\"])\n", + " for var_key, col_name in config:\n", + " key = var_key.__code__.co_consts[1]\n", + " key_bau = f\"{key}_bau\"\n", + " if key_bau in df_gen:\n", + " value = df_gen[key_bau]\n", + " if bau_values[col_name] is None:\n", + " bau_values[col_name] = value\n", + " elif bau_values[col_name] != value:\n", + " raise ValueError(f\"Inconsistent BAU values for {col_name}. This should only be used for portfolio cases with the same Site, ElectricLoad, and ElectricTariff for energy consumption and energy costs.\")\n", + " return bau_values\n", + "\n", + "def get_scenario_results(run_uuid):\n", + " results_url = f\"{root_url}/job/{run_uuid}/results/?api_key={API_KEY}\"\n", + " response = requests.get(results_url, verify=False)\n", + " response.raise_for_status()\n", + " return response.json()\n", + "\n", + "def sum_numeric(vector, default_value=0.0):\n", + " numeric_vector = [(x if isinstance(x, (int, float)) and not pd.isna(x) else default_value) for x in vector]\n", + " return sum(numeric_vector)\n", + "\n", + "def process_scenarios(scenarios):\n", + " config = [\n", + " (lambda df: get_with_suffix(df, \"PV.size_kw\", \"\"), \"PV Size (kW)\"),\n", + " (lambda df: get_with_suffix(df, \"ElectricStorage.size_kw\", \"\"), \"Battery Size (kW)\"),\n", + " (lambda df: get_with_suffix(df, \"ElectricStorage.size_kwh\", \"\"), \"Battery Capacity (kWh)\"),\n", + " # (lambda df: get_with_suffix(df, \"Wind.size_kw\", \"\"), \"Wind Size (kW)\"),\n", + " # (lambda df: get_with_suffix(df, \"CHP.size_kw\", \"\"), \"CHP Size (kW)\"),\n", + " (lambda df: get_with_suffix(df, \"PV.annual_energy_produced_kwh\", \"\"), \"PV Total Electricity Produced (kWh)\"),\n", + " # (lambda df: get_with_suffix(df, \"Wind.annual_energy_produced_kwh\", \"\"), \"Wind Total Electricity Produced (kWh)\"),\n", + " # (lambda df: get_with_suffix(df, \"CHP.annual_electric_energy_produced_kwh\", \"\"), \"CHP Total Electricity Produced (kWh)\"),\n", + " (lambda df: get_with_suffix(df, \"ElectricUtility.annual_energy_supplied_kwh\", \"\"), \"Grid Purchased Electricity (kWh)\"),\n", + " (lambda df: get_with_suffix(df, \"ElectricTariff.year_one_energy_cost_before_tax\", \"\"), \"Electricity Energy Cost ($)\"),\n", + " (lambda df: get_with_suffix(df, \"ElectricTariff.year_one_demand_cost_before_tax\", \"\"), \"Electricity Demand Cost ($)\"),\n", + " (lambda df: get_with_suffix(df, \"ElectricTariff.year_one_fixed_cost_before_tax\", \"\"), \"Utility Fixed Cost($)\"),\n", + " # (lambda df: get_with_suffix(df, \"ElectricTariff.year_one_export_benefit_before_tax\", \"\"), \"Electricity Export Benefit ($)\"),\n", + " # (lambda df: get_with_suffix(df, \"Boiler.fuel_used_mmbtu\", \"\"), \"Boiler Fuel (MMBtu)\"),\n", + " # (lambda df: get_with_suffix(df, \"CHP.fuel_used_mmbtu\", \"\"), \"CHP Fuel (MMBtu)\"),\n", + " # (lambda df: get_with_suffix(df, \"Boiler.annual_thermal_production_mmbtu\", \"\"), \"Boiler Thermal Production (MMBtu)\"),\n", + " # (lambda df: get_with_suffix(df, \"CHP.annual_thermal_production_mmbtu\", \"\"), \"CHP Thermal Production (MMBtu)\"),\n", + " # (lambda df: get_with_suffix(df, \"Financial.om_cost_escalation_rate_fraction\", \"\"), \"O&M Cost Increase ($)\"),\n", + " (lambda df: get_with_suffix(df, \"Financial.simple_payback_years\", \"\"), \"Payback Period (years)\"),\n", + " (lambda df: get_with_suffix(df, \"Financial.lifecycle_capital_costs\", \"\"), \"Gross Capital Cost ($)\"),\n", + " # (lambda df: get_with_suffix(df, \"Financial.total_incentives\", \"\"), \"Federal Tax Incentive (30%)\"),\n", + " # (lambda df: get_with_suffix(df, \"Financial.total_utility_annual_costs\", \"\"), \"IAC Grant ($)\"),\n", + " (lambda df: get_with_suffix(df, \"Site.annual_emissions_tonnes_CO2\", \"\"), \"CO2 Emissions (tonnes)\"),\n", + " (lambda df: round(100 * (get_with_suffix(df, \"ElectricTariff.year_one_bill_before_tax_bau\", \"\") - \n", + " get_with_suffix(df, \"ElectricTariff.year_one_bill_before_tax\", \"\")) / \n", + " (get_with_suffix(df, \"ElectricTariff.year_one_bill_before_tax_bau\", 1) + 1e-6)\n", + " ), \"Year 1 Utility Savings (%)\"),\n", + " (lambda df: round(100 * get_with_suffix(df, \"Site.renewable_electricity_fraction\", \"\")), \"RE Penetration (%)\"),\n", + " (lambda df: get_with_suffix(df, \"Site.annual_emissions_tonnes_CO2\", \"\"), \"Annual CO2 Emissions (Tons)\"),\n", + " (lambda df: get_with_suffix(df, \"Site.lifecycle_emissions_tonnes_CO2\", \"\"), \"Lifecycle CO2 Emissions (Tons)\"),\n", + " (lambda df: round(100 * get_with_suffix(df, \"Site.lifecycle_emissions_reduction_CO2_fraction\", \"\")), \"Lifecycle CO2 Reduction (%)\"),\n", + " (lambda df: get_with_suffix(df, \"ElectricUtility.annual_energy_supplied_kwh\", \"\"), \"Year 1 Electric Grid Purchases (kWh)\"),\n", + " (lambda df: get_with_suffix(df, \"ElectricTariff.year_one_energy_cost_before_tax\", \"\"), \"Year 1 Energy Charges ($)\"),\n", + " (lambda df: get_with_suffix(df, \"ElectricTariff.year_one_demand_cost_before_tax\", \"\"), \"Year 1 Demand Charges ($)\"),\n", + " (lambda df: get_with_suffix(df, \"ElectricTariff.year_one_fixed_cost_before_tax\", \"\"), \"Year 1 Fixed Cost Charges ($)\"),\n", + " (lambda df: get_with_suffix(df, \"ElectricTariff.year_one_coincident_peak_cost_before_tax\", \"\"), \"Year 1 Coincident Peak Charges ($)\"),\n", + " (lambda df: get_with_suffix(df, \"ElectricTariff.year_one_bill_before_tax\", \"\"), \"Year 1 Total Electric Bill Costs ($)\"),\n", + " (lambda df: get_with_suffix(df, \"CHP.year_one_fuel_cost_before_tax\", \"\"), \"Year 1 CHP Fuel Cost ($)\"),\n", + " (lambda df: get_with_suffix(df, \"ExistingBoiler.year_one_fuel_cost_before_tax\", \"\"), \"Year 1 Existing Boiler Fuel Cost ($)\"),\n", + " (lambda df: get_with_suffix(df, \"ElectricTariff.year_one_energy_cost_before_tax_bau\", \"\") - get_with_suffix(df, \"ElectricTariff.year_one_energy_cost_before_tax\", \"\"), \"Year 1 Energy Charge Savings ($)\"),\n", + " (lambda df: get_with_suffix(df, \"ElectricTariff.year_one_demand_cost_before_tax_bau\", \"\") - get_with_suffix(df, \"ElectricTariff.year_one_demand_cost_before_tax\", \"\"), \"Year 1 Demand Charge Savings ($)\"),\n", + " (lambda df: get_with_suffix(df, \"ElectricTariff.year_one_bill_before_tax_bau\", \"\") - get_with_suffix(df, \"ElectricTariff.year_one_bill_before_tax\", \"\"), \"Year 1 Total Electric Bill Savings ($)\"),\n", + " (lambda df: get_with_suffix(df, \"ElectricTariff.lifecycle_fixed_cost_after_tax\", \"\") + get_with_suffix(df, \"ElectricTariff.lifecycle_demand_cost_after_tax\", \"\") + get_with_suffix(df, \"ElectricTariff.lifecycle_energy_cost_after_tax\", \"\"), \"Lifecycle Utility Electricity Cost ($)\"),\n", + " (lambda df: get_with_suffix(df, \"Financial.lcc\", \"\"), \"Total Lifecycle Cost ($)\"),\n", + " (lambda df: get_with_suffix(df, \"Financial.npv\", \"\"), \"Net Present Value ($)\"),\n", + " (lambda df: round(100 * (get_with_suffix(df, \"Financial.npv\", \"\")) / (get_with_suffix(df, \"Financial.lcc_bau\", 1, \"\") + 1e-6)), \"Savings Compared to BAU (%)\"),\n", + " (lambda df: get_with_suffix(df, \"PV.annual_energy_produced_kwh\", \"\"), \"Annual PV Production (kWh)\"),\n", + " (lambda df: sum_numeric(get_with_suffix(df, \"PV.electric_curtailed_series_kw\", \"\", [])), \"PV Curtailed (kWh)\"),\n", + " (lambda df: get_with_suffix(df, \"PV.lcoe_per_kwh\", \"\"), \"PV Levelized Cost of Energy ($/kWh)\"),\n", + " (lambda df: get_with_suffix(df, \"ElectricTariff.year_one_export_benefit_before_tax\", \"\"), \"Year 1 Net Metering Benefit ($)\"),\n", + " (lambda df: get_with_suffix(df, \"Financial.annualized_payment_to_third_party\", \"\"), \"Annual Payment to Third-Party ($)\"),\n", + " (lambda df: round(get_with_suffix(df, \"Financial.annualized_payment_to_third_party\", \"\") / 12), \"Monthly Payment to Third-Party ($)\")\n", + " ]\n", + "\n", + " bau_values = get_bau_values(scenarios, config)\n", + "\n", + " combined_df = pd.DataFrame()\n", + " for scenario in scenarios:\n", + " run_uuid = scenario['run_uuid']\n", + " df_result = get_REopt_data(scenario[\"outputs\"], run_uuid, config)\n", + " df_result = df_result.set_index('Scenario').T\n", + " df_result.columns = [run_uuid]\n", + " if combined_df.empty:\n", + " combined_df = df_result\n", + " else:\n", + " combined_df = combined_df.join(df_result, how='outer')\n", + "\n", + " bau_data = {key: [value] for key, value in bau_values.items()}\n", + " bau_data[\"Scenario\"] = [\"BAU\"]\n", + " df_bau = pd.DataFrame(bau_data)\n", + "\n", + " combined_df = pd.concat([df_bau, combined_df.T]).reset_index(drop=True)\n", + " combined_df = clean_data_dict(combined_df.to_dict(orient=\"list\"))\n", + " combined_df = pd.DataFrame(combined_df)\n", + "\n", + " combined_df = combined_df[[\"Scenario\"] + [col for col in combined_df.columns if col != \"Scenario\"]]\n", + "\n", + " return combined_df\n", + "\n", + "def summary_by_runuuids(run_uuids):\n", + " if len(run_uuids) == 0:\n", + " return {'Error': 'Must provide one or more run_uuids'}\n", + "\n", + " for r_uuid in run_uuids:\n", + " if type(r_uuid) != str:\n", + " return {'Error': 'Provided run_uuids type error, must be string. ' + str(r_uuid)}\n", + " \n", + " try:\n", + " uuid.UUID(r_uuid)\n", + " except ValueError as e:\n", + " if e.args[0] == \"badly formed hexadecimal UUID string\":\n", + " return {\"Error\": str(e)}\n", + " else:\n", + " return {\"Error\": str(e)}\n", + " \n", + " try:\n", + " scenarios = []\n", + " for run_uuid in run_uuids:\n", + " results = get_scenario_results(run_uuid)\n", + " scenarios.append(results)\n", + " return {'scenarios': scenarios}\n", + " except Exception as e:\n", + " return {\"Error\": str(e)}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import json\n", + "\n", + "def main():\n", + " run_uuid_1 = \"0ea46c21-0076-4437-98b9-f1d3f4d5255d\"\n", + " run_uuid_2 = \"c9835dac-7ff7-43b1-9676-6bd135e3d167\"\n", + "\n", + " run_uuids = [run_uuid_1, run_uuid_2]\n", + "\n", + " scenarios = summary_by_runuuids(run_uuids)\n", + " if 'scenarios' not in scenarios:\n", + " print(f\"Error: {scenarios['Error']}\")\n", + " return\n", + "\n", + " final_df = process_scenarios(scenarios['scenarios'])\n", + " final_df = remove_empty_data_columns(final_df)\n", + "\n", + " final_df.iloc[1:, 0] = run_uuids\n", + " \n", + " display(final_df)\n", + " \n", + " # Convert the final DataFrame to JSON format\n", + " json_data = final_df.reset_index().to_dict(orient='records')\n", + " \n", + " with open('results.json', 'w') as json_file:\n", + " json.dump(json_data, json_file, indent=2)\n", + " \n", + " json_string = json.dumps(json_data, indent=2)\n", + " print(json_string)\n", + " \n", + " # Convert the JSON string back to a DataFrame for saving as CSV\n", + " json_df = pd.read_json(json_string)\n", + " json_df = json_df.T\n", + " \n", + " json_df.to_csv('transposed_results.csv')\n", + "\n", + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# def main():\n", + "# run_uuid_1 = \"0ea46c21-0076-4437-98b9-f1d3f4d5255d\"\n", + "# run_uuid_2 = \"c9835dac-7ff7-43b1-9676-6bd135e3d167\"\n", + "\n", + "# run_uuids = [run_uuid_1, run_uuid_2]\n", + "\n", + "# scenarios = summary_by_runuuids(run_uuids)\n", + "# if 'scenarios' not in scenarios:\n", + "# print(f\"Error: {scenarios['Error']}\")\n", + "# return\n", + "\n", + "# final_df = process_scenarios(scenarios['scenarios'])\n", + "# final_df = remove_empty_data_columns(final_df)\n", + "\n", + "# final_df.iloc[1:, 0] = run_uuids\n", + "# display(final_df)\n", + " \n", + "# # Save transposed DataFrame as CSV\n", + "# transposed_df = final_df.T\n", + "# transposed_df.to_csv('transposed_results.csv')\n", + "\n", + "# main()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/reoptjl/outputs/response_1fb7e69a-d75b-42a6-b818-eea2f824b57d.json b/reoptjl/outputs/response_1fb7e69a-d75b-42a6-b818-eea2f824b57d.json new file mode 100644 index 000000000..a0f8caf96 --- /dev/null +++ b/reoptjl/outputs/response_1fb7e69a-d75b-42a6-b818-eea2f824b57d.json @@ -0,0 +1,140650 @@ +{ + "run_uuid": "1fb7e69a-d75b-42a6-b818-eea2f824b57d", + "api_version": 3, + "user_uuid": "", + "webtool_uuid": "", + "job_type": "developer.nrel.gov", + "status": "optimal", + "created": "2024-07-24T02:40:37.275Z", + "reopt_version": "0.47.2", + "api_key": "9Yj9e7Il92xXdQkBcqrTf4WepVr6QeUCdUthVyYQ", + "inputs": { + "Financial": { + "analysis_years": 20, + "elec_cost_escalation_rate_fraction": 0.026, + "offtaker_discount_rate_fraction": 0.0638, + "offtaker_tax_rate_fraction": 0.4, + "om_cost_escalation_rate_fraction": 0.025, + "owner_discount_rate_fraction": 0.0638, + "owner_tax_rate_fraction": 0.4, + "third_party_ownership": false, + "value_of_lost_load_per_kwh": 0.0, + "microgrid_upgrade_cost_fraction": 0.0, + "offgrid_other_capital_costs": 0.0, + "offgrid_other_annual_costs": 0.0, + "CO2_cost_per_tonne": 51.0, + "CO2_cost_escalation_rate_fraction": 0.042173, + "NOx_grid_cost_per_tonne": 10337.724486281435, + "SO2_grid_cost_per_tonne": 25921.852796774776, + "PM25_grid_cost_per_tonne": 99776.26732993, + "NOx_onsite_fuelburn_cost_per_tonne": 9536.55958809451, + "SO2_onsite_fuelburn_cost_per_tonne": 25453.609756331767, + "PM25_onsite_fuelburn_cost_per_tonne": 179717.5775103194, + "NOx_cost_escalation_rate_fraction": 0.039740205364494054, + "SO2_cost_escalation_rate_fraction": 0.04964239816637659, + "PM25_cost_escalation_rate_fraction": 0.043590175213555106, + "generator_fuel_cost_escalation_rate_fraction": 0.012, + "existing_boiler_fuel_cost_escalation_rate_fraction": 0.015, + "boiler_fuel_cost_escalation_rate_fraction": 0.015, + "chp_fuel_cost_escalation_rate_fraction": 0.015 + }, + "ElectricLoad": { + "annual_kwh": 100000.0, + "doe_reference_name": "RetailStore", + "year": 2017, + "monthly_totals_kwh": [], + "loads_kw": [], + "critical_loads_kw": [], + "loads_kw_is_net": true, + "critical_loads_kw_is_net": false, + "critical_load_fraction": 0.5, + "operating_reserve_required_fraction": 0.0, + "min_load_met_annual_fraction": 1.0, + "blended_doe_reference_names": [], + "blended_doe_reference_percents": [] + }, + "Site": { + "latitude": 34.5794343, + "longitude": -118.1164613, + "land_acres": null, + "roof_squarefeet": null, + "min_resil_time_steps": null, + "CO2_emissions_reduction_min_fraction": null, + "CO2_emissions_reduction_max_fraction": null, + "renewable_electricity_min_fraction": null, + "renewable_electricity_max_fraction": null, + "include_exported_elec_emissions_in_total": true, + "include_exported_renewable_electricity_in_total": true + }, + "Settings": { + "timeout_seconds": 600, + "time_steps_per_hour": 1, + "optimality_tolerance": 0.001, + "add_soc_incentive": true, + "run_bau": true, + "include_climate_in_objective": false, + "include_health_in_objective": false, + "off_grid_flag": false, + "solver_name": "HiGHS" + }, + "PV": { + "name": "PV", + "existing_kw": 0.0, + "min_kw": 0.0, + "max_kw": 1000000000.0, + "installed_cost_per_kw": 1790.0, + "om_cost_per_kw": 18.0, + "macrs_option_years": 5, + "macrs_bonus_fraction": 0.6, + "macrs_itc_reduction": 0.5, + "federal_itc_fraction": 0.3, + "state_ibi_fraction": 0.0, + "state_ibi_max": 10000000000.0, + "utility_ibi_fraction": 0.0, + "utility_ibi_max": 10000000000.0, + "federal_rebate_per_kw": 0.0, + "state_rebate_per_kw": 0.0, + "state_rebate_max": 10000000000.0, + "utility_rebate_per_kw": 0.0, + "utility_rebate_max": 10000000000.0, + "production_incentive_per_kwh": 0.0, + "production_incentive_max_benefit": 1000000000.0, + "production_incentive_years": 1, + 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because the DoE load profiles start on a Sunday.", + "errors": {}, + "warnings": {}, + "has_stacktrace": false + } +} \ No newline at end of file diff --git a/reoptjl/outputs/response_788961e5-6575-4b7d-80d0-095c569e47f3.json b/reoptjl/outputs/response_788961e5-6575-4b7d-80d0-095c569e47f3.json new file mode 100644 index 000000000..5e57f045b --- /dev/null +++ b/reoptjl/outputs/response_788961e5-6575-4b7d-80d0-095c569e47f3.json @@ -0,0 +1,123094 @@ +{ + "run_uuid": "788961e5-6575-4b7d-80d0-095c569e47f3", + "api_version": 3, + "user_uuid": "", + "webtool_uuid": "", + "job_type": "developer.nrel.gov", + "status": "optimal", + "created": "2024-07-24T15:57:43.106Z", + "reopt_version": "0.47.2", + "api_key": "9Yj9e7Il92xXdQkBcqrTf4WepVr6QeUCdUthVyYQ", + "inputs": { + "Financial": { + "analysis_years": 20, + "elec_cost_escalation_rate_fraction": 0.026, + "offtaker_discount_rate_fraction": 0.0638, + "offtaker_tax_rate_fraction": 0.4, + "om_cost_escalation_rate_fraction": 0.025, + 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