notebooks/Hourly demand.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hourly demand\n",
    "Base demand data is currently restricted to 2012, although it is possible for a user to load 2011 data from IPM that is stored in the PUDL database. The 2012 demand data are aligned with renewables generation data provided with PowerGenome, and with electrification profiles included in the [NREL Electrification Futures Study](https://data.nrel.gov/submissions/126). Although 2012 is a leap year with 8784 hours, PowerGenome removes February 29 by default. If this is a problem, [file an issue](https://github.com/gschivley/PowerGenome/issues) on GitHub and I'll look into making it optional.\n",
    "\n",
    "**NOTE:** All 2012 load profiles have been compiled using FERC 714 data. Hourly demand from each entity is mapped to the counties that they serve (using EIA 861), and re-apportioned to IPM regions based on population. Anomalies were detected and fixed using code developed by [Tyler Ruggles and David Farnham](https://github.com/truggles/EIA_Cleaned_Hourly_Electricity_Demand_Code). Loads in each region are then adjusted based on the ratio of FERC load in 2011 to IPM load in 2011. **Finally, all load has been shifted to UTC time, which matches generation profiles linked to in the README.** The regions `WECC_SNV`, `MIS_MO`, and `PJM_SMAC` have total/peak load that is more than 30% off from IPM 2011 data. Future versions of the data may use different allocation methods that also account for large industrial/commercial loads. I'll post any updated on the [PowerGenome groups.io](https://groups.io/g/powergenome) site.\n",
    "\n",
    "Hourly demand is one of the few remaining areas of PowerGenome where users may need to supply data, although this is limited to hourly profiles of demand response resources (possibly from the NREL data) and hourly profiles/capacity of distributed generation. Rooftop solar PV profiles can be obtained from sources such as NREL or [Renewables.ninja](https://www.renewables.ninja/).\n",
    "\n",
    "Examples of user-supplied data - including load profiles - are included in `example_system/extra_inputs`. The corresponding parameters are\n",
    "- `distributed_gen_profiles_fn`\n",
    "- `demand_response_fn`\n",
    "- `regional_load_fn` \n",
    "\n",
    "Note that if external load profiles include electrification/demand reponse load you should set the parameter `regional_load_includes_demand_response` to `True`. In this case the demand response profiles are used to calculate capacity of the resource in each planning year (in the new generators data), but they are not added to the load profiles.\n",
    "\n",
    "Distributed generation profiles, which are normalized from 0-1, can be scaled as either a fraction of total load (in cases where DG is part of an RPS set-aside) or according to a total capacity (where capacity projections are available). This is done using the parameters\n",
    "- `distributed_gen_method` (key: region name, value: `capacity` or `fraction_load`)\n",
    "- `distributed_gen_values` (nested dictionary for each planning year)\n",
    "- `avg_distribution_loss` (simplified as a single value for all regions, used to scale up DG when subtracting from total load)\n",
    "\n",
    "Final load profiles are calculated as follows:\n",
    "1. Read hourly load data from either the PUDL sqlite database or the user-supplied CSV file.\n",
    "2. If read from sqlite:  \n",
    "    a. Combine IPM regions into model regions using the `region_aggregations` parameter.  \n",
    "    b. Scale up hourly load from base year (2012) to 2018 using historical demand from EIA AEO electricity market module (EMM) regions (AEO 2014 and AEO 2019, IPM regions mapped to EMM regions in the parameter `historical_load_region_maps`). Scale from 2019 to the model year using AEO 2020. EIA changed AEO EMM regions in 2020, and IPM regions are mapped to the new EMM regions in the parameter `future_load_region_map`.   \n",
    "3. If loaded from sqlite, or user-supplied data and values do not include demand response, add DR profiles to load profiles (if hourly DR demand is supplied by the user).\n",
    "4. If distributed generation curves and scaling method/values are provided, subtract DG from load curves.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from powergenome.load_profiles import (\n",
    "    make_load_curves, \n",
    "    add_load_growth, \n",
    "    make_final_load_curves, \n",
    "    make_distributed_gen_profiles,\n",
    ")\n",
    "from powergenome.external_data import make_demand_response_profiles\n",
    "from powergenome.generators import GeneratorClusters\n",
    "from powergenome.util import (\n",
    "    build_scenario_settings,\n",
    "    init_pudl_connection,\n",
    "    load_settings,\n",
    "    reverse_dict_of_lists,\n",
    "    remove_feb_29,\n",
    "    check_settings\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import settings\n",
    "This assumes that the settings file is set up for multiple scenarios/planning periods. If you are using a settings file with only a single scenario/planning period, remove or comment out the line with `build_scenario_settings`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cwd = Path.cwd()\n",
    "\n",
    "settings_path = (\n",
    "    cwd.parent / \"example_systems\" / \"CA_AZ\" / \"test_settings.yml\"\n",
    ")\n",
    "settings = load_settings(settings_path)\n",
    "settings[\"input_folder\"] = settings_path.parent / settings[\"input_folder\"]\n",
    "scenario_definitions = pd.read_csv(\n",
    "    settings[\"input_folder\"] / settings[\"scenario_definitions_fn\"]\n",
    ")\n",
    "scenario_settings = build_scenario_settings(settings, scenario_definitions)\n",
    "\n",
    "pudl_engine, pudl_out, pg_engine = init_pudl_connection(\n",
    "    freq=\"AS\",\n",
    "    start_year=min(settings.get(\"data_years\")),\n",
    "    end_year=max(settings.get(\"data_years\")),\n",
    ")\n",
    "\n",
    "check_settings(settings, pg_engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build load curves\n",
    "If you just want the final load curves that include any demand response/distributed generation, the function `make_final_load_curves` is all you need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>region</th>\n",
       "      <th>CA_N</th>\n",
       "      <th>CA_S</th>\n",
       "      <th>WECC_AZ</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14211</td>\n",
       "      <td>18064</td>\n",
       "      <td>10358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13539</td>\n",
       "      <td>17227</td>\n",
       "      <td>10054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12836</td>\n",
       "      <td>16394</td>\n",
       "      <td>9670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13052</td>\n",
       "      <td>14691</td>\n",
       "      <td>9545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12452</td>\n",
       "      <td>14045</td>\n",
       "      <td>9411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8756</th>\n",
       "      <td>13983</td>\n",
       "      <td>17833</td>\n",
       "      <td>11038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8757</th>\n",
       "      <td>15896</td>\n",
       "      <td>20164</td>\n",
       "      <td>11764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8758</th>\n",
       "      <td>16058</td>\n",
       "      <td>20372</td>\n",
       "      <td>11449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8759</th>\n",
       "      <td>15326</td>\n",
       "      <td>19448</td>\n",
       "      <td>11061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8760</th>\n",
       "      <td>14800</td>\n",
       "      <td>18801</td>\n",
       "      <td>10714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "region       CA_N   CA_S  WECC_AZ\n",
       "time_index                       \n",
       "1           14211  18064    10358\n",
       "2           13539  17227    10054\n",
       "3           12836  16394     9670\n",
       "4           13052  14691     9545\n",
       "5           12452  14045     9411\n",
       "...           ...    ...      ...\n",
       "8756        13983  17833    11038\n",
       "8757        15896  20164    11764\n",
       "8758        16058  20372    11449\n",
       "8759        15326  19448    11061\n",
       "8760        14800  18801    10714\n",
       "\n",
       "[8760 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_curves = make_final_load_curves(pg_engine, scenario_settings[2030][\"p1\"])\n",
    "load_curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='time_index'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "load_curves.plot(alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load curve components\n",
    "\n",
    "The individual components of a final load curve can also be accessed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaled 2012 load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_load = make_load_curves(pg_engine, scenario_settings[2030][\"p1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Demand response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CA_N</th>\n",
       "      <th>CA_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52.8</td>\n",
       "      <td>68.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49.3</td>\n",
       "      <td>63.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.0</td>\n",
       "      <td>24.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.4</td>\n",
       "      <td>18.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.1</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8755</th>\n",
       "      <td>367.7</td>\n",
       "      <td>474.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8756</th>\n",
       "      <td>357.6</td>\n",
       "      <td>461.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8757</th>\n",
       "      <td>302.8</td>\n",
       "      <td>390.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8758</th>\n",
       "      <td>52.8</td>\n",
       "      <td>68.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8759</th>\n",
       "      <td>52.8</td>\n",
       "      <td>68.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CA_N   CA_S\n",
       "0      52.8   68.1\n",
       "1      49.3   63.6\n",
       "2      19.0   24.5\n",
       "3      14.4   18.6\n",
       "4       9.1   11.8\n",
       "...     ...    ...\n",
       "8755  367.7  474.1\n",
       "8756  357.6  461.0\n",
       "8757  302.8  390.3\n",
       "8758   52.8   68.1\n",
       "8759   52.8   68.1\n",
       "\n",
       "[8760 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dr_path = settings[\"input_folder\"] / settings[\"demand_response_fn\"]\n",
    "dr_types = settings[\"demand_response_resources\"][scenario_settings[2030][\"p1\"][\"model_year\"]].keys()\n",
    "dr_profiles = make_demand_response_profiles(dr_path, list(dr_types)[0], scenario_settings[2030][\"p1\"])\n",
    "dr_profiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distributed generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CA_N</th>\n",
       "      <th>CA_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8755</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8756</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8757</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8758</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8759</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CA_N  CA_S\n",
       "0      0.0   0.0\n",
       "1      0.0   0.0\n",
       "2      0.0   0.0\n",
       "3      0.0   0.0\n",
       "4      0.0   0.0\n",
       "...    ...   ...\n",
       "8755   0.0   0.0\n",
       "8756   0.0   0.0\n",
       "8757   0.0   0.0\n",
       "8758   0.0   0.0\n",
       "8759   0.0   0.0\n",
       "\n",
       "[8760 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dg_profiles = make_distributed_gen_profiles(pg_engine, scenario_settings[2030][\"p1\"])\n",
    "dg_profiles"
   ]
  }
 ],
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   "display_name": "Python 3.9.7 64-bit ('powergenome': conda)",
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