13_simulate_request_with_buffer_byhour_hardcoded5pm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from icecream import ic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [\n",
    "            {\n",
    "                \"$id\": \"2\",\n",
    "                \"date\": \"2024-01-01T23:00:00Z\",\n",
    "                \"price\": 23.92\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"3\",\n",
    "                \"date\": \"2024-01-02T00:00:00Z\",\n",
    "                \"price\": 17.7\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"4\",\n",
    "                \"date\": \"2024-01-02T01:00:00Z\",\n",
    "                \"price\": 12.39\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"5\",\n",
    "                \"date\": \"2024-01-02T02:00:00Z\",\n",
    "                \"price\": 7.35\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"6\",\n",
    "                \"date\": \"2024-01-02T03:00:00Z\",\n",
    "                \"price\": 4.19\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"7\",\n",
    "                \"date\": \"2024-01-02T04:00:00Z\",\n",
    "                \"price\": 4.75\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"8\",\n",
    "                \"date\": \"2024-01-02T05:00:00Z\",\n",
    "                \"price\": 31.59\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"9\",\n",
    "                \"date\": \"2024-01-02T06:00:00Z\",\n",
    "                \"price\": 49.96\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"10\",\n",
    "                \"date\": \"2024-01-02T07:00:00Z\",\n",
    "                \"price\": 58.46\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"11\",\n",
    "                \"date\": \"2024-01-02T08:00:00Z\",\n",
    "                \"price\": 59.05\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"12\",\n",
    "                \"date\": \"2024-01-02T09:00:00Z\",\n",
    "                \"price\": 68.65\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"13\",\n",
    "                \"date\": \"2024-01-02T10:00:00Z\",\n",
    "                \"price\": 74.18\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"14\",\n",
    "                \"date\": \"2024-01-02T11:00:00Z\",\n",
    "                \"price\": 65.02\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"15\",\n",
    "                \"date\": \"2024-01-02T12:00:00Z\",\n",
    "                \"price\": 64.71\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"16\",\n",
    "                \"date\": \"2024-01-02T13:00:00Z\",\n",
    "                \"price\": 66.74\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"17\",\n",
    "                \"date\": \"2024-01-02T14:00:00Z\",\n",
    "                \"price\": 64.43\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"18\",\n",
    "                \"date\": \"2024-01-02T15:00:00Z\",\n",
    "                \"price\": 65.04\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"19\",\n",
    "                \"date\": \"2024-01-02T16:00:00Z\",\n",
    "                \"price\": 73.85\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"20\",\n",
    "                \"date\": \"2024-01-02T17:00:00Z\",\n",
    "                \"price\": 66.62\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"21\",\n",
    "                \"date\": \"2024-01-02T18:00:00Z\",\n",
    "                \"price\": 64.36\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"22\",\n",
    "                \"date\": \"2024-01-02T19:00:00Z\",\n",
    "                \"price\": 60\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"23\",\n",
    "                \"date\": \"2024-01-02T20:00:00Z\",\n",
    "                \"price\": 49.5\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"24\",\n",
    "                \"date\": \"2024-01-02T21:00:00Z\",\n",
    "                \"price\": 44.19\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"25\",\n",
    "                \"date\": \"2024-01-02T22:00:00Z\",\n",
    "                \"price\": 26.83\n",
    "            }\n",
    "        ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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></th>\n",
       "      <th>$id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2024-01-01T23:00:00Z</td>\n",
       "      <td>23.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2024-01-02T00:00:00Z</td>\n",
       "      <td>17.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2024-01-02T01:00:00Z</td>\n",
       "      <td>12.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>2024-01-02T02:00:00Z</td>\n",
       "      <td>7.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2024-01-02T03:00:00Z</td>\n",
       "      <td>4.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>2024-01-02T04:00:00Z</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>2024-01-02T05:00:00Z</td>\n",
       "      <td>31.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>2024-01-02T06:00:00Z</td>\n",
       "      <td>49.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>2024-01-02T07:00:00Z</td>\n",
       "      <td>58.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>2024-01-02T08:00:00Z</td>\n",
       "      <td>59.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12</td>\n",
       "      <td>2024-01-02T09:00:00Z</td>\n",
       "      <td>68.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13</td>\n",
       "      <td>2024-01-02T10:00:00Z</td>\n",
       "      <td>74.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14</td>\n",
       "      <td>2024-01-02T11:00:00Z</td>\n",
       "      <td>65.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15</td>\n",
       "      <td>2024-01-02T12:00:00Z</td>\n",
       "      <td>64.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>16</td>\n",
       "      <td>2024-01-02T13:00:00Z</td>\n",
       "      <td>66.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17</td>\n",
       "      <td>2024-01-02T14:00:00Z</td>\n",
       "      <td>64.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>18</td>\n",
       "      <td>2024-01-02T15:00:00Z</td>\n",
       "      <td>65.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>19</td>\n",
       "      <td>2024-01-02T16:00:00Z</td>\n",
       "      <td>73.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20</td>\n",
       "      <td>2024-01-02T17:00:00Z</td>\n",
       "      <td>66.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>21</td>\n",
       "      <td>2024-01-02T18:00:00Z</td>\n",
       "      <td>64.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>22</td>\n",
       "      <td>2024-01-02T19:00:00Z</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>23</td>\n",
       "      <td>2024-01-02T20:00:00Z</td>\n",
       "      <td>49.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>24</td>\n",
       "      <td>2024-01-02T21:00:00Z</td>\n",
       "      <td>44.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>25</td>\n",
       "      <td>2024-01-02T22:00:00Z</td>\n",
       "      <td>26.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   $id                  date  price\n",
       "0    2  2024-01-01T23:00:00Z  23.92\n",
       "1    3  2024-01-02T00:00:00Z  17.70\n",
       "2    4  2024-01-02T01:00:00Z  12.39\n",
       "3    5  2024-01-02T02:00:00Z   7.35\n",
       "4    6  2024-01-02T03:00:00Z   4.19\n",
       "5    7  2024-01-02T04:00:00Z   4.75\n",
       "6    8  2024-01-02T05:00:00Z  31.59\n",
       "7    9  2024-01-02T06:00:00Z  49.96\n",
       "8   10  2024-01-02T07:00:00Z  58.46\n",
       "9   11  2024-01-02T08:00:00Z  59.05\n",
       "10  12  2024-01-02T09:00:00Z  68.65\n",
       "11  13  2024-01-02T10:00:00Z  74.18\n",
       "12  14  2024-01-02T11:00:00Z  65.02\n",
       "13  15  2024-01-02T12:00:00Z  64.71\n",
       "14  16  2024-01-02T13:00:00Z  66.74\n",
       "15  17  2024-01-02T14:00:00Z  64.43\n",
       "16  18  2024-01-02T15:00:00Z  65.04\n",
       "17  19  2024-01-02T16:00:00Z  73.85\n",
       "18  20  2024-01-02T17:00:00Z  66.62\n",
       "19  21  2024-01-02T18:00:00Z  64.36\n",
       "20  22  2024-01-02T19:00:00Z  60.00\n",
       "21  23  2024-01-02T20:00:00Z  49.50\n",
       "22  24  2024-01-02T21:00:00Z  44.19\n",
       "23  25  2024-01-02T22:00:00Z  26.83"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# keep only the first 17 rows\n",
    "df = df.head(cutoff_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ic| ev_charging_hours_count_comfort: 9.090909090909092\n",
      "    ev_charging_hours_count_max: 13.636363636363637\n",
      "    total_hours_count: 17\n",
      "ic| ev_charging_hours_count_comfort / total_hours_count: 0.5347593582887701\n",
      "ic| percent_of_hours_needed_comfort: 0.5347593582887701\n",
      "    percent_of_hours_needed_max: 0.8021390374331551\n",
      "ic| percent_of_hours_needed_comfort: 0.8021390374331552\n",
      "    percent_of_hours_needed_max: 1.2032085561497325\n",
      "/var/folders/6s/rrw73w_s3nn1wfybdcbsv38m0000gp/T/ipykernel_24632/1846584528.py:35: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['is_in_lowest_hours_comfort'] = df[price_column_name] <= df[price_column_name].quantile(percent_of_hours_needed_comfort)\n",
      "/var/folders/6s/rrw73w_s3nn1wfybdcbsv38m0000gp/T/ipykernel_24632/1846584528.py:37: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['is_in_lowest_hours_max'] = df[price_column_name] <= df[price_column_name].quantile(percent_of_hours_needed_max)\n"
     ]
    }
   ],
   "source": [
    "price_column_name = \"price\"\n",
    "\n",
    "\n",
    "ev_comfort_charge_capacity_kwh = int(0.8 * 250)  # 80% of 60kWh\n",
    "ev_max_charge_capacity_kwh = 300  # 60kWh\n",
    "buffer = 0.5  # 50%\n",
    "\n",
    "\n",
    "# hardcoded for now\n",
    "ev_pmax = 22  # kW\n",
    "ev_charged_per_hour = ev_pmax  # kWh\n",
    "cutoff_time = 17  # 17pm(ish)\n",
    "\n",
    "\n",
    "ev_charging_hours_count_comfort = ev_comfort_charge_capacity_kwh / ev_charged_per_hour\n",
    "ev_charging_hours_count_max = ev_max_charge_capacity_kwh / ev_charged_per_hour\n",
    "total_hours_count = len(df)\n",
    "ic(ev_charging_hours_count_comfort, ev_charging_hours_count_max, total_hours_count)\n",
    "\n",
    "ic(ev_charging_hours_count_comfort / total_hours_count)\n",
    "\n",
    "percent_of_hours_needed_comfort = ev_charging_hours_count_comfort / total_hours_count\n",
    "percent_of_hours_needed_max = ev_charging_hours_count_max / total_hours_count\n",
    "ic(percent_of_hours_needed_comfort, percent_of_hours_needed_max)\n",
    "\n",
    "percent_of_hours_needed_comfort = percent_of_hours_needed_comfort * (1 + buffer)\n",
    "percent_of_hours_needed_max = percent_of_hours_needed_max * (1 + buffer)\n",
    "ic(percent_of_hours_needed_comfort, percent_of_hours_needed_max)\n",
    "\n",
    "if percent_of_hours_needed_comfort > 1:\n",
    "    percent_of_hours_needed_comfort = 1\n",
    "if percent_of_hours_needed_max > 1:\n",
    "    percent_of_hours_needed_max = 1\n",
    "\n",
    "df['is_in_lowest_hours_comfort'] = df[price_column_name] <= df[price_column_name].quantile(percent_of_hours_needed_comfort)\n",
    "\n",
    "df['is_in_lowest_hours_max'] = df[price_column_name] <= df[price_column_name].quantile(percent_of_hours_needed_max)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "highest_price_in_lowest_hours_comfort = df[df['is_in_lowest_hours_comfort']][price_column_name].max()\n",
    "\n",
    "highest_price_in_lowest_hours_max = df[df['is_in_lowest_hours_max']][price_column_name].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65.02, 74.18)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest_price_in_lowest_hours_comfort, highest_price_in_lowest_hours_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6s/rrw73w_s3nn1wfybdcbsv38m0000gp/T/ipykernel_24632/3483119753.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['DateTime'] = pd.to_datetime(df['date'])  # Ensure DateTime is in the correct format\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assume your dataframe is df\n",
    "df['DateTime'] = pd.to_datetime(df['date'])  # Ensure DateTime is in the correct format\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "\n",
    "# plot all the prices\n",
    "plt.plot(df['date'], df[price_column_name], label=price_column_name)\n",
    "\n",
    "# overlay the points where 'is_in_lowest_quarters' is True\n",
    "plt.scatter(df[df['is_in_lowest_hours_comfort']]['date'], df[df['is_in_lowest_hours_comfort']][price_column_name], color='red', label='Lowest hours')\n",
    "\n",
    "plt.xlabel('DateTime')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel(price_column_name)\n",
    "plt.title('Negative imbalance price over time')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}