simple-pv-simulator/main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import shutil\n",
    "\n",
    "def clear_folder_make_ess_pv(folder_path):\n",
    "    if os.path.isdir(folder_path):\n",
    "        shutil.rmtree(folder_path)\n",
    "    os.makedirs(folder_path)\n",
    "    os.makedirs(os.path.join(folder_path,'ess'))\n",
    "    os.makedirs(os.path.join(folder_path,'pv'))\n",
    "\n",
    "folder_path = 'plots'\n",
    "clear_folder_make_ess_pv(folder_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from EnergySystem import EnergySystem\n",
    "from config import pv_config, grid_config, ess_config\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('config.json', 'r') as f:\n",
    "    js_data = json.load(f)\n",
    "\n",
    "data = pd.read_csv('combined_data.csv')\n",
    "time_interval = js_data[\"time_interval\"][\"numerator\"] / js_data[\"time_interval\"][\"denominator\"]\n",
    "\n",
    "pv_loss = js_data[\"pv\"][\"loss\"]\n",
    "pv_cost_per_kW = js_data[\"pv\"][\"cost_per_kW\"]\n",
    "pv_lifetime = js_data[\"pv\"][\"lifetime\"]\n",
    "\n",
    "ess_loss = js_data[\"ess\"][\"loss\"]\n",
    "ess_cost_per_kW = js_data[\"ess\"][\"cost_per_kW\"]\n",
    "ess_lifetime = js_data[\"ess\"][\"lifetime\"]\n",
    "\n",
    "grid_loss = js_data[\"grid\"][\"loss\"]\n",
    "sell_price = js_data[\"grid\"][\"sell_price\"] #kWh\n",
    "grid_capacity = js_data[\"grid\"][\"capacity\"] #kWh\n",
    "\n",
    "pv_begin = js_data[\"pv_capacities\"][\"begin\"]\n",
    "pv_end = js_data[\"pv_capacities\"][\"end\"]\n",
    "pv_groups = js_data[\"pv_capacities\"][\"groups\"]\n",
    "\n",
    "ess_begin = js_data[\"ess_capacities\"][\"begin\"]\n",
    "ess_end = js_data[\"ess_capacities\"][\"end\"]\n",
    "ess_groups = js_data[\"ess_capacities\"][\"groups\"]\n",
    "\n",
    "annot_unmet = js_data[\"annotated\"][\"unmet_prob\"]\n",
    "annot_benefit = js_data[\"annotated\"][\"benefit\"]\n",
    "annot_cost = js_data[\"annotated\"][\"cost\"]\n",
    "\n",
    "title_unmet = js_data[\"plot_title\"][\"unmet_prob\"]\n",
    "title_cost = js_data[\"plot_title\"][\"cost\"]\n",
    "title_benefit = js_data[\"plot_title\"][\"benefit\"]\n",
    "\n",
    "figure_size = (js_data[\"figure_size\"][\"length\"], js_data[\"figure_size\"][\"height\"])\n",
    "\n",
    "pv_capacities = np.linspace(pv_begin, pv_end, pv_groups)\n",
    "ess_capacities = np.linspace(ess_begin, ess_end, ess_groups)\n",
    "results = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
    "affords = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
    "costs = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
    "overload_cnt = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "hour_demand = []\n",
    "for index, row in data.iterrows():\n",
    "    time = row['time']\n",
    "    demand = row['demand']\n",
    "    if time.endswith('00'):\n",
    "        hour_demand.append(demand)\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.plot(hour_demand)\n",
    "plt.ylabel('Demand Power / kW')\n",
    "plt.savefig('plots/demand.png')\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cal_profit(es: EnergySystem, saved_money):\n",
    "    profit = saved_money - es.ess.get_cost_per_year() - es.pv.get_cost_per_year()\n",
    "    return profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pv_capacity:5000.0\n",
      "ess_capacity:5000.0\n",
      "ess_capacity:27500.0\n",
      "ess_capacity:50000.0\n",
      "pv_capacity:27500.0\n",
      "ess_capacity:5000.0\n",
      "ess_capacity:27500.0\n",
      "ess_capacity:50000.0\n",
      "pv_capacity:50000.0\n",
      "ess_capacity:5000.0\n",
      "ess_capacity:27500.0\n",
      "ess_capacity:50000.0\n"
     ]
    }
   ],
   "source": [
    "for pv_capacity in pv_capacities:\n",
    "    print(f\"pv_capacity:{pv_capacity}\")\n",
    "    for ess_capacity in ess_capacities:\n",
    "        print(f\"ess_capacity:{ess_capacity}\")\n",
    "        pv = pv_config(capacity=pv_capacity, \n",
    "                        cost_per_kW=pv_cost_per_kW,\n",
    "                        lifetime=pv_lifetime, \n",
    "                        loss=pv_loss)\n",
    "        ess = ess_config(capacity=ess_capacity, \n",
    "                            cost_per_kW=ess_cost_per_kW, \n",
    "                            lifetime=ess_lifetime, \n",
    "                            loss=ess_loss,\n",
    "                            charge_power=ess_capacity,\n",
    "                            discharge_power=ess_capacity)\n",
    "        grid = grid_config(capacity=grid_capacity, \n",
    "                            grid_loss=grid_loss,\n",
    "                            sell_price= sell_price)\n",
    "        energySystem = EnergySystem(pv_type=pv, \n",
    "                                    ess_type=ess, \n",
    "                                    grid_type= grid)\n",
    "        benefit = energySystem.simulate(data, time_interval)\n",
    "        results.loc[pv_capacity,ess_capacity] = cal_profit(energySystem, benefit)\n",
    "        affords.loc[pv_capacity,ess_capacity] = energySystem.afford\n",
    "        overload_cnt.loc[pv_capacity,ess_capacity] = energySystem.overload_cnt\n",
    "        costs.loc[pv_capacity,ess_capacity] = energySystem.ess.capacity * energySystem.ess.cost_per_kW + energySystem.pv.capacity * energySystem.pv.cost_per_kW\n",
    "        pv_generated = energySystem.day_generated\n",
    "        ess_generated = energySystem.hour_stored\n",
    "        ess_generated_2 = energySystem.hour_stored_2\n",
    "        plt.figure(figsize=(10,8));\n",
    "        plt.plot(ess_generated)\n",
    "        plt.xlabel('day #')\n",
    "        plt.ylabel('SoC %')\n",
    "        plt.title(f'14:00 ESS SoC \\n PV cap:{pv_capacity}, ESS cap:{ess_capacity}')\n",
    "        plt.savefig(f'plots/ess/1400-{pv_capacity}-{ess_capacity}.png')\n",
    "        plt.close()\n",
    "        plt.figure(figsize=(10,8));\n",
    "        plt.plot(ess_generated_2)\n",
    "        plt.xlabel('day #')\n",
    "        plt.ylabel('SoC%')\n",
    "        plt.title(f'08:00 ESS SoC \\n PV cap:{pv_capacity}, ESS cap:{ess_capacity}')\n",
    "        plt.savefig(f'plots/ess/0800-{pv_capacity}-{ess_capacity}.png')\n",
    "        plt.close()\n",
    "        # print(energySystem.unmet)\n",
    "        spring_week_start = energySystem.season_start\n",
    "        spring_week_end = spring_week_start + energySystem.week_length\n",
    "        # summer_week_start = energySystem.season_start + 1 * energySystem.season_step\n",
    "        # summer_week_end = summer_week_start + energySystem.week_length\n",
    "        # autumn_week_start = energySystem.season_start + 2 * energySystem.season_step\n",
    "        # autumn_week_end = autumn_week_start + energySystem.week_length\n",
    "        # winter_week_start = energySystem.season_start + 3 * energySystem.season_step\n",
    "        # winter_week_end = winter_week_start+ energySystem.week_length\n",
    "\n",
    "        spring_consume_data = []\n",
    "        # summer_consume_data = []\n",
    "        # autumn_consume_data = []\n",
    "        # winter_consume_data = []\n",
    "        for index, row in data.iterrows():\n",
    "            if index in range(spring_week_start, spring_week_end):\n",
    "                spring_consume_data.append(row['demand'])\n",
    "        #     elif index in range(summer_week_start, summer_week_end):\n",
    "        #         summer_consume_data.append(row['demand'])\n",
    "        #     elif index in range(autumn_week_start, autumn_week_end):\n",
    "        #         autumn_consume_data.append(row['demand'])\n",
    "        #     elif index in range(winter_week_start, winter_week_end):\n",
    "        #         winter_consume_data.append(row['demand'])\n",
    "\n",
    "        spring_week_time = list(range(spring_week_start, spring_week_end))\n",
    "        # summer_week_time = list(range(summer_week_start, summer_week_end))\n",
    "        # autumn_week_time = list(range(autumn_week_start, autumn_week_end))\n",
    "        # winter_week_time = list(range(winter_week_start, winter_week_end))\n",
    "\n",
    "        spring_pv_generated = energySystem.spring_week_gen\n",
    "        # summer_pv_generated = energySystem.summer_week_gen\n",
    "        # autumn_pv_generated = energySystem.autumn_week_gen\n",
    "        # winter_pv_generated = energySystem.winter_week_gen\n",
    "\n",
    "        # spring_soc = energySystem.spring_week_soc\n",
    "        # summer_soc = energySystem.summer_week_soc\n",
    "        # autumn_soc = energySystem.autumn_week_soc\n",
    "        # winter_soc = energySystem.winter_week_soc\n",
    "\n",
    "\n",
    "        # fig, ax1 = plt.subplots()\n",
    "\n",
    "        plt.plot(spring_week_time, spring_pv_generated, label = 'pv generation')\n",
    "        plt.plot(spring_week_time, spring_consume_data, label = 'factory consume')\n",
    "        plt.ylabel('Power / kW')\n",
    "        plt.xlabel('15 min #')\n",
    "        plt.title(f'ess: {energySystem.ess.capacity/1000 } MWh pv: {energySystem.pv.capacity/1000 } MW spring week generate condition')\n",
    "        plt.legend()\n",
    "        plt.savefig(f'plots/{energySystem.ess.capacity}-{energySystem.pv.capacity}-spring.png')\n",
    "        plt.close()\n",
    "\n",
    "        # plt.plot(summer_week_time, summer_pv_generated, label = 'pv generation')\n",
    "        # plt.plot(summer_week_time, summer_consume_data, label = 'factory consume')\n",
    "        # plt.ylabel('Power / kW')\n",
    "        # plt.xlabel('15 min #')\n",
    "        # plt.title(f'ess: {energySystem.ess.capacity/1000 } MWh pv: {energySystem.pv.capacity/1000 } MW summer week generate condition')\n",
    "        # plt.legend()\n",
    "        # plt.savefig(f'plots/{energySystem.ess.capacity}-{energySystem.pv.capacity}-summer.png')\n",
    "        # plt.close()\n",
    "\n",
    "        # plt.plot(autumn_week_time, autumn_pv_generated, label = 'pv generation')\n",
    "        # plt.plot(autumn_week_time, autumn_consume_data, label = 'factory consume')\n",
    "        # plt.ylabel('Power / kW')\n",
    "        # plt.xlabel('15 min #')\n",
    "        # plt.title(f'ess: {energySystem.ess.capacity/1000 } MWh pv: {energySystem.pv.capacity/1000 } MW autumn week generate condition')\n",
    "        # plt.legend()\n",
    "        # plt.savefig(f'plots/{energySystem.ess.capacity}-{energySystem.pv.capacity}-autumn.png')\n",
    "        # plt.close()\n",
    "\n",
    "        # plt.plot(winter_week_time, winter_pv_generated, label = 'pv generation')\n",
    "        # plt.plot(winter_week_time, winter_consume_data, label = 'factory consume')\n",
    "        # plt.ylabel('Power / kW')\n",
    "        # plt.xlabel('15 min #')\n",
    "        # plt.title(f'ess: {energySystem.ess.capacity/1000 } MWh pv: {energySystem.pv.capacity/1000 } MW winter week generate condition')\n",
    "        # plt.legend()\n",
    "        # plt.savefig(f'plots/{energySystem.ess.capacity}-{energySystem.pv.capacity}-winter.png')\n",
    "        # plt.close()\n",
    "\n",
    "    plt.figure();\n",
    "    plt.plot(pv_generated)\n",
    "    plt.xlabel('day #')\n",
    "    plt.ylabel('Electricity kWh')\n",
    "    plt.title(f'PV generated pv cap:{pv_capacity}, ess cap:{ess_capacity}')\n",
    "    plt.savefig(f'plots/pv/{pv_capacity}-{ess_capacity}.png')\n",
    "    plt.close()\n",
    "\n",
    "\n",
    "        # plt.show()\n",
    "\n",
    "\n",
    "        \n",
    "\n",
    "# results = results.astype(float)\n",
    "\n",
    "\n",
    "# pv = pv_config(capacity=100000,cost_per_kW=200,lifetime=25,loss=0.95)\n",
    "# ess = ess_config(capacity=100000,cost_per_kW=300,lifetime=25,loss=0.95,charge_power=100000,discharge_power=100000)\n",
    "# grid = grid_config(price_schedule=price_schedule, capacity=5000, grid_loss=0.95, sell_price=0.4)\n",
    "# grid = grid_config(capacity=50000, grid_loss=0.95, sell_price=0.4)\n",
    "\n",
    "\n",
    "    # print(benefit)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       " (3100, '07:00', 12533.22, 0.0),\n",
       " ...]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energySystem.unmet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2000x1800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as ticker\n",
    "df=results\n",
    "df = df.astype(float)\n",
    "df.index = df.index / 1000\n",
    "df.columns = df.columns / 1000\n",
    "min_value = df.min().min()\n",
    "max_value = df.max().max()\n",
    "max_scale = max(abs(min_value/1000), abs(max_value/1000))\n",
    "plt.figure(figsize=figure_size)\n",
    "cmap = sns.color_palette(\"coolwarm\", as_cmap=True)\n",
    "ax = sns.heatmap(df/1000, fmt=\".1f\", cmap=cmap, vmin=-max_scale, vmax=max_scale, annot=annot_benefit)\n",
    "ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f\"{x:.2f}\"))\n",
    "# ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%.1f'))\n",
    "plt.title(title_benefit)\n",
    "plt.gca().invert_yaxis()\n",
    "plt.xlabel('ESS Capacity (MWh)')\n",
    "plt.ylabel('PV Capacity (MW)')\n",
    "plt.savefig('plots/benefit.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABdIAAAW/CAYAAAConAEjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAACkbklEQVR4nOzdd5hdZbk34N9OhkxCSIN0ShqHGooiTVpCCxylCShFQ+giRZogHiVwUCKgyAEVjgKhowIGEBSkhCDSm0gLCSTkAOmkkJA++/uDj9EhyWIGZthhct/XtS6y37XWu581ezD4m2fet1Qul8sBAAAAAACWqUWlCwAAAAAAgBWZIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQDgE3jqqafy5S9/OW3btk2pVMrzzz/f4Dl69+6dr371q41fHM1GqVTKOeecU/v6mmuuSalUyvjx42vHBgwYkAEDBtS+Hj9+fEqlUq655prPrE4AAGjuBOkAQKN4/fXXc+yxx6Zv375p3bp12rdvn+222y7/8z//k3nz5jX6+73//vs555xz8tBDDzX63B9n0aJFOfDAA/Puu+/mF7/4Ra6//vr06tVrmde+/PLLOeecc+oEn5+1AQMGpFQq1R6rr756ttxyy1x99dWpqanJokWL0rlz52y//fbLnaNcLmfttdfOF7/4xcL3mjNnToYOHZr+/funbdu2WWONNbL55pvnu9/9bt55553GfrQkyfnnn5/bb7+9SeZuqMsuuywdOnTIokWLKl1KRQwZMqTO99q/H61bt650eQAA8IlVVboAAODz7+67786BBx6Y6urqDB48OP3798/ChQvzyCOP5Hvf+15eeuml/OY3v2nU93z//fdz7rnnJkmdbtzPwuuvv54333wzv/3tb3PUUUcVXvvyyy/n3HPPzYABA9K7d+/PpsBlWGuttTJs2LAkydSpU3PdddflyCOPzGuvvZaf/vSnOfDAA/O///u/efPNN5f5Q4GHH344b731Vk455ZTlvseiRYuy44475tVXX81hhx2WE088MXPmzMlLL72Um266Kfvtt1969uzZ6M92/vnn54ADDsi+++7b6HM31N13353dd989q6yySqPMN2/evFRVNew/2Xv16pV58+Y1Wg0NVV1dnSuvvHKp8ZYtW1agGgAAaByCdADgUxk3blwOOuig9OrVKw8++GB69OhRe+7444/P2LFjc/fdd1ewwsY3ZcqUJEnHjh0rW0gDdOjQId/85jdrXx977LFZf/3188tf/jLnnXdeDj300FxxxRW5+eab8/3vf3+p+2+66aa0aNEiBx100HLf4/bbb89zzz2XG2+8MYccckidc/Pnz8/ChQsb74FWQO+//35GjRqVyy+/vNHm/CRd3JXu/q6qqqrzvdbYyuVy5s+fnzZt2jTZewAAwEdZ2gUA+FQuvPDCzJkzJ1dddVWdEP1D6667br773e/Wvl68eHHOO++89OvXL9XV1endu3d+8IMfZMGCBXXue/rppzNo0KB07tw5bdq0SZ8+fXLEEUck+WAN6C5duiRJzj333NqlIz5cS3rSpEk5/PDDs9Zaa6W6ujo9evTIPvvsU6/lVR588MHssMMOadu2bTp27Jh99tknr7zySu35IUOGZKeddkqSHHjggSmVSsvtiL/mmmty4IEHJkkGDhxYW+dHl6N55JFHstVWW6V169bp27dvrrvuuqXmmjlzZk4++eSsvfbaqa6uzrrrrpsLLrggNTU1H/tMy7Lqqqtmm222ydy5czN16tRst9126d27d2666aalrl20aFFuvfXWDBw4sLCj/PXXX0+SbLfddkud+3C5nyQZPnx4SqVSnnvuuaWuO//889OyZcu8/fbbSZIxY8Zk//33T/fu3dO6deustdZaOeiggzJr1qwkH4TGc+fOzbXXXlv79R0yZEjtfG+//XaOOOKIdOvWLdXV1dl4441z9dVX13nPhx56KKVSKX/4wx9y7rnnZs0110y7du1ywAEHZNasWVmwYEFOPvnkdO3aNauttloOP/zwpb5fk+SBBx7IggULsueeeyb513rmjzzySE466aR06dIlHTt2zLHHHpuFCxdm5syZGTx4cDp16pROnTrljDPOSLlcrjPnR9dIr4/lrZH+cd/bSXLOOeekVCpl7NixGTJkSDp27JgOHTrk8MMPz/vvv9+gOop8+D4ftaw14D/cS+Dee+/Nl770pbRp0yb/+7//22i1AABAfehIBwA+lT/96U/p27dvvvzlL9fr+qOOOirXXnttDjjggJx22ml54oknMmzYsLzyyisZMWJEkg86vnffffd06dIl3//+99OxY8eMHz8+f/zjH5MkXbp0yeWXX57jjjsu++23X772ta8lSTbddNMkyf7775+XXnopJ554Ynr37p0pU6bkvvvuy4QJEwqXV7n//vuz5557pm/fvjnnnHMyb968XHbZZdluu+3y7LPPpnfv3jn22GOz5ppr5vzzz89JJ52ULbfcMt26dVvmfDvuuGNOOumkXHrppfnBD36QDTfcMElq/5kkY8eOzQEHHJAjjzwyhx12WK6++uoMGTIkW2yxRTbeeOMkH3Q677TTTnn77bdz7LHHZp111smjjz6as846KxMnTswll1xSr6/9R73xxhtp2bJlOnbsmFKplEMOOSTnn39+Xnrppdr3TpJ77rkn7777bg499NDC+T5cEua6667LD3/4w2UGpUlywAEH5Pjjj8+NN96YL3zhC3XO3XjjjRkwYEDWXHPNLFy4MIMGDcqCBQty4oknpnv37nn77bdz1113ZebMmenQoUOuv/76HHXUUdlqq61yzDHHJEn69euXJJk8eXK22WablEqlnHDCCenSpUv+8pe/5Mgjj8zs2bNz8skn13nvYcOGpU2bNvn+97+fsWPH5rLLLssqq6ySFi1aZMaMGTnnnHPy+OOP55prrkmfPn1y9tln17n/z3/+c7bYYoulvh8+rP3cc8/N448/nt/85jfp2LFjHn300ayzzjo5//zz8+c//zkXXXRR+vfvn8GDBxd+nT+J+nxv/7uvf/3r6dOnT4YNG5Znn302V155Zbp27ZoLLrigXu83bdq0pcZatWpV+8OUhho9enQOPvjgHHvssTn66KOz/vrrf6J5AADgEysDAHxCs2bNKicp77PPPvW6/vnnny8nKR911FF1xk8//fRykvKDDz5YLpfL5REjRpSTlJ966qnlzjV16tRykvLQoUPrjM+YMaOcpHzRRRc16FnK5XJ58803L3ft2rU8ffr02rF//OMf5RYtWpQHDx5cOzZy5MhykvItt9zysXPecsst5STlkSNHLnWuV69e5STlhx9+uHZsypQp5erq6vJpp51WO3beeeeV27ZtW37ttdfq3P/973+/3LJly/KECRMKa9hpp53KG2ywQXnq1KnlqVOnll955ZXySSedVE5S3muvvWqve+mll8pJymeddVad+w866KBy69aty7NmzSp8n/fff7+8/vrrl5OUe/XqVR4yZEj5qquuKk+ePHmpaw8++OByz549y0uWLKkde/bZZ8tJysOHDy+Xy+Xyc889V6+vc9u2bcuHHXbYUuNHHnlkuUePHuVp06Yt9TwdOnQov//+++Vy+V+fZ//+/csLFy6sU2OpVCrvueeede7fdttty7169Vrq/dZZZ50634/Dhw8vJykPGjSoXFNTU+f+UqlU/va3v107tnjx4vJaa61V3mmnnerM+dHv8Q/nHDduXO3YTjvtVOe+cePG1fk6lsv1/94eOnRoOUn5iCOOqFPHfvvtV15jjTWWeuaPOuyww8pJlnkMGjRoqff5qGU934f/ntxzzz0f+/4AANBULO0CAHxis2fPTpK0a9euXtf/+c9/TpKceuqpdcZPO+20JKldS/3DtcfvuuuuLFq0qEE1tWnTJq1atcpDDz2UGTNm1Pu+iRMn5vnnn8+QIUOy+uqr145vuumm2W233Wprb2wbbbRRdthhh9rXXbp0yfrrr5833ni
      "text/plain": [
       "<Figure size 2000x1800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df = costs\n",
    "df = df.astype(int)\n",
    "df.index = df.index / 1000\n",
    "df.columns = df.columns / 1000\n",
    "\n",
    "plt.figure(figsize=figure_size)\n",
    "sns.heatmap(df/1000000,  fmt=\".1f\", cmap='viridis', annot=annot_cost)\n",
    "plt.title(title_cost)\n",
    "plt.gca().invert_yaxis()\n",
    "plt.xlabel('ESS Capacity (MWh)')\n",
    "plt.ylabel('PV Capacity (MW)')\n",
    "plt.savefig('plots/costs.png')\n",
    "\n",
    "    # pv = pv_config(capacity=100000,cost_per_kW=200,lifetime=25,loss=0.95)\n",
    "    # ess = ess_config(capacity=100000,cost_per_kW=300,lifetime=25,loss=0.95,charge_power=100000,discharge_power=100000)\n",
    "    # grid = grid_config(price_schedule=price_schedule, capacity=5000, grid_loss=0.95, sell_price=0.4)\n",
    "    # grid = grid_config(capacity=50000, grid_loss=0.95, sell_price=0.4)\n",
    "\n",
    "\n",
    "    # print(benefit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2000x1800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "df = overload_cnt\n",
    "df = df.astype(int)\n",
    "df.index = df.index / 1000\n",
    "df.columns = df.columns / 1000\n",
    "min_value = df.min().min()\n",
    "max_value = df.max().max()\n",
    "max_scale = max(abs(min_value/1000), abs(max_value/1000))\n",
    "\n",
    "plt.figure(figsize=figure_size)\n",
    "cmap = LinearSegmentedColormap.from_list(\"\", [\"white\", \"blue\"])\n",
    "ax = sns.heatmap(df/(4*24*365), fmt=\".00%\", cmap=cmap, vmin=0, vmax=1, annot=annot_unmet)\n",
    "cbar = ax.collections[0].colorbar\n",
    "cbar.set_ticks([0, 0.25, 0.5, 0.75, 1])\n",
    "cbar.set_ticklabels(['0%', '25%', '50%', '75%', '100%'])\n",
    "cbar.ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'{x:.0%}'))\n",
    "\n",
    "plt.title(title_unmet)\n",
    "plt.gca().invert_yaxis()\n",
    "plt.xlabel('ESS Capacity (MWh)')\n",
    "plt.ylabel('PV Capacity (MW)')\n",
    "plt.savefig('plots/unmet.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}