{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import shutil\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import seaborn as sns\n",
    "import json\n",
    "from matplotlib.colors import LinearSegmentedColormap\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": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "print(\"Version 0.0.4\")\n",
    "\n",
    "with open('config.json', 'r') as f:\n",
    "    js_data = json.load(f)\n",
    "\n",
    "\n",
    "    \n",
    "\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",
    "data = pd.read_csv('combined_data.csv')\n",
    "\n",
    "granularity = js_data[\"time_interval\"][\"numerator\"]\n",
    "\n",
    "months_days = [31,28,31,30,31,30,31,31,30,31,30,31]\n",
    "def get_month_coe(num, granularity):\n",
    "    return 60 / granularity * 24 * months_days[num]\n",
    "\n",
    "months_index = [get_month_coe(num, granularity) for num in range(12)]\n",
    "months_data = []\n",
    "for i in range(1,12):\n",
    "    months_index[i] += months_index[i-1]\n",
    "for i in range(12):\n",
    "    start = 0 if i == 0 else months_index[i-1]\n",
    "    end = months_index[i]\n",
    "    months_data.append(data.iloc[int(start):int(end)])\n",
    "    \n",
    "\n",
    "\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": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_results(results, filename, title_benefit, annot_benefit=False, figure_size=(10, 10)):\n",
    "    df=results\n",
    "    df = df.astype(float)\n",
    "    df.index = df.index / 1000\n",
    "    df.index = df.index.map(int)\n",
    "    df.columns = df.columns / 1000\n",
    "    df.columns = df.columns.map(int)\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",
    "    df[df.columns[-1] + 1] = df.iloc[:, -1] \n",
    "    new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns)\n",
    "    for i in df.columns:\n",
    "        new_Data[i] = df[i].iloc[-1]\n",
    "    df = pd.concat([df, new_Data])\n",
    "\n",
    "    X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))\n",
    "\n",
    "    def fmt(x,pos):\n",
    "        return '{:.0f}'.format(x/1000)\n",
    "\n",
    "    cmap = sns.color_palette(\"coolwarm\", as_cmap=True)\n",
    "    plt.figure(figsize=figure_size)\n",
    "    ax = sns.heatmap(df/1000, fmt=\".1f\", cmap=cmap, vmin=-max_scale, vmax=max_scale, annot=annot_benefit)\n",
    "    CS = ax.contour(X, Y, df,  colors='black', alpha=0.5)\n",
    "    ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt))\n",
    "    plt.title(title_benefit)\n",
    "    plt.gca().invert_yaxis()\n",
    "    plt.xlim(0, df.shape[1] - 1)\n",
    "    plt.ylim(0, df.shape[0] - 1)\n",
    "    plt.xlabel('ESS Capacity (MWh)')\n",
    "    plt.ylabel('PV Capacity (MW)')\n",
    "    plt.savefig(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_cost(costs, filename, title_cost, annot_cost=False, figure_size=(10, 10)):\n",
    "    df = costs\n",
    "    df = df.astype(int)\n",
    "    df.index = df.index / 1000\n",
    "    df.index = df.index.map(int)\n",
    "    df.columns = df.columns / 1000\n",
    "    df.columns = df.columns.map(int)\n",
    "\n",
    "    df[df.columns[-1] + 1] = df.iloc[:, -1] \n",
    "    new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns)\n",
    "    for i in df.columns:\n",
    "        new_Data[i] = df[i].iloc[-1]\n",
    "    df = pd.concat([df, new_Data])\n",
    "    X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))\n",
    "\n",
    "    def fmt(x, pos):\n",
    "        return '{:.0f}'.format(x / 1000000)\n",
    "\n",
    "    plt.figure(figsize=figure_size)\n",
    "    ax = sns.heatmap(df/1000000,  fmt=\".1f\", cmap='viridis', annot=annot_cost)\n",
    "    CS = ax.contour(X, Y, df,  colors='black', alpha=0.5)\n",
    "    ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt))\n",
    "    plt.title(title_cost)\n",
    "    plt.gca().invert_yaxis()\n",
    "    plt.xlim(0, df.shape[1] - 1)\n",
    "    plt.ylim(0, df.shape[0] - 1)\n",
    "    plt.xlabel('ESS Capacity (MWh)')\n",
    "    plt.ylabel('PV Capacity (MW)')\n",
    "    plt.savefig(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_overload(overload_cnt, filename, title_unmet, annot_unmet=False, figure_size=(10, 10), days=365, granularity=15):\n",
    "    df = overload_cnt\n",
    "    print(days, granularity)\n",
    "    coef = 60 / granularity * days * 24\n",
    "    print(coef)\n",
    "    print(df)\n",
    "    df = ( coef - df) / coef\n",
    "    print(df)\n",
    "\n",
    "    df = df.astype(float)\n",
    "    df.index = df.index / 1000\n",
    "    df.index = df.index.map(int)\n",
    "    df.columns = df.columns / 1000\n",
    "    df.columns = df.columns.map(int)\n",
    "\n",
    "\n",
    "    df[df.columns[-1] + 1] = df.iloc[:, -1] \n",
    "    new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns)\n",
    "    for i in df.columns:\n",
    "        new_Data[i] = df[i].iloc[-1]\n",
    "    # print(new_Data)\n",
    "    df = pd.concat([df, new_Data])\n",
    "\n",
    "\n",
    "    plt.figure(figsize=figure_size)\n",
    "    cmap = LinearSegmentedColormap.from_list(\"\", [\"white\", \"blue\"])\n",
    "    ax = sns.heatmap(df, fmt=\".00%\", cmap=cmap, vmin=0, vmax=1, annot=annot_unmet)\n",
    "\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",
    "    X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))\n",
    "\n",
    "    def fmt(x, pos):\n",
    "        return '{:.0f}%'.format(x * 100)\n",
    "    CS = ax.contour(X, Y, df,  colors='black', alpha=0.5)\n",
    "\n",
    "    ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt))\n",
    "\n",
    "    plt.xlim(0, df.shape[1] - 1)\n",
    "    plt.ylim(0, df.shape[0] - 1)\n",
    "    plt.title(title_unmet)\n",
    "    plt.xlabel('ESS Capacity (MWh)')\n",
    "    plt.ylabel('PV Capacity (MW)')\n",
    "    plt.savefig(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cal_profit(es: EnergySystem, saved_money, days):\n",
    "    profit = saved_money - es.ess.get_cost_per_year() / 365 * days - es.pv.get_cost_per_year() / 365 * days\n",
    "    return profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_data(pv_capacity, pv_cost_per_kW, pv_lifetime, pv_loss, ess_capacity, ess_cost_per_kW, ess_lifetime, ess_loss, grid_capacity, grid_loss, sell_price,time_interval, data, days):\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 = cal_profit(energySystem, benefit, days)\n",
    "    overload_cnt = energySystem.overload_cnt\n",
    "    costs = energySystem.ess.capacity * energySystem.ess.cost_per_kW + energySystem.pv.capacity * energySystem.pv.cost_per_kW\n",
    "    return (results, overload_cnt, costs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "months_results = []\n",
    "months_costs = []\n",
    "months_overload = []\n",
    "for index, month_data in enumerate(months_data):\n",
    "    results = 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",
    "    for pv_capacity in pv_capacities:\n",
    "        for ess_capacity in ess_capacities:\n",
    "            (result, overload, cost) = generate_data(pv_capacity=pv_capacity,pv_cost_per_kW=pv_cost_per_kW, pv_lifetime=pv_lifetime, pv_loss=pv_loss, ess_capacity=ess_capacity, ess_cost_per_kW=ess_cost_per_kW, ess_lifetime=ess_lifetime, ess_loss=ess_loss, grid_capacity=grid_capacity, grid_loss=grid_loss, sell_price=sell_price, time_interval=time_interval, data=month_data, days=months_days[index])\n",
    "            results.loc[pv_capacity,ess_capacity] = result\n",
    "            overload_cnt.loc[pv_capacity,ess_capacity] = overload\n",
    "            costs.loc[pv_capacity,ess_capacity] = cost\n",
    "    months_results.append(results)\n",
    "    months_costs.append(costs)\n",
    "    months_overload.append(overload_cnt)\n",
    "    draw_results(results=results, \n",
    "                    filename=f'plots/pv-{pv_capacity}-ess-{ess_capacity}-{index}-benefit.png',\n",
    "                    title_benefit=title_benefit,\n",
    "                    annot_benefit=annot_benefit,\n",
    "                    figure_size=figure_size)\n",
    "    draw_overload(overload_cnt=overload_cnt, \n",
    "                    filename=f'plots/pv-{pv_capacity}-ess-{ess_capacity}-{index}-unmet.png',\n",
    "                    title_unmet=title_unmet,\n",
    "                    annot_unmet=annot_unmet,\n",
    "                    figure_size=figure_size,\n",
    "                    days=months_days[index],\n",
    "                    granularity=granularity)\n",
    "\n",
    "annual_result = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
    "annual_costs = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
    "annual_overload = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
    "\n",
    "# get the yearly results\n",
    "for pv_capacity in pv_capacities:\n",
    "    for ess_capacity in ess_capacities:\n",
    "        results = 0\n",
    "        costs = 0\n",
    "        overload_cnt = 0\n",
    "        for index, month_data in enumerate(months_data):\n",
    "            results += months_results[index].loc[pv_capacity,ess_capacity]\n",
    "            costs += months_costs[index].loc[pv_capacity,ess_capacity]\n",
    "            overload_cnt += months_overload[index].loc[pv_capacity, ess_capacity]\n",
    "        annual_result.loc[pv_capacity, ess_capacity] = results\n",
    "        annual_costs.loc[pv_capacity, ess_capacity] = costs\n",
    "        annual_overload.loc[pv_capacity, ess_capacity] = overload_cnt\n",
    "\n",
    "draw_cost(costs=annual_costs,\n",
    "          filename='plots/annual_cost.png',\n",
    "        title_cost=title_cost,\n",
    "        annot_cost=annot_cost,\n",
    "        figure_size=figure_size)\n",
    "draw_results(results=annual_result,\n",
    "                filename='plots/annual_benefit.png',\n",
    "                title_benefit=title_benefit,\n",
    "                annot_benefit=annot_benefit,\n",
    "                figure_size=figure_size)\n",
    "draw_overload(overload_cnt=annual_overload,\n",
    "                filename='plots/annual_unmet.png',\n",
    "                title_unmet=title_unmet,\n",
    "                annot_unmet=annot_unmet,\n",
    "                figure_size=figure_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_data(data, filename):\n",
    "    data.to_csv(filename+'.csv')\n",
    "    data.to_json(filename + '.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isdir('data'):\n",
    "    os.makedirs('data')\n",
    "\n",
    "save_data(results, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-results')\n",
    "save_data(costs, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-costs')\n",
    "save_data(overload_cnt, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-overload_cnt')"
   ]
  }
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