ethylene 20 scenarios.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6bef64ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import cvxpy as cp\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "965f668a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Cost(C_i, i=0.04/12, periods=20*12): # i = interest rate per month, periods = payback periods (months), can change timescale to not be months\n",
    "    fv = C_i*((1+i)**periods) #future value calculation\n",
    "    cost = fv*(i/(((1+i)**periods) - 1))*(1/(1+i)) # monthly payment\n",
    "    return cost\n",
    "\n",
    "def GridCost(cg, mon, t, cost, c_fix=0):\n",
    "    if (mon==\"june\"):\n",
    "        df = pd.read_excel ('houston pricing.xlsx', sheet_name='Jun')\n",
    "        if (cost==\"fixed\"):\n",
    "            for n in range (0, t):\n",
    "                cg = cg + [c_fix*(P_g[n])] \n",
    "        elif (cost==\"wholesale\"):\n",
    "            for n in range (0, t):\n",
    "                cg = cg + [df.at[n, \"Settlement Point Price\"]*(P_g[n])]\n",
    "        else:\n",
    "            print('invalid cost input. must be \"fixed\" or \"wholesale\"')\n",
    "            return 0\n",
    "    elif (mon==\"december\"):\n",
    "        if (cost==\"fixed\"):\n",
    "            for n in range (0, t):\n",
    "                cg = cg + [c_fix*(P_g[n])] \n",
    "        elif (cost==\"wholesale\"):\n",
    "            df = pd.read_excel ('houston pricing.xlsx', sheet_name='Dec')\n",
    "            for n in range (0, t):\n",
    "                cg = cg + [df.at[n, \"Settlement Point Price\"]*(P_g[n])]\n",
    "        else:\n",
    "            print('invalid cost input. must be \"fixed\" or \"wholesale\"')\n",
    "            return 0\n",
    "    else:\n",
    "        print('invalid month input. must be \"june\" or \"december\"')\n",
    "        return 0\n",
    "    return cg\n",
    "    \n",
    "def Loads(pl, t):\n",
    "    df_loads = pd.read_excel(r'ethylene 14.xlsx')\n",
    "    for hour in range(t):\n",
    "        pl += [df_loads.at[hour, 'P_L_s']]\n",
    "    return pl\n",
    "\n",
    "def Solar(ps, mon):\n",
    "    if (mon==\"june\"):\n",
    "        df_solar = pd.read_excel('pvwatts_hourly_Houston_20000kwh.xlsx', sheet_name='j')\n",
    "        for hour in range(t):\n",
    "            ps += [df_solar.at[hour, \"ACMW\"]] \n",
    "    elif (mon==\"december\"):\n",
    "        df_solar = pd.read_excel('pvwatts_hourly_Houston_20000kwh.xlsx', sheet_name='d')\n",
    "        for hour in range(t):\n",
    "            ps += [df_solar.at[hour, \"ACMW\"]] \n",
    "    else:\n",
    "        print('invalid month input. must be \"june\" or \"december\"')\n",
    "        return 0\n",
    "    return ps\n",
    "\n",
    "def SellPrice(month, sp):\n",
    "    if (month==\"june\"):\n",
    "        df = pd.read_excel ('Mont Belvieu Ethylene Prices.xlsx', sheet_name='june')\n",
    "        for n in range (0, 30):\n",
    "            temp = [df.at[n, \"$/ton\"]] # cost per Mwh of solar\n",
    "            for m in range (0, 24):\n",
    "                sp += temp\n",
    "    elif (month==\"december\"):\n",
    "        df = pd.read_excel ('Mont Belvieu Ethylene Prices.xlsx', sheet_name='december')\n",
    "        for n in range (0, 31):\n",
    "            temp = [df.at[n, \"$/ton\"]] # cost per Mwh of solar\n",
    "            for m in range (0, 24):\n",
    "                sp += [temp]\n",
    "    else:\n",
    "        print(\"invalid month input\")\n",
    "        return 0\n",
    "    return sp\n",
    "\n",
    "def BuyPrice(month, bp):\n",
    "    if (month==\"june\"):\n",
    "        df = pd.read_excel ('Mont Belvieu Ethylene Prices.xlsx', sheet_name='june')\n",
    "        for n in range (0, 30):\n",
    "            temp = [df.at[n, \"$/ton\"]] # cost per Mwh of solar\n",
    "            for m in range (0, 24):\n",
    "                bp += temp\n",
    "    elif (month==\"december\"):\n",
    "        df = pd.read_excel ('Mont Belvieu Ethylene Prices.xlsx', sheet_name='december')\n",
    "        for n in range (0, 31):\n",
    "            temp = [df.at[n, \"$/ton\"]] # cost per Mwh of solar\n",
    "            for m in range (0, 24):\n",
    "                bp += [temp]\n",
    "    else:\n",
    "        print(\"invalid month input\")\n",
    "        return 0\n",
    "    return bp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa84a30f",
   "metadata": {},
   "source": [
    "### Scenario 1: June Fixed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d40fd04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "status: optimal\n",
      "Battery Capacity [MW]:  2.313182706545022e-07\n",
      "Minimized Cost: $ 3053566.9334807172\n",
      "Cost of Battery [$]:  0.00019978333575606456\n",
      "Solar Size [MW]:  -1.37567484746189e-09\n",
      "Cost of Bought Ethylene [tons]:  51636331.706797786\n",
      "Cost of Sold Ethylene [tons]:  54427184.77357586\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "P_ramp = 20*60 # ramp values # ~20 MW/min for gas, coal would be ~2 MW/min\n",
    "eff = 0.9 # battery efficiency\n",
    "t = 720 # one month duration (hours)\n",
    "month = \"june\" # or \"december\"\n",
    "cost = \"fixed\" # or \"wholesale\"\n",
    "c_fix = 19.75 # june 19.75, december 21.19\n",
    "storage_limit = 250 \n",
    "outage = \"no\" # or \"yes\"\n",
    "# outage_duration = 4 # hours # if outage = yes\n",
    "# outage_start = 100 # hour # if outage = yes\n",
    "\n",
    "E_max = cp.Variable() # establishing battery capacity (MW)\n",
    "E_b = cp.Variable(t+1) # defining battery charge variable\n",
    "P_g = cp.Variable(t) # power drawn from the grid\n",
    "P_ch = cp.Variable(t) # battery charge\n",
    "P_dis = cp.Variable(t) # battery discharge\n",
    "S_max = cp.Variable() # size of the solar panels\n",
    "St = cp.Variable(t+1) # ethylene storage\n",
    "Sl = cp.Variable(t) # sold ethylene\n",
    "Pd = cp.Variable(t) # production \n",
    "\n",
    "C_s = S_max*Cost(1000000*0.89*20)\n",
    "C_b = E_max*(Cost(143000))\n",
    "P_L = [] # loads\n",
    "P_L = Loads(P_L, t)\n",
    "P_s = [] # power generated by solar\n",
    "P_s = Solar(P_s, month)\n",
    "C_g = [] # cost of power from the grid\n",
    "C_g = GridCost(C_g, month, t, cost, c_fix)\n",
    "S_p = [] # sell price\n",
    "S_p = SellPrice(month, S_p)\n",
    "B_p = []\n",
    "B_p = BuyPrice(month, B_p)\n",
    "\n",
    "constraints = []\n",
    "constraints += [P_g >= 0] # power draw is not negative \n",
    "constraints += [P_L == P_g + P_dis + -P_ch + P_s*S_max] # the sources sum to load requirements\n",
    "constraints += [E_b[0] == 0] # battery charge at start is zero\n",
    "constraints += [P_ch >= 0] # charge into the is not negative\n",
    "constraints += [1 >= eff] #efficiency cannot be greater than one\n",
    "constraints += [P_dis >= 0] # discharge from battery is not negative\n",
    "constraints += [S_max >= 0] # can't have negative solar capacity\n",
    "constraints += [S_max <= 1] # solar 20000 kW or less\n",
    "constraints += [P_dis >= 0] # discharge from battery is not negative\n",
    "# ramp value constraints - power change between time steps can't exceed ramp limit\n",
    "for n in range (0, t-1): \n",
    "    a = P_g[n]\n",
    "    b = P_g[n+1]\n",
    "    constraints += [(a - b) <= P_ramp, (a - b) >= -P_ramp] \n",
    "    n+1\n",
    "for n in range (0, t):\n",
    "    constraints += [E_max/4 >= P_ch[n]] # battery charge in one time step cannot be greater than the battery capacity\n",
    "    constraints += [E_max/4 >= P_dis[n]] # battery discharge in one time step cannot be greater than the battery capaticy\n",
    "    constraints += [St[0] == 0] # storage at time 0 = 0\n",
    "    constraints += [St[n] >= 0] # storage between 0 and limit\n",
    "    constraints += [storage_limit >= St[n]] # storage between 0 and limit\n",
    "    constraints += [Sl[n] >= 0] # selling ethylene not buying\n",
    "    constraints += [Sl[0] == 0] # can't sell in at hour 0\n",
    "    constraints += [storage_limit >= Sl[n]] # can't sell more than storage limit in a given timestep\n",
    "for n in range (1, t+1):\n",
    "    constraints += [E_b[n] == E_b[n-1] + eff*P_ch[n-1] - 1/eff*P_dis[n-1]] # battery effectiveness impacts charge/discharge amounts of battery\n",
    "    constraints += [St[n] == St[n-1] + Pd[n-1] - Sl[n-1]] # storage at next time = current + produced - sold\n",
    "for n in range (0, t+1):\n",
    "    constraints += [E_b[n] >= 0] # battery charge is not negative\n",
    "    constraints += [E_max >= E_b[n]] # battery charge cannot be greater than the capacity\n",
    "if (outage==\"yes\"):\n",
    "    for n in range (outage_start, outage_start + outage_duration):\n",
    "        constraints += [P_g[n] == 0]    \n",
    "        constraints += [Pd[n] >= 0]\n",
    "        constraints += [Pd[n] <= (len(P_L)/sum(P_L))*134.7*P_L[n]]\n",
    "    for n in range(0, outage_start):\n",
    "        constraints += [Pd[n] == (len(P_L)/sum(P_L))*134.7*P_L[n]]\n",
    "    for n in range(outage_start + outage_duration, t):\n",
    "        constraints += [Pd[n] == (len(P_L)/sum(P_L))*134.7*P_L[n]]\n",
    "elif (outage==\"no\"):\n",
    "    for n in range(0, t):\n",
    "        constraints += [Pd[n] == (len(P_L)/sum(P_L))*134.7*P_L[n]]\n",
    "        \n",
    "P_c = [] # bought ethylene including price\n",
    "S_l = [] # sold ethylene including price\n",
    "for n in range(t):\n",
    "    P_c += [Pd[n] * B_p[n] * 0.95] # currently using buy price is 95% of sell price, with real buy prices, use 100%\n",
    "    S_l += [Sl[n] * S_p[n]]\n",
    "    \n",
    "objective = sum(C_g) + C_b + C_s + sum(P_c) - sum(S_l)\n",
    "\n",
    "prob = cp.Problem(cp.Minimize(objective), constraints)\n",
    "prob.solve() \n",
    "\n",
    "#printing results\n",
    "print(\"status:\", prob.status)\n",
    "print(\"Battery Capacity [MW]: \", E_max.value)\n",
    "print(\"Minimized Cost: $\", prob.value)\n",
    "print(\"Cost of Battery [$]: \", C_b.value)\n",
    "print(\"Solar Size [MW]: \", S_max.value*20)\n",
    "print(\"Cost of Bought Ethylene [tons]: \", sum(P_c).value)\n",
    "print(\"Cost of Sold Ethylene [tons]: \", sum(S_l).value)\n",
    "\n",
    "# battery charge variable for plotting\n",
    "E_plot = []\n",
    "for n in range (0, t):\n",
    "    E_plot = E_plot + [E_b.value[n]]\n",
    "    \n",
    "#plotting the results  \n",
    "SMALL_SIZE = 20\n",
    "MEDIUM_SIZE = 22\n",
    "BIGGER_SIZE = 24\n",
    "\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure tit\n",
    "matplotlib.rc('font', size=SMALL_SIZE)\n",
    "matplotlib.rc('axes', titlesize=SMALL_SIZE)\n",
    "matplotlib.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "matplotlib.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "matplotlib.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "matplotlib.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "matplotlib.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure tit\n",
    "\n",
    "plt.plot(range(0,t), P_g.value, \"red\", label = 'Power from Grid') \n",
    "plt.plot(range(0,t), P_dis.value, \"blue\", label = 'Power from Battery')\n",
    "plt.plot(range(0,t), P_L, \"black\", label = 'Demand')\n",
    "plt.plot(range(0,t), P_s*(S_max.value), \"green\", label = 'Power from Solar') \n",
    "plt.plot(range(0,t), E_plot, \"orange\", label = 'Battery State of Charge')\n",
    "plt.xlabel(\"Time (hours)\")\n",
    "plt.ylabel(\"Power Use (MW)\")\n",
    "# plt.title(\"Normal Operation: June\")\n",
    "leg = plt.legend()\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(17, 11)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4c43c88",
   "metadata": {},
   "source": [
    "### Scenario 2: June Wholesale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "68e70c79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "status: optimal\n",
      "Battery Capacity [MW]:  1.6946435446519873e-05\n",
      "Minimized Cost: $ 3056311.49436564\n",
      "Cost of Battery [$]:  0.014636178080966724\n",
      "Solar Size [MW]:  -1.0674678152470104e-08\n",
      "Cost of Bought Ethylene [tons]:  51636331.70679844\n",
      "Cost of Sold Ethylene [tons]:  54427184.77358714\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1224x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "P_ramp = 20*60 # ramp values # ~20 MW/min for gas, coal would be ~2 MW/min\n",
    "eff = 0.9 # battery efficiency\n",
    "t = 720 # one month duration (hours)\n",
    "month = \"june\" # or \"december\"\n",
    "cost = \"wholesale\" # or \"fixed\"\n",
    "c_fix = 19.75 # june 19.75, december 21.19\n",
    "storage_limit = 250 \n",
    "outage = \"no\" # or \"yes\"\n",
    "# outage_duration = 4 # hours # if outage = yes\n",
    "# outage_start = 100 # hour # if outage = yes\n",
    "\n",
    "E_max = cp.Variable() # establishing battery capacity (MW)\n",
    "E_b = cp.Variable(t+1) # defining battery charge variable\n",
    "P_g = cp.Variable(t) # power drawn from the grid\n",
    "P_ch = cp.Variable(t) # battery charge\n",
    "P_dis = cp.Variable(t) # battery discharge\n",
    "S_max = cp.Variable() # size of the solar panels\n",
    "St = cp.Variable(t+1) # ethylene storage\n",
    "Sl = cp.Variable(t) # sold ethylene\n",
    "Pd = cp.Variable(t) # production \n",
    "\n",
    "C_s = S_max*Cost(1000000*0.89*20)\n",
    "C_b = E_max*(Cost(143000))\n",
    "P_L = [] # loads\n",
    "P_L = Loads(P_L, t)\n",
    "P_s = [] # power generated by solar\n",
    "P_s = Solar(P_s, month)\n",
    "C_g = [] # cost of power from the grid\n",
    "C_g = GridCost(C_g, month, t, cost, c_fix)\n",
    "S_p = [] # sell price\n",
    "S_p = SellPrice(month, S_p)\n",
    "B_p = []\n",
    "B_p = BuyPrice(month, B_p)\n",
    "\n",
    "constraints = []\n",
    "constraints += [P_g >= 0] # power draw is not negative \n",
    "constraints += [P_L == P_g + P_dis + -P_ch + P_s*S_max] # the sources sum to load requirements\n",
    "constraints += [E_b[0] == 0] # battery charge at start is zero\n",
    "constraints += [P_ch >= 0] # charge into the is not negative\n",
    "constraints += [1 >= eff] #efficiency cannot be greater than one\n",
    "constraints += [P_dis >= 0] # discharge from battery is not negative\n",
    "constraints += [S_max >= 0] # can't have negative solar capacity\n",
    "constraints += [S_max <= 1] # solar 20000 kW or less\n",
    "constraints += [P_dis >= 0] # discharge from battery is not negative\n",
    "# ramp value constraints - power change between time steps can't exceed ramp limit\n",
    "for n in range (0, t-1): \n",
    "    a = P_g[n]\n",
    "    b = P_g[n+1]\n",
    "    constraints += [(a - b) <= P_ramp, (a - b) >= -P_ramp] \n",
    "    n+1\n",
    "for n in range (0, t):\n",
    "    constraints += [E_max/4 >= P_ch[n]] # battery charge in one time step cannot be greater than the battery capacity\n",
    "    constraints += [E_max/4 >= P_dis[n]] # battery discharge in one time step cannot be greater than the battery capaticy\n",
    "    constraints += [St[0] == 0] # storage at time 0 = 0\n",
    "    constraints += [St[n] >= 0] # storage between 0 and limit\n",
    "    constraints += [storage_limit >= St[n]] # storage between 0 and limit\n",
    "    constraints += [Sl[n] >= 0] # selling ethylene not buying\n",
    "    constraints += [Sl[0] == 0] # can't sell in at hour 0\n",
    "    constraints += [storage_limit >= Sl[n]] # can't sell more than storage limit in a given timestep\n",
    "for n in range (1, t+1):\n",
    "    constraints += [E_b[n] == E_b[n-1] + eff*P_ch[n-1] - 1/eff*P_dis[n-1]] # battery effectiveness impacts charge/discharge amounts of battery\n",
    "    constraints += [St[n] == St[n-1] + Pd[n-1] - Sl[n-1]] # storage at next time = current + produced - sold\n",
    "for n in range (0, t+1):\n",
    "    constraints += [E_b[n] >= 0] # battery charge is not negative\n",
    "    constraints += [E_max >= E_b[n]] # battery charge cannot be greater than the capacity\n",
    "if (outage==\"yes\"):\n",
    "    for n in range (outage_start, outage_start + outage_duration):\n",
    "        constraints += [P_g[n] == 0]    \n",
    "        constraints += [Pd[n] >= 0]\n",
    "        constraints += [Pd[n] <= (len(P_L)/sum(P_L))*134.7*P_L[n]]\n",
    "    for n in range(0, outage_start):\n",
    "        constraints += [Pd[n] == (len(P_L)/sum(P_L))*134.7*P_L[n]]\n",
    "    for n in range(outage_start + outage_duration, t):\n",
    "        constraints += [Pd[n] == (len(P_L)/sum(P_L))*134.7*P_L[n]]\n",
    "elif (outage==\"no\"):\n",
    "    for n in range(0, t):\n",
    "        constraints += [Pd[n] == (len(P_L)/sum(P_L))*134.7*P_L[n]]\n",
    "        \n",
    "P_c = [] # bought ethylene including price\n",
    "S_l = [] # sold ethylene including price\n",
    "for n in range(t):\n",
    "    P_c += [Pd[n] * B_p[n] * 0.95] # currently using buy price is 95% of sell price, with real buy prices, use 100%\n",
    "    S_l += [Sl[n] * S_p[n]]\n",
    "    \n",
    "objective = sum(C_g) + C_b + C_s + sum(P_c) - sum(S_l)\n",
    "\n",
    "prob = cp.Problem(cp.Minimize(objective), constraints)\n",
    "prob.solve() \n",
    "\n",
    "#printing results\n",
    "print(\"status:\", prob.status)\n",
    "print(\"Battery Capacity [MW]: \", E_max.value)\n",
    "print(\"Minimized Cost: $\", prob.value)\n",
    "print(\"Cost of Battery [$]: \", C_b.value)\n",
    "print(\"Solar Size [MW]: \", S_max.value*20)\n",
    "print(\"Cost of Bought Ethylene [tons]: \", sum(P_c).value)\n",
    "print(\"Cost of Sold Ethylene [tons]: \", sum(S_l).value)\n",
    "\n",
    "# battery charge variable for plotting\n",
    "E_plot = []\n",
    "for n in range (0, t):\n",
    "    E_plot = E_plot + [E_b.value[n]]\n",
    "    \n",
    "#plotting the results  \n",
    "SMALL_SIZE = 20\n",
    "MEDIUM_SIZE = 22\n",
    "BIGGER_SIZE = 24\n",
    "\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure tit\n",
    "matplotlib.rc('font', size=SMALL_SIZE)\n",
    "matplotlib.rc('axes', titlesize=SMALL_SIZE)\n",
    "matplotlib.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "matplotlib.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "matplotlib.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "matplotlib.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "matplotlib.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure tit\n",
    "\n",
    "plt.plot(range(0,t), P_g.value, \"red\", label = 'Power from Grid') \n",
    "plt.plot(range(0,t), P_dis.value, \"blue\", label = 'Power from Battery')\n",
    "plt.plot(range(0,t), P_L, \"black\", label = 'Demand')\n",
    "plt.plot(range(0,t), P_s*(S_max.value), \"green\", label = 'Power from Solar') \n",
    "plt.plot(range(0,t), E_plot, \"orange\", label = 'Battery State of Charge')\n",
    "plt.xlabel(\"Time (hours)\")\n",
    "plt.ylabel(\"Power Use (MW)\")\n",
    "# plt.title(\"Normal Operation: June\")\n",
    "leg = plt.legend()\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(17, 11)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79924cea",
   "metadata": {},
   "source": [
    "### Scenario 3: December Fixed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2760c0c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "status: optimal\n",
      "Battery Capacity [MW]:  1.143984141831884e-07\n",
      "Minimized Cost: $ 3427098.27703692\n",
      "Cost of Battery [$]:  9.880281711450896e-05\n",
      "Solar Size [MW]:  -5.490253895181011e-10\n",
      "Cost of Bought Ethylene [tons]:  [52163840.1885947]\n",
      "Cost of Sold Ethylene [tons]:  [55007286.7116785]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1224x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "P_ramp = 20*60 # ramp values # ~20 MW/min for gas, coal would be ~2 MW/min\n",
    "eff = 0.9 # battery efficiency\n",
    "t = 720 # one month duration (hours)\n",
    "month = \"december\" # or \"june\"\n",
    "cost = \"fixed\" # or \"wholesale\"\n",
    "c_fix = 21.19 # june 19.75, december 21.19\n",
    "storage_limit = 250 \n",
    "outage = \"no\" # or \"yes\"\n",
    "# outage_duration = 4 # hours # if outage = yes\n",
    "# outage_start = 100 # hour # if outage = yes\n",
    "\n",
    "E_max = cp.Variable() # establishing battery capacity (MW)\n",
    "E_b = cp.Variable(t+1) # defining battery charge variable\n",
    "P_g = cp.Variable(t) # power drawn from the grid\n",
    "P_ch = cp.Variable(t) # battery charge\n",
    "P_dis = cp.Variable(t) # battery discharge\n",
    "S_max = cp.Variable() # size of the solar panels\n",
    "St = cp.Variable(t+1) # ethylene storage\n",
    "Sl = cp.Variable(t) # sold ethylene\n",
    "Pd = cp.Variable(t) # production \n",
    "\n",
    "C_s = S_max*Cost(1000000*0.89*20)\n",
    "C_b = E_max*(Cost(143000))\n",
    "P_L = [] # loads\n",
    "P_L = Loads(P_L, t)\n",
    "P_s = [] # power generated by solar\n",
    "P_s = Solar(P_s, month)\n",
    "C_g = [] # cost of power from the grid\n",
    "C_g = GridCost(C_g, month, t, cost, c_fix)\n",
    "S_p = [] # sell price\n",
    "S_p = SellPrice(month, S_p)\n",
    "B_p = []\n",
    "B_p = BuyPrice(month, B_p)\n",
    "\n",
    "constraints = []\n",
    "constraints += [P_g >= 0] # power draw is not negative \n",
    "constraints += [P_L == P_g + P_dis + -P_ch + P_s*S_max] # the sources sum to load requirements\n",
    "constraints += [E_b[0] == 0] # battery charge at start is zero\n",
    "constraints += [P_ch >= 0] # charge into the is not negative\n",
    "constraints += [1 >= eff] #efficiency cannot be greater than one\n",
    "constraints += [P_dis >= 0] # discharge from battery is not negative\n",
    "constraints += [S_max >= 0] # can't have negative solar capacity\n",
    "constraints += [S_max <= 1] # solar 20000 kW or less\n",
    "constraints += [P_dis >= 0] # discharge from battery is not negative\n",
    "# ramp value constraints - power change between time steps can't exceed ramp limit\n",
    "for n in range (0, t-1): \n",
    "    a = P_g[n]\n",
    "    b = P_g[n+1]\n",
    "    constraints += [(a - b) <= P_ramp, (a - b) >= -P_ramp] \n",
    "    n+1\n",
    "for n in range (0, t):\n",
    "    constraints += [E_max/4 >= P_ch[n]] # battery charge in one time step cannot be greater than the battery capacity\n",
    "    constraints += [E_max/4 >= P_dis[n]] # battery discharge in one time step cannot be greater than the battery capaticy\n",
    "    constraints += [St[0] == 0] # storage at time 0 = 0\n",
    "    constraints += [St[n] >= 0] # storage between 0 and limit\n",
    "    constraints += [storage_limit >= St[n]] # storage between 0 and limit\n",
    "    constraints += [Sl[n] >= 0] # selling ethylene not buying\n",
    "    constraints += [Sl[0] == 0] # can't sell in at hour 0\n",
    "    constraints += [storage_limit >= Sl[n]] # can't sell more than storage limit in a given timestep\n",
    "for n in range (1, t+1):\n",
    "    constraints += [E_b[n] == E_b[n-1] + eff*P_ch[n-1] - 1/eff*P_dis[n-1]] # battery effectiveness impacts charge/discharge amounts of battery\n",
    "    constraints += [St[n] == St[n-1] + Pd[n-1] - Sl[n-1]] # storage at next time = current + produced - sold\n",
    "for n in range (0, t+1):\n",
    "    constraints += [E_b[n] >= 0] # battery charge is not negative\n",
    "    constraints += [E_max >= E_b[n]] # battery charge cannot be greater than the capacity\n",
    "if (outage==\"yes\"):\n",
    "    for n in range (outage_start, outage_start + outage_duration):\n",
    "        constraints += [P_g[n] == 0]    \n",
    "        constraints += [Pd[n] >= 0]\n",
    "        constraints += [Pd[n] <= (len(P_L)/sum(P_L))*134.7*P_L[n]]\n",
    "    for n in range(0, outage_start):\n",
    "        constraints += [Pd[n] == (len(P_L)/sum(P_L))*134.7*P_L[n]]\n",
    "    for n in range(outage_start + outage_duration, t):\n",
    "        constraints += [Pd[n] == (len(P_L)/sum(P_L))*134.7*P_L[n]]\n",
    "elif (outage==\"no\"):\n",
    "    for n in range(0, t):\n",
    "        constraints += [Pd[n] == (len(P_L)/sum(P_L))*134.7*P_L[n]]\n",
    "        \n",
    "P_c = [] # bought ethylene including price\n",
    "S_l = [] # sold ethylene including price\n",
    "for n in range(t):\n",
    "    P_c += [Pd[n] * B_p[n] * 0.95] # currently using buy price is 95% of sell price, with real buy prices, use 100%\n",
    "    S_l += [Sl[n] * S_p[n]]\n",
    "    \n",
    "objective = sum(C_g) + C_b + C_s + sum(P_c) - sum(S_l)\n",
    "\n",
    "prob = cp.Problem(cp.Minimize(objective), constraints)\n",
    "prob.solve() \n",
    "\n",
    "#printing results\n",
    "print(\"status:\", prob.status)\n",
    "print(\"Battery Capacity [MW]: \", E_max.value)\n",
    "print(\"Minimized Cost: $\", prob.value)\n",
    "print(\"Cost of Battery [$]: \", C_b.value)\n",
    "print(\"Solar Size [MW]: \", S_max.value*20)\n",
    "print(\"Cost of Bought Ethylene [tons]: \", sum(P_c).value)\n",
    "print(\"Cost of Sold Ethylene [tons]: \", sum(S_l).value)\n",
    "\n",
    "# battery charge variable for plotting\n",
    "E_plot = []\n",
    "for n in range (0, t):\n",
    "    E_plot = E_plot + [E_b.value[n]]\n",
    "    \n",
    "#plotting the results  \n",
    "SMALL_SIZE = 20\n",
    "MEDIUM_SIZE = 22\n",
    "BIGGER_SIZE = 24\n",
    "\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure tit\n",
    "matplotlib.rc('font', size=SMALL_SIZE)\n",
    "matplotlib.rc('axes', titlesize=SMALL_SIZE)\n",
    "matplotlib.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "matplotlib.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "matplotlib.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "matplotlib.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "matplotlib.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure tit\n",
    "\n",
    "plt.plot(range(0,t), P_g.value, \"red\", label = 'Power from Grid') \n",
    "plt.plot(range(0,t), P_dis.value, \"blue\", label = 'Power from Battery')\n",
    "plt.plot(range(0,t), P_L, \"black\", label = 'Demand')\n",
    "plt.plot(range(0,t), P_s*(S_max.value), \"green\", label = 'Power from Solar') \n",
    "plt.plot(range(0,t), E_plot, \"orange\", label = 'Battery State of Charge')\n",
    "plt.xlabel(\"Time (hours)\")\n",
    "plt.ylabel(\"Power Use (MW)\")\n",
    "# plt.title(\"Normal Operation: June\")\n",
    "leg = plt.legend()\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(17, 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4a975c8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6514843027796247e-13"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S_l[0][0].value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69ca8931",
   "metadata": {},
   "source": [
    "### Scenario 4: December Wholesale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3bb0da8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "status: optimal\n",
      "Battery Capacity [MW]:  8.304727646585881e-07\n",
      "Minimized Cost: $ 3386567.1074128\n",
      "Cost of Battery [$]:  0.0007172568717058436\n",
      "Solar Size [MW]:  -2.511856002202287e-09\n",
      "Cost of Bought Ethylene [tons]:  [52163840.18859477]\n",
      "Cost of Sold Ethylene [tons]:  [55007286.71167826]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "P_ramp = 20*60 # ramp values # ~20 MW/min for gas, coal would be ~2 MW/min\n",
    "eff = 0.9 # battery efficiency\n",
    "t = 720 # one month duration (hours)\n",
    "month = \"december\" # or \"june\"\n",
    "cost = \"wholesale\" # or \"fixed\"\n",
    "c_fix = 21.19 # june 19.75, december 21.19\n",
    "storage_limit = 250 \n",
    "outage = \"no\" # or \"yes\"\n",
    "# outage_duration = 4 # hours # if outage = yes\n",
    "# outage_start = 100 # hour # if outage = yes\n",
    "\n",
    "E_max = cp.Variable() # establishing battery capacity (MW)\n",
    "E_b = cp.Variable(t+1) # defining battery charge variable\n",
    "P_g = cp.Variable(t) # power drawn from the grid\n",
    "P_ch = cp.Variable(t) # battery charge\n",
    "P_dis = cp.Variable(t) # battery discharge\n",
    "S_max = cp.Variable() # size of the solar panels\n",
    "St = cp.Variable(t+1) # ethylene storage\n",
    "Sl = cp.Variable(t) # sold ethylene\n",
    "Pd = cp.Variable(t) # production \n",
    "\n",
    "C_s = S_max*Cost(1000000*0.89*20)\n",
    "C_b = E_max*(Cost(143000))\n",
    "P_L = [] # loads\n",
    "P_L = Loads(P_L, t)\n",
    "P_s = [] # power generated by solar\n",
    "P_s = Solar(P_s, month)\n",
    "C_g = [] # cost of power from the grid\n",
    "C_g = GridCost(C_g, month, t, cost, c_fix)\n",
    "S_p = [] # sell price\n",
    "S_p = SellPrice(month, S_p)\n",
    "B_p = []\n",
    "B_p = BuyPrice(month, B_p)\n",
    "\n",
    "constraints = []\n",
    "constraints += [P_g >= 0] # power draw is not negative \n",
    "constraints += [P_L == P_g + P_dis + -P_ch + P_s*S_max] # the sources sum to load requirements\n",
    "constraints += [E_b[0] == 0] # battery charge at start is zero\n",
    "constraints += [P_ch >= 0] # charge into the is not negative\n",
    "constraints += [1 >= eff] #efficiency cannot be greater than one\n",
    "constraints += [P_dis >= 0] # discharge from battery is not negative\n",
    "constraints += [S_max >= 0] # can't have negative solar capacity\n",
    "constraints += [S_max <= 1] # solar 20000 kW or less\n",
    "constraints += [P_dis >= 0] # discharge from battery is not negative\n",
    "# ramp value constraints - power change between time steps can't exceed ramp limit\n",
    "for n in range (0, t-1): \n",
    "    a = P_g[n]\n",
    "    b = P_g[n+1]\n",
    "    constraints += [(a - b) <= P_ramp, (a - b) >= -P_ramp] \n",
    "    n+1\n",
    "for n in range (0, t):\n",
    "    constraints += [E_max/4 >= P_ch[n]] # battery charge in one time step cannot be greater than the battery capacity\n",
    "    constraints += [E_max/4 >= P_dis[n]] # battery discharge in one time step cannot be greater than the battery capaticy\n",
    "    constraints += [St[0] == 0] # storage at time 0 = 0\n",
    "    constraints += [St[n] >= 0] # storage between 0 and limit\n",
    "    constraints += [storage_limit >= St[n]] # storage between 0 and limit\n",
    "    constraints += [Sl[n] >= 0] # selling ethylene not buying\n",
    "    constraints += [Sl[0] == 0] # can't sell in at hour 0\n",
    "    constraints += [storage_limit >= Sl[n]] # can't sell more than storage limit in a given timestep\n",
    "for n in range (1, t+1):\n",
    "    constraints += [E_b[n] == E_b[n-1] + eff*P_ch[n-1] - 1/eff*P_dis[n-1]] # battery effectiveness impacts charge/discharge amounts of battery\n",
    "    constraints += [St[n] == St[n-1] + Pd[n-1] - Sl[n-1]] # storage at next time = current + produced - sold\n",
    "for n in range (0, t+1):\n",
    "    constraints += [E_b[n] >= 0] # battery charge is not negative\n",
    "    constraints += [E_max >= E_b[n]] # battery charge cannot be greater than the capacity\n",
    "if (outage==\"yes\"):\n",
    "    for n in range (outage_start, outage_start + outage_duration):\n",
    "        constraints += [P_g[n] == 0]    \n",
    "        constraints += [Pd[n] >= 0]\n",
    "        constraints += [Pd[n] <= (len(P_L)/sum(P_L))*134.7*P_L[n]]\n",
    "    for n in range(0, outage_start):\n",
    "        constraints += [Pd[n] == (len(P_L)/sum(P_L))*134.7*P_L[n]]\n",
    "    for n in range(outage_start + outage_duration, t):\n",
    "        constraints += [Pd[n] == (len(P_L)/sum(P_L))*134.7*P_L[n]]\n",
    "elif (outage==\"no\"):\n",
    "    for n in range(0, t):\n",
    "        constraints += [Pd[n] == (len(P_L)/sum(P_L))*134.7*P_L[n]]\n",
    "        \n",
    "P_c = [] # bought ethylene including price\n",
    "S_l = [] # sold ethylene including price\n",
    "for n in range(t):\n",
    "    P_c += [Pd[n] * B_p[n] * 0.95] # currently using buy price is 95% of sell price, with real buy prices, use 100%\n",
    "    S_l += [Sl[n] * S_p[n]]\n",
    "    \n",
    "objective = sum(C_g) + C_b + C_s + sum(P_c) - sum(S_l)\n",
    "\n",
    "prob = cp.Problem(cp.Minimize(objective), constraints)\n",
    "prob.solve() \n",
    "\n",
    "#printing results\n",
    "print(\"status:\", prob.status)\n",
    "print(\"Battery Capacity [MW]: \", E_max.value)\n",
    "print(\"Minimized Cost: $\", prob.value)\n",
    "print(\"Cost of Battery [$]: \", C_b.value)\n",
    "print(\"Solar Size [MW]: \", S_max.value*20)\n",
    "print(\"Cost of Bought Ethylene [tons]: \", sum(P_c).value)\n",
    "print(\"Cost of Sold Ethylene [tons]: \", sum(S_l).value)\n",
    "\n",
    "# battery charge variable for plotting\n",
    "E_plot = []\n",
    "for n in range (0, t):\n",
    "    E_plot = E_plot + [E_b.value[n]]\n",
    "    \n",
    "#plotting the results  \n",
    "SMALL_SIZE = 20\n",
    "MEDIUM_SIZE = 22\n",
    "BIGGER_SIZE = 24\n",
    "\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure tit\n",
    "matplotlib.rc('font', size=SMALL_SIZE)\n",
    "matplotlib.rc('axes', titlesize=SMALL_SIZE)\n",
    "matplotlib.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "matplotlib.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "matplotlib.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "matplotlib.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "matplotlib.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure tit\n",
    "\n",
    "plt.plot(range(0,t), P_g.value, \"red\", label = 'Power from Grid') \n",
    "plt.plot(range(0,t), P_dis.value, \"blue\", label = 'Power from Battery')\n",
    "plt.plot(range(0,t), P_L, \"black\", label = 'Demand')\n",
    "plt.plot(range(0,t), P_s*(S_max.value), \"green\", label = 'Power from Solar') \n",
    "plt.plot(range(0,t), E_plot, \"orange\", label = 'Battery State of Charge')\n",
    "plt.xlabel(\"Time (hours)\")\n",
    "plt.ylabel(\"Power Use (MW)\")\n",
    "# plt.title(\"Normal Operation: June\")\n",
    "leg = plt.legend()\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(17, 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "682b7b0b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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