Allocation-N-Pricing-models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c4ebaed4",
   "metadata": {},
   "source": [
    "# Code for paper: *Why Do Pricing Rules Matter? Electricity Market Design with Electric Vehicle Participants.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "053c9b39",
   "metadata": {},
   "source": [
    "> Developed by Andrea Saumweber: a[dot]saumweber[at]moonroc[dot]de"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "40539aad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from ortools.linear_solver import pywraplp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42eb20cf",
   "metadata": {},
   "source": [
    "# 1. Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8fdd2e0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scenarios\n",
    "# S1: No Electric Vehicle (EV), No Demand Response (DR)\n",
    "# S2: EV, No DR\n",
    "# S3: EV, DR \n",
    "\n",
    "scenario = 'S3'\n",
    "\n",
    "\n",
    "data = 'data/Data_EV_Paper_' + scenario + '.xlsx'\n",
    "\n",
    "# Global Parameters\n",
    "timestep = 1\n",
    "big_M = 1000\n",
    "eff_IN = 1\n",
    "eff_OUT = 1\n",
    "\n",
    "# Generation \n",
    "df_GEN = pd.read_excel(data, sheet_name=\"Generation Resources\") \n",
    "G = set(df_GEN['Resource'])\n",
    "T = set(t for t in range(0, max(df_GEN['Hour'])+1))\n",
    "df_GEN.set_index(['Resource', 'Hour'], inplace=True)\n",
    "\n",
    "# Demand\n",
    "df_DEM = pd.read_excel(data, sheet_name=\"Demand Resources\") \n",
    "D = set(df_DEM['Resource'])\n",
    "df_DEM.set_index(['Resource', 'Hour'], inplace=True)\n",
    "\n",
    "# Transmission\n",
    "df_TRN = pd.read_excel(data, sheet_name=\"Transmission Lines\")\n",
    "N = set(df_TRN['Source'])\n",
    "L = set([(df_TRN.loc[i]['Source'],df_TRN.loc[i]['Sink']) for i in range(0, len(df_TRN))])\n",
    "df_TRN.set_index(['Source', 'Sink', 'Hour'], inplace=True)\n",
    "\n",
    "# Electric Vehicles\n",
    "df_EV = pd.read_excel(data, sheet_name=\"Electric Vehicles\")\n",
    "V = set(df_EV['Vehicle'])\n",
    "df_EV.set_index(['Vehicle', 'Hour'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eec8a1d1",
   "metadata": {},
   "source": [
    "# 2. Model\n",
    "## 2.1 Allocation Models\n",
    "### 2.1.1 Market Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "be99d11f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# setup model\n",
    "ucm = pywraplp.Solver.CreateSolver('SCIP')\n",
    "infinity = ucm.infinity()\n",
    "\n",
    "# define variables\n",
    "q_GEN = {(g,t): ucm.NumVar(0.0, infinity, \"q_GEN\"+str(g)+str(t)) for g in G for t in T}\n",
    "u_GEN = {(g,t): ucm.IntVar(0.0, 1.0, \"u_GEN\"+str(g)+str(t)) for g in G for t in T}\n",
    "q_ELA = {(d,t): ucm.NumVar(0.0, infinity, \"q_ELA\"+str(d)+str(t)) for d in D for t in T}\n",
    "u_DEM = {(d,t): ucm.IntVar(0.0, 1.0, \"u_DEM\"+str(d)+str(t)) for d in D for t in T}\n",
    "q_IN_EV = {(v,t): ucm.NumVar(0.0, infinity, \"q_IN_EV\"+str(v)+str(t)) for v in V for t in T}\n",
    "q_OUT_EV = {(v,t): ucm.NumVar(0.0, infinity, \"q_OUT_EV\"+str(v)+str(t)) for v in V for t in T}\n",
    "soc_EV = {(v,t): ucm.NumVar(0.0, infinity, \"soc_EV\"+str(v)+str(t)) for v in V for t in T}\n",
    "m_EV = {(v,t): ucm.IntVar(0.0, 1.0, \"m_EV\"+str(v)+str(t)) for v in V for t in T}\n",
    "q_TRN = {(n, m, t): ucm.NumVar(-infinity, infinity, \"q_TRN\"+str(n)+str(m)+str(t)) for (n, m) in L for t in T}\n",
    "d_TRN = {(n, t): ucm.NumVar(-infinity, infinity, \"d_TRN\"+str(n)+str(t)) for n in N for t in T}\n",
    "\n",
    "# define constraints\n",
    "output_GEN = {(g, t): ucm.Add(df_GEN.at[(g, t),'Q_MIN (MW)'] * u_GEN[g, t] <= q_GEN[g, t] <= df_GEN.at[(g, t),'Q_MAX (MW)'] * u_GEN[g, t]) for g in G for t in T}\n",
    "output_DEM = {(d, t): ucm.Add(df_DEM.at[(d, t),'Q_MIN (MW)'] * u_DEM[d, t] <= q_ELA[d, t] + df_DEM.at[(d, t),'Q_FIX (MWh)']  <= df_DEM.at[(d, t),'Q_MAX (MW)'] * u_DEM[d, t]) for d in D for t in T}\n",
    "soc_def_EV = {(v, t): ucm.Add(soc_EV[v, t] == soc_EV[v, t-1] + eff_IN * q_IN_EV[v, t] - (1/eff_OUT) * q_OUT_EV[v, t] - df_EV.at[(v, t), 'D_SOC_EV (MWh)']) for v in V for t in range(1, max(T)+1)}\n",
    "soc_def_EV2 = {(v, 0): ucm.Add(soc_EV[v, 0]==df_EV.at[(v, 0), 'SOC_EV_MAX (MWh)'] + eff_IN * q_IN_EV[v, 0] - (1/eff_OUT) * q_OUT_EV[v, 0] - df_EV.at[(v, 0), 'D_SOC_EV (MWh)']) for v in V}\n",
    "soc_def_EV3 = {(v, max(T)): ucm.Add(soc_EV[v, max(T)]==df_EV.at[(v, max(T)), 'SOC_EV_MAX (MWh)']) for v in V}\n",
    "soc_capacity_EV = {(v, t): ucm.Add(df_EV.at[(v, t), 'SOC_EV_MIN (MWh)'] <= soc_EV[v, t] <= df_EV.at[(v, t), 'SOC_EV_MAX (MWh)']) for v in V for t in T}\n",
    "charging_speed_EV = {(v, t): ucm.Add(q_IN_EV[v, t] + q_OUT_EV[v, t] <= df_EV.at[(v, t), 'Q_MAX (MW)']) for v in V for t in T}\n",
    "charging_mode_1_EV = {(v, t): ucm.Add(q_IN_EV[v, t] <= big_M * m_EV[v, t]) for v in V for t in T}\n",
    "charging_mode_2_EV = {(v, t): ucm.Add(q_OUT_EV[v, t] <= big_M * (1 - m_EV[v, t])) for v in V for t in T}\n",
    "capacity_TRAN = {(n, m, t): ucm.Add(-df_TRN.at[(n, m, t), 'Q_MAX (MW)'] <= q_TRN[n, m, t] <= df_TRN.at[(n, m, t), 'Q_MAX (MW)']) for (n, m) in L for t in T}\n",
    "second_law = {((n,m), t): ucm.Add(q_TRN[n, m, t] == -df_TRN.at[(n, m, t), 'B'] * (d_TRN[n, t] - d_TRN[m, t])) for (n,m) in L for t in T}\n",
    "second_law_fix = {t: ucm.Add(d_TRN[\"N1\", t] == 0) for t in T}\n",
    "first_law = {(n, t): ucm.Add(sum(q_GEN[g, t] for g in G if df_GEN.at[(g, t), 'Node']==n)\n",
    "                             + sum(q_OUT_EV[v, t] for v in V if df_EV.at[(v, t), 'Node']==n)\n",
    "                             - sum(df_DEM.at[(d, t),'Q_FIX (MWh)']+q_ELA[d, t] for d in D if df_DEM.at[(d, t), 'Node']==n)\n",
    "                             - sum(q_IN_EV[v, t] for v in V if df_EV.at[(v, t), 'Node']==n)   \n",
    "                             + sum(q_TRN[n_, m, t] for (n_, m) in L if n_ == n) == 0) for n in N for t in T}\n",
    "\n",
    "# define objective\n",
    "objective = ucm.Minimize(sum(df_GEN.at[(g, t),'C_Q ($/MWh)'] * q_GEN[g, t] \n",
    "                             + df_GEN.at[(g, t),'C_U ($/h)'] * u_GEN[g, t] for g in G for t in T) \n",
    "                         - sum(df_DEM.at[(d, t),'C_Q ($/MWh)'] * q_ELA[d, t] for d in D for t in T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4546a4ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... allocation computed \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/felipe/opt/anaconda3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3172: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "# solve model\n",
    "status = ucm.Solve()\n",
    "\n",
    "if status == pywraplp.Solver.OPTIMAL: \n",
    "    print('... allocation computed', \"\\n\")\n",
    "    \n",
    "    MARKET_RESULTS_GEN = pd.DataFrame(columns=['Resource', 'Hour', 'Node', 'q', 'u'])\n",
    "    MARKET_RESULTS_GEN.set_index(['Resource', 'Hour'], inplace=True)\n",
    "    MARKET_RESULTS_GEN.sort_values(by=['Resource', 'Hour'],inplace=True)\n",
    "    for t in T:\n",
    "        for g in G:\n",
    "            MARKET_RESULTS_GEN.at[(g, t), 'q'] = q_GEN[g, t].solution_value()\n",
    "            MARKET_RESULTS_GEN.at[(g, t), 'u'] = u_GEN[g, t].solution_value()\n",
    "            MARKET_RESULTS_GEN.at[(g, t), 'Node'] = df_GEN.at[(g, t), 'Node']\n",
    "            \n",
    "    MARKET_RESULTS_TRN = pd.DataFrame(columns=['Source', 'Sink', 'Hour', 'q', 'delta'])\n",
    "    MARKET_RESULTS_TRN.set_index(['Source', 'Sink', 'Hour'], inplace=True)\n",
    "    MARKET_RESULTS_TRN.sort_index() \n",
    "    for t in T:\n",
    "        for (n, m) in L:\n",
    "            MARKET_RESULTS_TRN.at[(n, m, t), 'q'] = q_TRN[n, m, t].solution_value()\n",
    "            MARKET_RESULTS_TRN.at[(n, m, t), 'delta'] = d_TRN[n, t].solution_value()\n",
    "    '''print(MARKET_RESULTS_TRN)'''\n",
    "            \n",
    "            \n",
    "    MARKET_RESULTS_DEM = pd.DataFrame(columns=['Resource', 'Hour', 'Node', 'q', 'u'])\n",
    "    MARKET_RESULTS_DEM.set_index(['Resource', 'Hour'], inplace=True)\n",
    "    MARKET_RESULTS_DEM.sort_values(by=['Resource', 'Hour'],inplace=True)\n",
    "    for t in T:\n",
    "        for d in D:\n",
    "            MARKET_RESULTS_DEM.at[(d, t), 'q'] = q_ELA[d, t].solution_value()\n",
    "            MARKET_RESULTS_DEM.at[(d, t), 'u'] = u_DEM[d, t].solution_value()\n",
    "            MARKET_RESULTS_DEM.at[(d, t), 'Node'] = df_DEM.at[(d, t), 'Node']\n",
    "            \n",
    "    MARKET_RESULTS_EV = pd.DataFrame(columns=['Vehicle', 'Hour', 'Node', 'q_in', 'q_out', 'soc', 'm'])\n",
    "    MARKET_RESULTS_EV.set_index(['Vehicle', 'Hour'], inplace=True)\n",
    "    MARKET_RESULTS_EV.sort_values(by=['Vehicle', 'Hour'],inplace=True) \n",
    "    for t in T:\n",
    "        for v in V:\n",
    "            MARKET_RESULTS_EV.at[(v, t), 'q_in'] = q_IN_EV[v, t].solution_value()\n",
    "            MARKET_RESULTS_EV.at[(v, t), 'q_out'] = q_OUT_EV[v, t].solution_value()\n",
    "            MARKET_RESULTS_EV.at[(v, t), 'soc'] = soc_EV[v, t].solution_value()\n",
    "            MARKET_RESULTS_EV.at[(v, t), 'm'] = m_EV[v, t].solution_value()\n",
    "            MARKET_RESULTS_EV.at[(v, t), 'Node'] = df_EV.at[(v, t), 'Node']\n",
    "            \n",
    "\n",
    "    MARKET_RESULTS_OBJECTIVE = ucm.Objective().Value()\n",
    "else:\n",
    "    print('The problem does not have an optimal solution.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "36d6074f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "MARKET_RESULTS_NODE = pd.DataFrame(columns=['Node', 'Hour', 'Generation', 'Demand'])\n",
    "MARKET_RESULTS_NODE.set_index(['Node', 'Hour'], inplace=True)\n",
    "for n in N:\n",
    "    for t in T:\n",
    "        MARKET_RESULTS_NODE.at[(n, t), 'Generation'] = (sum(MARKET_RESULTS_GEN.at[(g, t), 'q'] for g in G if df_GEN.at[(g, t), 'Node']==n) \n",
    "                                                        + sum(MARKET_RESULTS_EV.at[(v, t), 'q_out'] for v in V if df_EV.at[(v, t), 'Node']==n))\n",
    "        MARKET_RESULTS_NODE.at[(n, t), 'Demand'] = (sum(MARKET_RESULTS_DEM.at[(d, t), 'q'] for d in D if df_DEM.at[(d, t), 'Node']==n) + \n",
    "                                                    sum(df_DEM.at[(d, t),'Q_FIX (MWh)'] for d in D if df_DEM.at[(d, t), 'Node']==n)\n",
    "                                                   + sum(MARKET_RESULTS_EV.at[(v, t), 'q_in'] for v in V if df_EV.at[(v, t), 'Node']==n))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bf6d60e",
   "metadata": {},
   "source": [
    "### 2.1.2 Self-Schedules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "af943c7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GENERATION RESOURCE\n",
    "def f_self_GEN(g, n, market_prices, market_uplifts=None):\n",
    "    \n",
    "    # setup model\n",
    "    self_GEN = pywraplp.Solver.CreateSolver('SCIP')\n",
    "    infinity = self_GEN.infinity()\n",
    "    # define variables\n",
    "    self_q_GEN = {t: self_GEN.NumVar(0.0, infinity, \"q_GEN\"+str(t)) for t in T}\n",
    "    self_u_GEN = {t: self_GEN.IntVar(0.0, 1.0, \"u_GEN\"+str(t)) for t in T}\n",
    "    # define constraints\n",
    "    output_GEN = {t: self_GEN.Add(df_GEN.at[(g, t),'Q_MIN (MW)'] * self_u_GEN[t] <= self_q_GEN[t] <= df_GEN.at[(g, t),'Q_MAX (MW)'] * self_u_GEN[t]) for t in T}\n",
    "    # define objective\n",
    "    if market_uplifts is None:\n",
    "        objective = self_GEN.Maximize(sum(market_prices.at[(n,t), 'Price'] * self_q_GEN[t]\n",
    "                                          - df_GEN.at[(g, t),'C_Q ($/MWh)']  * self_q_GEN[t]\n",
    "                                          - df_GEN.at[(g, t),'C_U ($/h)']  * self_u_GEN[t] for t in T))\n",
    "    else:\n",
    "        objective = self_GEN.Maximize(sum(market_prices.at[(n,t), 'Price'] * self_q_GEN[t]\n",
    "                                          - df_GEN.at[(g, t),'C_Q ($/MWh)']  * self_q_GEN[t]\n",
    "                                          - df_GEN.at[(g, t),'C_U ($/h)']  * self_u_GEN[t]\n",
    "                                          + market_uplifts.at[(g, t),'Uplift Coefficient']  * self_u_GEN[t] for t in T))\n",
    "    # solve model\n",
    "    status = self_GEN.Solve()\n",
    "    if status == pywraplp.Solver.OPTIMAL: \n",
    "        # print('... allocation computed for', g, \"\\n\")\n",
    "        index = pd.MultiIndex.from_tuples([(g, t) for t in T], names=[\"Resource\", \"Hour\"])\n",
    "        SELF_RESULTS_GEN = pd.DataFrame(index=index, columns=['q', 'u'])\n",
    "        for t in T:\n",
    "            SELF_RESULTS_GEN.at[(g, t),'q'] = self_q_GEN[t].solution_value()\n",
    "            SELF_RESULTS_GEN.at[(g, t),'u'] = self_u_GEN[t].solution_value()\n",
    "        return self_GEN.Objective().Value(), SELF_RESULTS_GEN\n",
    "    else:\n",
    "        print('... failure at', g, \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "164b4b93",
   "metadata": {},
   "outputs": [],
   "source": [
    "# DEMAND RESOURCE\n",
    "def f_self_DEM(g, n, market_prices, market_uplifts=None):\n",
    "    # setup model\n",
    "    self_DEM = pywraplp.Solver.CreateSolver('SCIP')\n",
    "    infinity = self_DEM.infinity()\n",
    "    # define variables\n",
    "    self_q_ELA = {t: self_DEM.NumVar(0.0, infinity, \"q_ELA\"+str(t)) for t in T}\n",
    "    self_u_DEM = {t: self_DEM.IntVar(0.0, 1.0, \"u_DEM\"+str(t)) for t in T}\n",
    "    # define constraints\n",
    "    output_DEM = {t: self_DEM.Add(df_DEM.at[(d, t),'Q_MIN (MW)'] * self_u_DEM[t] <= self_q_ELA[t] + df_DEM.at[(d, t),'Q_FIX (MWh)']  <= df_DEM.at[(d, t),'Q_MAX (MW)'] * self_u_DEM[t]) for t in T}\n",
    "    # define objective\n",
    "    if market_uplifts is None:\n",
    "        objective = self_DEM.Maximize(sum(df_DEM.at[(d, t),'C_Q ($/MWh)']  * self_q_ELA[t] -\n",
    "                                          market_prices.at[(n,t), 'Price'] * self_q_ELA[t] for t in T))\n",
    "    else:\n",
    "        objective = self_DEM.Maximize(sum(df_DEM.at[(d, t),'C_Q ($/MWh)']  * self_q_ELA[t] -\n",
    "                                          market_prices.at[(n,t), 'Price'] * self_q_ELA[t] +\n",
    "                                          market_uplifts.at[(d,t), 'Uplift Coefficient'] * self_q_ELA[t] for t in T))\n",
    "    # solve model\n",
    "    status = self_DEM.Solve()\n",
    "    if status == pywraplp.Solver.OPTIMAL: \n",
    "        # print('... allocation computed for', g, \"\\n\")\n",
    "        index = pd.MultiIndex.from_tuples([(g, t) for t in T], names=[\"Resource\", \"Hour\"])\n",
    "        SELF_RESULTS_DEM = pd.DataFrame(index=index, columns=['q', 'u'])\n",
    "        for t in T:\n",
    "            SELF_RESULTS_DEM.at[(d, t),'q'] = self_q_ELA[t].solution_value()\n",
    "            SELF_RESULTS_DEM.at[(d, t),'u'] = self_u_DEM[t].solution_value()\n",
    "        return self_DEM.Objective().Value(), SELF_RESULTS_DEM\n",
    "    else:\n",
    "        print('... failure at', g, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7156252a",
   "metadata": {},
   "source": [
    "## 2.2 Pricing Model\n",
    "### 2.2.1 IP Pricing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5d5b6dac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# setup model\n",
    "ippm = pywraplp.Solver.CreateSolver('GLOP')\n",
    "infinity = ippm.infinity()\n",
    "\n",
    "# define variables\n",
    "q_GEN = {(g,t): ippm.NumVar(0.0, infinity, \"q_GEN\"+str(g)+str(t)) for g in G for t in T}\n",
    "u_GEN = {(g,t): ippm.NumVar(0.0, infinity, \"u_GEN\"+str(g)+str(t)) for g in G for t in T}\n",
    "q_ELA = {(d,t): ippm.NumVar(0.0, infinity, \"q_ELA\"+str(d)+str(t)) for d in D for t in T}\n",
    "u_DEM = {(d,t): ippm.NumVar(0.0, infinity, \"u_DEM\"+str(d)+str(t)) for d in D for t in T}\n",
    "q_IN_EV = {(v,t): ippm.NumVar(0.0, infinity, \"q_IN_EV\"+str(v)+str(t)) for v in V for t in T}\n",
    "q_OUT_EV = {(v,t): ippm.NumVar(0.0, infinity, \"q_OUT_EV\"+str(v)+str(t)) for v in V for t in T}\n",
    "soc_EV = {(v,t): ippm.NumVar(0.0, infinity, \"soc_EV\"+str(v)+str(t)) for v in V for t in T}\n",
    "m_EV = {(v,t): ippm.NumVar(0.0, infinity, \"m_EV\"+str(v)+str(t)) for v in V for t in T}\n",
    "q_TRN = {(n, m, t): ippm.NumVar(-infinity, infinity, \"q_TRN\"+str(n)+str(m)+str(t)) for (n, m) in L for t in T}\n",
    "d_TRN = {(n, t): ippm.NumVar(-infinity, infinity, \"d_TRN\"+str(n)+str(t)) for n in N for t in T}\n",
    "\n",
    "# define constraints\n",
    "fixed_GEN = {(g, t): ippm.Add(u_GEN[g, t] == MARKET_RESULTS_GEN.at[(g, t),'u']) for g in G for t in T}\n",
    "fixed_DEM = {(d, t): ippm.Add(u_DEM[d, t] == MARKET_RESULTS_DEM.at[(d, t),'u']) for d in D for t in T}\n",
    "fixed_EV = {(v, t): ippm.Add(m_EV[v, t] == MARKET_RESULTS_EV.at[(v, t), 'm']) for v in V for t in T}\n",
    "\n",
    "output_GEN = {(g, t): ippm.Add(df_GEN.at[(g, t),'Q_MIN (MW)'] * u_GEN[g, t] <= q_GEN[g, t] <= df_GEN.at[(g, t),'Q_MAX (MW)'] * u_GEN[g, t]) for g in G for t in T}\n",
    "output_DEM = {(d, t): ippm.Add(df_DEM.at[(d, t),'Q_MIN (MW)'] * u_DEM[d, t] <= q_ELA[d, t] + df_DEM.at[(d, t),'Q_FIX (MWh)']  <= df_DEM.at[(d, t),'Q_MAX (MW)'] * u_DEM[d, t]) for d in D for t in T}\n",
    "soc_def_EV = {(v, t): ippm.Add(soc_EV[v, t] == soc_EV[v, t-1] + eff_IN * q_IN_EV[v, t] - (1/eff_OUT) * q_OUT_EV[v, t] - df_EV.at[(v, t), 'D_SOC_EV (MWh)']) for v in V for t in range(1, max(T)+1)}\n",
    "soc_def_EV2 = {(v, 0): ippm.Add(soc_EV[v, 0]==df_EV.at[(v, 0), 'SOC_EV_MAX (MWh)'] + eff_IN * q_IN_EV[v, 0] - (1/eff_OUT) * q_OUT_EV[v, 0] - df_EV.at[(v, 0), 'D_SOC_EV (MWh)']) for v in V}\n",
    "soc_def_EV3 = {(v, max(T)): ippm.Add(soc_EV[v, max(T)]==df_EV.at[(v, max(T)), 'SOC_EV_MAX (MWh)']) for v in V}\n",
    "soc_capacity_EV = {(v, t): ippm.Add(df_EV.at[(v, t), 'SOC_EV_MIN (MWh)'] <= soc_EV[v, t] <= df_EV.at[(v, t), 'SOC_EV_MAX (MWh)']) for v in V for t in T}\n",
    "charging_speed_EV = {(v, t): ippm.Add(q_IN_EV[v, t] + q_OUT_EV[v, t] <= df_EV.at[(v, t), 'Q_MAX (MW)']) for v in V for t in T}\n",
    "charging_mode_1_EV = {(v, t): ippm.Add(q_IN_EV[v, t] <= big_M * m_EV[v, t]) for v in V for t in T}\n",
    "charging_mode_2_EV = {(v, t): ippm.Add(q_OUT_EV[v, t] <= big_M * (1 - m_EV[v, t])) for v in V for t in T}\n",
    "capacity_TRAN = {(n, m, t): ippm.Add(-df_TRN.at[(n, m, t), 'Q_MAX (MW)'] <= q_TRN[n, m, t] <= df_TRN.at[(n, m, t), 'Q_MAX (MW)']) for (n, m) in L for t in T}\n",
    "second_law = {((n,m), t): ippm.Add(q_TRN[n, m, t] == -df_TRN.at[(n, m, t), 'B'] * (d_TRN[n, t] - d_TRN[m, t])) for (n,m) in L for t in T}\n",
    "second_law_fix = {t: ippm.Add(d_TRN[\"N1\", t] == 0) for t in T}\n",
    "first_law = {(n, t): ippm.Add(sum(q_GEN[g, t] for g in G if df_GEN.at[(g, t), 'Node']==n)\n",
    "                             + sum(q_OUT_EV[v, t] for v in V if df_EV.at[(v, t), 'Node']==n)\n",
    "                             - sum(df_DEM.at[(d, t),'Q_FIX (MWh)']+q_ELA[d, t] for d in D if df_DEM.at[(d, t), 'Node']==n)\n",
    "                             - sum(q_IN_EV[v, t] for v in V if df_EV.at[(v, t), 'Node']==n)   \n",
    "                             + sum(q_TRN[n_, m, t] for (n_, m) in L if n_ == n) == 0) for n in N for t in T}\n",
    "\n",
    "# define objective\n",
    "objective = ippm.Minimize(sum(df_GEN.at[(g, t),'C_Q ($/MWh)'] * q_GEN[g, t] \n",
    "                             + df_GEN.at[(g, t),'C_U ($/h)'] * u_GEN[g, t] for g in G for t in T) \n",
    "                         - sum(df_DEM.at[(d, t),'C_Q ($/MWh)'] * q_ELA[d, t] for d in D for t in T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "17d38bd2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... ip pricing computed \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# solve model\n",
    "status = ippm.Solve()\n",
    "\n",
    "if status == pywraplp.Solver.OPTIMAL: \n",
    "    print('... ip pricing computed', \"\\n\")\n",
    "    # calculate prices\n",
    "    ip_prices = pd.DataFrame([[n, t, first_law[n, t].dual_value()] for n in N for t in T], columns=['Node', 'Hour', 'Price'])\n",
    "    ip_prices.set_index(['Node', 'Hour'], inplace=True)\n",
    "    # calculate uplifts\n",
    "    ip_uplifts = pd.DataFrame(columns=['Resource', 'Hour', 'Uplift', 'Uplift+', 'Uplift Coefficient'])\n",
    "    ip_uplifts.set_index(['Resource', 'Hour'], inplace=True)\n",
    "    for t in T:\n",
    "        for g in G:\n",
    "            ip_uplifts.at[(g, t), 'Uplift'] = fixed_GEN[g, t].dual_value() * u_GEN[g, t].solution_value()\n",
    "            ip_uplifts.at[(g, t), 'Uplift+'] = max(0,fixed_GEN[g, t].dual_value() * u_GEN[g, t].solution_value())\n",
    "            ip_uplifts.at[(g, t), 'Uplift Coefficient'] = fixed_GEN[g, t].dual_value()\n",
    "        for d in D:\n",
    "            ip_uplifts.at[(d, t), 'Uplift'] = output_DEM[d, t].dual_value() * q_ELA[d, t].solution_value()\n",
    "            ip_uplifts.at[(d, t), 'Uplift+'] = max(0,output_DEM[d, t].dual_value() * q_ELA[d, t].solution_value())\n",
    "            ip_uplifts.at[(d, t), 'Uplift Coefficient'] = output_DEM[d, t].dual_value()\n",
    "    \n",
    "    ip_uplifts_grouped = pd.DataFrame(columns=['Resource', 'Uplift', 'Uplift+'])\n",
    "    ip_uplifts_grouped.set_index(['Resource'], inplace=True)\n",
    "    for i in G.union(D):\n",
    "        ip_uplifts_grouped.at[i, 'Uplift'] = sum(ip_uplifts.at[(i, t), 'Uplift'] for t in T)\n",
    "        ip_uplifts_grouped.at[i, 'Uplift+'] = sum(ip_uplifts.at[(i, t), 'Uplift+'] for t in T)\n",
    "else: \n",
    "    print('The problem does not have an optimal solution.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "215bb901",
   "metadata": {},
   "source": [
    "### 2.2.2 ELM Pricing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "810adef1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# setup model\n",
    "elmpm = pywraplp.Solver.CreateSolver('GLOP')\n",
    "infinity = elmpm.infinity()\n",
    "\n",
    "# define variables\n",
    "q_GEN = {(g,t): elmpm.NumVar(0.0, infinity, \"q_GEN\"+str(g)+str(t)) for g in G for t in T}\n",
    "u_GEN = {(g,t): elmpm.NumVar(0.0, 1.0, \"u_GEN\"+str(g)+str(t)) for g in G for t in T}\n",
    "q_ELA = {(d,t): elmpm.NumVar(0.0, infinity, \"q_ELA\"+str(d)+str(t)) for d in D for t in T}\n",
    "u_DEM = {(d,t): elmpm.NumVar(0.0, 1.0, \"u_DEM\"+str(d)+str(t)) for d in D for t in T}\n",
    "q_IN_EV = {(v,t): elmpm.NumVar(0.0, infinity, \"q_IN_EV\"+str(v)+str(t)) for v in V for t in T}\n",
    "q_OUT_EV = {(v,t): elmpm.NumVar(0.0, infinity, \"q_OUT_EV\"+str(v)+str(t)) for v in V for t in T}\n",
    "soc_EV = {(v,t): elmpm.NumVar(0.0, infinity, \"soc_EV\"+str(v)+str(t)) for v in V for t in T}\n",
    "m_EV = {(v,t): elmpm.NumVar(0.0, 1.0, \"m_EV\"+str(v)+str(t)) for v in V for t in T}\n",
    "q_TRN = {(n, m, t): elmpm.NumVar(-infinity, infinity, \"q_TRN\"+str(n)+str(m)+str(t)) for (n, m) in L for t in T}\n",
    "d_TRN = {(n, t): elmpm.NumVar(-infinity, infinity, \"d_TRN\"+str(n)+str(t)) for n in N for t in T}\n",
    "\n",
    "# define constraints\n",
    "output_GEN = {(g, t): elmpm.Add(df_GEN.at[(g, t),'Q_MIN (MW)'] * u_GEN[g, t] <= q_GEN[g, t] <= df_GEN.at[(g, t),'Q_MAX (MW)'] * u_GEN[g, t]) for g in G for t in T}\n",
    "output_DEM = {(d, t): elmpm.Add(df_DEM.at[(d, t),'Q_MIN (MW)'] * u_DEM[d, t] <= q_ELA[d, t] + df_DEM.at[(d, t),'Q_FIX (MWh)']  <= df_DEM.at[(d, t),'Q_MAX (MW)'] * u_DEM[d, t]) for d in D for t in T}\n",
    "soc_def_EV = {(v, t): elmpm.Add(soc_EV[v, t] == soc_EV[v, t-1] + eff_IN * q_IN_EV[v, t] - (1/eff_OUT) * q_OUT_EV[v, t] - df_EV.at[(v, t), 'D_SOC_EV (MWh)']) for v in V for t in range(1, max(T)+1)}\n",
    "soc_def_EV2 = {(v, 0): elmpm.Add(soc_EV[v, 0]==df_EV.at[(v, 0), 'SOC_EV_MAX (MWh)'] + eff_IN * q_IN_EV[v, 0] - (1/eff_OUT) * q_OUT_EV[v, 0] - df_EV.at[(v, 0), 'D_SOC_EV (MWh)']) for v in V}\n",
    "soc_def_EV3 = {(v, max(T)): elmpm.Add(soc_EV[v, max(T)]==df_EV.at[(v, max(T)), 'SOC_EV_MAX (MWh)']) for v in V}\n",
    "soc_capacity_EV = {(v, t): elmpm.Add(df_EV.at[(v, t), 'SOC_EV_MIN (MWh)'] <= soc_EV[v, t] <= df_EV.at[(v, t), 'SOC_EV_MAX (MWh)']) for v in V for t in T}\n",
    "charging_speed_EV = {(v, t): elmpm.Add(q_IN_EV[v, t] + q_OUT_EV[v, t] <= df_EV.at[(v, t), 'Q_MAX (MW)']) for v in V for t in T}\n",
    "charging_mode_1_EV = {(v, t): elmpm.Add(q_IN_EV[v, t] <= big_M * m_EV[v, t]) for v in V for t in T}\n",
    "charging_mode_2_EV = {(v, t): elmpm.Add(q_OUT_EV[v, t] <= big_M * (1 - m_EV[v, t])) for v in V for t in T}\n",
    "capacity_TRAN = {(n, m, t): elmpm.Add(-df_TRN.at[(n, m, t), 'Q_MAX (MW)'] <= q_TRN[n, m, t] <= df_TRN.at[(n, m, t), 'Q_MAX (MW)']) for (n, m) in L for t in T}\n",
    "second_law = {((n,m), t): elmpm.Add(q_TRN[n, m, t] == -df_TRN.at[(n, m, t), 'B'] * (d_TRN[n, t] - d_TRN[m, t])) for (n,m) in L for t in T}\n",
    "second_law_fix = {t: elmpm.Add(d_TRN[\"N1\", t] == 0) for t in T}\n",
    "first_law = {(n, t): elmpm.Add(sum(q_GEN[g, t] for g in G if df_GEN.at[(g, t), 'Node']==n)\n",
    "                             + sum(q_OUT_EV[v, t] for v in V if df_EV.at[(v, t), 'Node']==n)\n",
    "                             - sum(df_DEM.at[(d, t),'Q_FIX (MWh)']+q_ELA[d, t] for d in D if df_DEM.at[(d, t), 'Node']==n)\n",
    "                             - sum(q_IN_EV[v, t] for v in V if df_EV.at[(v, t), 'Node']==n)   \n",
    "                             + sum(q_TRN[n_, m, t] for (n_, m) in L if n_ == n) == 0) for n in N for t in T}\n",
    "\n",
    "# define objective\n",
    "objective = elmpm.Minimize(sum(df_GEN.at[(g, t),'C_Q ($/MWh)'] * q_GEN[g, t] \n",
    "                             + df_GEN.at[(g, t),'C_U ($/h)'] * u_GEN[g, t] for g in G for t in T) \n",
    "                         - sum(df_DEM.at[(d, t),'C_Q ($/MWh)'] * q_ELA[d, t] for d in D for t in T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dade31ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... elm pricing computed \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# solve model\n",
    "status = elmpm.Solve()\n",
    "\n",
    "if status == pywraplp.Solver.OPTIMAL: \n",
    "    print('... elm pricing computed', \"\\n\")\n",
    "    # calculate prices\n",
    "    elmp_prices = pd.DataFrame([[n, t, first_law[n, t].dual_value()] for n in N for t in T], columns=['Node', 'Hour', 'Price'])\n",
    "    elmp_prices.set_index(['Node', 'Hour'], inplace=True)\n",
    "    \n",
    "    # calculate uplifts\n",
    "    elmp_uplifts = pd.DataFrame(columns=['Resource', 'Uplift'])\n",
    "    elmp_uplifts.set_index('Resource', inplace=True)\n",
    "    for n in N:\n",
    "        for g in G:\n",
    "            if df_GEN.at[(g, t), 'Node']==n:\n",
    "                objective, schedule = f_self_GEN(g, n, elmp_prices)\n",
    "                elmp_uplifts.at[g, 'Uplift'] = (objective \n",
    "                                                - sum(elmp_prices.at[(n, t), 'Price'] * MARKET_RESULTS_GEN.at[(g, t), 'q'] \n",
    "                                                      - df_GEN.at[(g, t),'C_Q ($/MWh)'] * MARKET_RESULTS_GEN.at[(g, t), 'q'] \n",
    "                                                      - df_GEN.at[(g, t),'C_U ($/h)'] * MARKET_RESULTS_GEN.at[(g, t), 'u'] for t in T))\n",
    "        for d in D: \n",
    "             if df_DEM.at[(d, t), 'Node']==n:\n",
    "                objective, schedule = f_self_DEM(d, n, elmp_prices)\n",
    "                elmp_uplifts.at[d, 'Uplift'] = (objective \n",
    "                                                - sum(df_DEM.at[(d, t),'C_Q ($/MWh)'] * MARKET_RESULTS_DEM.at[(d, t), 'q'] \n",
    "                                                      - elmp_prices.at[(n, t), 'Price'] *  MARKET_RESULTS_DEM.at[(d, t), 'q'] for t in T))\n",
    "else:\n",
    "    print('The problem does not have an optimal solution.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "401b548f",
   "metadata": {},
   "source": [
    "# 3 Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f4bf054",
   "metadata": {},
   "source": [
    "## 3.1 Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8362abf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>IP Prices</th>\n",
       "      <th>IP+ Prices</th>\n",
       "      <th>ELM Prices</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Node</th>\n",
       "      <th>Hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">N1</th>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">N2</th>\n",
       "      <th>0</th>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.2</td>\n",
       "      <td>14.2</td>\n",
       "      <td>14.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14.2</td>\n",
       "      <td>14.2</td>\n",
       "      <td>14.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>15.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>15.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>15.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>15.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>15.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>15.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>15.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">N3</th>\n",
       "      <th>0</th>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18.4</td>\n",
       "      <td>18.4</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18.4</td>\n",
       "      <td>18.4</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           IP Prices  IP+ Prices  ELM Prices\n",
       "Node Hour                                   \n",
       "N1   0          10.0        10.0        10.2\n",
       "     1          10.0        10.0        10.2\n",
       "     2          10.0        10.0        10.2\n",
       "     3          10.0        10.0        10.2\n",
       "     4          10.0        10.0        10.2\n",
       "     5          10.0        10.0        10.2\n",
       "     6          10.0        10.0        10.2\n",
       "     7          10.0        10.0        10.2\n",
       "     8          10.0        10.0        10.2\n",
       "     9          10.0        10.0        10.2\n",
       "     10         10.0        10.0        10.2\n",
       "     11         10.0        10.0        10.2\n",
       "     12         10.0        10.0        10.2\n",
       "     13         10.0        10.0        10.2\n",
       "     14         10.0        10.0        10.2\n",
       "     15         10.0        10.0        10.2\n",
       "     16         10.0        10.0        10.2\n",
       "     17         10.0        10.0        10.2\n",
       "     18         10.0        10.0        10.2\n",
       "     19         10.0        10.0        10.2\n",
       "     20         10.0        10.0        10.2\n",
       "     21         10.0        10.0        10.2\n",
       "     22         10.0        10.0        10.2\n",
       "     23         10.0        10.0        10.2\n",
       "N2   0          13.0        13.0        13.1\n",
       "     1          13.0        13.0        13.1\n",
       "     2          13.0        13.0        13.1\n",
       "     3          13.0        13.0        13.1\n",
       "     4          13.0        13.0        13.1\n",
       "     5          13.0        13.0        13.1\n",
       "     6          14.2        14.2        14.3\n",
       "     7          14.2        14.2        14.3\n",
       "     8          15.4        15.4        15.5\n",
       "     9          15.4        15.4        15.5\n",
       "     10         15.4        15.4        15.5\n",
       "     11         15.4        15.4        15.5\n",
       "     12         15.4        15.4        15.5\n",
       "     13         15.4        15.4        15.5\n",
       "     14         15.4        15.4        15.5\n",
       "     15         15.4        15.4        15.5\n",
       "     16         15.4        15.4        15.5\n",
       "     17         15.4        15.4        15.5\n",
       "     18         15.4        15.4        15.5\n",
       "     19         15.4        15.4        15.5\n",
       "     20         15.4        15.4        15.5\n",
       "     21         15.4        15.4        15.5\n",
       "     22         15.4        15.4        15.5\n",
       "     23         15.4        15.4        15.5\n",
       "N3   0          16.0        16.0        16.0\n",
       "     1          16.0        16.0        16.0\n",
       "     2          16.0        16.0        16.0\n",
       "     3          16.0        16.0        16.0\n",
       "     4          16.0        16.0        16.0\n",
       "     5          16.0        16.0        16.0\n",
       "     6          18.4        18.4        18.4\n",
       "     7          18.4        18.4        18.4\n",
       "     8          20.8        20.8        20.8\n",
       "     9          20.8        20.8        20.8\n",
       "     10         20.8        20.8        20.8\n",
       "     11         20.8        20.8        20.8\n",
       "     12         20.8        20.8        20.8\n",
       "     13         20.8        20.8        20.8\n",
       "     14         20.8        20.8        20.8\n",
       "     15         20.8        20.8        20.8\n",
       "     16         20.8        20.8        20.8\n",
       "     17         20.8        20.8        20.8\n",
       "     18         20.8        20.8        20.8\n",
       "     19         20.8        20.8        20.8\n",
       "     20         20.8        20.8        20.8\n",
       "     21         20.8        20.8        20.8\n",
       "     22         20.8        20.8        20.8\n",
       "     23         20.8        20.8        20.8"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MARKET_RESULTS_PRICES = pd.DataFrame(columns=['Node', 'Hour'])\n",
    "MARKET_RESULTS_PRICES.set_index(['Node', 'Hour'], inplace=True)\n",
    "\n",
    "MARKET_RESULTS_PRICES['IP Prices'] = ip_prices['Price']\n",
    "MARKET_RESULTS_PRICES['IP+ Prices'] = ip_prices['Price']\n",
    "MARKET_RESULTS_PRICES['ELM Prices'] = elmp_prices['Price']\n",
    "\n",
    "MARKET_RESULTS_PRICES.sort_values(by=['Node', 'Hour'],inplace=True)\n",
    "MARKET_RESULTS_PRICES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "074b38e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IP Uplift</th>\n",
       "      <th>IP+ Uplift</th>\n",
       "      <th>ELM Uplift</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Resource</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D3.1</th>\n",
       "      <td>-1442.874696</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D3.2</th>\n",
       "      <td>-646.442295</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D3.3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D3.4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D3.5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G1</th>\n",
       "      <td>2160.0</td>\n",
       "      <td>2160.0</td>\n",
       "      <td>139.04729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            IP Uplift IP+ Uplift ELM Uplift\n",
       "Resource                                   \n",
       "D2                0.0          0        0.0\n",
       "D3.1     -1442.874696          0        0.0\n",
       "D3.2      -646.442295          0        0.0\n",
       "D3.3              0.0          0        0.0\n",
       "D3.4              0.0          0        0.0\n",
       "D3.5              0.0          0        0.0\n",
       "G1             2160.0     2160.0  139.04729\n",
       "G2                0.0          0        0.0\n",
       "G3                0.0          0        0.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MARKET_RESULTS_UPLIFTS = pd.DataFrame(columns=['Resource'])\n",
    "MARKET_RESULTS_UPLIFTS.set_index(['Resource'], inplace=True)\n",
    "MARKET_RESULTS_UPLIFTS['IP Uplift'] = ip_uplifts_grouped['Uplift']\n",
    "MARKET_RESULTS_UPLIFTS['IP+ Uplift'] = ip_uplifts_grouped['Uplift+']\n",
    "MARKET_RESULTS_UPLIFTS['ELM Uplift'] = elmp_uplifts['Uplift']\n",
    "MARKET_RESULTS_UPLIFTS.sort_values(by=['Resource'],inplace=True)\n",
    "MARKET_RESULTS_UPLIFTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e6c658cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# profit / loss\n",
    "def f1_profit_GEN(g, n, market_prices, market_uplifts):\n",
    "    profit = (sum(market_prices.at[(n,t), 'Price'] * MARKET_RESULTS_GEN.at[(g, t), 'q'] for t in T) \n",
    "              - sum(df_GEN.at[(g, t),'C_Q ($/MWh)']  * MARKET_RESULTS_GEN.at[(g, t), 'q'] for t in T) \n",
    "              - sum(df_GEN.at[(g, t),'C_U ($/h)']  * MARKET_RESULTS_GEN.at[(g, t), 'u'] for t in T) \n",
    "             + market_uplifts.at[g, 'Uplift'])\n",
    "    return profit\n",
    "\n",
    "def f2_profit_GEN(g, n, market_prices, market_uplifts):\n",
    "    profit = (sum(market_prices.at[(n,t), 'Price'] * MARKET_RESULTS_GEN.at[(g, t), 'q'] for t in T) \n",
    "              - sum(df_GEN.at[(g, t),'C_Q ($/MWh)']  * MARKET_RESULTS_GEN.at[(g, t), 'q'] for t in T) \n",
    "              - sum(df_GEN.at[(g, t),'C_U ($/h)']  * MARKET_RESULTS_GEN.at[(g, t), 'u'] for t in T)\n",
    "              + sum(market_uplifts.at[(g, t),'Uplift'] for t in T))\n",
    "    return profit \n",
    "\n",
    "def f1_profit_DEM(d, n, market_prices, market_uplifts):\n",
    "    profit = (sum(df_DEM.at[(d, t),'C_Q ($/MWh)']  * MARKET_RESULTS_DEM.at[(d, t), 'q']\n",
    "                  - market_prices.at[(n,t), 'Price'] * MARKET_RESULTS_DEM.at[(d, t), 'q'] for t in T)\n",
    "             + market_uplifts.at[d, 'Uplift'])\n",
    "    return profit\n",
    "\n",
    "def f2_profit_DEM(d, n, market_prices, market_uplifts):\n",
    "    profit = (sum(df_DEM.at[(d, t),'C_Q ($/MWh)']  * MARKET_RESULTS_DEM.at[(d, t), 'q']\n",
    "                  - market_prices.at[(n,t), 'Price'] * MARKET_RESULTS_DEM.at[(d, t), 'q'] for t in T)\n",
    "              + sum(market_uplifts.at[(d, t),'Uplift'] for t in T))\n",
    "    return profit\n",
    "\n",
    "# opportunity cost\n",
    "def f1_opportunity_cost_GEN(g, n, market_prices, market_uplifts):\n",
    "    return f_self_GEN(g, n, market_prices)[0] - f1_profit_GEN(g, n, market_prices, market_uplifts)\n",
    "\n",
    "def f2_opportunity_cost_GEN(g, n, market_prices, market_uplifts):\n",
    "    return f_self_GEN(g, n, market_prices, market_uplifts)[0] - f2_profit_GEN(g, n, market_prices, market_uplifts)\n",
    "\n",
    "def f1_opportunity_cost_DEM(d, n, market_prices, market_uplifts):\n",
    "    return f_self_DEM(d, n, market_prices)[0] - f1_profit_DEM(d, n, market_prices, market_uplifts)\n",
    "\n",
    "def f2_opportunity_cost_DEM(d, n, market_prices, market_uplifts):\n",
    "    return f_self_DEM(n, n, market_prices, market_uplifts)[0] - f2_profit_DEM(d, n, market_prices, market_uplifts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "128dee7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\n PROFITS & LOSSES\n",
      "                      Profit\n",
      "Scheme Resource             \n",
      "G1     IP                0.0\n",
      "       ELM               0.0\n",
      "D3.4   IP               -0.0\n",
      "       ELM               0.0\n",
      "D3.5   IP                0.0\n",
      "       ELM               0.0\n",
      "D3.1   IP               -0.0\n",
      "       ELM       1442.874696\n",
      "D3.3   IP                0.0\n",
      "       ELM               0.0\n",
      "D3.2   IP               -0.0\n",
      "       ELM        646.442295\n",
      "G3     IP                0.0\n",
      "       ELM               0.0\n",
      "D2     IP                0.0\n",
      "       ELM               0.0\n",
      "G2     IP                0.0\n",
      "       ELM               0.0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Opportunity Cost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scheme</th>\n",
       "      <th>Resource</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">D2</th>\n",
       "      <th>ELM</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">D3.1</th>\n",
       "      <th>ELM</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">D3.2</th>\n",
       "      <th>ELM</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">D3.3</th>\n",
       "      <th>ELM</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">D3.4</th>\n",
       "      <th>ELM</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">D3.5</th>\n",
       "      <th>ELM</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">G1</th>\n",
       "      <th>ELM</th>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">G2</th>\n",
       "      <th>ELM</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">G3</th>\n",
       "      <th>ELM</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Opportunity Cost\n",
       "Scheme Resource                 \n",
       "D2     ELM                   0.0\n",
       "       IP                    0.0\n",
       "D3.1   ELM                   0.0\n",
       "       IP                    0.0\n",
       "D3.2   ELM                   0.0\n",
       "       IP                    0.0\n",
       "D3.3   ELM                   0.0\n",
       "       IP                    0.0\n",
       "D3.4   ELM                   0.0\n",
       "       IP                    0.0\n",
       "D3.5   ELM                   0.0\n",
       "       IP                    0.0\n",
       "G1     ELM                  -0.0\n",
       "       IP                    0.0\n",
       "G2     ELM                   0.0\n",
       "       IP                    0.0\n",
       "G3     ELM                   0.0\n",
       "       IP                    0.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PRICING_RESULTS_profits = pd.DataFrame(columns=['Resource', 'Scheme', 'Profit'])\n",
    "PRICING_RESULTS_profits.set_index(['Scheme', 'Resource'], inplace=True)\n",
    "for n in N:\n",
    "    for g in G:\n",
    "        if df_GEN.at[(g, t), 'Node']==n:\n",
    "            PRICING_RESULTS_profits.at[(g, 'IP'),'Profit'] = f2_profit_GEN(g, n, ip_prices, ip_uplifts)\n",
    "            PRICING_RESULTS_profits.at[(g, 'ELM'),'Profit'] = f1_profit_GEN(g, n, elmp_prices, elmp_uplifts)\n",
    "        for d in D: \n",
    "             if df_DEM.at[(d, t), 'Node']==n:\n",
    "                PRICING_RESULTS_profits.at[(d, 'IP'),'Profit'] = f2_profit_DEM(d, n, ip_prices, ip_uplifts)\n",
    "                PRICING_RESULTS_profits.at[(d, 'ELM'),'Profit'] = f1_profit_DEM(d, n, elmp_prices, elmp_uplifts)\n",
    "                \n",
    "print('\\\\n PROFITS & LOSSES')\n",
    "PRICING_RESULTS_profits.sort_values(by=['Scheme', 'Resource'])\n",
    "print(PRICING_RESULTS_profits)\n",
    "\n",
    "PRICING_RESULTS_oppcost = pd.DataFrame(columns=['Resource', 'Scheme', 'Opportunity Cost'])\n",
    "PRICING_RESULTS_oppcost.set_index(['Scheme', 'Resource'], inplace=True)\n",
    "for n in N:\n",
    "    for g in G:\n",
    "        if df_GEN.at[(g, t), 'Node']==n:\n",
    "            PRICING_RESULTS_oppcost.at[(g, 'IP'),'Opportunity Cost'] = f2_opportunity_cost_GEN(g, n, ip_prices, ip_uplifts)\n",
    "            PRICING_RESULTS_oppcost.at[(g, 'ELM'),'Opportunity Cost'] = f1_opportunity_cost_GEN(g, n, elmp_prices, elmp_uplifts)\n",
    "    for d in D: \n",
    "         if df_DEM.at[(d, t), 'Node']==n:\n",
    "            PRICING_RESULTS_oppcost.at[(d, 'IP'),'Opportunity Cost'] = f2_opportunity_cost_DEM(d, n, ip_prices, ip_uplifts)\n",
    "            PRICING_RESULTS_oppcost.at[(d, 'ELM'),'Opportunity Cost'] = f1_opportunity_cost_DEM(d, n, elmp_prices, elmp_uplifts)\n",
    "\n",
    "PRICING_RESULTS_oppcost.sort_values(by=['Scheme', 'Resource'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcd75559",
   "metadata": {},
   "source": [
    "## 3.2 Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2d4e765a",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use(['science', 'nature'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "49c05bc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lxhlMHTqx25o/Kd/CqyVr+KD8K2JHTuPjmEd54bxf8+KM+7Haatlff4SQoGCKG0t5ofg99tQVc6zpBOvinmDikDE8tP8fTAqL4KywsXxv9IXMGB7JT3aaWVO5jaFBQ/jFpBuYM2o6qy5eyvIjH3LJV/fy2Dk/5+6zFvTmbelWV5lQzkN3p04plQ3k4/ywp+D85pACRAGxwOy201rrzNZl09LStAxsJvyFUmqp1jqtD9YjmRCDwkDOhEM7+E/ZV5iL3iQ8aAg5sWaPedaWWkJVCPM23cfPz7yOn595HSOCh3KsqZKlha/w5rF13HXmtTx09kLGhnbc813UcIzXS9aSe2IHx5pO8MJ5v+biEdH8pfgdJoVFMCksgnOHTmJi2BieL17F+hO7+LJqD9OHTWL17Md4r/RLVpV9QW7lDoKUYt93X2JPnfPs6/OHTem2UVJQf4QfbH+ERkczD0Qm8OMJVzEkKLRHr0tFczUP7MtkVemX/HTi1TwQmUB40BAyD3/IS0c/IVSF8MqFKcwaOdXj9XJozbHmSpL3/Z31J3bS6LDxp6l3cM+k65m+4S4qbDW0aDtxo6axevZjPHPwbUJUMP89bl6HPYRdabQ3Y1BB1Nob+KDsKw43lXOosYyrTBdz6/jLePv4BmJGTiUy/IwOyx5trKDGXs+EISbeK/2SH0246qR7r3qjbSZ6vcep9Xi0Usqotc5SSuUDy12zFwE57aaFGNQkE0J48kUmglQQN46bx43j5lHadAKtNd/f+r9MHzaJD8s28V8Rc3jq3HvYMS/Do5FyxhATfz3/Xn4f9UOeKMqiyWHjUGMpBfVH+apqL7mVO3hpxv2UNlspbbbyy0k3cFbYWM4OP4NmbaOmpYFPyrdwqLGMBWPjSI36IbX2Bq6PuIQ/T72Tqa7DSa21gbMxo5Tic+tuzAdWYG2pZVTwMDZd8hzbagr59dfPE6SCCEKxdOrtfH+sc13XjY095cbBmNCRvDTjAY40lvNc8SoON5YzOmQ4BxuP8+qFDxE38twOjbYgFUSQgrPCInhtZioAJU0VhAcNASA37gkMKohgZSBUOZsTv5lyyym/Z2EGZ+NvdNAIfjIxvsP8W8Zf2uWyE8PGAGOw1JfwXPEqnitexdPT72HuqPOw6RbKm6tpdtho1i1EhI7CFDKSgw3HMYWMYLghvFd9rnq9x+l0yLdr4U/66tv16ZBMCH8y2DKxq+YAr5Ss4bqxsVw+emaPN5ZvlKzlqYNv8x3j+Vxhmsk1Y2Yz1BDWJzV15lhTJfX2JiaHjaPB0cSRpnIcWuPQDiaGjelwiE14cmgHr5R8yv8W/JMv5jzD3rpD3L5rGaEqhCFBIaRF/5SFZ1xO9Po7KLNVYXPYSTzjMl658CH+UPBPrjbN4grTRZ2uu20mfNJwuvPOO3VkZKT05RA+l5uby5VXXvkvrfWdvqxDMiF6IzMzk+zsbEwmE+Ds79T+NrPZTGZmJhUVFe7+TbNnz2b58uWddhSXTIhA0WhvptHRjDFkOHnV+5gQOsa1B8tT+0zIRX5FQJMLmoqBLjU1lZiYGLKyssjKyvK4rS2Lxdk/22q1djuCuGRCBIowQ6j7MOHskdO6vJ9c5FcIIQah+Ph4Nm/eDDiHI0hJSSElJcU932QyYbVa3eM+CSF6R658KoQQXpRW8DJLLa+4p7fMfR6A2I2/dt+2JOonpE29nYnrbqOkqdLjtp7obI9TYmIiK1asoLCw0OsDYAoxmEjDSQghvCht6u2dNoD0Nas73Hb08jd6vN6cnBzi4uKorKzsdH5UVBTZ2dk9L1QI0SmfHKprHeyvzcBSQviE6zMY6dsqJBOi99LT00lKSiI7O9tj5PDWQ3WtfZtaD9XFxcV1uz7JhBCe2mdChiMQAW+wnXotxOmSTAjhqW0mpHO4EEIIIUQPScNJCCGEEKKHpI+TCGjSn0MIT5IJITy1z4QMgCkCmgz2J4QnyYQQnmQATCGEEEKIXvLY46SUWgS0P82uUmv9tvdKEsJ/SCaE8CSZEIGu/R6nHOBTYD5gAQ4AC/v6QeXYtfAXPejPIZkQAUUyIYSnbvs4aa0PACillNZ6jevvpL4uQo5dC39xsv4ckgkRaCQTQnhqn4muOodnKKX+hnN37N/6vSoh/J9kQghPkgkRkLpqOG0GWi94dDWwpv0dlFIxQBKA1jpJKRUFZLim57ef7uO6hfA2yYQQniQTIiB11XD6O1AB5APRXdwnyhWExUqpeCDR9ROrlErAefzbPa21zurj2oXwJsmEEJ4kEyIgddVwsgBvAiYgqrM7aK2zXEFIwvltI0lrbVVKWYAEwNRu2q210x84jx26jh8K4TW5ubltO51G9mARyYQY1CQTQnjqKhNdNZyygUKcZ0oUdHaH1m8HrlNTfwBUKqWMgBGwdjLtJp3+hK+1/Ue8dOnSoh4sIpkQg5pkQghPXWWiuz5O812/s7taqVIqw/VnCs5TVJe7phd1Mi3EQCaZEMKTZEIEpK4aTsuBdEC5/u4wRofrWHTb49FWnMequ5oWYiCTTAjhSTIhAlJXl1xRWuttWuutOEPRp2RgM+EvTuGCppIJERAkE0J46ulFftPbjM/xaF8XIceuhb84hQuaSiZEQJBMCOGpRwNgur5B3AOglLob2NbvlQnhxyQTQniSTIhA1dWhura6Gp9DiEAlmRDCk2RCBAyPhpNSKrLdz9l0MT7H6ZBj18JfnKw/h2RCBBrJhBCeTtbHqbOzG7b0dRFy7Fr4ix7055BMiIAimRDCU7d9nLTWj3u3HCH8m2RCCE+SCRHoetLHSQghhBBCIA0nIYQQQoge8zhUp5R6DOeYHMr1GwCtdWpfPmhrpz+5cKPwtR50hJVMiIAimRDCU7edw7XWDwEopUbx7VkSV/d1EdLpT/iLk3WElUyIQCOZEMJTjwbABDKBSiAfGZ9DCJBMCNGeZEIEpK4aTgeANwET/TA+hxADkGRCCE+SCRGQuuocvhqwAPOBwr5+UBnYTPiLU7igqWRCBATJhBCeTnqRX9cosDE4A5EB3NrXRcixa+EvenJBU8mECCSSCSE89aSPUzwwB7C6pg90tiKlVAxgxhmcFJy7azMAtNbzlVJRbaf7oHYhfEUyIYQnyYQIWB0aTlrr5UqpFTiDoYGcLpaNbfPBX4yzc2AiEKuUSsC5+9Y9rbXO6pdnIEQ/k0wI4UkyIQJZV32cMnEesz4ALO/sDlrrTKWUEee3iSzApLW24vxmEdXJtBADmWRCCE+SCRGQujqrTmmttwEopVSnd3Dugl2otU50TVe6AmLEufu2/bRba6c/QAY3Ez6Rm5vbttNpZA8WkUyIQU0yIYSnrjKhtHYP/IpS6q9a618opWbh3K0KkNEajraUUitxjuEBsBLnNwaza3oRzmPZ7mnXtwoA0tLStHT6E/5CKbVUa53WxTzJhAg4kgkhPLXNRPs9TnFKqbtdf+e7fscC29qvpPUbRDttb7O2mxZiIJJMCOFJMiECWvuGkwXYgvMaREIIyYQQ7UkmREBr33Da3Nnu1r4mF28U/qIHg/1JJkRAkUwI4elkF/l93BtFyMBmwl/04IKmkgkRUCQTQnhqn4muhiMQQgghhBDtSMNJCCGEEKKHfNJwkos3Cn9xChc07VeSCeEvJBNCeDrpRX69QY5dC3/RkwuaeoNkQvgLyYQQngZUHyer1UpSUhIpKSlkZmZ2mJ+SkuKDqoQQQgjRHwbCdt+vG04rVqwgKSkJs9mM1Wr1dTlCCCGE6EcDYbvvk0N1PRUbG8uiRYtISkpi8WLnyP75+fmkp6cDEBUVRUpKChaLxf0ip6enYzKZMJvN7vvl5+cTExODxWJh+fLlWCwWj/vl5OSwefNmAMxmc+fFCCGEEKJfDYTtvse16rzlzjvv1JGRkT0e2CwrK4v09HTy8vJISkoiIyMDi8XCX/7yF+677z6sVisWi4Xs7Gyio6MpLCwkOjqaiooKUlNTWbFiBbGxsVgsFoxGIytXruxwv+joaOLj44mKkgt0B5Lc3FyuvPLKf2mt7/RlHaeaCSH6i2RC+AN/2u63z4Rfdw5PSUkhNTWVhIQELBYL+fn5He4TFRXlvt1oNJKcnIzVaqWyspKMjAyMRqN7Xqv29wPncdWUlBRWrlx52s9PDBzSEVYIT5IJ4Uv+uN1vnwm/PlSXlJREYmKiuzWYnJxMYmIiiYmJmEwmjxcFYOHChSQlJQHd73prf78VK1ZQWFiIyWTqnycihBBCiJMaCNt9nxyqS0tL0/JNQvgLpdRSrXWaL2uQTAh/IpkQwlPbTMgAmCKgyWB/QniSTAjhSQbAFKIN6c8hhCfJhBCe+nQATKVUvFIqwfV3lFIqWymV3dm0EIFAMiGEJ8mEGGx6vcdJKRUFJAJ5rptSXNOxrpDMbzuttc463WJ7IyUlxT2IVkpKCjk5OWRnZ7s7hJnNZvfopMnJyVgsFrKyskhOTvZFuWIAGyiZEMJbJBPCF/p7u9/rhpPW2qKUygBiXTeZtNZWpZQFSOhk2q312DXQr2N0ZGVlERcXR0JCgvu0w9mzZ5OamkpMTIzHfbOzs92DbYnBLzc3t23fici+WOdAyIQQXZFMiMGgL7f7XWWiL/s4VSqljIARsHYy7eatY9fZ2dlkZGQAzjEcMjIyyMzMJD093X2qY+vpiykpKaSnp7tPVxSDW9t/xEuXLi3qp4fxu0wI0RXJhBgM+nK731Um+vKsOjOwHEgFVnQyfVKPvr2DR9/eAcDFD77P/pJqth6o5LL/+wiA1NfyefajrwE45953KDlRz/qvj7uXaa/tIFdJSUnMnj3buZ7UVMxms8eYD7Gxse6RSIXoI6edCSEGGcmE6KAvt/3e2O6f1h4nrXU+kO/624LzWHUra7vpk3r4lpnuv7c9foP77/V/XABA+o++3c22/9mbAZgweiiXnTe+0/XNnz+fnJwc4uPjycjIIDo6utvHN5vNJCYmMn/+/FMpWwi3vs6EEAOdZEKcTF9u+72x3ffrkcNP1+LFi0lKSnIPp56Q4DyE3naXXdtddEajscMxUCGEEEIMDN7Y7g+Ii/wK0V/kgqZCeJJMCOFpQF3kV4j+JoP9CeFJMiGEpz4dAFMIIYQQIpAM6j5OmZmZHoNetZ6W2NlAWBUVFe7e9rNnz2b58uXS30kIIYQYQLyx3fdJw6l1YDNvHLtuHfQqKyuLrKwsj9vaaj0d0Wq1ukccFYOfv13QVPpziFNl13auy3uY4cHhDDeEM2/Uefxy8o28VrKGsuYqrjRdxIXDz0Yp1aP1SSbEQNfX2/2AvchvfHw86enpREdHdzoQlslkwmq1uk9jFIFB+nOIgWp7TSFZx9eTFv1THoxMpNbeSK29gTNCRwPQ5LCxt+4Qzxx8G1PICLbMfYFyWxWmkBEYlKHL9UomxGDRV9v99pkY1IfqutJZyzMxMZEVK1ZQWFgo4zgJIfzaK0dz+O03GTw7/ZcYlIFrxsZ2uM/PzryWn515LVprjjSVo5TiUcvrvFqyhhvGzeVXk24kZuQ5PqheCO/ry+2+X3UOTyt4GbX6GvdPXvU+8qr3edyWVvAyABPX3dbhtu7k5OQQFxfX5fyoqCgKCwv77LkIMVDZHC2+LkF04z9lG0krfIU1scv44YQrT3p/pRRnhUUA8PT0X7Bp7nPMHB5Fvb2pv0sVokf6a9vfX9t9v+rjlDb1dtKm3t7h/vqa1R1uO3r5Gz16rPT09A6dxDobCKt1l53sbQosg60/R6O9mSFBIaw4vo6vqvZy7+SbiAw/o9tlqmx1PH3wLYYawrhn0veZuv5Oro+Yy08mXMXlppndHtbpD1prHDho0XZCVQg23UJNSwMt2k6LtmMMHka4YQhHmypQKIKUIjxoCMaQ4VS31KE1hAQZCFHBhAQNnp3qJU0VHG4sZ8HYOC6beyGjQob1aj2R4Wdw35Sbu5w/2DIh/F9fb/v7ervfPhM+GQAzLS1Ny7Fr4S+UUku11mm+rOF0M2G11fL/Dr3Ps8Xv8nHMo0wcYuKJg1m8eOQTrhg9kxdn3M/I4I4b2mUHVvB40Uquj7iEP0T9mKihEyhpquD1krW8UrKGhPGX8XDUD/mm7hDnDptEo70Zm25hRPBQPqvcwcHGUsqarRiUgfum3MzKY5/xTunn1NobqG1p4MUZ91Npq+GnO5e5Gz4PRiZyz6TrOXfDXVTYqmnRduaMOpfVsx/jjp3LeLkkB4UiRAVz/Io3WVu5jbv3PE2wMhCsDDw3/VcsGBvH1A13otFoDTdEXELmBb/lpq1LWFO5HZtuIUQFU331uzx78B0e2v+iuzH1zsVLmBw2jvi8FEJVCCFBBm6fEM/vIhP48Y50DjeVE6wMTAqL4J8zHuSlI5+wqvQLDMpAkFI8c+4vqLHX84jldezagV07+PGEq7hx3Dxu2/EINS0N2LWDGcMjeeLcxaRbXiencisOrdFoPpj1JzZVf0P6gTcwEIRBGXggMoG5o87jrt1PMswQxlDDEC4ZNZ0fTbiKd45v4HBTOeYDK7g/8lZ+O+XW0/ik9cxgyIQQfaltJgbP1zEhAtTW6gLi81K4fuwl5Mx+jBkjzgZg2bRF/F/UT3i7dAMjDEPJOvYZLdrOf0XMYXPVPq4eM4uRwUPZMOcpzh02yb2+CUPG8LvIBH4XmUCLw84JWw3X5KVSYaum2dHCb6bczLJpi3jz+DqstloiQo1MCR8HwNnhZ3B9xCUMN4Qz3BBGRMgoIkJG8ebMhwl2NVzGhIwE4LO4JzGoIAwqiFDl/Ff04oz7eWnGAwSpb3sR3Dz+Um4ef2mH533k8tc73PburKUdbvvlpBu5+6wFNDtasOkWRrkakB/OeoRmbaPZ0cLYUGdNv51yK7V2Z8Mn1LW3as6oczEGD8OBxq4djAgOJ1gZuGbMbIKUwkAQ04adCcDtE+bjwIGBIEwhIwC4PmIuc0ZNR+E8bDYkKJTzhk3m/ikJroaXnalDJxKsDFwfcQl19kbq7I0MN4QDUNxYxq7aIpaf/xsWRMzp6cdCCNFPZI+TCHj+8O064q4Ybf/J2QwJCuHCEZGsnv0Yjx14g7eObyBEBWNQQay46Pccaazg4YIXMRCEA82iMxfw3+O+w9GmCqaEd36x61Y5Ffn8yfIqX1j3kDD+Ml67MLXHp6g7tIOalgZGBg/t8TKib+RZKthcUM7dV59DaVUjE01D+/0x/SETsp0Q/kT2OAnhZxadtYD7L02hyWFz3/bjCVdxlelibA47dm1ndPAIQsKDuX9KAi3ajkM7mGc8n5Cg4JM2mgDix8QQPyaGKlvdKfePCVJBve5TI3rP7nAwwRjOtIkj+fybUl7bcICMxfN8XZZPHK2sZ8PeUm65ZDK3PpnL8qR5jBsV7uuyRADyyVl1rZ3+XB2uhPAZf+kIe7T4CM89+hT7Nu5kYtgYACaFjWPOqOl8d/QFfM80kzBDKGNDR3Ht2FhmGS4k6PAkxoaOOqXH0Vrz+Fv7OFJZ3x9PQ/ShVZuLSXkln4mmoVw1YwKXn38Gf717LmXVjbz91cF+e1x/yUTrduLld7I5WlmPIUhxrKqBYEMQj9w2i3GjwsmzVFDfFLhngTa32DnqyvKD/97C4Yo6DpbV8tCreQC8tsFC1sYiH1YIxeV11DbaqG6w8cbnB3xaS2+1z4TPhiNoPVvCl/yp4eYvtQRaHf4y2B/0LBNbD1RSVt1Io83OvpJqtNbc8NinFJfXdbvc3iNVPPL2DpRSxEaNYczwIeRZKsjZcdTjfv7y/oP/1OLtOmoabBwsq2XBrDN5+JYLPeoIClKcqGvmmLWh3x7fXzJR3+wgLS2NytAz2X3YynhjOPcuOA+AGZOdg3y+vuEA+0uq+7WOvn7/e9s9pm0dtY02tNb8J+8wL3zyDQALZp3JyKGhjBoaynUXO/vcXTTFRFz0WKrqm/nNPzed9LH3l1Tz0dYjACQ8mcu+kmpqGmzsPVLVZS2dKatu5NOdJQCYV+1i1yEr1fXNHKpwNvKeeH83q7c7//ecqGvu2QvQia7qqHM1pqsbbNQ2Ovfil1Y10GJ3oLU+5ffAaxf5VUpFKaWylVLZ7ecVFRV1soT3+cs/ZvCfWqSO/tPbTGit3f+4Ptl+hIJjNUSNH8Fvv38+SimeuiOOSWOGkrWxiJfWFngs+/rnB9hVfIKJpqHERY8F4KY5kwkLNWCzO7DZnf9E3tp4EFuLw69ed3+p5XTr2FdSTXOLnT2Hrfz+9a2Ac09AvqUC+PaffKvV24/y+ucHCA02YBo+pEMd0yaM5JfXTmdX8Qn+58WvTqs2X+suE9l7q6httPGb75/P/JkTO13+idtjuSjSxJPv7+atjf2zF+503//j1ga2HqgE4AdPr+PLfWUUHKvmjuc3AHC4oo6aBlt3q3DXYXc4APjhM5+xregEN8+ZzCM/nAXAVTMmMDI8BOOwUK64wDkEyQWTjEyJGI4hSBF/4USUUryy3sK+do3NZz7YQ2VtE002O2XVjYDztY2MGIbleA3Pf7wXgBfXFrD1QCWfrs11/6/ZfcjK2l3HAPjHmv0Ul9dR3WBj4/4yAF74+SXMPSeCs8YM48EbLwDgxthJXDjZSHWDjcv+8BEOh+a1DRbM7+4EYHHGl+wqPsExawP3LN8IwMGy2g5fGNq+N6s2H+KYtYFvjlax4JEcADJz9vHyOuc4TQuf/oySEw18ua+MxKfWAfDV/rJe7X3vzz1OKUAiYFZKJfR0oe4+pP0xT+o4/Vr8pY7u5p3ksSJ7Vcip6zITRxvDOFHXzOGKOl50/UP6eNsRNhWU8/4na7nvn5uwOxw8dNOFzJsW4V4uNzeXcyaMRCnFvGnjmDctgiabnSUrtpGbm8vI8BCCDUGMDA/hmosmupcBmHtOBAtmnUltYwvr9x7HEKSw1A9jU0E5AE++v5u6phYKj9fw6noLubm5vLflENuKnBuBx97dia3Fwd4jVSz9x4cAvLXxIHsOW7E7HDz69g4AXnwrm1WbiwFng6HgWDX1TS088f5ucnNz2VxY7v6W+9LaAg6V12Gta2aL1blX4fNvSt3fXjNz9vHOh59SVt3I31Y7v2mv3XWMDXuPA/Cb5/9DZW1Tp69jbm5uh+cE8N6WQ/w9a3WH57TyyyIA9tYO7/Ccdhw8warNxeTm5nZ4TgBP/PtjXnGt//ev53O4op4Jo4dyQ+xZ5ObmMnZEGMPDQ6hpsHHRA+/hcGjWf32cx//9MbfOncJDN327p6kr0yaO5K4rz6G5xc49z7wPOPdIvp93CIBX1luwHK/ho+w1PPWfPYBzQ/HJdudr/daHn3a3+siTFtA3uszEzBFVDA8L6XSh9nm+7btnc+n0cZRVN5Lm+iy+s6mYXcUn0Frz57ec79tLb2Xz7ibnZ/GNzw+wr6SaxmY7v3C9fnmWCj7IPwzAv9YVcrCslgZ7EGkrt7mXych2fu6uSPuYl97OZl9JNQufdm6IV3xRxAf5h9Fac+PSd6httFFwvIYc1+f3r3fPZd60CCy78njophkAvLyukNXbj+JwaNJWOnPb2ftY2RzCgked71nW/Vcw62yT+0SNk/1PHB4WwvWzzwLAEKQIDzFwuKKO373wHwBMw4fQ3OJgxuTR3H55NABFu7cQGmzgokgTz//8EgAmjRmKcVgoAF8fsQLOvV8Vtc7GVlHhfoIURI8fwe9vmdnlezZtwkgmjB7KyPAQdj55I599to4Fs87iZ1dOBeCBGy8gctxwjENDOX+480tjzs4Ssl17yH/87Hoqa5s42BDOg//eAsCRSmeD7ZwzRrJmyTXk5ubywA0X8MtrpwOwNu1aJo0dxrxpEdw5w9kA/aqgnEOuhuttz3zG2rVru3wd8cY4TkqplVrrRKVUFJCgtV7WZt6XQOuwtUV47haOpOvdxH09z5uP1dt5/lJHd/P8pY7u5rW/PZJvgzBEa93vPW79KBN9vT5vz/OXOrqb5y91dDev/e2RSCb6an39Mc9f6uhunr/U0dt57W+PpJNM9OdZdZVKKSNgBKxtZ3gjkEL4IcmEEJ4kE2LA6c89TlGA2TW5SGtt7ZcHEmKAkEwI4UkyIQYinwyAKYQQQggxEHl1OILuzqDwch0JrjoyXN94fFVHfGuHSF++Nu3q8Mlro5SKafO4Rn/5rPQ3f3mekolu65BMeJG/PE/JRLd1BHQmvD1yeOsZFLFKqQStdZaXH79VFJAN5GutLb4owPVhSwTyXDf55LXppA5fvTaxWuv5rnoWA9H4x2elv0kmXCQTHUgmJBOSCU9+kQlvD4Bpch3DtuB84X0lH8gCopRS8b4owPVhy2hzk09em07q8Mlro7XOdHUSNbse318+K/3NX56nZKLrOiQT3uUvz1My0XUdAZ0JbzecujyDwttcH4Qc/OcfUEC/NkqpGCBVa53oeny/eT36md88T8lE1yQTXuU3z1My0bVAzoS3D9WZgeWuvxd5+bHbMiqlMnC+0Ok+rKOtQH9tUnGGIANYif+8Hv3NX56nZKJrkgnv8pfnKZnoWkBnQs6qE0IIIYToIZ9d5FcIIYQQYqCRhpMQQgghRA9Jw0kIIYQQooek4eTnlFKLXWcStA7+tdjXNQnhS5IJITxJJrxLGk5CCCGEED3k7eEIRO+kKqVaB/jKbh3LwjVvEc5xLVJct0cBca55Gb4a8VaIfiaZEMKTZMJLpOE0MKRrrfNdH/hYIElrneia7mrU1nS50rgYxCQTQniSTHiJHKobXNwjuEoYhAAkE0K0J5k4TbLHaWDKUEqtdP29CLC6RlIF52iqQgQayYQQniQT/URGDhdCCCGE6CE5VCeEEEII0UPScBJCCCGE6CFpOAkhhBBC9JA0nIQQQggheuj/A6e/K5wEIsiwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x144 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MARKET_RESULTS_GEN.sort_values(by=['Resource', 'Hour'], inplace=True)\n",
    "MARKET_RESULTS_DEM.sort_values(by=['Resource', 'Hour'], inplace=True)\n",
    "\n",
    "with plt.style.context(['science', 'nature']):\n",
    "    fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(10,2))\n",
    "\n",
    "    i=0\n",
    "    for n in sorted(N):\n",
    "        ax[i].plot([t for t in T], [MARKET_RESULTS_NODE.at[(n, t), 'Generation'] for t in T], label='GEN', linestyle='dotted')\n",
    "        ax[i].plot([t for t in T], [MARKET_RESULTS_NODE.at[(n, t), 'Demand'] for t in T], label='DEM', linestyle='dashed')\n",
    "        ax[i].set_title(n)\n",
    "        ax[i].set(xlabel ='Hour', ylabel ='Total Load', xlim =(0, 24), ylim =(0, 500)) #title =...\n",
    "        ax[i].legend(title='Schemes', loc = 'best')\n",
    "        i+=1\n",
    "    fig.savefig(scenario + 'system_load.jpg', dpi=300)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d92587cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x144 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prices over time\n",
    "with plt.style.context(['science', 'nature']):\n",
    "    fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(10,2))\n",
    "\n",
    "    i=0\n",
    "    for n in sorted(N):\n",
    "        ax[i].plot([t for t in T], [ip_prices.at[(n, t), 'Price'] for t in T], label='IP', linestyle='dotted')\n",
    "        ax[i].plot([t for t in T], [elmp_prices.at[(n, t), 'Price'] for t in T], label='ELM', linestyle='dashed')\n",
    "        ax[i].set_title(n)\n",
    "        ax[i].set(xlabel ='Hour', ylabel ='Price)', xlim =(0, 24), ylim =(0, 40)) #title =...\n",
    "        ax[i].legend(title='Schemes', loc = 'upper right')\n",
    "        i+=1\n",
    "    fig.savefig(scenario + '_prices.jpg', dpi=300)      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cca316d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# financial balance \n",
    "# note: financial balance is the only design criteria that is not fulfilled\n",
    "\n",
    "schemes = ['IP', 'ELM']#, 'CH']\n",
    "values_g1 = [ip_uplifts_grouped.at['G1', 'Uplift'], elmp_uplifts.at['G1', 'Uplift'], ip_uplifts_grouped.at['G1', 'Uplift+']]\n",
    "values_g2 =[ip_uplifts_grouped.at['G2', 'Uplift'], elmp_uplifts.at['G2', 'Uplift'], ip_uplifts_grouped.at['G2', 'Uplift+']]\n",
    "values_g3 =[ip_uplifts_grouped.at['G3', 'Uplift'], elmp_uplifts.at['G3', 'Uplift'],ip_uplifts_grouped.at['G3', 'Uplift+']]\n",
    "values_d31 =[ip_uplifts_grouped.at['D3.1', 'Uplift'], elmp_uplifts.at['D3.1', 'Uplift'],ip_uplifts_grouped.at['D3.1', 'Uplift+']]\n",
    "values_d32 =[ip_uplifts_grouped.at['D3.2', 'Uplift'], elmp_uplifts.at['D3.2', 'Uplift'],ip_uplifts_grouped.at['D3.2', 'Uplift+']]\n",
    "values_d33 =[ip_uplifts_grouped.at['D3.3', 'Uplift'], elmp_uplifts.at['D3.3', 'Uplift'],ip_uplifts_grouped.at['D3.3', 'Uplift+']]\n",
    "values_d34 =[ip_uplifts_grouped.at['D3.4', 'Uplift'], elmp_uplifts.at['D3.4', 'Uplift'],ip_uplifts_grouped.at['D3.4', 'Uplift+']]\n",
    "values_d35 =[ip_uplifts_grouped.at['D3.5', 'Uplift'], elmp_uplifts.at['D3.5', 'Uplift'],ip_uplifts_grouped.at['D3.5', 'Uplift+']]\n",
    "\n",
    "data = np.array([values_g1, values_g2, values_g3, values_d31, values_d32, values_d33, values_d34, values_d35])\n",
    "data_shape = np.shape(data)\n",
    "\n",
    "# Take negative and positive data apart and cumulate\n",
    "def get_cumulated_array(data, **kwargs):\n",
    "    cum = data.clip(**kwargs)\n",
    "    cum = np.cumsum(cum, axis=0)\n",
    "    d = np.zeros(np.shape(data))\n",
    "    d[1:] = cum[:-1]\n",
    "    return d  \n",
    "\n",
    "cumulated_data = get_cumulated_array(data, min=0)\n",
    "cumulated_data_neg = get_cumulated_array(data, max=0)\n",
    "\n",
    "# Re-merge negative and positive data.\n",
    "row_mask = (data<0)\n",
    "cumulated_data[row_mask] = cumulated_data_neg[row_mask]\n",
    "data_stack = cumulated_data\n",
    "\n",
    "cols = ['IP', 'ELM', 'IP+']\n",
    "resources =['G1', 'G2', 'G3', 'D2', 'D3', 'D4', 'D5', 'D6']\n",
    "\n",
    "with plt.style.context(['science', 'nature']):\n",
    "    fig, ax = plt.subplots(1, figsize=(5, 4))\n",
    "\n",
    "    for i in np.arange(0, data_shape[0]):\n",
    "        ax.bar(cols, data[i], width=0.4, bottom=data_stack[i], label=resources[i])  \n",
    "    ax.set(xlabel ='Scheme', ylabel ='Uplift)', ylim =(-3000, 3000)) #title =...\n",
    "    ax.axhline(0, color='black', linewidth=0.5, linestyle='--')\n",
    "    ax.legend(title='Resources', loc = 'lower right')\n",
    "    fig.savefig(scenario + 'uplifts.jpg', dpi=300)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6debd82",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "393a1223",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e0fa8ba",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}