Hydrology_Git.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1b12121f",
   "metadata": {},
   "source": [
    "\n",
    "Hydropower System Performance and Financial Analysis Script\n",
    "============================================================\n",
    "\n",
    "This script performs a comprehensive analysis of a hydropower system, calculating\n",
    "its technical and financial metrics over a project's lifetime. The script handles\n",
    "data preprocessing, turbine efficiency modeling, renewable energy estimation, and\n",
    "cost-benefit analysis, presenting results through plots and outputs.\n",
    "\n",
    "Key Features:\n",
    "-------------\n",
    "1. Load and preprocess historical flow data.\n",
    "2. Calculate flow-duration and power-duration curves.\n",
    "3. Model turbine efficiency and plant performance.\n",
    "4. Estimate renewable energy available and firm power.\n",
    "5. Perform financial analysis, including cash flow, NPV, IRR, and payback period.\n",
    "\n",
    "Inputs:\n",
    "-------\n",
    "1. Historical flow data in an Excel file with columns:\n",
    "   - \"Date\": Timestamps of flow readings.\n",
    "   - \"Flow (m³/s)\": Recorded flow rates.\n",
    "2. Plant and turbine parameters (e.g., gross head, design flow).\n",
    "3. Financial parameters (e.g., inflation rate, discount rate).\n",
    "\n",
    "Outputs:\n",
    "--------\n",
    "1. Efficiency, flow, and power data tables.\n",
    "2. Power-duration and cash-flow plots.\n",
    "3. Key metrics: NPV, IRR, payback period, and firm power energy.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c4625548",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install numpy_financial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d2421fec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy_financial as npf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6b737e8",
   "metadata": {},
   "source": [
    "\n",
    "SECTION 1: Load and Preprocess Flow Data\n",
    "=============================================================\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3bd4e946",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  pip uninstall openpyxl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "01321747",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install openpyxl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "271c2188",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: openpyxl in c:\\users\\kumab\\anaconda3\\lib\\site-packages (3.1.5)\n",
      "Requirement already satisfied: et-xmlfile in c:\\users\\kumab\\anaconda3\\lib\\site-packages (from openpyxl) (1.1.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install --upgrade openpyxl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c05666f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Set the parameters for artificial flow data\n",
    "days_in_year = 365  # Number of days in a year\n",
    "min_flow = 1  # Minimum flow value\n",
    "max_flow = 10  # Maximum flow value\n",
    "\n",
    "# Generate random flow data using a non-uniform distribution (normal distribution, clipped to the range)\n",
    "mean_flow = (min_flow + max_flow) / 9  # Mean flow value\n",
    "std_dev = (max_flow - min_flow) / 10  # Standard deviation (to keep most values within range)\n",
    "\n",
    "# Generate random data and clip it to the desired range\n",
    "flow_data = np.random.normal(mean_flow, std_dev, days_in_year)\n",
    "flow_data = np.clip(flow_data, min_flow, max_flow)\n",
    "\n",
    "\n",
    "# Create a DataFrame with the generated data\n",
    "dates = pd.date_range(start=\"2024-01-01\", periods=days_in_year, freq=\"D\")\n",
    "Input_flow_data = pd.DataFrame({\n",
    "    \"Date\": dates,\n",
    "    \"Flow (m³/s)\": flow_data\n",
    "})\n",
    "\n",
    "    \n",
    "# Rename columns for consistency\n",
    "Input_flow_data.columns = [\"Date\", \"Flow (m³/s)\"]\n",
    "Input_flow_data[\"Date\"] = pd.to_datetime(Input_flow_data[\"Date\"])\n",
    "Input_flow_data[\"Flow (m³/s)\"] = pd.to_numeric(Input_flow_data[\"Flow (m³/s)\"], errors=\"coerce\")\n",
    "Input_flow_data = Input_flow_data.dropna(subset=[\"Flow (m³/s)\"])\n",
    "\n",
    "# Filter and sort flow data\n",
    "flow_raw = Input_flow_data[\"Flow (m³/s)\"].values\n",
    "non_zero_flow = flow_raw[flow_raw > 0]\n",
    "sorted_flow = np.sort(non_zero_flow)[::-1]\n",
    "\n",
    "# Calculate exceedance percentages and flow rates\n",
    "percentiles = np.arange(0, 105, 5)\n",
    "len_sorted_flow = len(sorted_flow)\n",
    "index = (percentiles / 100.0) * (len_sorted_flow - 1)\n",
    "rate_flow = np.interp(index, np.arange(len_sorted_flow), sorted_flow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0059cf72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: openpyxl\n",
      "Version: 3.1.5\n",
      "Summary: A Python library to read/write Excel 2010 xlsx/xlsm files\n",
      "Home-page: https://openpyxl.readthedocs.io\n",
      "Author: See AUTHORS\n",
      "Author-email: charlie.clark@clark-consulting.eu\n",
      "License: MIT\n",
      "Location: c:\\users\\kumab\\anaconda3\\lib\\site-packages\n",
      "Requires: et-xmlfile\n",
      "Required-by: \n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip show openpyxl\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "988f775e",
   "metadata": {},
   "source": [
    "Section 2: Define Plant and Financial Parameters\n",
    "=============================================================\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "79b76057",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define physical parameters\n",
    "Q_des = 1  # Design flow rate for a single turbine (m^3/s)\n",
    "j = 2  # Number of jets\n",
    "num_turbines = 2  # Total number of turbines\n",
    "# Efficiency adjustment percentage (input parameter)\n",
    "efficiency_adjustment = 3.0  # Enter value between -5% and 5%\n",
    "rho = 1000  # Density of water (kg/m^3)\n",
    "g = 9.81  # Gravitational acceleration (m/s^2)\n",
    "l_t = 0.02  # Transformer loss\n",
    "l_para = 0.01  # Parasitic losses\n",
    "e_g = 0.97  # Generator efficiency\n",
    "l_hydr_max = 0.03  # Max hydraulic loss\n",
    "h_tail_max = 3  # Max tailwater effect (m)\n",
    "Q_max = np.max(flow_raw)\n",
    "l_dt = 0.02  # downtime loss (upto 5%)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e3a113f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define financial parameters\n",
    "Inflation_rate=10/100 #12%\n",
    "discount_rate=10/100 #12%\n",
    "Project_life_time=30 #years\n",
    "Debt_to_equity_ratio=66/100 #66%\n",
    "Interest_rate_on_debt=8/100 #8%\n",
    "Loan_term=15 #15years\n",
    "project_lifetime = 30  # years\n",
    "revenue_per_kwh = 0.1500  # $/kWh\n",
    "base_om_cost = 931813  # Base O&M cost ($)\n",
    "total_capital_cost = 25927641  # Initial equity investment ($)\n",
    "residual_value_base = 30803782  # Residual value of the project ($), to be added at year 30\n",
    "turbine_upgrade_cost_nominal=150000\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9afca323",
   "metadata": {},
   "source": [
    "Section 3: Define Functions for energy modeling\n",
    "=============================================================\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d870f3f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rotational_speed(h, Q_d, j):\n",
    "    return 31 * (h * Q_d / j) ** 0.5\n",
    "\n",
    "def runner_diameter(h, rpm, j):\n",
    "    return (49.4 * h**0.5 * j**0.02) / rpm\n",
    "\n",
    "def turbine_peak_efficiency(d):\n",
    "    return 0.864 * d**0.04\n",
    "\n",
    "def peak_efficiency_flow(Q_d, j):\n",
    "    return (0.662 + 0.001 * j) * Q_d\n",
    "\n",
    "def efficiency_at_flow(Q, Q_p, e_p, j):\n",
    "    if np.isscalar(Q):\n",
    "        return 0 if Q <= 0 else e_p * (1 - ((1.31 + 0.025 * j) * abs((Q_p - Q) / Q_p) ** (5.6 + 0.4 * j)))\n",
    "    else:\n",
    "        Q = np.array(Q)\n",
    "        efficiency = np.where(\n",
    "            Q <= 0,\n",
    "            0,\n",
    "            e_p * (1 - ((1.31 + 0.025 * j) * abs((Q_p - Q) / Q_p) ** (5.6 + 0.4 * j)))\n",
    "        )\n",
    "        return efficiency\n",
    "    \n",
    "def hydraulic_loss(Q, Q_des):\n",
    "    return gross_head * l_hydr_max * (Q**2 / Q_des**2)\n",
    "\n",
    "def tailwater_effect(Q, Q_des, Q_max):\n",
    "    if np.isscalar(Q):  # Handle single scalar values\n",
    "        if Q > Q_des:\n",
    "            return h_tail_max * ((Q - Q_des) ** 2 / (Q_max - Q_des) ** 2)\n",
    "        return 0\n",
    "    else:  # Handle arrays element-wise\n",
    "        return np.where(\n",
    "            Q > Q_des,\n",
    "            h_tail_max * ((Q - Q_des) ** 2 / (Q_max - Q_des) ** 2), 0)\n",
    "\n",
    "def extract_efficiency_at_flow(Q_des, efficiency_data):\n",
    "  \n",
    "    row = efficiency_data[efficiency_data['Flow Rate (m³/s)'] == Q_des]\n",
    "    if not row.empty:\n",
    "        # Extract the adjusted efficiency value and convert to decimal\n",
    "        return row['Adjusted Efficiency (%)'].values[0] / 100\n",
    "    else:\n",
    "        return None  # Handle missing data gracefully\n",
    "\n",
    "\n",
    "def plant_capacity(Q_des):\n",
    "    h_hydr_des = 0.03\n",
    "    h_tail_des = 0.1\n",
    "    e_t_des = extract_efficiency_at_flow(Q_des, efficiency_data)\n",
    "    # e_t = 0.894\n",
    "\n",
    "    H_net = gross_head - hydraulic_losses - h_tail_des\n",
    "    return rho * g * Q_des * gross_head * e_t_des * e_g * (1 - l_t) * (1 - l_para)*(1-h_hydr_des)\n",
    "\n",
    "def power_output(Q):\n",
    "    # Ensure Q is limited to Q_des if Q > Q_des\n",
    "    \n",
    "  \n",
    "    # Calculate tailwater effect\n",
    "    h_tail = tailwater_effect(Q, Q_des, Q_max)\n",
    "    \n",
    "    Q = np.minimum(Q, Q_des)  # Sets Q = Q_des if Q > Q_des, otherwise Q remains the same\n",
    "    \n",
    "    # Calculate hydraulic losses\n",
    "    h_hydr = hydraulic_loss(Q, Q_des)\n",
    "    \n",
    "       \n",
    "    # Calculate net head\n",
    "    H_net = gross_head - h_hydr - h_tail\n",
    "\n",
    "    # Calculate turbine efficiency\n",
    "    e_t = efficiency_at_flow(Q, Q_p, e_p, j)  # Efficiency for the (possibly adjusted) flow\n",
    "    \n",
    "    # Return power output\n",
    "    return rho * g * Q * H_net * e_t * e_g * (1 - l_t) * (1 - l_para)\n",
    "\n",
    "# Renewable energy calculation\n",
    "def renewable_energy_available(power_output, percentiles, l_dt=0):\n",
    " \n",
    "    # Ensure power_output and percentiles are NumPy arrays\n",
    "    power_output = np.array(power_output)\n",
    "    percentiles = np.array(percentiles)\n",
    "    \n",
    "    # Convert power_output to kW if not already\n",
    "    power_output_kW = power_output   # Convert MW to kW if necessary (adjust if already in kW)\n",
    "    \n",
    "    # Annual hours represented by each 5% increment\n",
    "    hours_per_percent = 0.05 * 8760  # 5% of annual hours (8760 hours)\n",
    "    \n",
    "    # Initialize energy available\n",
    "    energy_available = 0.0\n",
    "    \n",
    "    # Loop through each interval of the power curve (trapezoidal rule)\n",
    "    for k in range(1, len(power_output_kW)):\n",
    "        # Average power output over the interval\n",
    "        avg_power = (power_output_kW[k] + power_output_kW[k - 1]) / 2\n",
    "        \n",
    "        # Add the contribution of this interval\n",
    "        energy_available += avg_power * hours_per_percent\n",
    "    \n",
    "    # Adjust for downtime losses\n",
    "    energy_available *= (1 - l_dt)  # Apply downtime factor\n",
    "    \n",
    "    return energy_available, avg_power # Energy available in kWh/year\n",
    "\n",
    "\n",
    "def calculate_firm_power_energy_with_details(firm_power_curve, percentiles):\n",
    "  \n",
    "    # Split the Firm Power Curve into two segments\n",
    "    higher_exceedance_power = firm_power_curve[18:]  # High exceedance probabilities\n",
    "    lower_exceedance_power = firm_power_curve[:19]  # Low exceedance probabilities (constant section)\n",
    "    \n",
    "    # Constants\n",
    "    time_factor = 0.98  # 5% of annual hours with 2% downtime\n",
    "\n",
    "    # Energy and details for higher exceedance probabilities (trapezoidal rule)\n",
    "    energy_higher_exceedance = 0.0\n",
    "    contributing_power_higher = []  # Store trapezoidal power values\n",
    "    for k in range(1, len(higher_exceedance_power)):\n",
    "        # Trapezoidal area for each interval\n",
    "        trapezoid_area = (higher_exceedance_power[k] + higher_exceedance_power[k - 1]) / 2\n",
    "        contributing_power_higher.append((higher_exceedance_power[k], higher_exceedance_power[k - 1]))\n",
    "        energy_higher_exceedance += trapezoid_area * time_factor\n",
    "\n",
    "    # Energy and details for lower exceedance probabilities (constant power value)\n",
    "    energy_lower_exceedance = 0.0\n",
    "    contributing_power_lower = []  # Store constant power values\n",
    "    for _ in range(1, len(lower_exceedance_power)):\n",
    "        contributing_power_lower.append(firm_power_curve[18])  # Constant firm power value\n",
    "        energy_lower_exceedance += firm_power_curve[18] * time_factor\n",
    "\n",
    "    # Total energy under the Firm Power Curve\n",
    "    firm_power_energy = energy_higher_exceedance + energy_lower_exceedance\n",
    "    \n",
    "    return firm_power_energy, contributing_power_higher, contributing_power_lower\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d83f533",
   "metadata": {},
   "source": [
    "Section 4: main energy modeling calculations\n",
    "=============================================================\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9a821861",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total energy under the Firm Power Curve: 46.21 x timefactor MWh:where  𝑡𝑖𝑚𝑒𝑓𝑎𝑐𝑡𝑜𝑟  is defined as follows: for the annual firm energy delivered  𝑡𝑖𝑚𝑒𝑓𝑎𝑐𝑡𝑜𝑟=0.05∗86700ℎ , while for the daily firm energy delivered  𝑡𝑖𝑚𝑒𝑓𝑎𝑐𝑡𝑜𝑟=0.05∗24ℎ \n",
      "Annual available energy: 20223.80 MWh\n",
      "Plant capacity: 2.44 MW\n",
      "gross head: 300.00 MW\n"
     ]
    }
   ],
   "source": [
    "\n",
    "gross_head=300\n",
    "hydraulic_losses = 0.03 * gross_head  # 3% of gross head\n",
    "rated_head = gross_head - hydraulic_losses  # Rated head (effective head)\n",
    "rpm = rotational_speed(rated_head, Q_des, j)\n",
    "d = runner_diameter(rated_head, rpm, j)\n",
    "e_p = turbine_peak_efficiency(d)\n",
    "Q_p = peak_efficiency_flow(Q_des, j)\n",
    "\n",
    "# Generate flow data for plotting: 0% to 100% of total plant design flow at 5% intervals\n",
    "total_Q_des = num_turbines * Q_des  # Total plant design flow\n",
    "flow_percentages = np.arange(0, 101, 5)\n",
    "flows = [total_Q_des * (i / 100) for i in flow_percentages]  # Flow rates as percentages of total Q_des\n",
    "\n",
    "# Calculate turbine efficiencies for each flow rate\n",
    "combined_efficiencies = []\n",
    "turbine_count = []  # Track the number of turbines in use\n",
    "\n",
    "for Q in flows:\n",
    "    # Determine the number of turbines in use\n",
    "    active_turbines = max(1, min(num_turbines, int(np.ceil(Q / Q_des))))  # Ensure at least 1 turbine is active\n",
    "    turbine_count.append(active_turbines)\n",
    "    \n",
    "    # Split flow among active turbines\n",
    "    flow_per_turbine = Q / active_turbines if active_turbines > 0 else 0  # Avoid division by zero\n",
    "    \n",
    "    # Calculate efficiency for each active turbine\n",
    "    individual_efficiencies = []\n",
    "    for _ in range(active_turbines):\n",
    "        individual_efficiencies.append(efficiency_at_flow(flow_per_turbine, Q_p, e_p, j))\n",
    "    \n",
    "    # Combined efficiency (weighted average of individual turbine efficiencies)\n",
    "    combined_efficiency = sum(individual_efficiencies) / active_turbines if active_turbines > 0 else 0\n",
    "    combined_efficiencies.append(combined_efficiency)\n",
    "\n",
    "# Apply efficiency adjustment\n",
    "adjustment_factor = (efficiency_adjustment / 100)\n",
    "adjusted_efficiencies = [eff + adjustment_factor for eff in combined_efficiencies]\n",
    "\n",
    "# Create a DataFrame to store results\n",
    "efficiency_data = pd.DataFrame({\n",
    "    'Flow Rate (%)': flow_percentages,\n",
    "    'Flow Rate (m³/s)': flows,\n",
    "    'Number of Turbines': turbine_count,\n",
    "    'Original Efficiency (%)': [eff * 100 for eff in combined_efficiencies],\n",
    "    'Adjusted Efficiency (%)': [eff * 100 for eff in adjusted_efficiencies]\n",
    "})\n",
    "\n",
    "\n",
    "e_t_des = extract_efficiency_at_flow(Q_des, efficiency_data)\n",
    "P_des=plant_capacity(Q_des)/10**6\n",
    "Power_output= power_output(rate_flow)/10**6\n",
    "renewable_energy = renewable_energy_available(Power_output, percentiles, l_dt=l_dt)\n",
    "Firm_power_curve = Power_output  # Full power duration curve (Firm Power values)\n",
    "firm_energy, contributing_higher, contributing_lower = calculate_firm_power_energy_with_details(Firm_power_curve, percentiles)\n",
    "energy_production_mwh =  renewable_energy[0]# MWh/year\n",
    "\n",
    "# # NPV and IRR\n",
    "print(f\"Total energy under the Firm Power Curve: {firm_energy:.2f} x timefactor MWh:where  𝑡𝑖𝑚𝑒𝑓𝑎𝑐𝑡𝑜𝑟  is defined as follows: for the annual firm energy delivered  𝑡𝑖𝑚𝑒𝑓𝑎𝑐𝑡𝑜𝑟=0.05∗86700ℎ , while for the daily firm energy delivered  𝑡𝑖𝑚𝑒𝑓𝑎𝑐𝑡𝑜𝑟=0.05∗24ℎ \")\n",
    "print(f\"Annual available energy: {energy_production_mwh:.2f} MWh\")\n",
    "print(f\"Plant capacity: {P_des:.2f} MW\")\n",
    "print(f\"gross head: {gross_head:.2f} MW\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e873630d",
   "metadata": {},
   "source": [
    " Section 5: Functions for financial analysis\n",
    "=============================================================\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d01c0609",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_om_cost(base_cost, inflation_rate, year):\n",
    "    return base_cost * ((1 + inflation_rate) ** (year - 1))\n",
    "\n",
    "def calculate_periodic_cost(base_turbine_cost, inflation_rate, year):\n",
    "    return base_turbine_cost * ((1 + inflation_rate) ** (year - 1))\n",
    "\n",
    "# Function to calculate inflation-adjusted residual value at the end of project life\n",
    "def calculate_residual_value(base_value, inflation_rate, project_lifetime):\n",
    "    return base_value * ((1 + inflation_rate) ** project_lifetime)\n",
    "\n",
    "# Function to calculate annual revenue\n",
    "def calculate_revenue(energy_production_mwh, revenue_per_kwh):\n",
    "    return energy_production_mwh * revenue_per_kwh * 1000  # Convert MWh to kWh\n",
    "\n",
    "# Calculate annual debt payment (using loan amortization formula)\n",
    "def calculate_annual_debt_payment(principal, interest_rate, loan_term):\n",
    "    r = interest_rate  # Interest rate\n",
    "    n = loan_term  # Loan term in years\n",
    "    return principal * (r * (1 + r) ** n) / ((1 + r) ** n - 1)\n",
    "\n",
    "def calculate_payback_period(initial_investment, cash_flows):\n",
    "    cumulative_cash_flow = 0  # Start with the initial investment\n",
    "    for year, cash_flow in enumerate(cash_flows, start=1):\n",
    "        cumulative_cash_flow += cash_flow\n",
    "        if cumulative_cash_flow >= 0:\n",
    "            # Interpolate fractional year\n",
    "            fractional_year = (cumulative_cash_flow - cash_flow) / cash_flow\n",
    "            return year - fractional_year\n",
    "    return None  # If never paid back\n",
    "def year_to_positive_cash_flow(initial_investment, cash_flows):\n",
    "    cumulative_cash_flow = 0  # Start with the initial investment\n",
    "    for year, cash_flow in enumerate(cash_flows, start=0):\n",
    "        cumulative_cash_flow += cash_flow\n",
    "        if cumulative_cash_flow >= 0:\n",
    "            return year, cumulative_cash_flow  # Return the first year when cumulative cash flow is positive\n",
    "    return None  # If cash flow never becomes positive\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34eb2fb0",
   "metadata": {},
   "source": [
    " Section 6: main financial analysis calculations\n",
    "=============================================================\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6bd88e3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net Present Value (NPV): $6,975,011.46\n",
      "Internal Rate of Return (IRR): 11.48%\n"
     ]
    }
   ],
   "source": [
    "debt_amount = total_capital_cost * Debt_to_equity_ratio\n",
    "equity_amount = total_capital_cost - debt_amount\n",
    "initial_investment = -equity_amount\n",
    "annual_debt_payment = calculate_annual_debt_payment(debt_amount, Interest_rate_on_debt, Loan_term)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Initialize lists to store cash flow and cumulative cash flow\n",
    "cash_flows = []\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Calculate cash flows for each year\n",
    "# Loop through project years\n",
    "for year in range(1, Project_life_time + 2):\n",
    "    annual_revenue = calculate_revenue(energy_production_mwh, revenue_per_kwh)\n",
    "    om_cost = calculate_om_cost(base_om_cost, Inflation_rate, year)\n",
    "    turbine_cost_ir = calculate_periodic_cost(turbine_upgrade_cost_nominal, Inflation_rate, year)\n",
    "    \n",
    "    # Debt payment (only for the loan term)\n",
    "    debt_payment = annual_debt_payment if year <= Loan_term +1 else 0\n",
    "\n",
    "    # Calculate cash flow\n",
    "    cash_flow = annual_revenue - om_cost - debt_payment\n",
    "\n",
    "    # Add residual value in the last year\n",
    "    if year == Project_life_time + 1:\n",
    "        residual_value = calculate_residual_value(residual_value_base, Inflation_rate, Project_life_time)\n",
    "        cash_flow += residual_value\n",
    "\n",
    "    # Subtract turbine upgrade cost every 10 years\n",
    "    if year % 10 == 1:\n",
    "        cash_flow -= turbine_cost_ir\n",
    "\n",
    "    # Update cumulative cash flow\n",
    "    cash_flows.append(cash_flow)\n",
    "cash_flows[0] = initial_investment\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# NPV and IRR\n",
    "npv = sum(cf / (1 + discount_rate) ** year for year, cf in enumerate(cash_flows, start=0))\n",
    "irr = npf.irr(cash_flows)\n",
    "\n",
    "# # NPV and IRR\n",
    "print(f\"Net Present Value (NPV): ${npv:,.2f}\")\n",
    "print(f\"Internal Rate of Return (IRR): {irr:.2%}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdd943c1",
   "metadata": {},
   "source": [
    "Plots\n",
    "======"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ecfee2ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plotting the cash flow\n",
    "years = list(range(1, project_lifetime + 2))\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(years, cash_flows, label=\"Annual Cash Flow\", marker='o')\n",
    "# plt.plot(years, cumulative_cash_flows, label=\"Cumulative Cash Flow\", linestyle=\"--\")\n",
    "\n",
    "# Adding labels, title, and legend\n",
    "plt.title(\"Project Cash Flow Over Lifetime (Including Residual Value)\", fontsize=14)\n",
    "plt.xlabel(\"Year\", fontsize=12)\n",
    "plt.ylabel(\"Cash Flow ($)\", fontsize=12)\n",
    "plt.axhline(0, color='gray', linestyle='--', linewidth=0.8)  # Add a horizontal line at y=0\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.7)\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b84d46c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MQwrnTIslfajwWXpHob9eUjie5T2XMrP9Fe4ZtNVxuTPbx+5D3P2vkXVGyxzjvijpYEk7Khzfeinca3lF0pOSbnb3lyPrW0PS5xTu4eyiVfujOQr7o+cl/VMhc3LZBhmaWW+F8/DdFc7Fh0jaUNKaktZQ2HfMUzgPn6JwDv6Au88qV5tiZdp+mKSDFNq+mUK7Fyjcl3lD0r8U9ulvJ6j3cK363LdYM3L13c0sZsD6Qne/O7ZNpWBmGyncw8qn4EB4d18kaVKh62fqaDaz7yrsk2L1U/icVsvpik9ocH6+Au7+lJn9XXGZ8U8xs++4+9Isr62dY72Y/coCrf5dyFu3mdVLurKTl39VieN7B08qnGPkmg2hQWF/cmtFWgQAAIDKcXcePHjw4MGDBw8ePHjwqNhD0vUK2W5iHicXUH9s3aMT1DkkQb1DylWfwo8t31MI4Mi1zm+ybPPkyO1N77DeYEk3SloRuf4MSV+WZGX+fFxfzvoUAjX+Jqkpcv33Mn/jgt53J203SV+Q9G+FjOCxn5mWx0KFqaaHVvt7n+W9jUvwPqK/qwW2ZUgBf9skj+kFvOdCHqv9nQr9/BfwN1xT0lkK06IX2v4mSY8rZAxbM2KbFfn8SPqMpDslLSvgPS2TdIuknYtsw8lJPmtt1usv6TKFH75j1l+kEOjdr8TfsY0lXaIQVFPo52NRph+O7WQbD0XW839FvI9Jkdv4fDn3WW3as5Wk5oR/x4PL1JYeeV6/PrJ91yfc7rjIei8uZ30KQWUv5FlnXqbshMhtnFLkdy7m/GWlpI0T1l31fWIJP7eTI9u9XmR9X42o64uRdQ2PbNsjOeoYHds3nax/cgF9XMjj5CK3P73DemtJukLhPDhm/bIc+8r0mb0r8j3dIekHkWUnlamtX5D0WmQbmhUyuA7tUMe4yPUvjmxTyepTie8PlOp7m6fuHRSubxclaHtn/fWKpKsVsiJvUOTfu6hHKftbYdDutxUGcsV+1zbJ0QaTdIbCvYKY+mYpHEvqS/x93FXhXKyQvl8p6X6FwcXFtGF0IX2qEKB/gUJga8z6KyT9TtK6ke2aXoHP6fRS9mfk+/pGRLverHS7crQ39pjtkgZWua2x14QvJajzsATv//BO6rggxzo3R7RhRo71R+ZY77xO1nlPEfd1ytRHN0b8Hf9R7c89Dx48ePDgwYMHj9I/6gQAAAAA6PIyWZaflvQTSQMrtM3DFbIlfknxM4ptKOlPkv6VyWJec8zsbIXswkdL0dfNG0m6TtKNmYxIxbZhlEJmuL8rZMdKkvG3xZoKP7xPNbNfZbKoASVjZnVm9lWFqbZ/rxBkUag6SXtJ+rWkoSVoXlHMbISZjZP0iKQjFLJAJtVL0rGSnjOz681srRI2MafMPmSqQjBPZ5ndOuor6fuSnjSz9UrQhv5m9huFDI0/UMgYXai+Cv1wdSevd5aFrqMzCtm4mQ1TmI0jnw8VMnpWwpcVAqxiXe3uD5SjIe7eVI56uzoLfibpUYWMsTH+ElnuSwU1KhijuPOXB939vZgKa32f2ImHI8uNKmG5vSPrii33SGS5VDCzAxUCdL+u+Iy+JT32lYuZra2QnTrG3yTdHll2hJntUFirVmdmfczsrwrXMLHnc6YQBDjZzM4sVVsQmNkaZvZnhWvLoxU+80VVKWmYpDMl3Szp50XW1yWY2YYKs6pdpjCgJsaRkl40s89kqW+QwnHmjwr3CmKsI+kqSfdkMvcXxcw2y2SaflZhlrBC+r6Hwr7nUTO718w2KbZdsTIzST2v8BmLvbdUL+lshXONql9TVtEhEWXGlbsRCSyMLLdYUvQscqWWmTEy5ppQCgktYj2qMMgoxmGdPP9xjnUaIurNVWZOticz500Xd7LONz3MGlAN4yPKfCYzWwcAAAC6EQLhAQAAAKCLywTBP6UwjXaltnm2QgDDgAKr2F/SE5kflGuCmfUws6sVMogVGjR+gqSbimhDnZldKukxxQfV5dNTISPYBDPbokR1IuXMbGOFYJGrJHXZwLFCmNm5kp5TfCBi3ioVgk9eNLOy78fN7GSF4MiNC6xiB4X9d/8i2tASiP81hUyO5Xa/pGkR5XYys0IGbBwTWe4md19ZQP2FOCJB2SUKgZ4orT9K+q6SDUi4RSEjaz57Z/azhTghslxUUH6t7xNziA0iJxC+BpjZ1xSOBesXWEXRx74yO1ZxA1AWS7rP3f+nMOtBjBMLblUbZtZPIfDv+AKrWEPS1WbWLQKruwIza5D0oKTTxe/Bncocb5+UtFsBq68t6T4z+3Sb+jZSSGKwWoB8pIMzdRbcZ2b2RYXM1V8otI4sPqdw7D6ghHVmldnG05K2LbCKTRUGOBUzELcmZfbFn85bMC5YuOzMrK+kdSOLP+vuXs725JHkXPjZ2ILu3ijpf5HFOzvf/CjHOjnvA5rZuso9eH5WJ89fquz3jMe7+625tllmMZ/tNRUSMQAAAKAb4cYHAAAAAHRtfSQ9IGmzSm3QzL6kEAyeJLArm20k3W9msRkZq8kkXauQ2a5Yx5rZsYkbYNZTYXr176g81+sjFH4MLvTHZECSZGY7Kvywu0eVm1JyZnaVpN+qsGzH+Wwq6bFMkHhZmNlpCrNTFDszxVaSLi+wDV9SyIBZaBBvYu7eLOn/RRYvJCv8FyPLXVdA3YmZ2TbKE9DQwS3unjWTHwpjZt9VyMqfSCbQ5W8RResUMrsnbddwxWWqnCnpvoj6anqfmMd4STEDV/K2z8y2VlwA9rZmtk4ptqmQEfW5iHLdnpl9S9JvVPz5c8HHvgqIDVa/390XZ/4fmxV+jJkVMvtUq0zA7u2KC77M5wIzY/BWafxS0uhqN6KLW1NhNp9iBoz3kXSrma1tZgMk/UvFz3D1GUnnFbJiZp94q6RyDOxZW9K9mUD7ssjM7nGv4me16sx6CrMVps0+ijtve7LcDYl0pOKP33eWsyERdk9Q9uWEdb8SWW6LTmawyRV4PyzPTEy53ter7r6g45Nm9klJp2Qp3yTpnBz1lZ27vyHpg4iiZR/UAwAAgMoiEB4AAAAAurbLFTIUVsq6kv5cwvp2kvSzEtZXLkepRNkQM36bZJpdMzNJf1VpM7Zls6Gkf9ZSpn50LZkA3IckbVDttpSamV0u6atl3kyDpH+UaUDKBgoZqkvlDDPbOckKZnaMpOsVZqKotGslNUaUOy6TKTGKmY1QXDbK/7p70oCHQiUJwpAiM38j2qckXVLE+rHnWbGZ3dv6UmS56/PNXtAN9ok5ZQYlxGTrHJEneEiKzxBqyhPkbmbDFBdUP87dmyK3251tpNIGryc+9pWbmW2lsN+J8bdO/p/LBpI+m6hRq/u6pAOLrKOtS1TZa+BuJzPjRuIBWyn0VUnDS1DPRpK+p3Auvl0J6pOkn2QyNUfLzOx3uYpPapBLT0k3mdk+Zar/TpXuWuIAMyv3PZauJmZmg4WS3ix3Q/IxsyGKP4bPUrjOrKYk+4q3E9adpPxq+xh3nyHpjU7K91CYWaYzx+V47fGOT2TuX16l7PuZ37l77Iw05TQpokwhs4AAAACgCyMQHgAAAAC6ts9VeHt9FaalL6WvmllXzxzdt8T1radkQe3fVHzG4WJtqvDDdTl/HEc3lAkevktSTDbbmmJmR0v6VoU2N0DS7WbWu8T1rqHwI3epmBJkTzez7RUyolflfmMmU90NEUX7KfeP/R11qWzwGbsmKLtEccG+iHegiph1wd2fljQ1ouiIzPcqSua4HvPZdknX5KmrO+wTYzwcUaZO+bNcJ8lqny9oPjao/pEE2+zOip0BpaNEx74KiR2su0jS/S0L7v6qpNhgtIIHBJvZJ1Tc4KCs1UoaWOI60+ZElTcYurso5bHnPEnHlLC+PpJOji1sZrsrzOJSCb0k3ZI0UD9Sqe/NlGLWv1ryyYgyU9zdy96STphZLzM7VdJ/FTfA3iWdmRnEWE1bR5ZbWEBbZyQoO6yT52/Osc4PsmWSN7NPSzo6x3o3ZXnuZGUfoDdL0g9z1FVJMYHwOxc7Iw4AAAC6llLfJAQAAAAAdC9Nkh6VNE7ShwrBSttLOjzz/xh1kn6nkB2+ViyT9ICkZyTNkTRY4cehEQnqOEVhSvKczGyopJ8kbN9EheCttyWtlLSZpEMk7Ri5/r6STlNtZAl+rAQx++PdfXQJ2pJ2lyr+x9+2pirsR95R+D71Uwim30HSXpLWLlUDC2Fma0v6Q8LVXpH0oKRpkpYqZIH8rKQ9I9ffVtL3M49yekrSvxR+WB+g0Mb9FR8YdZyZfc3dl+UqlPkB+UaFgJ0kVigEQDyhcIxZIGkthWzIn8o8kgQoXSXpK8r//s5QfFbumED4pYrY35fQlgnKPpcv8zeq4hpJV0SUO0HStyPrHC1pk4hy49y9s4yR3X2f2NEjkn4QUW6UpHvzvB6LQPjyq8ixrxIyA1yOjyx+n7sv6fDc3xR3/XKYmfXPDCpL6ldKFri6XNK/JT2pEDQ3SGGA1+cS1oPcYgfUL1L4vrwkaabCdfCaCgMR1lHIPDxc4Xo4DZ5T2N+/r3BO+nnF7+OzDQZtlHS7wt93icL38UTF30s5VdIv8hUys54K2bKT/O7/tsI9j/8pZAhfT+FcYn/FDWxdX9KvVdgMNklMUuiTdxT2EZ+WdJji3+v+ZraRu79fpvZ1NbtElClbxu5MYPWQDk/3VNivbKxwzjdaYVagGC7pK+5+V4maWJDMjI+xCQHmFLCJJOt0dr7//xSuG7Jdk28o6QkzO08hy3tvheQdv1Tng+mfcfcn2j5hZv0V7gtl8113n5ez5ZUTEwjfV9I2kqaUuS0AAACoEALhAQAAAKC2vKTwA+fDCsEdjQo/Ug9TCJw5soTbmizp+GzT2prZQIXg9jGRde1oZnu6+39K2L5yeULSie4+ve2TZvYTSd+V9NPIevYwszp3b85T7jKFjGoxpks6xd3HZXnt+2Z2rEJw+5oRdf3YzP7q7ksjt91dzZL0pQ7PraPwo36Mr0uaneP1hZl/f6rVBx58T51n82rrLkl/z1PmlYh6CmZmwySdlXC1f0q60N1fzFGvKQzM+KpCoEk1sol/XyEIK8ZshayCd2XJoneJme0raaxCIEk+3zCz37n7B/FNjfauwn5sXIfnf2VmB0i6U3H7if4Kg5/yZRQ/VWFgQ6wmhWPIpe4+s7NCmQzRxyh8PvJmQXf318zsQUkH5Sm6q5nt5O4v5CpkZjtJ2irfdiX93d3nR5Qrlc0SlH2pbK1AiwUK+/f7JL2qcFzprRAEtKvCQLW9Oqxzo0IQSb5ZeMaY2XciziWk+GDZfINAuuM+sTNPS1qs/MG3nQZBmtnmihuA0GKEma3l7nOTbquND9z95QTbTMzdr1e45mhlZicrbvaLt919SMkblV+lj32VMEqrBxR25m+dPPfjiHX7SDpK0rWR25IkmdlmCudvsZ5VuL58PUtdGyp8vg5I0gaszszqFHeucI+kk2MCFzOZhPfNPD6rPPu9bIOQzWyc4gb7/MjdL44oV0rLJH3Z3TtmP/6lmV2iuEFTHT0q6Th3/6jtk2b2K4UBOzGDC4aZ2Trunut6UwrXaTHnrFI47n1d0jXu3tThtZ9nzn//priBl2PM7Ff5zqkLNFfSGe5+R4fnf2NmuyoEx8dkE6+TtIfCgIR2sh2rzGy64r4/p2SOlV2GmQ1S3CDzt8vYjK+qdDMjvCnptE7uf1Xa+gnKFjKoLEkG+azn1e4+28wuVOf3srZSGPwSY5mkc7I8f7Gy/y2eVcJziDKL/YxvJQLhAQAAug0C4QEAAACgNjQp/Fj5uyzBULMyjyck/STz41ex3pD0mc5+cHX3eWZ2gkJmxeMi6/w/SV09EP4pSQdkyaaoTIDXz8xsW8UFm/VT+FHlf50VMLNPKGQzizFN0u7uPquzAu5+q5nNVBgokW+K3w0UBjJ0pR+rKs7dF0n6a9vnzGyI4gPh7+44aKKT7TzU8TkzO11xgfCT3P2v+YuV1fnK/5lq6/vunnfQSOZ79bCkhzNBF78vsH0FyWQ0Oz2y+GxJe2QL3mrh7o+Y2WhJExT2Abn0UQhauShy+7FmKey/s2Z8dvd/ZTLBxWZE30U5ggEzgxm+maB9jZK+6O4P5iuYGahzg6QbzOxoxU21fqXyB8JLISt8vsEdMdngpbjA0FKKzUYoFZaREPEekHSqu3/Y4fkVCgMKJ0u6ruO5mbvPMbO7lT9QaCOFrJmP5iqUyVIZMxjyY+UYWNVN94mdcvflZvakQlBnLjub2ZqZc4aOYjO4t6hTyGa7WoZ5M9tSoc/zIRv86ip67KugEyPLLVSW4LbMALFJCoH9MdtKel1whuIHMU6VtH9nA8fc/QMzO1Rhdol9ErYD7Q1S/vP25ZJOih3IlwnmvkXSLZlzv70Uromz7Rdr0ZlZguBb/FjhXsa6CeqbLOnQbMcNd3/bzC6Q1Nn2OtpFIWt/VpmBD+dF1rVU4Xv4VGcF3P2FTFbv55U/WN8U7lPF7qtiLZF0sLs/k+1Fd3/OzL4kabVr7E7soiyB8N3QkMhyXTk7fpPCvc3rJI3tQjNbrZWg7OIC6k+yTqeDHdz9N2a2p8LgtmKc5+7PtX3CzLZR9uB4l/TVLINiqyn2M55kgDkAAAC6uGpk2QIAAAAAJPcld78qJiOou5ci6O3MfFnHMj9ynK0QVBXjKDOLzXxeDcsknZAtCL6DqxLUuV2e109U3LW5SxqTKwi+tWDIlvW7iDol6aTIckixTFbuYxOs8uuYIPiOMpkE91CZs9t3cKTip2X/v1wBny3c/RXFB3KW4zv4tc4CAdu4UfGZ6vLtxz6t+AyUUjie5Q2C78jdb1dEVniFQKFXI8qNMbN8mYFjAuHfUZ4g5TLIl726rXnlagR0v6TDswTBr6aTc7OOs4R05oSIMp9XmCEon5vcfVmO17vjPjGfhyPK1Cscn7KJyeDeUWfB87FB9QTCr67Sx76yM7OWLO0x7s0xy1O2TPHZjMpkeE8iyfnhGfmCrt19uaTTFIK0UTiLKDO30NlsPHjc3c90928UUkcX87i739DZi+6+QtJjCes8p5PBUy3+rnD/IUa+/dHekjaPrOv7uYLgW2TObb4aWefRZpbk3DTGjzsLgm/h7g8rR9KBDqq+T6+Q2H14Vw6Ef08hEP6/XSgIXgqzPcVaUUD9SdbJN6PUcYof+JetHae6+9VZXrtS2ZNsXufuEwrcXrkQCA8AAJBCBMIDAAAAQNf3V3e/pYLbe8rdo4L6MtOoxwZd95a0Y4FtqoRr3P2tiHIvSor9QW5gntdjs8Hfl/CHpdhg/T3NLGbqbKTb/sqfybfFNEkXFLqhTGBNbFBIKcR+Bye5+50J6v2T4oJbNjWzHRLUm8/UmONFJtDspcg6B+Z5/fDIeqQQhHtPgvLt5Ajya1vGFbcP7K8cAXxmtqukLSLquT5mkFqJ5Qt+aGth2VqRbnMUgkQKCXRp8YikmPOOIzMDknKJmalGyh983932iTFig8o7C3jvLHj9ZYWs+UnWIRC+MNU49lXCYQrHqhi5gt1jA+FNcQNvQmGz9RV3nJSkcTHBt5KUuR6r5LVvdzRHYSB1LuubWdIZLbqrKyLKTE5Q3/PuPj5XAXdfrPjBvwPzvB577J6tZIP675b0bkS53grXi6XyseJnZ4uduWNgYU2pObGzQxY0CKZCNpP0A0kvm9nfMrModgVJkno0FVB/knVyXgu6+0p3P0PhWve1BPU+ojAL5WqznZnZEZL2y7LOfEnf6axCM+tjZl80s6vN7AUze9/MlprZAjN7y8zGm9klmSz2JZO5pxRz7ZFkpjUAAAB0cQTCAwAAAEDX5go/AlVS0imjk5TfLWHdlfTXmEKZH1RmRtY5oLMXzKxB0ojIesZGlpMkZTJizogo2kPSp5LUjVTaK0HZXxcZGFppnWX47ShRQFYmA+TEErchxs0Jyr4dWa7T/VhGks/H5QnKFuMGxQV4fDnHa8dErO+ZbVVaksEisYNYkMyV7v5RMRVkBm1cG1G0v6RDO3vRzNaSdHBEPc+4+5Q8ZbrbPjHGi4qb3Wi1QHgz20idBwKPV8homs1OZpYtwDkmKPV1d48JTEyTahz7KuHEyHKNkjqdaSUzc0PsAIAvRZaTpN0TlL0jQdlCyqMNd29SGJyazwNm9gsz2yNiwFV3tVTSvyPKxV7/S9I/Isu9E1luYJ7XYwNI78wMCIqSOU/p7DjWUSmP3XfEDH7NqKV9eiXkm+2qRb5ZELuCOklHS3rRzKIHaZVRkoHPMbNyFLNOVFvc/TZJ20o6RCGb+0SFTOnLJC2SNF3S45IulvRJd9/P3Vc7X8/MUPOrTjZzUbaZK82s3szOVxj0e5ukMxUSowxWCORvkDRE4fz6B5KeNLOnzGyfmPcWaXFEmVLPZgEAAIAqIhAeAAAAALq2x9x9eoW3+WSSwu4+WSEAI8bOyZtTEUsk/TdB+dgMWrkCEHdQ/HV5VAbFDmKnAt6xgLqRLjsmKJt0IE3VmNnGktaNLF4r38HHEpQtej9mZiZp+8h6XokIwi0Jd18oabVMdll8ysw6a//REeuPd/c341tWMjE/6rcYWK5GpNz1JarnOsVlgMwVnPpFxWWpzJkNvpvuE/PKzOgQs+8caWYdM3DmClwfrxBclE0PdQhcNLMhkjaNaMfDEWXSpqLHvkrIZFv/bGTxf0QEjcaen21tZrGDZHeKLCcl32cUso9BezH7ir6SvinpP5IWmdl0M3vIzH5nZueZ2cFmtkl5m1l1U9w9Jii4sxk+sonNUh47a0+uc/E6xZ+L18qxu9vt0ysoNhA+ybVEtfWTdKOZnVnldiQZiFxfQP09EpSNbou7N7n7A+7+NXff1d03dvfe7t7P3Td3973d/Ufu/lyOai5QCFrvaIqyzBBqZuspZJf/paT1Y9uqMMDuETP7QeY+Q7FiPuex3xkAAADUAALhAQAAAKBryzmldhm4pJcLWC92Wu+uOu3slEwgVKzYHw5zXXcPSbC9t83MkzwkfTKy7iTtQDptHlnu9WIzJFfYkARlxxfwHYwJpE7ajlxc0uQE5UuxH2vJ6BYj0SCrErhKcdnyzuj4hJntJmmziHVjgu3LIUkw1tpla0V6veXusVlcc3L39yX9M6LoAWbW2TlUTJbMRoVsjLkMiainRS3sE5OICRjtLWlkh+dyBcI/rtzn8R3XjckGL4XAIqxSjWNfJRyv+KC4v5WoTIvYTPSxA2dc0v8SbF/u/rGkWjqn7Ir+ovC3j1WncO6zn6SzJf1a0v2S3jGzWWZ2j5mdkxm0053EfjaTBMG+GlkudhatfOfiMYPhJOmGAo7d34qse0hkuRiTEpStlX16V1OKIONKMkm/N7NKzxrUVpLBA4XMsNEnQdmKDWQws80kfbuTl8/JzEDStnx/hfPf1WZSit2kpEsk/bzA9TvWVYoyAAAAqBFc+AEAAABA1/Z8hbe30N0L+VElNlBhrQLqroQPE5aPnlI8h/VKUEcpJMnQVA37uLsV+Rhd7TdR42I/q9PL2Ygy6G7fwUXuvihB+Urvx6aXYHvRMpna748oeoKZdZwS/ZiI9Rol3ZG4YaXxdoKyO5StFelV6nOznJnaM3oqy+cyE5yy5+rFV3NrZqaEXLrbPjGJ2ODyjkE9nQX5vOHuH0h6SZ1nq+24bkwgfGz2+jRJeuyLDTyttlyzQLQ1X9K/8hVy99clvRhZ5zFm1jOiXOx13cLIjNsdEQhfBHefKOnmElW3jqRDJV0p6U0zG2dmh5So7mqbE1kuSSB8bJ2l0B2P3UnuzZTieqY7ib2XlyToOhF3P7bj/ZjM9tZRuC45WtL/k5R0UGmdpGsysyBUQ5KByIVkGe94PZrLrALqL9QVyv55uc3dx2V5/iZJw0qw3W+bWewg2s7E/E1raXYEAAAA5EEgPAAAAAB0bUkDtIsVOz13R42R5bpqIPyChOWTZI/vTNl+fEwoyQ9uSKfYz8jHZW1F6XW372A19mNJ2l6Nz8eVEWUGSPpiy0JmGvaYH91vK3DgWClMS1B2VzOrL1tL0qnU52b3S/ogoly2zO/HKy6T4Z8jynS3fWK0TJDwuxFFW4PXzWw9dR7oMz5Tb7M6nw1jVzNrGygVEwj/YiZTNlZJeuxryl+kusxsuKQdI4v/w91jA3Rvjyw3SFJMkPPAyPrKfX2Jzp0h6YkS12kK+6v7zOxeMxtQ4vorLfbzmeS8udDPfCG647E7yX69FNcz3UnswLCKfm7cfam7z3H3Se5+h7ufI2kLSSer8wGD2QyTdFA52hhhtuLPIQq575lkFq+K3Cc2s30lHZHlpUWSvpml/P4Kg6Y686TCuXRfhVllTlfuewS/jByY15mYz3mSwZQAAADo4giEBwAAAJBWSaY/LebGe7GS/ChUCkmmT28r9u/ZVaedTZodsjv94FrNzzeA0n0H07wfy8rdH5Y0NaLoGW3+v6ekjSLWua6gRpXGswnK9pW0a7kaUmJJzxGqdfwq6bmZu6+UdH1E0d3M7BMdnjs+Yr1J7p7kM1Nt1erXmKzwe5hZj8z/O8sGL0mPd/L/tnpK2l2SzGxjhYCwfB6OKJM23fHYd1KCsgvN7ISYh5L9HnhiRJnYfXa5ry+7uqrdd8hk4j9AYWDgylLWnfE5SfeYWe8y1F0pJf+7ZI7raVOyz667J9mv18I+vZJiB8sNLGcjYrh7k7vfIGk3JRv8cFyZmpRTZnBjbBb7tQvIXL9OgrJvJaw7scxA6s4Glf/U3d/L8vz3clT5tKR93f0Jd1/i7rPd/RpJn1XnMztsquwDgfPKHJfWiCjKAFMAAIBuhEB4AAAAAGnVI3+RVv3L1or8Kj3Vc0OB6/WLLDe3wPq7o64yBW93CTJB+cR+VpNkMesK+A4WL8nfsFqfj6siyuyeycArScdElH/N3Z8qok3FeiZh+S+XpRWll+TcTKre+Vk5zs2uUVywaGswiJntLGnbiHVissFL7BNjgsz7Sdo58/9cGdzbBr+Pz1Fu7w7/5hMTrI8algmcG5NglbMk3RT5+HGCeg8xs3zH7djrunJfX3Z1Vb3vkAk4/JqkrSVdobgZSJLYW9IFJa4T8brKsRtdw9uR5TYuaysScPf/SbowwSp7lqstEf4XWa5e0gYJ607SJ68krLsQ5yj7dcYbCseSdjLnDHvlqO9r7r7aNZS7T5T0lxzrHZ67mZ2K/XvGfmcAAABQAwiEBwAAAJBWvRKUXa9sreh6+plZIdNax/6NCIRfZXa1GwBEmhVZbkg5G1EGfAeLF/vZkKr3+bhJcceeL2cCEI+KKFvNbPBy95eVLBPgcREBjV1BknMzqRudn7n7NEnjIooe38n/O7NU0s2RzUj7PjE2yHxUh387esfdp7dZnihpUSdlkwTCL5f0ZEQ51Lb9JQ2udiMU9sfH5ikTe13Xz8z6FNCG7rKP7xL3Hdz9TXc/X2HWm09K+qak2xQCO4sd4PUNM6tm8oA0S/uxG+1NjywXM/tVJf1V8bOHDDGzas0e9EKCslsmrHtoZLkVkl5OWHciZraepB928vLX3H1ZludHq/O4ozfyzE41NsdrowrIri/Ff8anF1A3AAAAuigC4QEAAACk1ZoJym5ftlZ0Paa47KKrVjAzSdtEFueH2lWmR5ZbKqmnu1uZHqPL9xbRTcQG3A7N/GhaK6YnKLtJGb+DQ8r0/iphhuKDpz5dzoZ0xt0XK3eWuRZfknSA8mfva5J0Y7HtKoG/JyjbR9JPytWQCLGBLdHnZpl9TS3tb2LEfE6HmtmnMgEh+YJUJekOd48NVp0eWU7qhvtEd5+puMCiUZmBJSM6eb1tNni5+0pJnc0gMdLMeisuEP7pzP4M3duXqt2ANvK1Jfa6Lsm1YljBbJC69j4+9rgmdbH7Dh485+6/cvdj3X0bSX0VBix+RtIZCtl+/6NwzhOjv0IQJCpvhkJgbIy9ynjsruUZrroNd58laV5E0SHlbUky7j5fye4VrlWutuTxnwRlk+7POzuv7Oh5d1+SsO6kfi5pQJbn73P3BzpZZ1iO+p7Ls73nJTV38tpASevnWT+bzSLLvV5A3QAAAOiiCIQHAAAA0N0sjCw3MEGdnWV87K6SBiuOUPyU988nrLs7m6y4IIreknYoc1uAXF5MUDYmm3ZX8Z6kjyPLfqqcDalV7t4saVJk8W3MbLtytieH3yl/MNdakv4YUde/3H1G8U0q2p+VLBDvLDM7qBwNMbMeeYqU49wsJnC41typuH3SCZL2VVzW6Jjg+hbsE+Oywn9a4dqgs6C/8VmeezzLc5K0hqTDJW0Vsd2HI8qUW5J9DhIys36SvlDtdrSxm5nlylCbJDPuHgm3vXvC8pUWe1yTkh3b9krYjpJw9yZ3f9vdH3P3P7v7+e7+aYVAwgcjq4m5Z8I+pMTcvUnS1Mji3fXYXWq1/jmNuec2vOytSC7JzCH5rj3K5QnFDwLfM7ZSM9ta0qDI4rEzGBXEzEZKOjnLS8sknZdj1XVyvPZerm1mAvtzDdzNVXdnYgYiLFX8/hMAAAA1gEB4AAAAAN3N/MhyW8QUMrN1FKaoT5OjE5ZPEvj6TMK6uy13n6f4KY1PLGNTgHyeTFD261WcqjwRd3dJT0cW5zvYuSSfj2+VrRU5uPvbku6JKLpJRJnrimxOSbj7q5L+mXC1m8xs11K1wczWN7PblT+DYUnPzTLGJChbE9x9maS/RhQ9RtkDVDp63d2zBWV3tn32iXHB5mtLOjvH69mC3nP1w/cjtimVOfApUmwG0iTBbFjlKIXM3F1Jru967P5Cko5MuN2uPrAy9rgmxd932EFStQYMZuXu70s6TnEZx2MGZ7EPKY/YLNVdacaJrqzWP6f5sm9L0naZmR27hEwgeL8Eq0Ttg81stJl5zCOmPndvVPz52AFm1iuy7KGR5aQwcLYsMp+Jq5R9sOcv3X1ajtVzJUeJ+U7lKhObeKWtmED4lzIzNwEAAKCbIBAeAAAAQHeTK4tMWyMjy10kqSaCOktoDzP7TExBMxso6SuR9S5VssyBafCPyHJnZH4cLAkzG2pmvzczssIhxr8lLYosu6WknxW6ITOrM7Peha5fgNjv4OfNbHSpNmpmg83sUjM7vFR1VtFdCcqeaGafK3RDZlZMgOCVRazb4mPFf2Yq4UJ1Po18NoMkPWpmRQ3wM7M+ZvZtSa8pLlgx9txsAzPLOxjBzHaRdFhknbXmzxFl1lUITMwnSTb4FmnfJ45T/tkjpM4Hyc5099eyPD9B4Tw4m5jA10ZJz0aUK7cFkeXWM7PYzKZYpSsOMDmhs2BJd58paXpkPaPNLCorvJltrrh9XNVkAudis8LH3nf4cYHNycrMBpSinszg7ZjZQmK+87H7kG0iyyGIPXbvYGYnlWqjZra2mX3XzM4sVZ1dRK1/TmOST6ypcN1ekDLcR0oyWPoDd19c4u0nETNoVQqzjeUdfJIJlo/9Dr3i7uWcZfMUZT9mvav893hyDU6IGTSSq0ySwWctYgLhSdQCAADQzRAIDwAAAKC7eSWy3Ggzyxl4YmYHSjqr+CbVpKvzBbBkgiJ+r5AZM8bf3T12GuG0uElxU2/3lvQPM9u00A2ZWU8zO8DM7pH0P4XPdlfNYoYuJDNV9d8SrHK+mV2QdDtmNkwhu/iwpOsW4Q5JMT+km6RbzSzmB9XsFYQg/0+b2U0KgWPfkTSw0Pq6kCcl5coO15ZJ+mshgdhmdrCk/yZdr0UmM/ZLha6fcXNXOo65+0uS/pRwtQZJ/zKzm5MeU8xsUzP7oaS3JV0mqX/kqrHnZpL01TxtGCjpemXPVFjz3H2KQtB0Pvne/wpJNxTQhFTvE919geIyqXYmWzb4lmz/Be+/JI3vIhkrY4JhW3y9bK3ohjKDgPaudjuyGCJprxyv35qgrj+aWc7jRmZWob9Iis2iW02xx7ajzGz9XAXM7HRJny++Se38PzMbb2ZjzGyNQisxs6GS1osoGjMwIHYf8tl892rQzqOS3oss+zsz27eYjZnZTmb2O60Kjt2wmPq6oNjP6bFm1hXf+6OSYs4Zcu3b8/mXmT1vZicWs3+RJDP7uqTTEqxSzPlUKdwhaWZk2Z9FDAy8QNInIuv7f5HlEssMnrq0k5fPjxh8MDvHaxvl2XYf5b6vPCfPtjvWt5WknMfdjH8lqRcAAABdH4HwAAAAALqb2OCVOkl3mVnWLEhmdorCDxz1pWpYjRmqkDV2eLYXMz+S3Khk2fquLkXDuhN3f0XSPyOLbyXpBTP7qplFTRttZuuY2aFmdp2kDyU9qDDtci3dD3gsdjrriMfF1X4zNeyXis88bZJ+bmb3xgRJmtlIM/urpEmSdi+ijYm5+8cKQbUx1pf0tJn9IDbTrZn1N7P9M8Ei70l6QtIJ6kYzjbh7s8LnI9YASQ+Y2RURgWH1ZvYFMxsn6X5JebOF51FsVvjrily/HL6pkJk9CZM0RtIbZvaQmZ2X+R5ulsn23jcT9L6zmR1tZleZ2UsKwcoXK2QkTyJJJutvmtnJWRttto2kRyRlPTfpRmKywudzr7t/mHQl9omSwmesUFkD4TPGF1Hvw0WsW0ovK/5c4HuZINwfmNlpZnZClkfBAym6oRMUf35+nLtbMQ9JOyRoW65M9X9U/GdiuKSHMoHVqzGzDRQyW0fNTNYFxN536C/p7mznPGbWIzN4tBzXySZplKSbJX1oZneY2elJBsGZ2baS/q64wWcfRJSZErnpNST918yuz5yjnNTJPmTNyPq6tcxAqdhz3DUlPWhmvzazwTErZM4LP21mvzCzNyU9L+lsScXM1NSVxX5O15U02cyuztyjOTHb57ScDc3G3edLeiqiaLGDr3ZSGHQ5y8xuMbMvmtlasSub2e5m9oCkKxJu956E5UsqMyj68sji60kaZ2abdXzBgq9L+lFkXe+pvNeiP1L2QU+PuvvtEeu/nOO1XfOsu5M6P87MVriXmUTMZ3upwkxQAAAA6EbSGtABAAAAoPvKFYDS0VBJL5nZ7Qo/ZK9QyHp3uCqbkbi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      "text/plain": [
       "<Figure size 3600x2400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "# #=============================TURBINE EFFECIENCY CURVE PLOT\n",
    "# Plot the Turbine Efficiency Curve with adjusted values\n",
    "plt.figure(figsize=(12, 8), dpi=300)\n",
    "plt.plot(flow_percentages, adjusted_efficiencies, marker='o', linestyle='-', color='b', label=\"Efficiency Curve\")\n",
    "plt.title(f\"Turbine Efficiency Curve with Adjustment ({efficiency_adjustment:.1f}%)\", fontsize=14,   weight='bold')\n",
    "plt.xlabel(\"Flow Rate (%)\", fontsize=12)\n",
    "plt.ylabel(\"Efficiency (%)\", fontsize=12)\n",
    "plt.grid(True)\n",
    "plt.xticks(np.arange(0, 101, 5))\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6c83257e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3000x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# #============================================FLOW DURATION CURVE\n",
    "\n",
    "# Plot the Flow Duration Curve (FDC)\n",
    "design_flow_30=rate_flow[6] \n",
    "Firm_flow_90=rate_flow[18] \n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 6), dpi=300)\n",
    "plt.plot(percentiles, rate_flow, marker='o', linestyle='-', color='b', label=\"Flow Duration Curve\")\n",
    "\n",
    "# Show the design flow at 30th percentile\n",
    "# Add a horizontal line for the 30th percentile design flow\n",
    "plt.axhline(design_flow_30, color='red', linestyle='--', label=f'Design Flow Rate (m³/s) ({design_flow_30:.2f} m³/s)')\n",
    "plt.axhline(Firm_flow_90, color='green', linestyle='--', label=f'Firm flow rate (m³/s) ({Firm_flow_90:.2f} m³/s)')\n",
    "\n",
    "plt.title(\"Flow Duration Curve\",fontsize=14,   weight='bold' )\n",
    "plt.xlabel(\"Exceedance Probability (%)\")\n",
    "plt.ylabel(\"Flow (m³/s)\")\n",
    "plt.grid(True)\n",
    "plt.xticks(np.arange(0, 101, 5))\n",
    "plt.yticks(np.linspace(np.min(sorted_flow), np.max(sorted_flow), num=10))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "eed7631e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "##============================================ # #POWER DURATION CURVE PLOT\n",
    "Firm_power=Power_output[18]\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.plot(percentiles, Power_output, marker='o', linestyle='-', color='b', label=\"Power Duration Curve\")\n",
    "plt.axhline(Firm_power, color='red', linestyle='--', label=f'Firm Power Output(MW) ({Firm_power:.2f} MW)')\n",
    "\n",
    "\n",
    "plt.title(\"Flow Duration Curve\")\n",
    "plt.xlabel(\"Exceedance Probability (%)\")\n",
    "plt.title(\"Power Duration Curve\")\n",
    "plt.xlabel(\"Exceedance Probability (%)\")\n",
    "plt.ylabel(\"Power (MW)\")\n",
    "plt.grid(True)\n",
    "plt.xticks(np.arange(0, 101, 5))\n",
    "plt.yticks(np.linspace(np.min(Power_output), np.max(Power_output), num=10))\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "PDC=[]\n",
    "PDC.append(percentiles)\n",
    "PDC.append(Power_output)\n",
    "pdc=pd.DataFrame(PDC)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2102ae16",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}