Seminar_Exercise.ipynb

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      "cell_type": "markdown",
      "source": [
        "**Seminar on Modeling and Simulation for Energy Systems**\n",
        "\n",
        "SYDSEN Research Group, AIFB-KIT\n",
        "\n",
        "---\n",
        "# Exercise on Wind Power Calculation Using Weather Data"
      ],
      "metadata": {
        "id": "358MpvrO4WUl"
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    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "FxuKkvJoMwBl"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1. Loading Data**\n"
      ],
      "metadata": {
        "id": "47fKoxIo5rU5"
      }
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    {
      "cell_type": "code",
      "source": [
        "# Import csv file using pandas read_csv command and select time as the index_col\n",
        "wind_data = None\n",
        "\n",
        "# Fill any NaN values in the dataframe with 0\n",
        "wind_data = None\n",
        "\n",
        "# Convert the index to datetime object\n",
        "wind_data.index = pd.to_datetime(wind_data.index, format='%Y%m%d%H%M')\n",
        "\n",
        "# Assumed Parameters\n",
        "air_density = 1.2  # kg/m^3\n",
        "diameter = 82  # meters\n",
        "nominal_power = 2.35 # MW\n",
        "power_coeff =  0.47\n"
      ],
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        "outputId": "e5bacb71-e02d-4860-a49c-dd4eb59b314d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'NoneType' object has no attribute 'index'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-a7a5c94b6402>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Convert the index to datetime object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mwind_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwind_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'%Y%m%d%H%M'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m# Assumed Parameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'index'"
          ]
        }
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    },
    {
      "cell_type": "markdown",
      "source": [
        "**2. Wind Power Model**"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a functon to calculate wind power using parameters\n",
        "def calculate_wind_power(wind_speed, diameter, air_density):\n",
        "    \"\"\"\n",
        "    Calculate the power generated by a wind turbine.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    wind_speed : list or array\n",
        "        Speed values in m/s.\n",
        "    diameter : float\n",
        "        Diameter of the wind turbine in meters.\n",
        "    air_density : float\n",
        "        Air density in kg/m^3.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    wind_power : numpy array\n",
        "        Power values in MW for each wind speed.\n",
        "    \"\"\"\n",
        "    return None"
      ],
      "metadata": {
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3. Applying Model to Data**"
      ],
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make a new dataframe to filter the data for year 2019\n",
        "wind_data_2019 = pd.DataFrame()\n",
        "\n",
        "# Filter data of wind speed for year 2019\n",
        "wind_data_2019['Wind Speed m/s'] = None\n",
        "\n",
        "# Use the function above to calculate wind power using the wind speed data\n",
        "wind_data_2019['Wind Power MW'] = None\n",
        "\n",
        "# Adjust the wind turbine output to min & max speeds (<3m/s = 0, >25m/s = 0), and max power output (2.35MW)\n",
        "wind_data_2019['Wind Power MW'] = None"
      ],
      "metadata": {
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4. Plotting Results**"
      ],
      "metadata": {
        "id": "ADZkM7hE59SG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the wind power over the year\n",
        "plt.figure()\n",
        "None\n",
        "plt.show()\n",
        "\n",
        "# PLot the wind power over one week\n",
        "plt.figure()\n",
        "None\n",
        "plt.show()\n",
        "\n",
        "# Plot the weekly average wind speeds for year 2019\n",
        "plt.figure()\n",
        "None\n",
        "plt.show()\n",
        "\n",
        "# Plot the wind speed distribution histogram\n",
        "plt.figure()\n",
        "None\n",
        "plt.show()\n",
        "\n",
        "# Plot a comparison between weekly average actual and simulated power (use column 'Leistung' from original data as the actual power)\n",
        "plt.figure()\n",
        "None\n",
        "plt.show()"
      ],
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      "execution_count": null,
      "outputs": []
    }
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