monte_carlo_cs3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PIDE\n",
    "##### **P**hotovoltaic **I**ntegration **D**ynamics and **E**fficiency for Autonomous Control on Power Distribution Grids\n",
    "## **Case study 3: Comparison of decentralized and distributed grid control strategies**\n",
    "\n",
    "This third case study evaluates decentralized versus distributed grid control strategies using 100 Monte Carlo sampleson a summer day. \n",
    "\n",
    "The analysis focuses on the effects of different grid control strategies:\n",
    "\n",
    "- `PV-BES Strategy 1` (pvbes_decentralized_sc_ctrl)\n",
    "- `PV-BES Strategy 2` (pvbes_distributed_sc_ctrl)\n",
    "- `PV-BES Strategy 3` (pvbes_distributed_sc_dnc_ctrl)\n",
    "\n",
    "For all strategies, `Q(V)` was selected as the reactive power control mode.\n",
    "\n",
    "### **Execution Workflow**\n",
    "1. **Run Simulations**:\n",
    "   - Execute `./exe_server_master_cs3_job.sh` to generate simulation data.\n",
    "   - Results are stored as `.pkl` files in the `mcs_folder` directory.\n",
    "2. **Load and Organize**:\n",
    "   - Retrieve and sort simulations into categories: `pvbes_decentralized_sc_ctrl`, `pvbes_distributed_sc_ctrl`, and `pvbes_distributed_sc_dnc_ctrl`.\n",
    "   - Ensure no duplicate assignments with tracking mechanisms.\n",
    "3. **Visualization**:\n",
    "   - Plot different grid control strategies in a 3x3 subplot.\n",
    "       - `storage.p_mw` (*BES Power (MW)*)\n",
    "       - `storage.soc_percent` (*SoC (%)*)\n",
    "       - `res_line.loading_percent` (*LL (%)*)\n",
    "   - Use shaded areas to represent the 1st to 99th percentile range."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **(1): Executing Batch Scripts**\n",
    "1. Make the script executable:  \n",
    "   `chmod +x ./exe_server_master_cs3_job.sh`\n",
    "2. Run the script:  \n",
    "   `./exe_server_master_cs3_job.sh`\n",
    "\n",
    "This script executes 100 default Monte Carlo runs and saves the resulting pickle files in the *`mcs_folder`* folder. \n",
    "You can modify the *`exe_server_master_cs3_job.sh`* script directly to adjust the behavior as needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **(2): Loading and Sorting Monte Carlo Simulations**\n",
    "\n",
    "- **Folder**: `/hkfs/home/haicore/iai/ii6824/Documents/pide/output/mcs_folder`\n",
    "- **Categories**:  \n",
    "  - `pvbes_decentralized_sc_ctrl`  \n",
    "  - `pvbes_distributed_sc_ctrl`  \n",
    "  - `pvbes_distributed_sc_dnc_ctrl`  \n",
    "\n",
    "**Workflow**:\n",
    "1. Load `.pkl` files from the folder.\n",
    "2. Sort simulations based on a predefined order.\n",
    "3. Extract and group simulations into categories.\n",
    "\n",
    "- Prevents duplicate assignments with a tracking mechanism.\n",
    "- Prints a summary of simulations extracted for each category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from cycler import cycler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracted Monte Carlo simulations:\n",
      "PV-BES Strategy 1 (voltage_reactive_power_ctrl_rbc_pvbes_decentralized_sc_ctrl): 100 simulations extracted.\n",
      "PV-BES Strategy 2 (voltage_reactive_power_ctrl_rbc_pvbes_distributed_sc_ctrl): 100 simulations extracted.\n",
      "PV-BES Strategy 3 (voltage_reactive_power_ctrl_rbc_pvbes_distributed_sc_dnc_ctrl): 100 simulations extracted.\n"
     ]
    }
   ],
   "source": [
    "# Directory containing the Pickle files\n",
    "folder_name = \"./output/mcs_folder\"\n",
    "save_folder_name = \"./output/mcs_results\"\n",
    "\n",
    "os.makedirs(save_folder_name, exist_ok=True)\n",
    "\n",
    "# Desired order for categorizing simulations\n",
    "desired_order_list = [\n",
    "    \"voltage_reactive_power_ctrl_rbc_pvbes_decentralized_sc_ctrl\",\n",
    "    \"voltage_reactive_power_ctrl_rbc_pvbes_distributed_sc_ctrl\",\n",
    "    \"voltage_reactive_power_ctrl_rbc_pvbes_distributed_sc_dnc_ctrl\"\n",
    "]\n",
    "desired_order_list_name = [\"PV-BES Strategy 1\", \"PV-BES Strategy 2\", \"PV-BES Strategy 3\"]\n",
    "\n",
    "# Load and sort simulations based on the desired order\n",
    "def load_and_sort_simulations(folder, order_list):\n",
    "    sims = {}\n",
    "    # Load all Pickle files in the folder\n",
    "    for filename in filter(lambda f: f.endswith(\".pkl\"), os.listdir(folder)):\n",
    "        with open(os.path.join(folder, filename), \"rb\") as f:\n",
    "            sims[os.path.splitext(filename)[0]] = pickle.load(f)\n",
    "\n",
    "    # Define sorting order: prioritize keys matching the desired order\n",
    "    def order_key(key):\n",
    "        return next((i for i, o in enumerate(order_list) if o in key), len(order_list)), key\n",
    "\n",
    "    # Return simulations sorted by the desired order\n",
    "    sorted_sims = {k: sims[k] for k in sorted(sims.keys(), key=order_key)}\n",
    "    return sorted_sims\n",
    "\n",
    "# Extract simulations into categories based on the desired order\n",
    "def extract_simulations(sims, order_list):\n",
    "    extracted_data = {desired: [] for desired in order_list}\n",
    "    used_keys = set()  # Prevent duplicate assignments\n",
    "    for key, value in sims.items():\n",
    "        for desired in order_list:\n",
    "            if desired in key and key not in used_keys:\n",
    "                extracted_data[desired].append(value)\n",
    "                used_keys.add(key)\n",
    "                break\n",
    "    return extracted_data\n",
    "\n",
    "# Load, sort, and extract simulations\n",
    "sorted_sims = load_and_sort_simulations(folder_name, desired_order_list)\n",
    "extracted_data = extract_simulations(sorted_sims, desired_order_list)\n",
    "\n",
    "# Display the extracted simulations with labels\n",
    "print(\"Extracted Monte Carlo simulations:\")\n",
    "for name, (category, simulations) in zip(desired_order_list_name, extracted_data.items()):\n",
    "    print(f\"{name} ({category}): {len(simulations)} simulations extracted.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **(3.1): Figure Configuration**\n",
    "\n",
    "- **Fonts**: LaTeX with Computer Modern, consistent sizes.\n",
    "- **Style**: Custom colors, line styles, and 2px line width.\n",
    "- **Datasets**: BES Power (MW), SoC (\\%), LL (\\%).\n",
    "- **Layout**: Rows for datasets, columns for control strategies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enable LaTeX for text rendering and set default font and sizes\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,               # Enable LaTeX\n",
    "    \"font.family\": \"serif\",            # Use serif fonts\n",
    "    \"font.serif\": [\"Computer Modern\"], # LaTeX default font\n",
    "    \"axes.labelsize\": 9,               # Font size for axis labels\n",
    "    \"axes.titlesize\": 9,               # Font size for titles\n",
    "    \"xtick.labelsize\": 9,              # Font size for x-tick labels\n",
    "    \"ytick.labelsize\": 9,              # Font size for y-tick labels\n",
    "    \"legend.fontsize\": 9,              # Font size for legend\n",
    "    \"axes.prop_cycle\": cycler(color=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#e377c2']) + \\\n",
    "                       cycler(linestyle=['-', '--', '-.', ':', '-']),\n",
    "    \"lines.linewidth\": 2               # Default line width\n",
    "})\n",
    "# Example list of datasets\n",
    "data_sets = [\"storage.p_mw\", \"storage.soc_percent\", \"res_line.loading_percent\"]\n",
    "y_label_names = ['BES Power (MW)', r'SoC (\\%)', r'LL (\\%)']\n",
    "\n",
    "# Folder labels for legend\n",
    "folder_labels = ['BES Bus 12', 'BES Bus 14', 'BES Bus 10', 'BES Bus 9', 'BES Bus 6']\n",
    "\n",
    "# Define line styles for different plots\n",
    "line_styles = ['-', '--', '-.', \":\", (0, (3, 5, 1, 10))]\n",
    "\n",
    "# Configure plot dimensions and subplots layout\n",
    "fig_width = 5.5\n",
    "fig_height = 3.375 * 1.70  # Höhe verdoppeln für zwei Subplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **(3): Plot Code for Figure 6: Case Study 3**\n",
    "\n",
    "**Decentralized and Distributed Grid Control Strategies**  \n",
    "Under Different Control Methods, Shown Over 100 Monte Carlo Runs on a Summer Day.  \n",
    "Shaded Areas Indicate the 1st to 99th Percentile Range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['serif'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Computer Modern\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 550x573.75 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(len(data_sets), len(desired_order_list), figsize=(fig_width, fig_height))\n",
    "\n",
    "# Add a list to collect handles and labels for the average lines\n",
    "handles = []\n",
    "# Loop through each dataset (rows)\n",
    "for i, data_set in enumerate(data_sets):\n",
    "    # Loop through each category (columns)\n",
    "    for j, category in enumerate(desired_order_list):\n",
    "        simulations = extracted_data.get(category, [])\n",
    "        number_of_simulations = len(simulations)\n",
    "        \n",
    "        if number_of_simulations == 0:\n",
    "            print(f\"No simulations found for category: {category}\")\n",
    "            continue\n",
    "\n",
    "        # Collect all tables into a list for stacking\n",
    "        all_tables = [simulations[k][\"simulation\"][data_set] for k in range(number_of_simulations)]\n",
    "\n",
    "        # Stack tables into a 3D NumPy array: (simulations, rows, columns)\n",
    "        stacked_tables = np.stack([table.to_numpy() for table in all_tables])\n",
    "\n",
    "        # Calculate average, 1st percentile, and 99th percentile\n",
    "        avg_table = np.mean(stacked_tables, axis=0)\n",
    "        quantile1_table = np.percentile(stacked_tables, 1, axis=0)\n",
    "        quantile99_table = np.percentile(stacked_tables, 99, axis=0)\n",
    "\n",
    "        # Convert NumPy arrays back to DataFrames\n",
    "        avg_table_df = pd.DataFrame(avg_table, columns=all_tables[0].columns)\n",
    "        quantile1_table_df = pd.DataFrame(quantile1_table, columns=all_tables[0].columns)\n",
    "        quantile99_table_df = pd.DataFrame(quantile99_table, columns=all_tables[0].columns)\n",
    "\n",
    "        # Plot data for each column in the current dataset\n",
    "        x = range(len(avg_table_df))  # Assuming rows represent sequential time steps\n",
    "\n",
    "        if i == len(data_sets) - 1:  # Last row\n",
    "            # Combine all columns in a single subplot\n",
    "            for idx, column in enumerate(avg_table_df.columns):\n",
    "                y_avg = avg_table_df.mean(axis=1)\n",
    "                y_quantile1 = quantile1_table_df.mean(axis=1)\n",
    "                y_quantile99 = quantile99_table_df.mean(axis=1)\n",
    "                \n",
    "                # Plot average line\n",
    "                line, = axes[i, j].plot(x, y_avg, color=\"black\", linewidth=2)\n",
    "                if j == 0 and idx == 0:  # Add a single handle for this category\n",
    "                    handles.append(line)\n",
    "                # Plot shaded quantile area\n",
    "                axes[i, j].fill_between(x, y_quantile1, y_quantile99, alpha=0.2)\n",
    "        else:  # All other rows\n",
    "            for idx, column in enumerate(avg_table_df.columns):\n",
    "                label = folder_labels[idx] if idx < len(folder_labels) else f\"Column {idx}\"\n",
    "                y_avg = avg_table_df[column]\n",
    "                y_quantile1 = quantile1_table_df[column]\n",
    "                y_quantile99 = quantile99_table_df[column]\n",
    "\n",
    "                # Plot average line\n",
    "                line, = axes[i, j].plot(x, y_avg, label=label, linestyle=line_styles[idx % len(line_styles)], linewidth=2)\n",
    "                if i == 0 and j == 0:  # Collect only one handle per label for the legend\n",
    "                    handles.append(line)\n",
    "\n",
    "                # Plot shaded quantile area\n",
    "                axes[i, j].fill_between(x, y_quantile1, y_quantile99, alpha=0.4)\n",
    "\n",
    "        # Set specific ylim for the first row\n",
    "        if i == 0:  # First row\n",
    "            axes[i, j].set_ylim(-0.08, 0.08)\n",
    "        else:  # All other rows\n",
    "            axes[i, j].set_ylim(0, 100.1)\n",
    "\n",
    "        # Customize subplot\n",
    "        if i == len(data_sets) - 1:\n",
    "            axes[i, j].set_xlabel(\"Time (Hours)\", fontsize=10)\n",
    "        if j == 0:\n",
    "            axes[i, j].set_ylabel(f\"{y_label_names[i]}\", fontsize=10)\n",
    "        else:\n",
    "            # Remove Y-axis ticks for all other columns\n",
    "            axes[i, j].set_yticks([])\n",
    "        \n",
    "        # Add x-ticks and labels\n",
    "        ticks = [0, 6*4, 12*4, 18*4, 24*4]\n",
    "        tick_labels = [\"0h\", \"6h\", \"12h\", \"18h\", \"24h\"]\n",
    "        axes[i, j].set_xticks(ticks)\n",
    "        axes[i, j].set_xticklabels(tick_labels, fontsize=8)\n",
    "        # axes[i, j].grid()\n",
    "        # Setze explizite Major-Ticks für jede Achse\n",
    "        axes[i, j].set_xticks(ticks)\n",
    "        axes[i, j].set_yticks(np.linspace(axes[i, j].get_ylim()[0], axes[i, j].get_ylim()[1], 5))  # Beispiel für Y-Ticks\n",
    "        axes[i, j].grid(True, which='both', linestyle='-', linewidth=1.0)\n",
    "\n",
    "# Add a common legend for the figure using only the average lines\n",
    "fig.legend(handles, folder_labels, loc='upper center', bbox_to_anchor=(0.5, 1.01), ncol=3, fontsize=10, frameon=False)\n",
    "\n",
    "# Adjust layout and save the plot\n",
    "fig.tight_layout(rect=[0, 0, 1, 0.95])\n",
    "plt.savefig(f\"{save_folder_name}/fig06montecarlo.pgf\", format=\"pgf\", bbox_inches=\"tight\")\n",
    "plt.savefig(f\"{save_folder_name}/fig06montecarlo.pdf\", format=\"pdf\", dpi=300, bbox_inches=\"tight\")\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jupyter Notebook exploration complete. Thank you for your commitment to advancing sustainable energy solutions.\n"
     ]
    }
   ],
   "source": [
    "print(\"Jupyter Notebook exploration complete. Thank you for your commitment to advancing sustainable energy solutions.\")"
   ]
  }
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