{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customising the environment class - Timing of actions and inputs\n",
    "\n",
    "Beside the action and observation spaces (see example 3), the timing between fmu timesteps, the interval between two agent actions and the resolution of external data is something that you might want to customize.\n",
    "\n",
    "Like in example 3, the energy managenment of a DC microgrid is chosen. The microgrid consist of a PV array, a battery storage, a load and a grid connection. The main idea is to optimise the use of the battery storage to minimise the energy consumption from the main grid and maximise the use of the PV energy. As basis of this example, the same customised environment with the reduced action and observation spaces is used. So the environment definition is imported from an external file, for details see example 3. \n",
    "\n",
    "The fmu has a base time step of 0.01 seconds which is very short but necessary to simulate all dynamic effects. It isn't realistic that the agent will take an action every 10 ms, so this time shall be changed to an action every ten seconds. Also, the simulation time is changed to fifteen minutes. This is updated in the config file for this example (04-config.cfg).\n",
    "\n",
    "```\n",
    "[FMU]\n",
    "FMU_path = 03-MicrogridFMU.fmu\n",
    "stop_time = 900.0\n",
    "dt = 0.01\n",
    "action_interval = 10.0\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All data handling and simulations necessary between two agent actions are done by the StableRLS package. Only the timing of external inputs needs to handled manually. For this, the pre-defined function *FMU_external_input* is modified. Every second, the load power is updated from a load profile which is imported during the *reset_*. Every 60 seconds the PV inputs (irradiance/module temperature) are updated from the imported data. In between this updates the normal cycle of fmu and agent steps is done.\n",
    "\n",
    "\n",
    "To run this example you might need to run '03-Customising_Actions_Observations.ipynb' first. This depends on your operating system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages as in the other examples\n",
    "import stablerls.configreader as cfg_reader\n",
    "import stablerls.gymFMU as gymFMU\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import gymnasium as gym\n",
    "import logging\n",
    "import random\n",
    "import datetime\n",
    "import os\n",
    "\n",
    "logger = logging.getLogger(__name__)\n",
    "\n",
    "class GridEnv(gymFMU.StableRLS):\n",
    "    def set_action_space(self):\n",
    "        \"\"\"Setter function for the action space of the agent.\n",
    "        \n",
    "        Returns\n",
    "        -------\n",
    "        space : gymnasium.space\n",
    "            Returns the action space defined by specified FMU inputs\n",
    "        \"\"\"\n",
    "        return gym.spaces.MultiDiscrete([11, 11])\n",
    "    \n",
    "    def set_observation_space(self):\n",
    "        \"\"\"Setter function for the observation space of the agent.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        space : gymnasium.space\n",
    "            Returns the observation space defined by specified FMU outputs\n",
    "        \"\"\"\n",
    "        high = np.arange(8).astype(np.float32)\n",
    "        high[:] = 1\n",
    "        low = high * -1\n",
    "        return gym.spaces.Box(low, high)\n",
    "    \n",
    "    def assignAction(self, action):\n",
    "        \"\"\"Changed assignment of actions to the FMU because only certain inputs\n",
    "        are used for the agent actions.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        action : list\n",
    "            An action provided by the agent to update the environment state.\n",
    "        \"\"\"\n",
    "        # assign actions to inputs\n",
    "        # check if actions are within action space\n",
    "        if not self.action_space.contains(action):\n",
    "            logger.info(f\"The actions are not within the action space. Action: {action}. Time: {self.time}\")\n",
    "\n",
    "        # convert discrete actions into steps of voltage references\n",
    "        vStepGrid = (action[0] - 5) * 0.04    \n",
    "        vStepBat = (action[1] - 5) * 0.04\n",
    "\n",
    "        # add them to actual references to get new setpoints\n",
    "        vRefGrid = self.fmu.fmu.getReal([self.fmu.input[3].valueReference])[0] + vStepGrid  \n",
    "        vRefBat = self.fmu.fmu.getReal([self.fmu.input[5].valueReference])[0] + vStepBat\n",
    "\n",
    "        # assign actions to the FMU inputs - take care of right indices!\n",
    "        self.fmu.fmu.setReal([self.fmu.input[3].valueReference], [vRefGrid])\n",
    "        self.fmu.fmu.setReal([self.fmu.input[5].valueReference], [vRefBat])\n",
    "\n",
    "    def obs_processing(self, raw_obs):\n",
    "        \"\"\"Customised action processing: Only specific outputs are evaluated. Additionally,\n",
    "        they are normalised to +-1.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        raw_obs : ObsType\n",
    "            The raw observation defined by all FMU outputs.\n",
    "        Returns\n",
    "        -------\n",
    "        observation : ObsType\n",
    "            The processed observation for the agent.\n",
    "        \"\"\"\n",
    "\n",
    "        nDec = 2\n",
    "        observation = np.array([  round((raw_obs[0] - self.nominal_voltage) / (0.1*self.nominal_voltage), 2),   # PV.V\n",
    "                                  round((raw_obs[3] - self.nominal_voltage) / (0.1*self.nominal_voltage), 2),   # Grid.V                     \n",
    "                                  round((raw_obs[6] - self.nominal_voltage) / (0.1*self.nominal_voltage), 2),   # Load.V\n",
    "                                  round((raw_obs[13] - self.nominal_voltage) / (0.1*self.nominal_voltage), 2),  # Bat.V  \n",
    "                                  round(raw_obs[2] / self.nominal_current, 2),                                  # PV.I\n",
    "                                  round(raw_obs[5] / self.nominal_current, 2),                                  # Grid.I\n",
    "                                  round(raw_obs[12] / self.nominal_current, 2),                                 # Bat.Inet\n",
    "                                  round(raw_obs[11], nDec),                                                     # Bat.SOC\n",
    "                               ]).astype(np.float32)\n",
    "\n",
    "        return observation\n",
    "\n",
    "    def reset_(self, seed=None):\n",
    "        \"\"\"Since zeros make no sense for the voltage references, the input reset is\n",
    "        changed. Also, the initial SOC is chosen randomly between limits.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        seed : int, optional\n",
    "            - None -\n",
    "        \"\"\"\n",
    "        # set voltage references to nominal voltage\n",
    "        self.fmu.fmu.setReal([self.fmu.input[3].valueReference], [self.nominal_voltage])\n",
    "        self.fmu.fmu.setReal([self.fmu.input[5].valueReference], [self.nominal_voltage])\n",
    "\n",
    "        # set initial SOC, randomly chosen\n",
    "        rand = random.random()\n",
    "        if rand < 0.15:\n",
    "            self.soc_init = 0.15\n",
    "        elif rand > 0.85:\n",
    "            self.soc_init = 0.85\n",
    "        else:\n",
    "            self.soc_init = rand \n",
    "        self.fmu.fmu.setReal([self.fmu.input[4].valueReference], [self.soc_init])\n",
    "\n",
    "        # for the other inputs external data is imported\n",
    "        load = pd.read_pickle(os.path.join(\"04-loadProfile.pkl\"))\n",
    "        pv = pd.read_pickle(os.path.join(\"04-inputPV.pkl\"))\n",
    "\n",
    "        # from this profiles a random intervall is selected\n",
    "        delta = pv.loc[len(pv)-1, \"Time\"] - pv.loc[0, \"Time\"]\n",
    "        delta -= datetime.timedelta(seconds=self.stop_time)\n",
    "        deltaMins = delta.days*1440 + delta.seconds/60\n",
    "        randOffset = random.randint(0, deltaMins)*60\n",
    "        startDate = pv.loc[0, \"Time\"] + datetime.timedelta(seconds=randOffset)\n",
    "        stopDate = startDate + datetime.timedelta(seconds=self.stop_time)\n",
    "\n",
    "        # input data for PV panel\n",
    "        self.inptPV = pv.loc[(pv[\"Time\"] >= startDate) & (pv[\"Time\"] <= stopDate),:]\n",
    "        \n",
    "        # load profile data\n",
    "        self.inptLoad = load.loc[(load[\"Time\"] >= startDate) & (load[\"Time\"] <= stopDate),:]\n",
    "\n",
    "        # get the first observation as specified by gymnaisum\n",
    "        self._next_observation(steps=1)\n",
    "        return self.obs_processing(self.outputs[self.step_count, :])\n",
    "\n",
    "    def FMU_external_input(self):\n",
    "        \"\"\"This function is called before each FMU step. Here external FMU\n",
    "        inputs independent of the agent action are set. In this case, this includes\n",
    "        weather data and the load power.\n",
    "\n",
    "        Use the code below to access the FMU inputs.\n",
    "        self.fmu.fmu.setReal([self.fmu.input[0].valueReference], [value])\n",
    "        \"\"\"\n",
    "        # update PV input every minute\n",
    "        t_in = round(self.time,3)\n",
    "        if (t_in % 60 == 0):\n",
    "            # Irradiance\n",
    "            self.fmu.fmu.setReal([self.fmu.input[1].valueReference], [self.inptPV.iloc[int(self.time/60),0]])\n",
    "\n",
    "            # ModuleTemperature\n",
    "            self.fmu.fmu.setReal([self.fmu.input[0].valueReference], [self.inptPV.iloc[int(self.time/60),1]])\n",
    "\n",
    "        # update LoadPower every second\n",
    "        if t_in.is_integer():\n",
    "            self.fmu.fmu.setReal([self.fmu.input[2].valueReference], [self.inptLoad.iloc[int(self.time),0]])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this modified environment class ten minutes are simulated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action: [5 5]\n",
      "Observation: [-0.08 -0.09 -0.16 -0.1   0.19  0.08  0.08  0.32]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [-0.08 -0.09 -0.16 -0.1   0.19  0.08  0.08  0.32]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [-0.08 -0.09 -0.16 -0.1   0.19  0.08  0.08  0.32]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [-0.08 -0.09 -0.16 -0.1   0.19  0.08  0.08  0.32]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [-0.08 -0.09 -0.16 -0.1   0.19  0.08  0.08  0.32]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [-0.08 -0.09 -0.16 -0.1   0.19  0.08  0.08  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [-0.08 -0.09 -0.16 -0.1   0.19  0.08  0.08  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [-0.08 -0.09 -0.16 -0.1   0.19  0.08  0.08  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [-0.08 -0.09 -0.16 -0.1   0.19  0.08  0.08  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [-0.08 -0.09 -0.16 -0.1   0.19  0.08  0.08  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [-0.08 -0.09 -0.16 -0.1   0.19  0.08  0.08  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [-0.08 -0.09 -0.16 -0.1   0.19  0.08  0.08  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.02 -0.01 -0.05 -0.02  0.23  0.01  0.01  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.02 -0.01 -0.05 -0.02  0.23  0.01  0.01  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.02 -0.01 -0.05 -0.02  0.23  0.01  0.01  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.02 -0.01 -0.05 -0.02  0.23  0.01  0.01  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.02 -0.01 -0.05 -0.02  0.23  0.01  0.01  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.02 -0.01 -0.05 -0.02  0.23  0.01  0.01  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.02 -0.01 -0.05 -0.02  0.23  0.01  0.01  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.02 -0.01 -0.05 -0.02  0.23  0.01  0.01  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.02 -0.01 -0.05 -0.02  0.23  0.01  0.01  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.02 -0.01 -0.05 -0.02  0.23  0.01  0.01  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.02 -0.01 -0.05 -0.02  0.23  0.01  0.01  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.02 -0.01 -0.05 -0.02  0.23  0.01  0.01  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.06  0.03  0.03  0.04  0.14 -0.03 -0.03  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.06  0.03  0.03  0.04  0.14 -0.03 -0.03  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.06  0.03  0.03  0.04  0.14 -0.03 -0.03  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.06  0.03  0.03  0.04  0.14 -0.03 -0.03  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.06  0.03  0.03  0.04  0.14 -0.03 -0.03  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.06  0.03  0.03  0.04  0.14 -0.03 -0.03  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.1   0.06  0.06  0.06  0.18 -0.05 -0.05  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.1   0.06  0.06  0.06  0.18 -0.05 -0.05  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.1   0.06  0.06  0.06  0.18 -0.05 -0.05  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.1   0.06  0.06  0.06  0.18 -0.05 -0.05  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.1   0.06  0.06  0.06  0.18 -0.05 -0.05  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.1   0.06  0.06  0.06  0.18 -0.05 -0.05  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.17  0.11  0.12  0.12  0.26 -0.09 -0.09  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.17  0.11  0.12  0.12  0.26 -0.09 -0.09  0.31]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.17  0.11  0.12  0.12  0.26 -0.09 -0.09  0.32]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.17  0.11  0.12  0.12  0.26 -0.09 -0.09  0.32]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.17  0.11  0.12  0.12  0.26 -0.09 -0.09  0.32]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.17  0.11  0.12  0.12  0.26 -0.09 -0.09  0.32]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.13  0.09  0.09  0.09  0.22 -0.07 -0.07  0.32]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.13  0.09  0.09  0.09  0.22 -0.07 -0.07  0.32]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.13  0.09  0.09  0.09  0.22 -0.07 -0.07  0.32]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.13  0.09  0.09  0.09  0.22 -0.07 -0.07  0.32]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.13  0.09  0.09  0.09  0.22 -0.07 -0.07  0.32]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.13  0.09  0.09  0.09  0.22 -0.07 -0.07  0.33]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.   -0.02 -0.04 -0.02  0.14  0.01  0.01  0.33]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.   -0.02 -0.04 -0.02  0.14  0.01  0.01  0.33]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.   -0.02 -0.04 -0.02  0.14  0.01  0.01  0.33]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.   -0.02 -0.04 -0.02  0.14  0.01  0.01  0.33]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.   -0.02 -0.04 -0.02  0.14  0.01  0.01  0.32]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.   -0.02 -0.04 -0.02  0.14  0.01  0.01  0.32]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.05  0.02 -0.01  0.02  0.19 -0.01 -0.01  0.32]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.05  0.02 -0.01  0.02  0.19 -0.01 -0.01  0.33]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.05  0.02 -0.01  0.02  0.19 -0.01 -0.01  0.33]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.05  0.02 -0.01  0.02  0.19 -0.01 -0.01  0.33]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.05  0.02 -0.01  0.02  0.19 -0.01 -0.01  0.33]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.05  0.02 -0.01  0.02  0.19 -0.01 -0.01  0.33]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.13  0.09  0.09  0.09  0.22 -0.07 -0.07  0.33]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.13  0.09  0.09  0.09  0.22 -0.07 -0.07  0.33]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.13  0.09  0.09  0.09  0.22 -0.07 -0.07  0.33]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.13  0.09  0.09  0.09  0.22 -0.07 -0.07  0.33]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.13  0.09  0.09  0.09  0.22 -0.07 -0.07  0.33]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.13  0.09  0.09  0.09  0.22 -0.07 -0.07  0.33]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.16  0.1   0.11  0.11  0.25 -0.08 -0.09  0.33]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.16  0.1   0.11  0.11  0.25 -0.08 -0.09  0.33]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.16  0.1   0.11  0.11  0.25 -0.08 -0.09  0.34]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.16  0.1   0.11  0.11  0.25 -0.08 -0.09  0.34]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.16  0.1   0.11  0.11  0.25 -0.08 -0.09  0.34]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.16  0.1   0.11  0.11  0.25 -0.08 -0.09  0.34]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.09  0.05  0.05  0.05  0.17 -0.04 -0.04  0.34]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.09  0.05  0.05  0.05  0.17 -0.04 -0.04  0.34]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.09  0.05  0.05  0.05  0.17 -0.04 -0.04  0.34]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.09  0.05  0.05  0.05  0.17 -0.04 -0.04  0.34]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.09  0.05  0.05  0.05  0.17 -0.04 -0.04  0.34]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.09  0.05  0.05  0.05  0.17 -0.04 -0.04  0.34]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.12  0.08  0.08  0.08  0.21 -0.06 -0.06  0.34]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.12  0.08  0.08  0.08  0.21 -0.06 -0.06  0.34]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.12  0.08  0.08  0.08  0.21 -0.06 -0.06  0.35]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.12  0.08  0.08  0.08  0.21 -0.06 -0.06  0.35]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.12  0.08  0.08  0.08  0.21 -0.06 -0.06  0.35]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.12  0.08  0.08  0.08  0.21 -0.06 -0.06  0.35]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.05  0.02  0.    0.02  0.2  -0.02 -0.02  0.35]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.05  0.02  0.    0.02  0.2  -0.02 -0.02  0.35]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.05  0.02  0.    0.02  0.2  -0.02 -0.02  0.35]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.05  0.02  0.    0.02  0.2  -0.02 -0.02  0.35]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.05  0.02  0.    0.02  0.2  -0.02 -0.02  0.35]\n",
      "\n",
      "Action: [5 5]\n",
      "Observation: [ 0.05  0.02  0.    0.02  0.2  -0.02 -0.02  0.35]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# read config-file\n",
    "config = cfg_reader.configreader('04-config.cfg')\n",
    "\n",
    "# create new env object and reset it before simulating\n",
    "microgrid = GridEnv(config)\n",
    "obs = microgrid.reset()\n",
    "\n",
    "# for this example, the actions are kept constant at the reference value of 48 V\n",
    "action = np.array([5,5])\n",
    "\n",
    "terminated = False\n",
    "truncated = False\n",
    "while not (terminated or truncated):\n",
    "    observation, reward, terminated, truncated, info  = microgrid.step(action)\n",
    "    print(f'Action: {action}\\nObservation: {observation}\\n')\n",
    "\n",
    "microgrid.close()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To show the simulation results, they are plotted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A6jTuswOgtgg7AADA0gg7APwC99kB4C7CDoBqIWwA8FeEHQA14vDxIBoHd9oBUEuEHQAAYGmEHQAAYGmEHQB1GpeeA6itILMrAACwr4oKQzM+3KVvi0/5vOwAh0Mjr0jQ9clxPi8bvkXYAeAXDC4Hs6Rvik9qwYYDppVfVHKOsGMDhB0A1cKzqeANF8r//XcVVT9YU4cm+azcb4tPafaavbpQwd+1HRB2ANRpDNmxtsoGu7CQQN3UtbnPyt2076hmr9nrs/JgLgYoAwAASyPsAPALdDZYGy148CbCDgDANIwFgy8QdgDUbdxoB0AtEXYAAKbz9TPXKnFLA3sg7ADwCxyTrIntCl8wNexMnTpVDofD5dWhQwfn/HPnzikrK0uNGzdWRESERowYoaKiIpfPyM/P1+DBgxUWFqZmzZrpoYce0oULF3y9KoDlcVAC4K9Mv89Op06dtGrVKuf7oKD/VmnChAn68MMPtXjxYkVFRenee+/V8OHDtWHDBklSeXm5Bg8erNjYWG3cuFEFBQUaPXq0goODNXPmTJ+vC2AHvu5tYMSOtZmVofm7shfTw05QUJBiY2Mvmn7ixAnNnz9fCxcuVN++fSVJCxYsUMeOHbVp0yZdeeWVWrFihXbt2qVVq1YpJiZGXbp00YwZMzRp0iRNnTpVISEhvl4dAF7CVTsA3GX6mJ09e/YoPj5el19+udLT05Wfny9J2rJli86fP6/+/fs7l+3QoYMSExOVm5srScrNzVVKSopiYmKcywwcOFAlJSXauXPnJcssLS1VSUmJywsAYB4uuoM3mRp2evbsqVdeeUUfffSR5s6dq/3796tXr146efKkCgsLFRISoujoaJefiYmJUWFhoSSpsLDQJehUzq+cdyk5OTmKiopyvhISEjy7YgA8hoOgtXE1FHzB1G6stLQ05/87d+6snj17qkWLFnrzzTdVv359r5WbnZ2tiRMnOt+XlJQQeAAAsCjTu7F+KDo6Wu3atdO3336r2NhYlZWV6fjx4y7LFBUVOcf4xMbGXnR1VuX7qsYBVQoNDVVkZKTLC0DdRgOAtZnVgseflT3UqbBz6tQp7d27V3FxcerevbuCg4O1evVq5/zdu3crPz9fqampkqTU1FTl5eWpuLjYuczKlSsVGRmppKQkn9cfAFAzhA34gqndWA8++KCGDBmiFi1a6MiRI3rssccUGBiokSNHKioqSnfddZcmTpyoRo0aKTIyUuPGjVNqaqquvPJKSdKAAQOUlJSkUaNG6cknn1RhYaEmT56srKwshYaGmrlqgOVUHpQcPr5o19flwR7MumMzzGFq2Dl06JBGjhypo0ePqmnTpvrFL36hTZs2qWnTppKkp59+WgEBARoxYoRKS0s1cOBAzZkzx/nzgYGBWrJkiTIzM5Wamqrw8HBlZGRo+vTpZq0SAKAGKrsnCbXwJlPDzqJFi35yfr169TR79mzNnj37ksu0aNFCS5cu9XTVANQxdHcAcJfpNxUEAIBeJWs6X16hjXuPSpJ6tWmigABzNjRhB0CdxkHQ6mizs7IzZeXKePlzSdKex9MUYFJ3ZZ26GgsAAJ8ia9kCYQeAX+A+O9b03wHKgPcQdgAAgKURdgBUi/MM3Men4JzxwxsYC2YvhB0AfoJ+LCty3qyS9AEvIuwAAABLI+wAqNM44bcHNjO8ibADADANV9nBFwg7APwCB0V4A39W9kDYAQCYxuBGO/ABwg6AGvH1MYmrdADUFmEHQLUYNPjDi3weon1cHsxF2AHgFxizY01sVvgCYQcAAFgaYQcAYDrGZsGbCDsAANOY3T1pmF0B+ARhB4BfYIA0AHcRdgAApqkMsXRiwZsIOwCqp/Lebz4+KjGUA97A35W9EHYA+AWGVlgb4QPeRNgBAJiHEAsfIOwAAABLI+wAqNMcDF21tMqGHbO2Mw1L9kDYAeAXOCgBcBdhBwAAWBphB0CN+Pq2/lylY22GSbc0gL0QdgBUC91IsBbSlZ0QdgD4Be6zA8BdhB0AgGl45hl8gbADoE6jswFAbRF2AACm+e8AZZPus0PDki0QdgD4Bbo7ALiLsAMAMB3dlfAmwg6AGvH1QYn7r1gb7XXwBcIOgGoxGNwACyFE2wthB4B/IGtZGuED3kTYAQCYhhZD+AJhB0Cd5mDoKryIq/zsgbADwC9wSLKmyu1KNxa8qcZh5+zZs1q/fr127dp10bxz587pr3/9q0cqBgAA4Ak1CjvffPONOnbsqGuuuUYpKSnq3bu3CgoKnPNPnDihO+64w+OVBFCH+PgMnDN+e6C7Et5Uo7AzadIkJScnq7i4WLt371aDBg109dVXKz8/31v1AwBYmUn9k0Qre6lR2Nm4caNycnLUpEkTtWnTRh988IEGDhyoXr16ad++fd6qI4A6wOyLZrhqB4C7ahR2zp49q6CgIOd7h8OhuXPnasiQIerdu7e++eYbj1cQAGBdlVdD0V0Jbwr634v8V4cOHfTFF1+oY8eOLtNfeOEFSdLQoUM9VzMAAAAPqFHYuemmm/TGG29o1KhRF8174YUXVFFRoRdffNFjlYN/eXPzQf2/FbtVXmGf7oZmDUK14I6fKy6qvtlVAeAGekftoUZhJzs7W9nZ2ZecP2fOHM2ZM6fWlYJ/em/rYf3zZKnZ1fCpY6fL9Pn+Y7qxy2VmV8XyOCZZU2XYoBcL3lSjsAP8lMqd1m8HddC17ZuZWxkfePjt7fpH/nGzqwEA+B/cCjt79uxR27ZtPV0XWERcVH21jWlgdjW8LjzEnucKvr4fioORq/bAdoYX1fgOylu2bFGfPn28UBUAgN2YNWaGEG0vNTo1/fjjjzVixAg999xz3qoP/BgP1LM2s7fu8p2FuipntU/LrBcSqN8PS9ZVrZv4tFwAnlXtlp133nlHN9xwg3Jycqq8Gqu2Zs2aJYfDofHjxzun9enTRw6Hw+V1zz33uPxcfn6+Bg8erLCwMDVr1kwPPfSQLly44PH6ATBH66bhkqRz5yt05MQ5n772/fO0Ptxe8D9qiNpwPgjU1FrA6qrdsnPLLbdo6tSpF4UNT9i8ebPmzZunzp07XzRvzJgxmj59uvN9WFiY8//l5eUaPHiwYmNjtXHjRhUUFGj06NEKDg7WzJkzPV5PVA+tw/CkPu2b6dOHr9XxM+d9Wu4bm/O18LN801u04F1cem4P1Q47rVu31ooVKzRx4kTVr++5e4qcOnVK6enpeumll/T73//+ovlhYWGKjY2t8mdXrFihXbt2adWqVYqJiVGXLl00Y8YMTZo0SVOnTlVISIjH6glcCjtL70toFKaERr4tc83uer4t0OY4SYI3VTvsrF+/Xmlpabrpppv0wQcfKDg42CMVyMrK0uDBg9W/f/8qw87rr7+u1157TbGxsRoyZIimTJnibN3Jzc1VSkqKYmJinMsPHDhQmZmZ2rlzp7p27VplmaWlpSot/e/9YEpKSjyyLnbHQR/wX4Zh6K0th3TwX2d9Wu6+f57yaXmwp2qHnSZNmmjNmjUaOnSofvnLX+rdd9+tdeGLFi3Sl19+qc2bN1c5/9Zbb1WLFi0UHx+v7du3a9KkSdq9e7feeecdSVJhYaFL0JHkfF9YWHjJcnNycjRt2rRa1x/2ZtczUbuut9XtPFKih97ablr5YSGBppUN66vR1VgRERFatmyZbr311loXfPDgQd1///1auXKl6tWrurl47Nixzv+npKQoLi5O/fr10969e9W6dWu3y87OztbEiROd70tKSpSQkOD258GVr+/DAqD2Ss7+e0xUVP1gDf1ZvE/LDgxw6P+6N/dpmbCXGt8VLTQ0VIsXL651wVu2bFFxcbG6devmnFZeXq5PPvlEL7zwgkpLSxUY6Jr0e/bsKUn69ttv1bp1a8XGxurzzz93WaaoqEiSLjnOp3IdQkNDa70OgJ3QTWkPsZH1NGNYstnV8DpOyeylxjcVlKSAALd+zEW/fv2Ul5enrVu3Ol89evRQenq6tm7delHQkaStW7dKkuLi4iRJqampysvLU3FxsXOZlStXKjIyUklJSbWuI2qGYyGsyC4hzyarCZuq1f3ui4uLVVxcrIqKCpfpVV1C/mMNGjRQcrLr2UN4eLgaN26s5ORk7d27VwsXLtSgQYPUuHFjbd++XRMmTNA111zj/PwBAwYoKSlJo0aN0pNPPqnCwkJNnjxZWVlZtNwAAABJboadLVu2KCMjQ1999ZWM/5z2OBwOGYYhh8Oh8vLyWlcsJCREq1at0jPPPKPTp08rISFBI0aM0OTJk53LBAYGasmSJcrMzFRqaqrCw8OVkZHhcl8e+B4DWGEFdvszdj593G4rDltwK+zceeedateunebPn6+YmBiPPWNk7dq1zv8nJCRo3bp1//NnWrRooaVLl3qkfMAdPCYDAOo2t8LOvn379Pbbb6tNmzaerg/8Gcd8AEAd5NZI4379+mnbtm2ergsAP0AvhzXRQgkrc6tl589//rMyMjK0Y8cOJScnX3Q35aFDh3qkcvBPHAwBAHWJW2EnNzdXGzZs0LJlyy6a56kBygDqFs787cFTYzDrOpusJv7DrW6scePG6bbbblNBQYEqKipcXgQd++JgCGuyx9+1Xe4nBHtyK+wcPXpUEyZMuOi5VAAA+BODlGcLboWd4cOHa82aNZ6uCyzCbs3D7CutyXZ/x//512arDZtwa8xOu3btlJ2drfXr1yslJeWiAcr33XefRyoHAABQW25fjRUREaF169ZddOM/h8NB2LEpWjgA/2e3Fi3Yg1thZ//+/Z6uB+B37HLVyo/ZdLUtj7ErsLIajdnp1auX/vCHP2jPnj3eqg8sgaMhgLrNwX7KVmoUdsaMGaPc3Fx169ZNHTt21KRJk7RhwwbOCAAbsOvX3G7rTcsdrKhGYWf06NF6++239f333+uPf/yjjh8/rptvvlmxsbG688479d577+ns2bPeqivqOJsdEwBL4fsLK3Pr0vPQ0FANGjRI8+bN05EjR/T+++8rLi5OU6ZMUePGjXXDDTdow4YNnq4rAAAeRcizB7fCzo/17NlTjz/+uPLy8pSXl6d+/fqpoKDAEx8NP2S3ZnC7dXPYhe0GoP/n75ixLLCiGl2NlZGRoX79+qlPnz5KTEyscpnWrVtrwoQJHqkcAABAbdUo7Hz33Xe6++67VVZWppYtW+raa6/Vtddeq759+youLs5bdYSfYKC6PXDmb212a9CCPdQo7Kxdu1alpaXauHGj1q5dq7Vr1+q1117T+fPn1bZtW2fwufnmm71VX6DO4JgAK+FBvrCyGt9UMDQ01NmiI0nnzp3Txo0btWzZMv3pT3/Sn/70J8KOzRECANR1tGDZi1t3UJaksrIy5ebmau3atVqzZo0+++wzxcfHa8SIEZ6sHwCYym69s2QAWFGNws4nn3ziEm4SExPVu3dvjR07Vq+99pqaN2/urXrCD9jsmABYit1CXSW7rrfd1CjsVF6FNWnSJC1atEgxMTHeqhfgN9hXAkDdVqP77Dz88MOKjY3V+PHjdd1112ncuHHOOyoDlWx3fxLAApwtHHx/YUE1CjuzZs3Spk2bdPToUT3xxBMKCwvTk08+qfj4eCUnJysrK0tvvfWWt+oKAABQY24NUI6IiFBaWprS0tIkSceOHdNTTz2l559/Xi+++KLKy8s9Wkn4B/q+7YETfwD+xq2wU1FRoc2bNzvvtbNhwwadOnVKiYmJGj58uKfrCNRJHPRhJc5eLFNrAXhHjcLOk08+6Qw3J0+e1GWXXaY+ffromWee0bXXXqtWrVp5q57wI+wsrcmud8jmZnuA/6tR2HnmmWfUp08f/eEPf9C1116rNm3aeKteAAAT2K3FkjBrDzUKO0eOHPFWPWAB7DIA/2XXljvYQ42uxgJwMQ4S1mS3Fg7Aygg78DgOEoD/YYAyrIywA6BGOBgC8DeEHXgO3TkAgDqIsAO4iRYOWEnluYpdHvdik9XEf7gVdoqKijRq1CjFx8crKChIgYGBLi/YGzsRa7Jrux0NloD/c+sOyrfffrvy8/M1ZcoUxcXF2eZMAACszm57c8KsPbgVdtavX69PP/1UXbp08XB14M/YZwD+jG8wrMutsJOQkMC9RYD/4JtgTQ7btXHAV0ovlKu4pNSUsuOj6yswwH5/226FnWeeeUaPPPKI5s2bp5YtW3q4SvB3HCQA/8WoBO+6UF6hgU9/ogNHz5hS/hUtG+nNe1JNKdtMboWdW265RWfOnFHr1q0VFham4OBgl/nHjh3zSOUA1EEcDS2JxnrfOHnugjPo1A8O9NnXqcIwdO58hbYfPu6bAusYt1t2gB+z286SgfkAamPntIEK8FGX0pHjZ3XVrI9VYbP9dCW3wk5GRoan6wEAdZJdjg3/fVyEPUK8XdazUsB/Ts4qbJp2qh12SkpKFBkZ6fz/T6lcDjZlr32Ibdit5Q724Os/a7O+RpUNSBU2/SJXO+w0bNhQBQUFatasmaKjo6tswjcMQw6HQ+Xl5R6tJADARzhZsaTK7jKbNuxUP+x8/PHHatSokSRpzZo1XqsQ/Jdhmwb/H7HpasNabHrCbypfDvsL+EFhFRWGz8YK1RXVDju9e/eu8v8AYEWMP4c3mHWPuh9mmwrDUIDNmvB4ECg8zl5fIfth+1ob29eafjj0xI5dWYQdAIB9u6FN5MvbV/zwrsl2HKRM2IHH2O37wxkwAH/x424suyHsAMBPsMtxoXI97TJWyaz1NO/Sc7qx3HLhwgWtWrVK8+bN08mTJyVJR44c0alTpzxWOfgn7ixsTXRzwIrsEmZdw45NVvoH3LqD8nfffafrr79e+fn5Ki0t1XXXXacGDRroiSeeUGlpqV588UVP1xMA4AN2u7OwXfywG6vHjFW+64evI7nKrbBz//33q0ePHtq2bZsaN27snH7TTTdpzJgxHqsc/IsNTxYk0eIBa+Cv2DfM2k8GBjiUclmU8g6fUFl5hc/L7xQfqSAT7+3jVtj59NNPtXHjRoWEhLhMb9mypQ4fPuxWRWbNmqXs7Gzdf//9zgeNnjt3Tg888IAWLVqk0tJSDRw4UHPmzFFMTIzz5/Lz85WZmak1a9YoIiJCGRkZysnJUVCQW6sGAJIYgA5rcTgcevc3V6n4ZKkp5TdrEGrqEAe3EkFFRUWVj4Q4dOiQGjRoUOPP27x5s+bNm6fOnTu7TJ8wYYI+/PBDLV68WFFRUbr33ns1fPhwbdiwQZJUXl6uwYMHKzY2Vhs3blRBQYFGjx6t4OBgzZw5051VgwdwkLA2hmRZG9vXN8z4PQcFBig+ur7vC64D3BqgPGDAAGfri/TvxHjq1Ck99thjGjRoUI0+69SpU0pPT9dLL72khg0bOqefOHFC8+fP11NPPaW+ffuqe/fuWrBggTZu3KhNmzZJklasWKFdu3bptddeU5cuXZSWlqYZM2Zo9uzZKisrc2fVAMCWzLqzL+ALboWdP/7xj9qwYYOSkpJ07tw53Xrrrc4urCeeeKJGn5WVlaXBgwerf//+LtO3bNmi8+fPu0zv0KGDEhMTlZubK0nKzc1VSkqKS7fWwIEDVVJSop07d16yzNLSUpWUlLi8UHt221VyBmwPjMmyJvMuPefvyQxudWM1b95c27Zt09/+9jdt27ZNp06d0l133aX09HTVr1/9JrJFixbpyy+/1ObNmy+aV1hYqJCQEEVHR7tMj4mJUWFhoXOZHwadyvmV8y4lJydH06ZNq3Y9AcAu7BfiCR924PYo3qCgIKWnpys9Pd2tnz948KDuv/9+rVy5UvXq1XO3Gm7Jzs7WxIkTne9LSkqUkJDg0zpYmf12lvZALwfgOewmfcutbqycnBy9/PLLF01/+eWXq92NtWXLFhUXF6tbt24KCgpSUFCQ1q1bp+eee05BQUGKiYlRWVmZjh8/7vJzRUVFio2NlSTFxsaqqKjoovmV8y4lNDRUkZGRLi/AXYQAWAn32fEy9hemcCvszJs3Tx06dLhoeqdOnap9Q8F+/fopLy9PW7dudb569Oih9PR05/+Dg4O1evVq58/s3r1b+fn5Sk1NlSSlpqYqLy9PxcXFzmVWrlypyMhIJSUlubNqqAUGOMJK7NZCydcXVuZWN1ZhYaHi4uIumt60aVMVFBRU6zMaNGig5ORkl2nh4eFq3Lixc/pdd92liRMnqlGjRoqMjNS4ceOUmpqqK6+8UtK/rwpLSkrSqFGj9OSTT6qwsFCTJ09WVlaWQkND3Vk1AP8DZ/5A7fFYHd9yK+wkJCRow4YNatWqlcv0DRs2KD4+3iMVk6Snn35aAQEBGjFihMtNBSsFBgZqyZIlyszMVGpqqsLDw5WRkaHp06d7rA6oOQ6GgP/iGAwrcivsjBkzRuPHj9f58+fVt29fSdLq1av18MMP64EHHnC7MmvXrnV5X69ePc2ePVuzZ8++5M+0aNFCS5cudbtMAACXRPsKv2VzuBV2HnroIR09elS/+c1vnDfvq1evniZNmqTs7GyPVhCouzgFtgWOTpZEC7S9uBV2HA6HnnjiCU2ZMkVfffWV6tevr7Zt2zJOBrAwjvnWZtcBymatN1HLt2r1tMyIiAj9/Oc/91RdYBH0+QNA1ewaKs3mVtg5ffq0Zs2apdWrV6u4uFgVFa6Pi9+3b59HKgf4A/ZdsBKuEoIVuRV2fv3rX2vdunUaNWqU4uLi+HJAEmcssBa7jeng++tbHDZ9y62ws2zZMn344Ye6+uqrPV0fAHUcO2kA/satOyg3bNhQjRo18nRdYBEcCwH/xffXu7jE3xxutezMmDFDjz76qP7yl78oLCzM03UCgDrD14emf50u06//+oUKT5zzabmnyy74tDzAl9wKO3/84x+1d+9excTEqGXLlgoODnaZ/+WXX3qkcvAvdjtjoTsH3vD5gWPa8t2/TCv/8qbhppXtS5XfX7P2WnYbE2Y2t8LOsGHDPFwNAHUdA1h9o/L33CG2gZ78v84+LTsoIEAdYhv4tEy74XtkDrfCzmOPPebpesBKbHbCws4L3hARGqTOzaPNrgZgCW4NUAYAALVgs5NCs7nVslNeXq6nn35ab775pvLz853Px6p07Ngxj1QO/oUWDnuwyz6aMVmAdbjVsjNt2jQ99dRTuuWWW3TixAlNnDhRw4cPV0BAgKZOnerhKgKAnXDWYGVsXXO4FXZef/11vfTSS3rggQcUFBSkkSNH6s9//rMeffRRbdq0ydN1hJ/hKgMAQF3iVtgpLCxUSkqKpH8/DPTEiROSpBtuuEEffvih52oHACYzTOqfpRvN2ti8vuVW2GnevLkKCgokSa1bt9aKFSskSZs3b1ZoaKjnage/YrfmWXZWgP+q/P76OsyaFZ7tzq2wc9NNN2n16tWSpHHjxmnKlClq27atRo8erTvvvNOjFQRQV7CT9gWOhYDnuXU11qxZs5z/v+WWW5SYmKjc3Fy1bdtWQ4YM8Vjl4J/s1vxutztHA6g9u+0nzeZW2Pmx1NRUpaameuKjAABioD/gSdUOO++//77S0tIUHBys999//yeXHTp0aK0rBv9DX7Q9cEYKuI/dpDmqHXaGDRumwsJCNWvW7CefjeVwOFReXu6JugEAANRatcNORUVFlf8HfowTf1iJr0/EOfG3B7opfYtnYwFuojsH8F+V31/CpT1Uu2Xnueeeq/aH3nfffW5VBv6NnQbgQYRpwGOqHXaefvppl/f//Oc/debMGUVHR0uSjh8/rrCwMDVr1oywA1gQAysBz6Fl2Leq3Y21f/9+5+vxxx9Xly5d9NVXX+nYsWM6duyYvvrqK3Xr1k0zZszwZn3hBxw2+xYTAgBUF/sLc7g1ZmfKlCl6/vnn1b59e+e09u3b6+mnn9bkyZM9VjkAdY9dwqxZ68nBEPA8t8JOQUGBLly4cNH08vJyFRUV1bpS8FPspAEAdZBbYadfv366++679eWXXzqnbdmyRZmZmerfv7/HKgcAdmWP9jP7Yvv6llth5+WXX1ZsbKx69Oih0NBQhYaG6oorrlBMTIz+/Oc/e7qO8DM26eWATdCtZG2+3r48S88cbj0bq2nTplq6dKm++eYbff3115KkDh06qF27dh6tHPyL3b7C3BQM8Gd8f+2kVg8CbdeuHQEHADyIM397sMtA/7rC7bBz6NAhvf/++8rPz1dZWZnLvKeeeqrWFQP8hV0OTXTnALXH98gcboWd1atXa+jQobr88sv19ddfKzk5WQcOHJBhGOrWrZun6wg/w/kKUHuc+AOe41bYyc7O1oMPPqhp06apQYMGevvtt9WsWTOlp6fr+uuv93Qd/VZFhb0ivMEpCyyErAFv4u/Lt9wKO1999ZXeeOONf39AUJDOnj2riIgITZ8+XTfeeKMyMzM9Wkl/1eHRj1R2gSfEAwBgJrcuPQ8PD3eO04mLi9PevXud877//nvP1Ax+qXF4iFo3jTC7GoDH+Lq9kgZSa2PzmsOtlp0rr7xS69evV8eOHTVo0CA98MADysvL0zvvvKMrr7zS03X0W5uy+5ldBZ+LCA1SSJBbGRoAfI7ud3twK+w89dRTOnXqlCRp2rRpOnXqlP72t7+pbdu2XIn1A43CQ8yuAryIAaTwJu7j5F2mf3/NLt9mahx2ysvLdejQIXXu3FnSv7u0XnzxRY9XDMClnSm74PPxYKUXyn1aHmBFtCSZo8ZhJzAwUAMGDNBXX32l6OhoL1QJ8DM+3nktzSvQfW/8QxdsdrUfALjLrcEVycnJ2rdvn6frAqAavjjwL9OCToPQIPVs1ciUsu2CCGsP9GL5lltjdn7/+9/rwQcf1IwZM9S9e3eFh4e7zI+MjPRI5QBc2t29L9dDA9r7tMwAh0MBAfbYTZs+pgOAx7gVdgYNGiRJGjp0qMvzPQzDkMPhUHk5ffuAtwU6HAoK5Mo3qyJsWRMtd+ZwK+ysWbPG0/UAUE08KNK3GFBqbWxde6hx2Dl//rymT5+uF198UW3btvVGnQAA8CqzG8546rlv1bgNPDg4WNu3b/dGXQC/Yva+yuzy4R20JFkbm9ccbnX433bbbZo/f76n6wL4JfZdAFC3uTVm58KFC3r55Ze1atWqKq/G4i7KgPdwZmgPtNxZG9vXt9wKOzt27FC3bt0kSd98843LPPohAQBAXcLVWICf4tlJ3sVvF95B06wZuEkHAPwEDk2A/6tRy87w4cOrtdw777xTreXmzp2ruXPn6sCBA5KkTp066dFHH1VaWpokqU+fPlq3bp3Lz9x9990uDx7Nz89XZmam1qxZo4iICGVkZCgnJ0dBQW41WgEA7MSkNEvLoW/VKBFERUV5tPDmzZtr1qxZatu2rQzD0F/+8hfdeOON+sc//qFOnTpJksaMGaPp06c7fyYsLMz5//Lycg0ePFixsbHauHGjCgoKNHr0aAUHB2vmzJkerStQ1zA8ztropvQus8aXcoGBOWoUdhYsWODRwocMGeLy/vHHH9fcuXO1adMmZ9gJCwtTbGxslT+/YsUK7dq1S6tWrVJMTIy6dOmiGTNmaNKkSZo6dapCQkI8Wl/ghzgYAYB/qDNjdsrLy7Vo0SKdPn1aqampzumvv/66mjRpouTkZGVnZ+vMmTPOebm5uUpJSVFMTIxz2sCBA1VSUqKdO3f6tP6wL1+fqXHTOcD/ceWyb5k+sCUvL0+pqak6d+6cIiIi9O677yopKUmSdOutt6pFixaKj4/X9u3bNWnSJO3evds5JqiwsNAl6Ehyvi8sLLxkmaWlpSotLXW+Lykp8fRqAYBbyLKA55kedtq3b6+tW7fqxIkTeuutt5SRkaF169YpKSlJY8eOdS6XkpKiuLg49evXT3v37lXr1q3dLjMnJ0fTpk3zRPUB03Be6F2cecMbyLLmML0bKyQkRG3atFH37t2Vk5Ojn/3sZ3r22WerXLZnz56SpG+//VaSFBsbq6KiIpdlKt9fapyPJGVnZ+vEiRPO18GDBz2xKgCsyKyrdchagMeYHnZ+rKKiwqWL6Ye2bt0qSYqLi5MkpaamKi8vT8XFxc5lVq5cqcjISGdXWFVCQ0MVGRnp8gL8BWeGgOeY9X0iy/qWqd1Y2dnZSktLU2Jiok6ePKmFCxdq7dq1Wr58ufbu3auFCxdq0KBBaty4sbZv364JEybommuuUefOnSVJAwYMUFJSkkaNGqUnn3xShYWFmjx5srKyshQaGmrmqgGAWwzirKUxJsscpoad4uJijR49WgUFBYqKilLnzp21fPlyXXfddTp48KBWrVqlZ555RqdPn1ZCQoJGjBihyZMnO38+MDBQS5YsUWZmplJTUxUeHq6MjAyX+/IAlkU/B+A2vj32YmrYmT9//iXnJSQkXHT35Kq0aNFCS5cu9WS1gOr5z97S15eCc2YI+D/OVXyrzo3ZAQAA8CTCDuCnODEE/A9jssxB2AGAKpjVzUA3JeB5hB3Az3Bm6Fv8vuEdtM36EmEHAOog7uDsG1xgYA+EHcBPcSwE3Mf3x14IO4CbzNpXcmYI+D/Clm8RdoBaInvAkwizgOcRdgAA8BHCrDkIO4CfcnA1h6WxdQHPIewAfoYTQ9+oDBucicMbCLO+ZeqzsQAAMFNZeYVeXLfXZ+UVlZzzWVn4L8IO4Ke4msOaaEjyjXrBgZKk8+WGZi372rTy4RuEHQCA7cRE1tPUIUnKO1zi87IdDmlw5zifl2tnhB3ATWbd4ZYxJPZAy5333X51K7OrAB9hgDJQS4QPAKjbCDuAn+LEHwCqh7AD+B2akqzM1w+mBOyAsAMAVfnPoBmyB+D/CDuAn2IAq7WxeQHPIewAAABLI+wAfoZuFQCoGcIO4Cbns5NMrQWshr8nwPMIO4CfMuumhgDgbwg7AFAHEWYBzyHsAH6GMTu+8d9uSn7hgL8j7AAAAEsj7ABAXUJDEuBxhB3Az9CtAgA1Q9gBgDqI4cmA5xB2ADdVXixj1oMbuVgHAKqHsAMAACyNsAP4GS49tzbGZAGeR9gBgCr8t5vS3HoAqD3CDuCnHAxhtTTGZAGeQ9gB/AwNDQBQM4QdAABgaYQdAKhDGCMEeB5hB3CT2UMqGNMBANVD2AH8DGf+dkGaBTyFsAMAP4FsCfg/wg7gpzjv9y6zLu0nXAGeR9gBAACWRtgB/AyPE7AHBqADnkPYAQAAlkbYAWrJrKujOPMHgOoh7ABucpiVNujFsjRuLQB4HmEHAABYGmEH8FM89dw3TOumNKdYwJIIOwBQBcZEAdZB2AH8DEM6AKBmCDsAUIdwHyXA8wg7QC2ZdXCimwUAqoewA7iJrAFvIswCnmNq2Jk7d646d+6syMhIRUZGKjU1VcuWLXPOP3funLKystS4cWNFRERoxIgRKioqcvmM/Px8DR48WGFhYWrWrJkeeughXbhwwderAviMwY1YAKBGTA07zZs316xZs7RlyxZ98cUX6tu3r2688Ubt3LlTkjRhwgR98MEHWrx4sdatW6cjR45o+PDhzp8vLy/X4MGDVVZWpo0bN+ovf/mLXnnlFT366KNmrRIAyyFcAv4uyMzChwwZ4vL+8ccf19y5c7Vp0yY1b95c8+fP18KFC9W3b19J0oIFC9SxY0dt2rRJV155pVasWKFdu3Zp1apViomJUZcuXTRjxgxNmjRJU6dOVUhIiBmrBcACzOpFouEO8Lw6M2anvLxcixYt0unTp5WamqotW7bo/Pnz6t+/v3OZDh06KDExUbm5uZKk3NxcpaSkKCYmxrnMwIEDVVJS4mwdqkppaalKSkpcXoC/4FgIADVjetjJy8tTRESEQkNDdc899+jdd99VUlKSCgsLFRISoujoaJflY2JiVFhYKEkqLCx0CTqV8yvnXUpOTo6ioqKcr4SEBM+uFADUEnfIBjzH9LDTvn17bd26VZ999pkyMzOVkZGhXbt2ebXM7OxsnThxwvk6ePCgV8sDvMG0B5ECgJ8xdcyOJIWEhKhNmzaSpO7du2vz5s169tlndcstt6isrEzHjx93ad0pKipSbGysJCk2Nlaff/65y+dVXq1VuUxVQkNDFRoa6uE1gV0xxgIA6jbTW3Z+rKKiQqWlperevbuCg4O1evVq57zdu3crPz9fqampkqTU1FTl5eWpuLjYuczKlSsVGRmppKQkn9cdNmNSwwrhytrYvIDnmdqyk52drbS0NCUmJurkyZNauHCh1q5dq+XLlysqKkp33XWXJk6cqEaNGikyMlLjxo1TamqqrrzySknSgAEDlJSUpFGjRunJJ59UYWGhJk+erKysLFpuAACAJJPDTnFxsUaPHq2CggJFRUWpc+fOWr58ua677jpJ0tNPP62AgACNGDFCpaWlGjhwoObMmeP8+cDAQC1ZskSZmZlKTU1VeHi4MjIyNH36dLNWCfAZRuz4hlktaQzJAjzH1LAzf/78n5xfr149zZ49W7Nnz77kMi1atNDSpUs9XTWgzqKbwzcIG4B11LkxOwAAAJ5E2AGAuoQR6IDHEXaAWjLr0EQ3CwBUD2EH8DM89dweCLOA5xB2ADdxO38A8A+EHcBPEbV8g3Y0wP8RdgCgDiFcAZ5H2AH8DAdD36CbErAOwg4A1EGELcBzCDuAn3JwuQ4AVAthB6gln18JTj8WANQIYQcA6hBuowR4HmEHcJPZvUhmlw8A/oKwAwA/wbQ7VhNmAY8h7AB+xmDQDgDUCGEHAKpiUssKzz4DPI+wA/gpejkAoHoIOwAAwNKCzK4A4O9e+HiPFmzY77Pyjp8977OyYB5a7gDPIewAbmoXEyFJOl1WrtNl5T4vv3XTCJ+XCQD+iLADuGlMr8vVr2OMzp33fdBpGBai+Oj6Pi/Xjs6eL9ehf53xWXm03AGeR9gB3ORwOGhdsYFN+47pF0+sMbsaAGqBsAMAVejZqpEui66v70+V+rzskKAAXZcU4/NyAasi7ABAFVo0DteGR/qaXQ0AHsCl5wAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNKCzK5AXWAYhiSppKTE5JoAAIDqqjxuVx7HL4WwI+nkyZOSpISEBJNrAgAAaurkyZOKioq65HyH8b/ikA1UVFToyJEjatCggRwOh8c+t6SkRAkJCTp48KAiIyM99rlwD9ujbmF71D1sk7qF7fG/GYahkydPKj4+XgEBlx6ZQ8uOpICAADVv3txrnx8ZGckfah3C9qhb2B51D9ukbmF7/LSfatGpxABlAABgaYQdAABgaYQdLwoNDdVjjz2m0NBQs6sCsT3qGrZH3cM2qVvYHp7DAGUAAGBptOwAAABLI+wAAABLI+wAAABLI+wAAABLI+x40ezZs9WyZUvVq1dPPXv21Oeff252lSwnJydHP//5z9WgQQM1a9ZMw4YN0+7du12WOXfunLKystS4cWNFRERoxIgRKioqclkmPz9fgwcPVlhYmJo1a6aHHnpIFy5c8OWqWNKsWbPkcDg0fvx45zS2h28dPnxYt912mxo3bqz69esrJSVFX3zxhXO+YRh69NFHFRcXp/r166t///7as2ePy2ccO3ZM6enpioyMVHR0tO666y6dOnXK16tiCeXl5ZoyZYpatWql+vXrq3Xr1poxY4bLs53YJl5gwCsWLVpkhISEGC+//LKxc+dOY8yYMUZ0dLRRVFRkdtUsZeDAgcaCBQuMHTt2GFu3bjUGDRpkJCYmGqdOnXIuc8899xgJCQnG6tWrjS+++MK48sorjauuuso5/8KFC0ZycrLRv39/4x//+IexdOlSo0mTJkZ2drYZq2QZn3/+udGyZUujc+fOxv333++czvbwnWPHjhktWrQwbr/9duOzzz4z9u3bZyxfvtz49ttvncvMmjXLiIqKMt577z1j27ZtxtChQ41WrVoZZ8+edS5z/fXXGz/72c+MTZs2GZ9++qnRpk0bY+TIkWaskt97/PHHjcaNGxtLliwx9u/fbyxevNiIiIgwnn32WecybBPPI+x4yRVXXGFkZWU535eXlxvx8fFGTk6OibWyvuLiYkOSsW7dOsMwDOP48eNGcHCwsXjxYucyX331lSHJyM3NNQzDMJYuXWoEBAQYhYWFzmXmzp1rREZGGqWlpb5dAYs4efKk0bZtW2PlypVG7969nWGH7eFbkyZNMn7xi19ccn5FRYURGxtr/L//9/+c044fP26EhoYab7zxhmEYhrFr1y5DkrF582bnMsuWLTMcDodx+PBh71XeogYPHmzceeedLtOGDx9upKenG4bBNvEWurG8oKysTFu2bFH//v2d0wICAtS/f3/l5uaaWDPrO3HihCSpUaNGkqQtW7bo/PnzLtuiQ4cOSkxMdG6L3NxcpaSkKCYmxrnMwIEDVVJSop07d/qw9taRlZWlwYMHu/zeJbaHr73//vvq0aOHbr75ZjVr1kxdu3bVSy+95Jy/f/9+FRYWumyPqKgo9ezZ02V7REdHq0ePHs5l+vfvr4CAAH322We+WxmLuOqqq7R69Wp98803kqRt27Zp/fr1SktLk8Q28RYeBOoF33//vcrLy1121pIUExOjr7/+2qRaWV9FRYXGjx+vq6++WsnJyZKkwsJChYSEKDo62mXZmJgYFRYWOpepaltVzkPNLFq0SF9++aU2b9580Ty2h2/t27dPc+fO1cSJE/Xb3/5Wmzdv1n333aeQkBBlZGQ4f59V/b5/uD2aNWvmMj8oKEiNGjVie7jhkUceUUlJiTp06KDAwECVl5fr8ccfV3p6uiSxTbyEsAPLyMrK0o4dO7R+/Xqzq2JbBw8e1P3336+VK1eqXr16ZlfH9ioqKtSjRw/NnDlTktS1a1ft2LFDL774ojIyMkyunT29+eabev3117Vw4UJ16tRJW7du1fjx4xUfH8828SK6sbygSZMmCgwMvOgKk6KiIsXGxppUK2u79957tWTJEq1Zs0bNmzd3To+NjVVZWZmOHz/usvwPt0VsbGyV26pyHqpvy5YtKi4uVrdu3RQUFKSgoCCtW7dOzz33nIKCghQTE8P28KG4uDglJSW5TOvYsaPy8/Ml/ff3+VP7qtjYWBUXF7vMv3Dhgo4dO8b2cMNDDz2kRx55RL/61a+UkpKiUaNGacKECcrJyZHENvEWwo4XhISEqHv37lq9erVzWkVFhVavXq3U1FQTa2Y9hmHo3nvv1bvvvquPP/5YrVq1cpnfvXt3BQcHu2yL3bt3Kz8/37ktUlNTlZeX57LzWLlypSIjIy86UOCn9evXT3l5edq6davz1aNHD6Wnpzv/z/bwnauvvvqiWzF88803atGihSSpVatWio2NddkeJSUl+uyzz1y2x/Hjx7VlyxbnMh9//LEqKirUs2dPH6yFtZw5c0YBAa6H3sDAQFVUVEhim3iN2SOkrWrRokVGaGio8corrxi7du0yxo4da0RHR7tcYYLay8zMNKKiooy1a9caBQUFzteZM2ecy9xzzz1GYmKi8fHHHxtffPGFkZqaaqSmpjrnV17qPGDAAGPr1q3GRx99ZDRt2pRLnT3kh1djGQbbw5c+//xzIygoyHj88ceNPXv2GK+//roRFhZmvPbaa85lZs2aZURHRxt///vfje3btxs33nhjlZc5d+3a1fjss8+M9evXG23btuUyZzdlZGQYl112mfPS83feecdo0qSJ8fDDDzuXYZt4HmHHi55//nkjMTHRCAkJMa644gpj06ZNZlfJciRV+VqwYIFzmbNnzxq/+c1vjIYNGxphYWHGTTfdZBQUFLh8zoEDB4y0tDSjfv36RpMmTYwHHnjAOH/+vI/Xxpp+HHbYHr71wQcfGMnJyUZoaKjRoUMH409/+pPL/IqKCmPKlClGTEyMERoaavTr18/YvXu3yzJHjx41Ro4caURERBiRkZHGHXfcYZw8edKXq2EZJSUlxv33328kJiYa9erVMy6//HLjd7/7ncttFdgmnucwjB/cthEAAMBiGLMDAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADoE64/fbbNWzYMJ+X+8orr1z0FHYA1sJNBQF4ncPh+Mn5jz32mCZMmCDDMHwePM6ePauTJ0+qWbNmPi0XgO8QdgB4XWFhofP/f/vb3/Too4+6PKAyIiJCERERZlQNgA3QjQXA62JjY52vqKgoORwOl2kREREXdWP16dNH48aN0/jx49WwYUPFxMTopZde0unTp3XHHXeoQYMGatOmjZYtW+ZS1o4dO5SWlqaIiAjFxMRo1KhR+v777y9Ztx93Y02dOlVdunTRq6++qpYtWyoqKkq/+tWvdPLkyUt+xnfffachQ4aoYcOGCg8PV6dOnbR06VK3f18APIuwA6DO+stf/qImTZro888/17hx45SZmambb75ZV111lb788ksNGDBAo0aN0pkzZyRJx48fV9++fdW1a1d98cUX+uijj1RUVKRf/vKXNSp37969eu+997RkyRItWbJE69at06xZsy65fFZWlkpLS/XJJ58oLy9PTzzxBC1VQB1C2AFQZ/3sZz/T5MmT1bZtW2VnZ6tevXpq0qSJxowZo7Zt2+rRRx/V0aNHtX37dknSCy+8oK5du2rmzJnq0KGDunbtqpdffllr1qzRN998U+1yKyoq9Morryg5OVm9evXSqFGjtHr16ksun5+fr6uvvlopKSm6/PLLdcMNN+iaa66p9foD8IwgsysAAJfSuXNn5/8DAwPVuHFjpaSkOKfFxMRIkoqLiyVJ27Zt05o1a6psVdm7d6/atWtXrXJbtmypBg0aON/HxcU5y6jKfffdp8zMTK1YsUL9+/fXiBEjXOoOwFy07ACos4KDg13eOxwOl2mVV3lVVFRIkk6dOqUhQ4Zo69atLq89e/bUqKWlqnIry6jKr3/9a+3bt0+jRo1SXl6eevTooeeff77a5QHwLsIOAMvo1q2bdu7cqZYtW6pNmzYur/DwcK+WnZCQoHvuuUfvvPOOHnjgAb300kteLQ9A9RF2AFhGVlaWjh07ppEjR2rz5s3au3evli9frjvuuEPl5eVeK3f8+PFavny59u/fry+//FJr1qxRx44dvVYegJoh7ACwjPj4eG3YsEHl5eUaMGCAUlJSNH78eEVHRysgwHu7u/LycmVlZaljx466/vrr1a5dO82ZM8dr5QGoGW4qCAAALI2WHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGn/H+V4vGO5bAf9AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plot external inputs\n",
    "figIrr, axsIrr = plt.subplots(1,1)\n",
    "axsIrr.plot(microgrid.times, microgrid.inputs[:,1])\n",
    "axsIrr.set_title(\"PV Irradiance\")\n",
    "axsIrr.set_xlabel(\"Time in s\")\n",
    "axsIrr.set_ylabel(\"Irradiance in W/m^2\")\n",
    "figIrr.canvas.draw()\n",
    "#figIrr.show()\n",
    "\n",
    "figLoad, axsLoad= plt.subplots(1,1)\n",
    "axsLoad.plot(microgrid.times, microgrid.inputs[:,2])\n",
    "axsLoad.set_title(\"Load Power\")\n",
    "axsLoad.set_xlabel(\"Time in s\")\n",
    "axsLoad.set_ylabel(\"Power in W\")\n",
    "figLoad.canvas.draw()\n",
    "#figLoad.show()\n",
    "\n",
    "# plot agent actions\n",
    "figAct, axsAct = plt.subplots(1,1)\n",
    "line1, line2, = axsAct.plot(microgrid.times, microgrid.inputs[:,3], microgrid.times, microgrid.inputs[:,5])\n",
    "axsAct.legend([line1, line2],[\"Battery\", \"Grid\"])\n",
    "axsAct.set_title(\"Voltage References\")\n",
    "axsAct.set_xlabel(\"Time in s\")\n",
    "axsAct.set_ylabel(\"Voltage in V\")\n",
    "figAct.canvas.draw()\n",
    "#figAct.show()\n",
    "        \n",
    "# plot outputs (voltages/source currents)\n",
    "figOutV, axsOutV = plt.subplots(1,1)\n",
    "line3, line4, line5, line6, = axsOutV.plot(microgrid.times,microgrid.outputs[:,0],microgrid.times,microgrid.outputs[:,3],microgrid.times,microgrid.outputs[:,6],microgrid.times,microgrid.outputs[:,13])\n",
    "axsOutV.legend([line3, line4, line5, line6],[\"PV\", \"Grid\",\"Load\",\"Battery\"])\n",
    "axsOutV.set_title(\"Voltages\")\n",
    "axsOutV.set_xlabel(\"Time in s\")\n",
    "axsOutV.set_ylabel(\"Voltage in V\")\n",
    "figOutV.canvas.draw()\n",
    "#figOutV.show()\n",
    "\n",
    "figOutI, axsOutI = plt.subplots(1,1)\n",
    "line7, line8, line9, = axsOutI.plot(microgrid.times,microgrid.outputs[:,2],microgrid.times,microgrid.outputs[:,5],microgrid.times,microgrid.outputs[:,12])\n",
    "axsOutI.legend([line7, line8, line9],[\"PV\", \"Grid\", \"Battery\"])\n",
    "axsOutI.set_title(\"Source Currents\")\n",
    "axsOutI.set_xlabel(\"Time in s\")\n",
    "axsOutI.set_ylabel(\"Current in A\")\n",
    "figOutI.canvas.draw()\n",
    "#figOutI.show()\n",
    "        \n",
    "# plot SOC\n",
    "figBat, axsBat = plt.subplots(1,1)\n",
    "axsBat.plot(microgrid.times,microgrid.outputs[:,11])\n",
    "axsBat.set_xlabel(\"Time in s\")\n",
    "axsBat.set_ylabel(\"SOC\")\n",
    "axsBat.set_title(\"SOC\")\n",
    "figBat.canvas.draw()\n",
    "#figBat.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following plots are used for the offical JOSS paper and are included here for the sake of completeness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "#from matplotlib.backends.backend_pgf import FigureCanvasPgf\n",
    "#mpl.backend_bases.register_backend('pgf', FigureCanvasPgf)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['text.usetex'] = True\n",
    "\n",
    "from math import sqrt\n",
    "\n",
    "default_width = 6.3 # width in inches\n",
    "default_ratio = (sqrt(5.0) - 1.0) / 1.5 \n",
    "def figure_two(height=default_width*default_ratio, width=default_width, *args, **kwargs):\n",
    "    fig = plt.figure(figsize=(width, height),*args,**kwargs)\n",
    "    ax = [\n",
    "        fig.add_axes([0.2, 0.49, 0.6, 0.2], xticklabels=[]),\n",
    "        fig.add_axes([0.2, 0.28, 0.6, 0.2]),\n",
    "    ]\n",
    "    return fig, ax\n",
    "\n",
    "def figure_one(height=default_width*default_ratio, width=default_width, *args, **kwargs):\n",
    "    fig = plt.figure(figsize=(width, height),*args,**kwargs)\n",
    "    ax = [\n",
    "        fig.add_axes([0.2, 0.28, 0.6, 0.2]),\n",
    "    ]\n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 630x519.149 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = figure_one()\n",
    "\n",
    "ax[0].plot(microgrid.times, microgrid.inputs[:,2],c=u'#1f77b4')\n",
    "ax[0].set(xlabel='Time in s')\n",
    "plt.ylabel('Power in W', color=u'#1f77b4')\n",
    "\n",
    "\n",
    "ax2 = ax[0].twinx()\n",
    "ax2.plot(microgrid.times, microgrid.inputs[:,1], c= u'#ff7f0e')\n",
    "plt.ylabel(r'Irradiance in $\\frac{W}{m^2}$', c=u'#ff7f0e')\n",
    "ax[0].grid()\n",
    "\n",
    "#fig.savefig('../joss/result_input.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 630x519.149 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#mpl.rcParams['axes.prop_cycle'] = mpl.cycler(color=[u'b', u'g', u'c', u'r', u'm', u'y', u'k']) \n",
    "#mpl.rcParams['lines.linewidth'] = 1\n",
    "\n",
    "fig, ax = figure_two()\n",
    "\n",
    "\n",
    "ax[0].legend()\n",
    "ax[0].set(xlabel='Time in s')\n",
    "\n",
    "# plot outputs (voltages/source currents)\n",
    "line3, line4, line5, line6, = ax[0].plot(microgrid.times,microgrid.outputs[:,0],microgrid.times,microgrid.outputs[:,3],microgrid.times,microgrid.outputs[:,6],microgrid.times,microgrid.outputs[:,13])\n",
    "ax[0].legend([line3, line4, line5, line6],[\"PV\", \"Grid\",\"Load\",\"Battery\"],bbox_to_anchor=(1.0, 1.0))\n",
    "ax[0].set_ylabel(\"Voltage in V\")\n",
    "\n",
    "line7, line8, line9, line10 = ax[1].plot(microgrid.times,microgrid.outputs[:,2],microgrid.times,microgrid.outputs[:,5],microgrid.times,microgrid.outputs[:,7],microgrid.times,microgrid.outputs[:,12])\n",
    "#ax[1].legend([line7, line8, line9],[\"PV\", \"Grid\", \"Battery\"], bbox_to_anchor=(1.0, 1.0))\n",
    "ax[1].set_xlabel(\"Time in s\")\n",
    "ax[1].set_ylabel(\"Current in A\")\n",
    "\n",
    "[x.grid() for x in ax]\n",
    "fig.align_ylabels(ax)\n",
    "ax[1].yaxis.set_label_coords(-0.1,0.5)\n",
    "ax[0].yaxis.set_label_coords(-0.1,0.5)\n",
    "\n",
    "#fig.savefig('../joss/result_ui.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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