{
 "cells": [
  {
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   "id": "6a3770a2",
   "metadata": {},
   "source": [
    "# Green PPA Pricing - Expected Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c2d0f955",
   "metadata": {
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     "end_time": "2023-06-16T08:35:18.182289Z",
     "start_time": "2023-06-16T08:35:17.417889Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:80% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import datetime as dt\n",
    "sys.path.append('../../../..')\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import logging\n",
    "logger = logging.getLogger('rivapy.pricing')\n",
    "logger.setLevel(logging.DEBUG)\n",
    "\n",
    "from rivapy.instruments import SimpleSchedule,  GreenPPASpecification\n",
    "from rivapy.marketdata import PowerPriceForwardCurve\n",
    "import rivapy.sample_data.residual_demand_models as rdm_sample\n",
    "from rivapy.pricing.pricing_request import GreenPPAPricingRequest\n",
    "from rivapy.pricing import green_ppa_mean_pricer\n",
    "\n",
    "from IPython.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:80% !important; }</style>\"))\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eacff24",
   "metadata": {},
   "source": [
    "## Green PPA Specification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ca679b34",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-16T12:47:19.274633Z",
     "start_time": "2023-06-16T12:47:18.535730Z"
    }
   },
   "outputs": [],
   "source": [
    "simple_schedule = SimpleSchedule(dt.datetime(2022,12,1), dt.datetime(2023,12,1,4,0,0), freq='1H')\n",
    "green_ppa = GreenPPASpecification(simple_schedule, \n",
    "                                  fixed_price = 10.0, \n",
    "                                  max_capacity=10, \n",
    "                                  technology = 'wind', \n",
    "                                  udl = 'power',\n",
    "                                  location='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0315764f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-16T12:47:31.411336Z",
     "start_time": "2023-06-16T12:47:31.387602Z"
    }
   },
   "outputs": [],
   "source": [
    "pr = GreenPPAPricingRequest(theo_val=True, cf_expected=True, cf_paths=True)\n",
    "rd_model = rdm_sample.WagnerModel.residual_demand_model(capacity_wind=25, capacity_solar=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d4ed5552",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-16T12:47:33.215775Z",
     "start_time": "2023-06-16T12:47:32.219539Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/doeltz/doeltz/development/RiVaPy/rivapy/tools/datetime_grid.py:24: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.\n",
      "  self.dates = pd.date_range(start, end, freq=freq, tz=tz, closed=closed).to_pydatetime()\n"
     ]
    }
   ],
   "source": [
    "results = green_ppa_mean_pricer.price(dt.datetime(2022,12,1), green_ppa, rd_model, pr, n_sims=10, seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b755b16a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-16T12:47:33.239924Z",
     "start_time": "2023-06-16T12:47:33.216689Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'theo_val': 106.33955937000052,\n",
       " 'cf_expected': array([88.65277778, 73.38856324, 81.46445778, ..., 35.01461317,\n",
       "        16.81824448, 35.65894031]),\n",
       " 'cf_paths': array([[ 88.65277778,  88.65277778,  88.65277778, ...,  88.65277778,\n",
       "          88.65277778,  88.65277778],\n",
       "        [120.59769197,  73.59260888,  85.56582072, ...,  56.00689862,\n",
       "         124.17623418,  70.07224311],\n",
       "        [150.66679196,  55.10256533,  27.52760532, ..., -50.1915637 ,\n",
       "         153.27188838, 117.63575482],\n",
       "        ...,\n",
       "        [ 41.34946064,  80.04721905,  89.40655705, ..., 144.98860263,\n",
       "          52.47875851,  31.48219098],\n",
       "        [ 41.83472586,  82.56825408,  91.22650758, ..., 150.22908045,\n",
       "          42.48726577,  27.08113649],\n",
       "        [ 44.12449773,  52.31010233, 117.90576107, ..., 101.5151421 ,\n",
       "          36.99336384,  30.78853215]])}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f3a5502",
   "metadata": {},
   "outputs": [],
   "source": [
    "values = np.ones((len(simple_schedule.get_schedule()),))\n",
    "hpfc = PowerPriceForwardCurve( dt.datetime(2022,12,1), dt.datetime(2022,12,1), \n",
    "                              dt.datetime(2023,12,1,4,0,0), freq='1H', values = values)"
   ]
  }
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