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Author: ngjiayi93

Model_withdata/analysis_genmix_cost_emi.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline \n",
+    "import os\n",
+    "import csv\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yr = 2016\n",
+    "run_no = 1 \n",
+    "rundays = 365\n",
+    "runhours = rundays*24\n",
+    "\n",
+    "hr_range = pd.date_range('1/1/'+str(yr)+'', periods=runhours, freq='H')\n",
+    "day_range = pd.date_range('1/1/'+str(yr)+'', periods=rundays, freq='D')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_gen = pd.read_csv('input/data_camb_genparams.csv',header=0)\n",
+    "df_load = pd.read_csv('input/data_camb_load_2016.csv',header=0)\n",
+    "    \n",
+    "sys_load = (df_load.iloc[:,4:].sum(axis=1)).values\n",
+    "#reserve = (sys_load*0.15).values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gen_name = df_gen['name']\n",
+    "gen_type = df_gen['typ']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7.999093686386829, 22.35092073298965)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "srsv = pd.read_csv('output/out_camb_R'+str(run_no)+'_'+str(yr)+'_srsv.csv',header=0)\n",
+    "nrsv = pd.read_csv('output/out_camb_R'+str(run_no)+'_'+str(yr)+'_nrsv.csv',header=0)\n",
+    "       \n",
+    "###Include Generator_type to the srsv data\n",
+    "for x in range(len(gen_name)):\n",
+    "    srsv.loc[srsv.Generator == gen_name[x], 'Type'] = gen_type[x]\n",
+    "    nrsv.loc[nrsv.Generator == gen_name[x], 'Type'] = gen_type[x]\n",
+    " \n",
+    "####Reserve_GWh by Type\n",
+    "srsv_bytype = round(srsv.groupby(['Type'])['Value'].sum()/1000,1)\n",
+    "nrsv_bytype = round(nrsv.groupby(['Type'])['Value'].sum()/1000,1)\n",
+    "    \n",
+    "\n",
+    "###Reserve_ratios by Time only\n",
+    "srsv_bytime = srsv.groupby(['Time'])['Value'].sum().values\n",
+    "nrsv_bytime = nrsv.groupby(['Time'])['Value'].sum().values\n",
+    "trsv_bytime = srsv_bytime +nrsv_bytime \n",
+    "    \n",
+    "srsv_ratio = srsv_bytime*100/sys_load\n",
+    "nrsv_ratio = nrsv_bytime*100/sys_load\n",
+    "trsv_ratio = trsv_bytime*100/sys_load\n",
+    "\n",
+    "avg_srsv = np.mean(srsv_ratio)\n",
+    "avg_trsv = np.mean(trsv_ratio)\n",
+    "\n",
+    "\n",
+    "avg_srsv,avg_trsv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}