575 lines
11 KiB
Plaintext
575 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **Import all functions and libraries**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from src.Functions_General import *\n",
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"from src.Functions_Energy_Model import *\n",
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"from src.Functions_Financial_Model import *\n",
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"from src.Functions_Load_Emulator_and_DSM import *"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## **External inputs to set before to run the simulation**\n",
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"\n",
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"Before to run the simulation be sure to have set correctly the following input files:\n",
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" \n",
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"- config.yml [[link yaml](config.yml)]\n",
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"\n",
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"- users CACER.xlsx [[link excel](users%20CACER.xlsx)]\n",
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"\n",
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"- inputs_FM.xlsx [[link excel](files/inputs_FM.xlsx)]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## **Initializing CACER configuration**"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **1. Generating the calendar file**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"generate_calendar()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **2. Generating the yaml and csv files with all the inputs of the CACER configuration**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"initialization_users()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **3. Generating the emulated load profile of the users**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"flag_last_dict = False # if false we create the appliance start time dictionary; if true we import the last one created (default: False)\n",
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"flag_optDSM = False # if true we simulate the optimized DSM case (default: False)\n",
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"flag_all_appliance = True # if true we use all appliance for the load profile emulation (default: True)\n",
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"flag_daily_activation = True # if false we dont'use a daily usage activation for some specified appliances (only washing machine at the moment, default: True)\n",
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"flag_multi_use = True # if true we activate the possibility to have multiple activations for the selected appliances during the day (default: True)\n",
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"\n",
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"create_emulated_users(flag_last_dict, flag_optDSM, flag_all_appliance, flag_daily_activation, flag_multi_use)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **4. Generating all load profile of the users (domestic users, commercial users, industrial users, etc.)**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"load_profile_all_users()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **5. Generating productivity of the photovoltaic generators**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"simulate_configuration_productivity()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **6. Generating all energy flows of the single users**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"CACER_energy_flows()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## **Simulate CACER configuration**"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **7. Calculate the electricity bills for each user types**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"create_users_bill()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **8. Setting name and type of the configuration and editing of the incentive repartition**\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"case_denomination = \"prova\"\n",
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"CACER_type = \"CER\" # CER; AUD; AID; NO_CACER\n",
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"repartition_scheme = \"Egualitario\" # Egualitario; Prosumer; PNRR; Equitario; Misto CER; Misto AUC; Misto - ESCo; Social Fund; Misto - Social Fund; Misto - Social Fund 2; Misto - Social Fund PA; Misto - PA; CACER\n",
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"\n",
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"setting_CACER_scenario(case_denomination, CACER_type, repartition_scheme)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **9. Calculate CACER energy shared**\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"CACER_shared_energy()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **10. Calculate energy sold revenues**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"RID_calculation()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **11. Aggregate bills costs and energy sold revenues for the entire CACER configuration**\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"aggregate_CACER_bills()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"aggregate_CACER_RID()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **12. Generating the xlsx and csv files with all the financial inputs of the CACER configuration**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"FM_initialization()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **13. Calculate incentives revenues**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"incentives()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **14. All the economic cash flows for each users and for each stakeholders are calculated**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"cash_flows_for_all_plants() # plants first..."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"cash_flows_for_all_users() # ...then users..."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **15. All the economic cash flows are aggregated for the project and for the CACER configuration (<u>for single primary station</u>)**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"aggregate_FM() # ...then configurations!"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **16.1 The simulation results are organized for the export and for the creation of the report**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"organize_simulation_results_for_reporting()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **16.2 Creating the report with results**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%run Reporting.ipynb"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **16.3 Creating the report with results**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"save_simulation_results(simulation_name = case_denomination)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **17. Killing all excel processes in background**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"kill_excel_processes()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n",
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"---"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.11"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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