ConnectToEvolve_hackathonChallenge.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO allow Monte Carlo runs on output data structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cadCAD Open Questions\n",
    "\n",
    "# 1. Is it possible to run substeps (PSubs) in a non-deterministic order? Or order that was determined by a function that's output changes each step?\n",
    "# 2. How to best handle variables that are only relevant in the step? Or across substeps?\n",
    "# 3. Can you update multiple state variables in a single policy without doing this silly dictionary pass I've done? Or is it possible to immediately update the state variable itself instead of waiting for state update?\n",
    "# 4. Can initial conditions be non-deterministic? Can I pass in a random number generator that acts as the dataset, and will it generate new random numbers each Monte Carlo run?\n",
    "# 5. Need to walk through an ideal model map with somebody. How can I build in a market? Starting to see the light but still wrapped up in fitting within structure\n",
    "# 6. can't have two functions updating same state variable in the same substep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Power to the People Hackathon\n",
    "## Energy System Dynamics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import math\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set Simulation Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot = True\n",
    "# simulation parameters\n",
    "days_to_simulate = 365*15 # days to simulate\n",
    "# revenues\n",
    "price_per_kwh = 0.07 # cost per kwh of electricity\n",
    "max_price_per_kwh = 0.13 # max cost of electricity\n",
    "# operating decisions\n",
    "production_decay_rate = 0.99992 # decay rate of equimpent (solartainer production, battery max level)\n",
    "maintenance_rate = 150 # days between maintenance\n",
    "# operating costs\n",
    "op_cost_per_hour = 0.1 # operating costs per hour of operation\n",
    "op_cost_per_kwh = 0.035 # operating cost per kwh produced\n",
    "maintenance_cost_per = 300 # maintenance cost per time maintained (increases production capability of equipment back to 100%)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set Initial Conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# List of all the state variables in the system and their initial values\n",
    "initial_conditions = {\n",
    "    # INITIAL CONDITIONS TO PLAY WITH\n",
    "    'bat_lim': 25, # max kWh able to be stored in battery\n",
    "    'price_per_kwh': {'price': price_per_kwh, 'max_price': max_price_per_kwh, 'min_price': 0.0}, # EUR cents\n",
    "    'maintenance_rate': maintenance_rate, # days between each maintenance call\n",
    "    # INITIAL CONDITIONS TO LET BE, but no hard rules on that :)\n",
    "    # initial ownership breakdown\n",
    "    'ownership': {'solartainer': 1., 'operator': 0., 'priority': 0., 'business': 0., 'leisure': 0., 'maintenance': 0., 'crowdfunders': 0.},\n",
    "    # total tracking of max values\n",
    "    'energy_prod': {'available': 0., 'remaining': 0.}, # total energy produced by the system per timestep\n",
    "    'energy_appetite': {'priority': 0., 'business': 0., 'leisure': 0.}, # amount of energy appetite\n",
    "    # total tracking of actual usage\n",
    "    'energy_cons': {'tot': 0, 'prod': 0., 'bat_use': 0., 'priority': 0., 'business': 0., 'leisure': 0., 'bybat': 0}, # total energy consumed\n",
    "    'energy_revenue': {'tot': 0, 'prod': 0., 'bat_use': 0., 'priority': 0., 'business': 0., 'leisure': 0., 'bybat': 0}, # total energy consumed\n",
    "    # time tracking\n",
    "    'hour': 0,\n",
    "    # track when maintenance occurs (binary)\n",
    "    'maintenance_cost': {'maintenance_cost': 0, 'maintenance_occured': 0, 'maintenance_cost_per': maintenance_cost_per},\n",
    "    'op_cost': {'operations': op_cost_per_hour, 'op_cost_per_kwh': op_cost_per_kwh}, # 4 EUR cents per kwh of operations, 10 EUR cents per hour of operations\n",
    "    # track time-based variances\n",
    "    'annual_var': {'prod': 0., 'priority': 0., 'business': 0., 'leisure': 0.},\n",
    "    'daily_mult': {'prod': .8, 'priority': .8, 'business': .8, 'leisure': .8},\n",
    "    # limited mechanics tracking\n",
    "    'bat_level': 0, # amount of energy currently stored in the battery  \n",
    "    'production_efficiency': {'value': 1., 'decay': production_decay_rate} # % of production and battery storage usable, decay=0.9999 to start\n",
    "}\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Data Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# Specify the energy generation and usage functions\n",
    "\n",
    "def hourly_production_capacity(params, step, sL, s):\n",
    "    hourly_0_prod_chance = 0.1\n",
    "    hour = s['hour']\n",
    "    # set cosine function attributes\n",
    "    y_int = 33 # tuned to make energy output over year match reported 59000 per Solartainer\n",
    "    cos_mult = 2.\n",
    "    x_mult = 1./365/24*2*np.pi\n",
    "    # get variances\n",
    "    # get annual mu\n",
    "    if hour % (365*24) == 0:\n",
    "        mu, sigma = y_int, .03 * y_int\n",
    "        annual_var = np.random.normal(mu, sigma, 1)[0]\n",
    "    else:\n",
    "        annual_var = s['annual_var']['prod']\n",
    "    # get daily mu\n",
    "    # daily (multiplier)\n",
    "    if hour % 24 == 0:\n",
    "        mu, sigma = 0.8, .2\n",
    "        daily_mult = abs(np.random.normal(mu, sigma, 1)[0])\n",
    "    else:\n",
    "        daily_mult = s['daily_mult']['prod']\n",
    "    # hourly var\n",
    "    mu, sigma = annual_var, .1 * annual_var\n",
    "    hourly_var = np.random.normal(mu, sigma, 1)[0]\n",
    "    # hourly mult\n",
    "    hourly_mult = max(np.sin((hour)/24*2*np.pi - np.pi/2) + 0.5, 0)\n",
    "    if random.random() < hourly_0_prod_chance:\n",
    "        hourly_mult = 0\n",
    "    # get core waveform\n",
    "    prod = ((cos_mult * np.cos(step * x_mult) + hourly_var) * daily_mult) * hourly_mult\n",
    "    # set limits\n",
    "    prod = max(0, prod) # must be >= 0\n",
    "    prod = min(prod, y_int) # maximum generation capacity of equipment per hour\n",
    "    prod *= s['production_efficiency']['value']\n",
    "    return(prod, annual_var, daily_mult)\n",
    "\n",
    "def hourly_usage_appetite(params, step, sL, s, tier):\n",
    "    hour = s['hour']\n",
    "    # get different cosine manipulators for each tier of user\n",
    "    if tier == 'priority':\n",
    "        y_int = 9\n",
    "        annual_var = s['annual_var']['priority']\n",
    "        daily_mult = s['daily_mult']['priority']\n",
    "        daily_mult_sigma = 0.01\n",
    "        cos_mult = 0.1\n",
    "        hourly_cos_mult = 0.1\n",
    "        hourly_sig = 0.01\n",
    "    if tier == 'business':\n",
    "        y_int = 3 / (s['price_per_kwh']['price'] / s['price_per_kwh']['max_price'])\n",
    "        annual_var = s['annual_var']['business']\n",
    "        daily_mult = s['daily_mult']['business']\n",
    "        daily_mult_sigma = 0.1\n",
    "        cos_mult = 0.5\n",
    "        hourly_cos_mult = 0.8\n",
    "        hourly_sig = 0.1\n",
    "    if tier == 'leisure':\n",
    "        y_int = 3 / (s['price_per_kwh']['price'] / s['price_per_kwh']['max_price'])\n",
    "        annual_var = s['annual_var']['leisure']\n",
    "        daily_mult = s['daily_mult']['leisure']\n",
    "        daily_mult_sigma = 0.2\n",
    "        cos_mult = 1.2\n",
    "        hourly_cos_mult = 1.0\n",
    "        hourly_sig = 0.2\n",
    "    # set cosine function attributes\n",
    "    x_mult = 1./365/24*2*np.pi\n",
    "    # get variances\n",
    "    # get annual mu\n",
    "    if hour % (365*24) == 0:\n",
    "        mu, sigma = y_int, .03 * y_int\n",
    "        annual_var = np.random.normal(mu, sigma, 1)[0]\n",
    "    # get daily mu\n",
    "    # daily (multiplier)\n",
    "    if hour % 24 == 0:\n",
    "        mu, sigma = 0.8, daily_mult_sigma\n",
    "        daily_mult = abs(np.random.normal(mu, sigma, 1)[0])\n",
    "    # hourly var\n",
    "    mu, sigma = annual_var, hourly_sig * annual_var\n",
    "    hourly_var = np.random.normal(mu, sigma, 1)[0]\n",
    "    # hourly mult\n",
    "    hourly_mult = max(hourly_cos_mult * (np.sin((hour)/24*4*np.pi - np.pi/2)+0.5*np.sin((hour)/24*2*np.pi - np.pi/2)) + 0.8, 0)\n",
    "    # get core waveform\n",
    "    cons = ((cos_mult * np.sin(step * x_mult) + hourly_var) * daily_mult) * hourly_mult\n",
    "    # set limits\n",
    "    cons = max(0, cons) # must be >= 0\n",
    "    cons = min(cons, y_int) # maximum usage appetite per hour\n",
    "    return(cons, annual_var, daily_mult)\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specify Agent Energy Consumption Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #  \n",
    "# Specify the Solartainer production and battery logic in a Policy function\n",
    "def solartainer_produce(params, step, sL, s):\n",
    "    # calculate production from random variables\n",
    "    energy_prod_remaining, annual_var, daily_mult = hourly_production_capacity(params, step, sL, s)\n",
    "    if s['hour'] % (24*10) == 0: # print day every 10 days\n",
    "        print(\"year:\", int(s['hour']/24/365.),\"day:\", int(s['hour']/24)%365)\n",
    "    # reset state trackers\n",
    "    energy_prod_ = s['energy_prod'] # total energy produced by the system per timestep\n",
    "    energy_prod_['available'] = energy_prod_remaining\n",
    "    energy_prod_['remaining'] = energy_prod_remaining\n",
    "    annual_var_ = s['annual_var']\n",
    "    annual_var_['prod'] = annual_var\n",
    "    daily_mult_ = s['daily_mult']\n",
    "    daily_mult_['prod'] = daily_mult\n",
    "    return({'energy_prod': energy_prod_,\n",
    "            'annual_var': annual_var_,\n",
    "            'daily_mult': daily_mult_\n",
    "            })\n",
    "\n",
    "# Specify the user of electricity logic in a Policy function\n",
    "def consume_electricity(params, step, sL, s, energy_appetite, use_bat):\n",
    "    energy_avail_prod = s['energy_prod']['remaining']\n",
    "    energy_cons_prod = min(energy_avail_prod, energy_appetite)\n",
    "    # TODO can you change the circumstances under which the user uses the battery? Sounds exciting!\n",
    "    if use_bat:\n",
    "        energy_cons_bat = min(energy_appetite - energy_cons_prod, s['bat_level'])\n",
    "    else:\n",
    "        energy_cons_bat = 0\n",
    "    return(energy_cons_prod, energy_cons_bat)\n",
    "\n",
    "def userGroup_usage(params, step, sL, s, tier):\n",
    "    # calculate energy appetite from random variables, something about the state?\n",
    "    energy_appetite, annual_var, daily_mult = hourly_usage_appetite(params, step, sL, s, tier=tier)\n",
    "    # consume electricity based on appetite\n",
    "    energy_cons_prod, energy_cons_bat = consume_electricity(params, step, sL, s, energy_appetite, use_bat=True)\n",
    "    # prepare dictionaries\n",
    "    # appetite\n",
    "    energy_appetite_ = s['energy_appetite']\n",
    "    energy_appetite_[tier] = energy_appetite\n",
    "    # energy consumption\n",
    "    energy_cons_ = s['energy_cons']\n",
    "    energy_cons_[tier] += (energy_cons_prod + energy_cons_bat)\n",
    "    energy_cons_['tot'] += energy_cons_prod\n",
    "    energy_cons_['prod'] += energy_cons_prod\n",
    "    energy_cons_['bat_use'] += energy_cons_bat\n",
    "    # revenues from sale\n",
    "    energy_revenue_ = s['energy_revenue']\n",
    "    energy_revenue_[tier] += (energy_cons_prod + energy_cons_bat) * s['price_per_kwh']['price']\n",
    "    energy_revenue_['tot'] += (energy_cons_prod + energy_cons_bat) * s['price_per_kwh']['price']\n",
    "    energy_revenue_['prod'] += energy_cons_prod * s['price_per_kwh']['price']\n",
    "    energy_revenue_['bat_use'] += energy_cons_bat * s['price_per_kwh']['price']\n",
    "    # operating costs\n",
    "    op_cost_ = s['op_cost']\n",
    "    op_cost_['operations'] += (energy_cons_prod + energy_cons_bat) * s['op_cost']['op_cost_per_kwh']\n",
    "    # production tracking\n",
    "    energy_prod_ = s['energy_prod']\n",
    "    energy_prod_['remaining'] -= energy_cons_prod\n",
    "    # variances\n",
    "    annual_var_ = s['annual_var']\n",
    "    annual_var_[tier] = annual_var\n",
    "    daily_mult_ = s['daily_mult']\n",
    "    daily_mult_[tier] = daily_mult\n",
    "    return({'energy_appetite': energy_appetite_,\n",
    "            'energy_cons': energy_cons_, \n",
    "            'op_cost': op_cost_,\n",
    "            'energy_revenue': energy_revenue_,\n",
    "            'energy_prod': energy_prod_,\n",
    "            'bat_use': energy_cons_bat,\n",
    "            'annual_var': annual_var_,\n",
    "            'daily_mult': daily_mult_})\n",
    "\n",
    "# Specify the individual policy functions of the electricity consumers and battery\n",
    "def userGroup_priority(params, step, sL, s):\n",
    "    tier = 'priority'\n",
    "    return userGroup_usage(params, step, sL, s, tier)\n",
    "\n",
    "def userGroup_business(params, step, sL, s):\n",
    "    tier = 'business'\n",
    "    return userGroup_usage(params, step, sL, s, tier)\n",
    "\n",
    "def userGroup_leisure(params, step, sL, s):\n",
    "    tier = 'leisure'\n",
    "    return userGroup_usage(params, step, sL, s, tier)\n",
    "\n",
    "# Specify the actions of the battery based on leftover electricity\n",
    "def battery_tracker(params, step, sL, s):\n",
    "    # calculate energy appetite based on battery cap\n",
    "    energy_appetite = s['bat_lim']*s['production_efficiency']['value'] - s['bat_level']\n",
    "    # consume electricity based on appetite\n",
    "    energy_cons_prod, energy_cons_bat = consume_electricity(params, step, sL, s, energy_appetite, use_bat=False)\n",
    "    #energy_cons_ = s['energy_cons']\n",
    "    energy_cons_ = s['energy_cons']\n",
    "    energy_cons_['bybat'] = energy_cons_prod\n",
    "    energy_cons_['bat_use'] += energy_cons_bat\n",
    "    energy_cons_['tot'] += (energy_cons_prod)\n",
    "    op_cost_ = s['op_cost']\n",
    "    op_cost_['operations'] += (energy_cons_prod) * s['op_cost']['op_cost_per_kwh'] / 2\n",
    "    return({'energy_cons': energy_cons_,\n",
    "            'op_cost': op_cost_,\n",
    "            'bat_use': 0,\n",
    "            'hour_inc': 1})\n",
    "\n",
    "# Specify maintenance schedule\n",
    "def maintenance(params, step, sL, s):\n",
    "    # TODO this looks like it would be fun to play with as a hacker!\n",
    "    maintenance_ = s['maintenance_cost']\n",
    "    if (s['hour']+1) % (s['maintenance_rate']*24) == 0:\n",
    "        maintenance_['maintenance_occured'] = 1\n",
    "        maintenance_['maintenance_cost'] = maintenance_['maintenance_cost_per']\n",
    "    else:\n",
    "        maintenance_['maintenance_occured'] = 0\n",
    "        maintenance_['maintenance_cost'] = 0\n",
    "    return({'maintenance_cost': maintenance_})\n",
    "\n",
    "def ownership_dynamics(params, step, sL, s):\n",
    "    # currently set to stay the same over the entire time period\n",
    "    # TODO maybe you can play with the ownership model dynamics here based on consumption? or revenues?\n",
    "    ownership_ = s['ownership']\n",
    "    return({'ownership': ownership_})\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define State Update Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# We make the state update functions less \"intelligent\",\n",
    "# ie. they simply add the amount of electricity produced / consumed\n",
    "\n",
    "# set    \n",
    "\n",
    "# update potential max values\n",
    "def set_energy_prod(params, step, sL, s, _input):\n",
    "    y = 'energy_prod'\n",
    "    x = {'available': 0., 'remaining': 0.}\n",
    "    return (y, x)\n",
    "\n",
    "def set_energy_appetite(params, step, sL, s, _input):\n",
    "    y = 'energy_appetite'\n",
    "    x = {'priority': 0., 'business': 0., 'leisure': 0.}\n",
    "    return (y, x)\n",
    "\n",
    "# update actual use values\n",
    "def set_energy_cons(params, step, sL, s, _input):\n",
    "    y = 'energy_cons'\n",
    "    x = {'tot': 0, 'prod': 0., 'bat_use': 0., 'priority': 0., 'business': 0., 'leisure': 0., 'bybat': 0}\n",
    "    return (y, x)\n",
    "\n",
    "# costs and revenues\n",
    "def set_energy_revenue(params, step, sL, s, _input):\n",
    "    y = 'energy_revenue'\n",
    "    x = {'tot': 0, 'prod': 0., 'bat_use': 0., 'priority': 0., 'business': 0., 'leisure': 0., 'bybat': 0}\n",
    "    return (y, x) \n",
    "\n",
    "def set_op_cost(params, step, sL, s, _input):\n",
    "    y = 'op_cost'\n",
    "    x = {'operations': op_cost_per_hour, 'op_cost_per_kwh': op_cost_per_kwh}\n",
    "    return (y, x) \n",
    "\n",
    "def set_maintenance_cost(params, step, sL, s, _input):\n",
    "    y = 'maintenance_cost'\n",
    "    x = _input['maintenance_cost']\n",
    "    return (y, x)    \n",
    "\n",
    "# increments\n",
    "\n",
    "def increment_energy_prod(params, step, sL, s, _input):\n",
    "    y = 'energy_prod'\n",
    "    x = _input['energy_prod']\n",
    "    return (y, x)\n",
    "\n",
    "def increment_energy_appetite(params, step, sL, s, _input):\n",
    "    y = 'energy_appetite'\n",
    "    x = _input['energy_appetite']\n",
    "    return (y, x)\n",
    "\n",
    "# update actual use values\n",
    "def increment_energy_cons(params, step, sL, s, _input):\n",
    "    y = 'energy_cons'\n",
    "    x = _input['energy_cons']\n",
    "    return (y, x)\n",
    "\n",
    "# update battery state\n",
    "def increment_bat_level(params, step, sL, s, _input):\n",
    "    y = 'bat_level'\n",
    "    x = s['bat_level'] + _input['energy_cons']['bybat'] - _input['bat_use']\n",
    "    return (y, x)\n",
    "\n",
    "# update input variances\n",
    "def update_annual_var(params, step, sL, s, _input):\n",
    "    y = 'annual_var'\n",
    "    x = _input['annual_var']\n",
    "    return (y, x)\n",
    "\n",
    "def update_daily_mult(params, step, sL, s, _input):\n",
    "    y = 'daily_mult'\n",
    "    x = _input['daily_mult']\n",
    "    return (y, x)\n",
    "\n",
    "# update tracker of hours passed\n",
    "def increment_hour(params, step, sL, s, _input):\n",
    "    y = 'hour'\n",
    "    x = s['hour'] + _input['hour_inc']\n",
    "    return (y, x)\n",
    "\n",
    "# degrade operational efficiency\n",
    "def increment_production_efficiency(params, step, sL, s, _input):\n",
    "    y = 'production_efficiency'\n",
    "    x = s['production_efficiency']\n",
    "    x['value'] *= x['decay']\n",
    "    if s['maintenance_cost']['maintenance_occured'] == 1:\n",
    "        x['value'] = 1.\n",
    "    return (y, x)\n",
    "\n",
    "def set_maintenance_occured(params, step, sL, s, _input):\n",
    "    y = 'maintenance_occured'\n",
    "    x = _input['maintenance_occured']\n",
    "    return (y, x)\n",
    "\n",
    "# update costs\n",
    "def increment_energy_revenue(params, step, sL, s, _input):\n",
    "    y = 'energy_revenue'\n",
    "    x = _input['energy_revenue']\n",
    "    return (y, x)\n",
    "\n",
    "def increment_op_cost(params, step, sL, s, _input):\n",
    "    y = 'op_cost'\n",
    "    x = _input['op_cost']\n",
    "    return (y, x)\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# TODO here is where you could play with how some policy updates occur\n",
    "\n",
    "# update price of electricity\n",
    "def update_price_per_kwh(params, step, sL, s, _input):\n",
    "    y = 'price_per_kwh'\n",
    "    price = max(min(s['price_per_kwh']['price'], s['price_per_kwh']['max_price']),s['price_per_kwh']['min_price']) # make sure in bounds\n",
    "    x = s['price_per_kwh']\n",
    "    x['price'] = price\n",
    "    return (y, x)\n",
    "\n",
    "# update ownership\n",
    "def update_ownership(params, step, sL, s, _input):\n",
    "    y = 'ownership'\n",
    "    x = _input['ownership']\n",
    "    return (y, x)\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Partial State Update Blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# In the Partial State Update Blocks, \n",
    "# the user specifies if state update functions will be run in series or in parallel\n",
    "# and the policy functions that will be evaluated in that block\n",
    "user_variables = { # The following state variables will be updated simultaneously\n",
    "            'energy_appetite': increment_energy_appetite,\n",
    "            'energy_cons': increment_energy_cons,\n",
    "            'bat_level': increment_bat_level,\n",
    "            'annual_var': update_annual_var,\n",
    "            'daily_mult': update_daily_mult,\n",
    "            'energy_revenue': increment_energy_revenue,\n",
    "            'op_cost': increment_op_cost\n",
    "        }\n",
    "\n",
    "partial_state_update_blocks = [\n",
    "    { # Maintenance if needed\n",
    "        'policies': { # The following policy functions will be evaluated and their returns will be passed to the state update functions\n",
    "            'maintenance': maintenance\n",
    "        },\n",
    "        'variables': { # The following state variables will be updated simultaneously\n",
    "            'maintenance_cost': set_maintenance_cost,\n",
    "        }\n",
    "    },\n",
    "    { # Get energy available to be used in this timestep\n",
    "        'policies': { # The following policy functions will be evaluated and their returns will be passed to the state update functions\n",
    "            'solartainer_produce': solartainer_produce\n",
    "        },\n",
    "        'variables': { # The following state variables will be updated simultaneously\n",
    "            'energy_prod': set_energy_prod,\n",
    "            'energy_appetite': set_energy_appetite,\n",
    "            'energy_cons': set_energy_cons,\n",
    "            'energy_prod': increment_energy_prod,\n",
    "            'annual_var': update_annual_var,\n",
    "            'daily_mult': update_daily_mult,\n",
    "            'price_per_kwh': update_price_per_kwh,\n",
    "            'energy_revenue': set_energy_revenue,\n",
    "            'op_cost': set_op_cost\n",
    "        }\n",
    "    },\n",
    "    { # Get priority users usage (Tier 1 users)\n",
    "        'policies': { # The following policy functions will be evaluated and their returns will be passed to the state update functions\n",
    "            'userGroup_priority': userGroup_priority\n",
    "        },\n",
    "        'variables': user_variables\n",
    "    },\n",
    "    { # Get business users usage (Tier 2 users)\n",
    "        'policies': { # The following policy functions will be evaluated and their returns will be passed to the state update functions\n",
    "            'userGroup_business': userGroup_business\n",
    "        },\n",
    "        'variables': user_variables\n",
    "    },\n",
    "    { # Get leisure users usage (Tier 3 users)\n",
    "        'policies': { # The following policy functions will be evaluated and their returns will be passed to the state update functions\n",
    "            'userGroup_leisure': userGroup_leisure\n",
    "        },\n",
    "        'variables': user_variables\n",
    "    },\n",
    "    { # Get battery usage (Tier 4)\n",
    "        'policies': { # The following policy functions will be evaluated and their returns will be passed to the state update functions\n",
    "            'battery_tracker': battery_tracker,\n",
    "        },\n",
    "        'variables': { # The following state variables will be updated simultaneously\n",
    "            'energy_cons': increment_energy_cons,\n",
    "            'op_cost': increment_op_cost,\n",
    "            'bat_level': increment_bat_level,\n",
    "            'hour': increment_hour\n",
    "        }\n",
    "    },\n",
    "    { # Operate on ownership\n",
    "        'policies': { # The following policy functions will be evaluated and their returns will be passed to the state update functions\n",
    "            'ownership_dynamics': ownership_dynamics,\n",
    "        },\n",
    "        'variables': { # The following state variables will be updated simultaneously\n",
    "            'ownership': update_ownership\n",
    "        }\n",
    "    }\n",
    "]\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run cadCAD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                            __________   ____ \n",
      "          ________ __ _____/ ____/   |  / __ \\\n",
      "         / ___/ __` / __  / /   / /| | / / / /\n",
      "        / /__/ /_/ / /_/ / /___/ ___ |/ /_/ / \n",
      "        \\___/\\__,_/\\__,_/\\____/_/  |_/_____/  \n",
      "        by BlockScience\n",
      "        \n",
      "Execution Mode: single_proc: [<cadCAD.configuration.Configuration object at 0x000001D8A7ACC358>]\n",
      "Configurations: [<cadCAD.configuration.Configuration object at 0x000001D8A7ACC358>]\n",
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      "year: 12 day: 100\n",
      "year: 12 day: 110\n",
      "year: 12 day: 120\n",
      "year: 12 day: 130\n",
      "year: 12 day: 140\n",
      "year: 12 day: 150\n",
      "year: 12 day: 160\n",
      "year: 12 day: 170\n",
      "year: 12 day: 180\n",
      "year: 12 day: 190\n",
      "year: 12 day: 200\n",
      "year: 12 day: 210\n",
      "year: 12 day: 220\n",
      "year: 12 day: 230\n",
      "year: 12 day: 240\n",
      "year: 12 day: 250\n",
      "year: 12 day: 260\n",
      "year: 12 day: 270\n",
      "year: 12 day: 280\n",
      "year: 12 day: 290\n",
      "year: 12 day: 300\n",
      "year: 12 day: 310\n",
      "year: 12 day: 320\n",
      "year: 12 day: 330\n",
      "year: 12 day: 340\n",
      "year: 12 day: 350\n",
      "year: 12 day: 360\n",
      "year: 13 day: 5\n",
      "year: 13 day: 15\n",
      "year: 13 day: 25\n",
      "year: 13 day: 35\n",
      "year: 13 day: 45\n",
      "year: 13 day: 55\n",
      "year: 13 day: 65\n",
      "year: 13 day: 75\n",
      "year: 13 day: 85\n",
      "year: 13 day: 95\n",
      "year: 13 day: 105\n",
      "year: 13 day: 115\n",
      "year: 13 day: 125\n",
      "year: 13 day: 135\n",
      "year: 13 day: 145\n",
      "year: 13 day: 155\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year: 13 day: 165\n",
      "year: 13 day: 175\n",
      "year: 13 day: 185\n",
      "year: 13 day: 195\n",
      "year: 13 day: 205\n",
      "year: 13 day: 215\n",
      "year: 13 day: 225\n",
      "year: 13 day: 235\n",
      "year: 13 day: 245\n",
      "year: 13 day: 255\n",
      "year: 13 day: 265\n",
      "year: 13 day: 275\n",
      "year: 13 day: 285\n",
      "year: 13 day: 295\n",
      "year: 13 day: 305\n",
      "year: 13 day: 315\n",
      "year: 13 day: 325\n",
      "year: 13 day: 335\n",
      "year: 13 day: 345\n",
      "year: 13 day: 355\n",
      "year: 14 day: 0\n",
      "year: 14 day: 10\n",
      "year: 14 day: 20\n",
      "year: 14 day: 30\n",
      "year: 14 day: 40\n",
      "year: 14 day: 50\n",
      "year: 14 day: 60\n",
      "year: 14 day: 70\n",
      "year: 14 day: 80\n",
      "year: 14 day: 90\n",
      "year: 14 day: 100\n",
      "year: 14 day: 110\n",
      "year: 14 day: 120\n",
      "year: 14 day: 130\n",
      "year: 14 day: 140\n",
      "year: 14 day: 150\n",
      "year: 14 day: 160\n",
      "year: 14 day: 170\n",
      "year: 14 day: 180\n",
      "year: 14 day: 190\n",
      "year: 14 day: 200\n",
      "year: 14 day: 210\n",
      "year: 14 day: 220\n",
      "year: 14 day: 230\n",
      "year: 14 day: 240\n",
      "year: 14 day: 250\n",
      "year: 14 day: 260\n",
      "year: 14 day: 270\n",
      "year: 14 day: 280\n",
      "year: 14 day: 290\n",
      "year: 14 day: 300\n",
      "year: 14 day: 310\n",
      "year: 14 day: 320\n",
      "year: 14 day: 330\n",
      "year: 14 day: 340\n",
      "year: 14 day: 350\n",
      "year: 14 day: 360\n"
     ]
    }
   ],
   "source": [
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# Settings of general simulation parameters, unrelated to the system itself\n",
    "# `T` is a range with the number of discrete units of time the simulation will run for;\n",
    "# `N` is the number of times the simulation will be run (Monte Carlo runs)\n",
    "# `M` is not currently used\n",
    "\n",
    "simulation_parameters = {\n",
    "    'T': range(24*days_to_simulate),\n",
    "    'N': 1,\n",
    "    'M': {}\n",
    "}\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "\n",
    "from cadCAD.configuration import Configuration\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# The configurations above are then packaged into a `Configuration` object\n",
    "config = Configuration(initial_state=initial_conditions, #dict containing variable names and initial values\n",
    "                       partial_state_update_blocks=partial_state_update_blocks, #dict containing state update functions\n",
    "                       sim_config=simulation_parameters #dict containing simulation parameters\n",
    "                      )\n",
    "\n",
    "from cadCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
    "exec_mode = ExecutionMode()\n",
    "exec_context = ExecutionContext(exec_mode.single_proc)\n",
    "executor = Executor(exec_context, [config]) # Pass the configuration object inside an array\n",
    "raw_result, tensor = executor.execute() # The `execute()` method returns a tuple; its first elements contains the raw results\n",
    "\n",
    "\n",
    "raw_result = [d for d in raw_result if d['substep'] == len(partial_state_update_blocks)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Data Save / Load Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get data in an easier to reference frame\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "df = np.asarray(raw_result)\n",
    "df2 = {k: [dic[k] for dic in df] for k in df[0]}\n",
    "inverts = ['price_per_kwh', 'maintenance_cost', 'ownership', 'energy_prod', 'energy_appetite', 'energy_cons', 'annual_var', 'daily_mult', 'op_cost', 'energy_revenue']\n",
    "for invert in inverts:\n",
    "    df3 = df2.copy()\n",
    "    df3[invert] = {k: [dic[k] for dic in df2[invert]] for k in df2[invert][0]}\n",
    "    df2 = df3.copy()\n",
    "df = df2.copy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Output into csv\n",
    "import pickle\n",
    "def save_obj(obj, name):\n",
    "    num = str(int(random.random()*100))\n",
    "    with open('data/'+name + num + '.pkl', 'wb') as f:\n",
    "        pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "# you can load objects from csv like this\n",
    "def load_obj(name):\n",
    "    with open('data/' + name + '.pkl', 'rb') as f:\n",
    "        return pickle.load(f)\n",
    "\n",
    "save_obj(df, 'data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Some Stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------\n",
      "kwh produced: 1111137 of 1744410 available which is 64.0 % capacity\n",
      "total appetite was: 1600961\n",
      "leaving 489824 kwh instead generated by diesel which is 31.0 %\n",
      "over 131400 hours\n",
      "total revenues EUR 77779 with op cost 55156 and maintenance 10800 occuring 36 times\n",
      "kwh produced: 1111137 with 178674 being routed through battery and 932463 directly from Solartainer\n",
      "priority users used 496809 out of 746673 appetite\n",
      "business users used 327834 out of 435139 appetite\n",
      "leisure users used 286494 out of 419149 appetite\n",
      "-----------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "av = int(sum(df['energy_prod']['available']))\n",
    "tot = int(sum(df['energy_cons']['tot']))\n",
    "tot_revenue = int(sum(df['energy_revenue']['tot']))\n",
    "tot_op_cost = int(sum(df['op_cost']['operations']))\n",
    "maintenance_count = int(sum(df['maintenance_cost']['maintenance_occured']))\n",
    "maintenance_cost = int(sum(df['maintenance_cost']['maintenance_cost']))\n",
    "priority = int(sum(df['energy_cons']['priority']))\n",
    "business = int(sum(df['energy_cons']['business']))\n",
    "leisure = int(sum(df['energy_cons']['leisure']))\n",
    "priority_app = int(sum(df['energy_appetite']['priority']))\n",
    "business_app = int(sum(df['energy_appetite']['business']))\n",
    "leisure_app = int(sum(df['energy_appetite']['leisure']))\n",
    "bat_use = int(sum(df['energy_cons']['bat_use']))\n",
    "bat = int(sum(df['energy_cons']['bybat']))\n",
    "prod = int(sum(df['energy_cons']['prod']))\n",
    "\n",
    "print('----------------------------')\n",
    "print('kwh produced:', tot,'of', av,'available which is', np.round(100.*tot/av,),'% capacity')\n",
    "app_tot = priority_app+business_app+leisure_app\n",
    "print('total appetite was:', app_tot)\n",
    "print('leaving', app_tot - tot, 'kwh instead generated by diesel which is', np.round(100*((app_tot - tot) / app_tot),0),'%')\n",
    "\n",
    "print('over', max(df['hour']), 'hours')\n",
    "print('total revenues EUR', tot_revenue, 'with op cost', tot_op_cost, 'and maintenance', maintenance_cost, 'occuring', maintenance_count, 'times')\n",
    "print('kwh produced:', tot, 'with', bat_use, 'being routed through battery and', prod,'directly from Solartainer')\n",
    "print('priority users used', priority, 'out of', priority_app, 'appetite')\n",
    "print('business users used', business, 'out of', business_app, 'appetite')\n",
    "print('leisure users used', leisure, 'out of', leisure_app, 'appetite')\n",
    "print('-----------------------------')\n",
    "\n",
    "def moving_average(mylist, N=24*20):\n",
    "    cumsum, moving_aves = [0], []\n",
    "    for i, x in enumerate(mylist, 1):\n",
    "        cumsum.append(cumsum[i-1] + x)\n",
    "        if i>=N:\n",
    "            moving_ave = (cumsum[i] - cumsum[i-N])/N\n",
    "            moving_aves.append(moving_ave)\n",
    "    return cumsum, moving_aves\n",
    "\n",
    "av = moving_average(df['energy_prod']['available'])\n",
    "tot = moving_average(df['energy_cons']['tot'])\n",
    "tot_revenue = moving_average(df['energy_revenue']['tot'])\n",
    "tot_op_cost = moving_average(df['op_cost']['operations'])\n",
    "maintenance_count = moving_average(df['maintenance_cost']['maintenance_occured'])\n",
    "maintenance_cost = moving_average(df['maintenance_cost']['maintenance_cost'])\n",
    "priority = moving_average(df['energy_cons']['priority'])\n",
    "business = moving_average(df['energy_cons']['business'])\n",
    "leisure = moving_average(df['energy_cons']['leisure'])\n",
    "priority_app = moving_average(df['energy_appetite']['priority'])\n",
    "business_app = moving_average(df['energy_appetite']['business'])\n",
    "leisure_app = moving_average(df['energy_appetite']['leisure'])\n",
    "bat_use = moving_average(df['energy_cons']['bat_use'])\n",
    "bat = moving_average(df['energy_cons']['bybat'])\n",
    "prod = moving_average(df['energy_cons']['prod'])\n",
    "\n",
    "plot_distance = 24 * 2\n",
    "if plot:\n",
    "    # plot \n",
    "    plt.plot(df['timestep'][0:plot_distance], df['bat_level'][0:plot_distance], label='battery level')\n",
    "    plt.plot(df['timestep'][0:plot_distance], df['energy_cons']['tot'][0:plot_distance], label='total consumption')\n",
    "    plt.plot(df['timestep'][0:plot_distance], df['energy_prod']['available'][0:plot_distance], label='production capacity')\n",
    "    plt.legend()\n",
    "    plt.title('production, consumption, and battery level')\n",
    "    plt.show()\n",
    "\n",
    "    plt.plot(df['timestep'][0:plot_distance], df['energy_cons']['tot'][0:plot_distance], label='total production')\n",
    "    plt.plot(df['timestep'][0:plot_distance], df['energy_cons']['prod'][0:plot_distance], label='consumption from solartainer')\n",
    "    plt.plot(df['timestep'][0:plot_distance], df['energy_cons']['bat_use'][0:plot_distance], label='consumption from battery')\n",
    "    plt.plot(df['timestep'][0:plot_distance], df['energy_cons']['bybat'][0:plot_distance], label='production into battery')\n",
    "    plt.legend()\n",
    "    plt.title('breakdown of energy consumption type')\n",
    "    plt.show()\n",
    "\n",
    "    plt.plot(df['timestep'][0:plot_distance], df['energy_cons']['tot'][0:plot_distance], label='production')\n",
    "    plt.plot(df['timestep'][0:plot_distance], df['energy_prod']['available'][0:plot_distance], label='production capacity')\n",
    "    plt.plot(df['timestep'][0:plot_distance], df['energy_cons']['bat_use'][0:plot_distance], label='battery use')\n",
    "    plt.legend()\n",
    "    plt.title('production vs. capacity and battery use')\n",
    "    plt.show()\n",
    "\n",
    "    plt.plot(df['timestep'][0:plot_distance], df['energy_cons']['tot'][0:plot_distance], label='total production')\n",
    "    plt.plot(df['timestep'][0:plot_distance], df['energy_cons']['priority'][0:plot_distance], label='priority use')\n",
    "    plt.plot(df['timestep'][0:plot_distance], df['energy_cons']['business'][0:plot_distance], label='business use')\n",
    "    plt.plot(df['timestep'][0:plot_distance], df['energy_cons']['leisure'][0:plot_distance], label='leisure use')\n",
    "    plt.plot(df['timestep'][0:plot_distance], df['energy_cons']['bybat'][0:plot_distance], label='to battery')\n",
    "    plt.legend()\n",
    "    plt.title('consumption by tier')\n",
    "    plt.show()\n",
    "    \n",
    "    owners = ['solartainer', 'operator', 'priority', 'business', 'leisure', 'maintenance', 'crowdfunders']\n",
    "    for owner in owners:\n",
    "        plt.plot(df['timestep'], df['ownership'][owner], label=owner)\n",
    "    plt.legend()\n",
    "    plt.title('ownership levels')\n",
    "    plt.show()\n",
    "\n",
    "    plt.plot(av[1], label='available')\n",
    "    plt.plot(tot[1], label='produced')\n",
    "    plt.plot(priority[1], label='priority use')\n",
    "    plt.plot(business[1], label='business use')\n",
    "    plt.plot(leisure[1], label='leisure use')\n",
    "    plt.plot(bat[1], label='use of battery')\n",
    "    plt.legend()\n",
    "    plt.title('20 day consumption moving averages')\n",
    "    plt.show()\n",
    "\n",
    "    plt.plot(av[0], label='av')\n",
    "    plt.plot(tot[0], label='tot')\n",
    "    plt.plot(priority[0], label='priority')\n",
    "    plt.plot(business[0], label='business')\n",
    "    plt.plot(leisure[0], label='leisure')\n",
    "    plt.plot(bat[0], label='bat_use')\n",
    "    plt.legend()\n",
    "    plt.title('running total consumption')\n",
    "    plt.show()\n",
    "    \n",
    "    plt.plot(tot_revenue[0], label='revenue')\n",
    "    plt.plot(tot_op_cost[0], label='operating cost')\n",
    "    plt.plot(maintenance_cost[0], label='maintenance cost')\n",
    "    plt.legend()\n",
    "    plt.title('running financials')\n",
    "    plt.show()\n",
    "    \n",
    "    plt.plot(tot_revenue[0], label='revenue')\n",
    "    plt.plot(tot_op_cost[0], label='operating cost')\n",
    "    plt.plot(maintenance_cost[0], label='maintenance cost')\n",
    "    plt.legend()\n",
    "    plt.title('20 day moving average financials')\n",
    "    plt.show()\n",
    "    \n",
    "\n"
   ]
  }
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