LCOE_optimize simplified_thermal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import numpy as np\n",
    "import pygad\n",
    "import warnings\n",
    "import time\n",
    "import csv\n",
    "import pandas as pd\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "from pathlib import Path\n",
    "sys.path.insert(1, str( (Path().absolute())  ) + \"/src\")\n",
    "\n",
    "from parameters import  repeat_elements, refueling_duration_estimate, fuel_cycle_length \n",
    "from schedule_mixed_reactor_optimizer import capacity_factor_weeks_approach_mix_reactors\n",
    "from economic_FOMs import level_cost_of_energy_reactor_mix_starting_from_BOAK\n",
    "from parameters import OM_cost_per_MWh_thermal, fuel_cycle_length, refueling_duration_estimate\n",
    "from economic_FOMs import tot_TCI_multiple_reactors_mix_starting_from_BOAK_thermal\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The O&M cost for multiple reactors (thermal) assuming\n",
    "\n",
    "def simplified_schedule_thermal(power_list, num_of_reactors_list, demand, levelization_period_years):\n",
    "    OM__cost_per_year_per_reactor_list = []\n",
    "    actual_output_per_reactor_list = []\n",
    "\n",
    "    for i in range(len(power_list)):\n",
    "        p = power_list[i]\n",
    "        \n",
    "        # calculate the capacity factor\n",
    "        CF = 1- (refueling_duration_estimate(p)/fuel_cycle_length(p))\n",
    "        \n",
    "        if num_of_reactors_list[i] >0:\n",
    "            \n",
    "            OM_for_specific_power = OM_cost_per_MWh_thermal(p/0.35, num_of_reactors_list[i]) # $/MWh for one reactor\n",
    "        else:\n",
    "            OM_for_specific_power = 0\n",
    "        \n",
    "        \n",
    "        OM__cost_per_year_per_reactor = OM_for_specific_power *power_list[i]*num_of_reactors_list[i]*8760 * CF # dollar per year for specific power\n",
    "        \n",
    "        OM__cost_per_year_per_reactor_list.append(OM__cost_per_year_per_reactor)\n",
    "        \n",
    "        # total energy produced\n",
    "        actual_output_per_reactor_per_year = p*num_of_reactors_list[i ] * 8760*CF # MWh (in a year)for specific power\n",
    "        actual_output_per_reactor_list.append(actual_output_per_reactor_per_year )\n",
    "       \n",
    "        \n",
    "    total_OM__cost_per_year = sum(OM__cost_per_year_per_reactor_list) # In dollars for all the reactors\n",
    "    actual_output_per_year = sum (actual_output_per_reactor_list)\n",
    "    \n",
    "    \n",
    "    total_OM__cost_per_year_list = [total_OM__cost_per_year] * int(levelization_period_years) # dollars per year\n",
    "    actual_output_per_year_list = [actual_output_per_year] * int(levelization_period_years) # MWh\n",
    "    actual_demand_per_year_list = [demand*8760] * int(levelization_period_years) # in mwh\n",
    "\n",
    "    if actual_output_per_year >= demand*8760:\n",
    "        actual_output_per_year_per_demand = demand*8760  #MWh\n",
    "        \n",
    "    elif actual_output_per_year < demand*8760: \n",
    "        actual_output_per_year_per_demand  = actual_output_per_year\n",
    "        \n",
    "    overall_capacity_factor = (actual_output_per_year/ (demand*8760))\n",
    "    actual_output_per_year_per_demand_list = [actual_output_per_year_per_demand] * int(levelization_period_years)  # MWh  \n",
    "\n",
    "    return  total_OM__cost_per_year_list, actual_output_per_year_list, actual_output_per_year_per_demand_list , overall_capacity_factor\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def level_cost_of_energy_reactor_mix_starting_from_BOAK_simplified_thermal( demand, interest_rate, num_of_reactors_list, power_list, levelization_period_years):\n",
    "    \n",
    "    simplified_schedule_results = simplified_schedule_thermal(power_list, num_of_reactors_list, demand, levelization_period_years)\n",
    "    list_of_OM_cost_per_year_all_reactors = simplified_schedule_results[0]\n",
    "    list_of_generated_MWh_per_year_from_all_reactors_per_demand = simplified_schedule_results[2]\n",
    "    overall_capacity_factor =  simplified_schedule_results [3]\n",
    "    \n",
    "    \n",
    "    \n",
    "    sum_cost = 0 # initialization \n",
    "    sum_elec = 0 # initialization \n",
    "    for year in range( len(list_of_generated_MWh_per_year_from_all_reactors_per_demand)):\n",
    "        if year == 0:\n",
    "            cap_cost =  tot_TCI_multiple_reactors_mix_starting_from_BOAK_thermal(power_list, interest_rate, num_of_reactors_list)\n",
    "            OM_cost_per_year = 0\n",
    "            elec_gen = 0\n",
    "          \n",
    "        elif year > 0:\n",
    "         \n",
    "            cap_cost = 0 \n",
    "            \n",
    "            OM_cost_per_year =  list_of_OM_cost_per_year_all_reactors[year-1]\n",
    "            elec_gen =  list_of_generated_MWh_per_year_from_all_reactors_per_demand[year-1]\n",
    "        \n",
    "        sum_cost += (cap_cost + OM_cost_per_year) / ((1 +interest_rate)**(year) ) \n",
    "        sum_elec += elec_gen/ ((1 + interest_rate)**year) \n",
    "    LCOE =  sum_cost/ sum_elec\n",
    "    # print(\"LCOE NOW is: \", LCOE)\n",
    "    return LCOE, overall_capacity_factor\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LCOE Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# delete the output file is exists\n",
    "filename = \"GA_results_simple_case.csv\"\n",
    "try:\n",
    "    os.remove(filename)\n",
    "except OSError:\n",
    "    pass\n",
    "\n",
    "\n",
    "def initial_pop_reactors(power_list, sol_per_pop, possible_solutions):\n",
    "    \"\"\"\n",
    "    This function initializes a population of potential solutions for the reactor mix optimization problem.\n",
    "\n",
    "    Parameters:\n",
    "    - power_list (list): A list of possible reactor capacities.\n",
    "    - sol_per_pop (int): The number of potential solutions per population.\n",
    "    - possible_solutions (list): A list of upper limits for each reactor capacity.\n",
    "\n",
    "    Returns:\n",
    "    - pop (list): A list of potential solutions, where each sub-list represents a solution and contains integers representing the number of each reactor type.\n",
    "    \"\"\"\n",
    "    pop = np.zeros((sol_per_pop, len(power_list)))\n",
    "    for i in range(len(power_list)):\n",
    "        temp = np.random.randint(0, max(possible_solutions[i]) + 1, size=sol_per_pop).tolist()\n",
    "        pop[:, i] = temp\n",
    "    return pop.tolist()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def gene_upper_limit(power_list, demand): # limiting the maximum number of each type of reactors\n",
    "\n",
    "    upper_limit_list = []\n",
    "    for i in range(len(power_list )):\n",
    "        # I use the multiplier 1.05 because of the ratio between refueling duration and operational lifetime is will nnot be higher than 1.05\n",
    "        upper_limit =range(int(np.ceil(1.05*demand/power_list[i]))+2) ## TODO check the +1\n",
    "        upper_limit_list.append(upper_limit )\n",
    "\n",
    "    return  upper_limit_list\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "fitness_list = []\n",
    "gen_sol_list = []\n",
    "\n",
    "def on_gen(ga_instance):\n",
    "    pass\n",
    "    # print(\"Generation : \", ga_instance.generations_completed,  ga_instance.best_solution()[0], ga_instance.best_solution()[1])\n",
    "    # fitness_list.append(ga_instance.best_solution()[1])\n",
    "    # gen_sol_list.append(ga_instance.best_solution()[0])\n",
    "\n",
    "def optimize_lcoe(power_list,  levelization_period_weeks, demand , interest_rate, capacity_factor_criteria):\n",
    "    solutions_list = []\n",
    "    CF_list = []\n",
    "    LCOE_list = []\n",
    "    start_time = time.time()\n",
    "    levelization_period_years = int(levelization_period_weeks/52.1428571429)\n",
    "    \n",
    "    # GA params\n",
    "    sol_per_pop =  200 *int( (len(power_list)))\n",
    "    \n",
    "    num_generations = 5000\n",
    "    \n",
    "    num_parents_mating = 4 # int(sol_per_pop /5) # int(np.ceil(sol_per_pop/3))# consider increasing this\n",
    "    num_genes = len(power_list)\n",
    "\n",
    "    parent_selection_type = \"rank\"\n",
    "    keep_parents = 2 # int(np.ceil(sol_per_pop/5))\n",
    "    \n",
    "    mutation_type = \"random\"\n",
    "    mutation_percent_genes = 10\n",
    "    \n",
    "    crossover_type = \"single_point\"\n",
    "    # mutation_type = \"adaptive\"\n",
    "    # mutation_percent_genes =  mutation_percent_genes = (20, 1) \n",
    "    \n",
    "    possible_solutions = gene_upper_limit(power_list, demand) # gene space\n",
    "    \n",
    "    initial_pop  = initial_pop_reactors(power_list,  sol_per_pop, possible_solutions)\n",
    "    \n",
    "    allow_dup  = True\n",
    "    \n",
    "    \n",
    "\n",
    "\n",
    "    \n",
    "    def fitness_func(ga_instance, solution, solution_idx):\n",
    "        # print(\"\\n\\n proposed solution\", solution)\n",
    "       \n",
    "        power_list_modified = [power_list [i] for i in range(len(solution)) if solution[i] != 0]\n",
    "      \n",
    "        Num_of_each_reactor_type_modified = [x for x in solution if x != 0]\n",
    "      \n",
    "        long_list_power = repeat_elements(power_list_modified, Num_of_each_reactor_type_modified)\n",
    "       \n",
    "        \n",
    "        if sum(long_list_power) >= demand * capacity_factor_criteria and  sum(long_list_power) <= 2*demand : #  ((sum(long_list_power)) - min(long_list_power) ) <= demand:\n",
    "            \n",
    "               \n",
    "                output_lcoe_results = level_cost_of_energy_reactor_mix_starting_from_BOAK_simplified_thermal(demand, interest_rate, solution, power_list, levelization_period_years)\n",
    "                output_lcoe  = output_lcoe_results[0]\n",
    "                system_CF =  output_lcoe_results[1]\n",
    "\n",
    "            \n",
    "            \n",
    "                fitness_1 = 1000 / ( output_lcoe) \n",
    "                \n",
    "                if    3*capacity_factor_criteria >=system_CF >= capacity_factor_criteria:\n",
    "                    fitness_2 = 0\n",
    "                    \n",
    "                else:\n",
    "                    fitness_2 = -2\n",
    "                \n",
    "                fitness = fitness_1 + fitness_2\n",
    "                \n",
    "                GA_results =  { 'sol' :solution , 'CF' : np.round(system_CF, 3), 'LCOE' : np.round(output_lcoe, 3) }\n",
    "                solutions_list.append(solution )\n",
    "                CF_list.append(np.round(system_CF, 3))\n",
    "                LCOE_list.append(np.round(output_lcoe, 3))\n",
    "                \n",
    "                with open(\"GA_results_simple_case.csv\", \"a\") as csv_file:\n",
    "                    writer = csv.writer(csv_file)\n",
    "                    for key, value in GA_results.items():\n",
    "                        writer.writerow([key, value])\n",
    "         \n",
    "                \n",
    "                # print(\"solution is : \" , solution)\n",
    "                # print(\"Total Power is : \" , sum(long_list_power))\n",
    "                # print(\"MW discrepancy : \" , np.abs(demand - sum(long_list_power)))\n",
    "                # print(\"Capacity Factor (min): \" , np.round(capacity_factor_t_min, 3))\n",
    "                # print(\"LCOE : \" , np.round(output_lcoe, 3))\n",
    "                \n",
    "        else:\n",
    "            fitness = -2\n",
    "        # if fitness >=0:    \n",
    "        # print( \"FITNESS NOW: \", fitness, \"\\n\")\n",
    "        \n",
    "        \n",
    "\n",
    "        return fitness\n",
    "            \n",
    "    ga_instance = pygad.GA(num_generations=num_generations,\n",
    "                       num_parents_mating= num_parents_mating,\n",
    "                       fitness_func= fitness_func,\n",
    "                       sol_per_pop=sol_per_pop,\n",
    "                       num_genes=num_genes,\n",
    "                     \n",
    "                       parent_selection_type=parent_selection_type,\n",
    "                       keep_parents=keep_parents, crossover_type=crossover_type,\n",
    "                       \n",
    "                    #    mutation_percent_genes= mutation_percent_genes,\n",
    "                       \n",
    "                       # pick 'possible_solutions' if you want to give more freedom,  or 'solutions_P_list' if you want to be faster.\n",
    "                       gene_space=  possible_solutions ,\n",
    "                       #mutation_type=mutation_type, \n",
    "                       stop_criteria= [\"saturate_500\"], \n",
    "                      \n",
    "                       on_generation= on_gen,\n",
    "                        fitness_batch_size=1,\n",
    "                        keep_elitism = 0,\n",
    "                        gene_type = int, initial_population = initial_pop,\n",
    "                        parallel_processing = 8,\n",
    "                    \n",
    "                    \n",
    "                       allow_duplicate_genes=allow_dup )\n",
    "\n",
    "    \n",
    " \n",
    "    ga_instance.run()\n",
    "  \n",
    "    end_time = time.time() \n",
    "    \n",
    "    solution1, solution_fitness1, solution_idx1 = ga_instance.best_solution()\n",
    "    print(\"Parameters of the best solution : {solution}\".format(solution=solution1))\n",
    "    print(\"Fitness value of the best solution = {solution_fitness}\".format(solution_fitness=solution_fitness1))\n",
    "\n",
    "    \n",
    "    \n",
    "    sol, sol_fitness, _ = ga_instance.best_solution()\n",
    "    print(\"\\n The optimization program runtime is \" , np.round( (end_time -start_time), 0), \" sec\", \" & The Number of Generations Passed is \",\\\n",
    "        ga_instance.generations_completed, \"...... Fitness value of the best solution = {solution_fitness}\".format(solution_fitness=sol_fitness))\n",
    "    # print(sol, 'gg') \n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "    return solution1, solutions_list, CF_list, LCOE_list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Demand is 1310 MWe  and the Availability Criteria is : 0.95\n",
      "Parameters of the best solution : [ 0  4 10]\n",
      "Fitness value of the best solution = 12.927017366202119\n",
      "\n",
      " The optimization program runtime is  28.0  sec  & The Number of Generations Passed is  638 ...... Fitness value of the best solution = 12.927017366202119\n",
      "Demand is 1310 MWe  and the Availability Criteria is : 0.96\n",
      "Parameters of the best solution : [ 0  4 11]\n",
      "Fitness value of the best solution = 12.890631261734217\n",
      "\n",
      " The optimization program runtime is  26.0  sec  & The Number of Generations Passed is  571 ...... Fitness value of the best solution = 12.890631261734217\n",
      "Demand is 1310 MWe  and the Availability Criteria is : 0.97\n",
      "Parameters of the best solution : [ 0  4 12]\n",
      "Fitness value of the best solution = 12.856164097069351\n",
      "\n",
      " The optimization program runtime is  31.0  sec  & The Number of Generations Passed is  674 ...... Fitness value of the best solution = 12.856164097069351\n",
      "Demand is 1310 MWe  and the Availability Criteria is : 0.98\n",
      "Parameters of the best solution : [ 0  4 14]\n",
      "Fitness value of the best solution = 12.792338045216209\n",
      "\n",
      " The optimization program runtime is  25.0  sec  & The Number of Generations Passed is  562 ...... Fitness value of the best solution = 12.792338045216209\n",
      "Demand is 1310 MWe  and the Availability Criteria is : 0.99\n",
      "Parameters of the best solution : [ 0  4 15]\n",
      "Fitness value of the best solution = 12.762708929925864\n",
      "\n",
      " The optimization program runtime is  25.0  sec  & The Number of Generations Passed is  554 ...... Fitness value of the best solution = 12.762708929925864\n",
      "Demand is 1310 MWe  and the Availability Criteria is : 1\n",
      "Parameters of the best solution : [ 0  4 16]\n",
      "Fitness value of the best solution = 12.732613195198475\n",
      "\n",
      " The optimization program runtime is  24.0  sec  & The Number of Generations Passed is  549 ...... Fitness value of the best solution = 12.732613195198475\n",
      "Demand is 1742 MWe  and the Availability Criteria is : 0.95\n",
      "Parameters of the best solution : [0 6 0]\n",
      "Fitness value of the best solution = 14.14494465886815\n",
      "\n",
      " The optimization program runtime is  26.0  sec  & The Number of Generations Passed is  503 ...... Fitness value of the best solution = 14.14494465886815\n",
      "Demand is 1742 MWe  and the Availability Criteria is : 0.96\n",
      "Parameters of the best solution : [0 6 0]\n",
      "Fitness value of the best solution = 14.14494465886815\n",
      "\n",
      " The optimization program runtime is  28.0  sec  & The Number of Generations Passed is  545 ...... Fitness value of the best solution = 14.14494465886815\n",
      "Demand is 1742 MWe  and the Availability Criteria is : 0.97\n",
      "Parameters of the best solution : [0 6 0]\n",
      "Fitness value of the best solution = 14.14494465886815\n",
      "\n",
      " The optimization program runtime is  32.0  sec  & The Number of Generations Passed is  613 ...... Fitness value of the best solution = 14.14494465886815\n",
      "Demand is 1742 MWe  and the Availability Criteria is : 0.98\n",
      "Parameters of the best solution : [0 6 0]\n",
      "Fitness value of the best solution = 14.14494465886815\n",
      "\n",
      " The optimization program runtime is  30.0  sec  & The Number of Generations Passed is  580 ...... Fitness value of the best solution = 14.14494465886815\n",
      "Demand is 1742 MWe  and the Availability Criteria is : 0.99\n",
      "Parameters of the best solution : [0 6 0]\n",
      "Fitness value of the best solution = 14.14494465886815\n",
      "\n",
      " The optimization program runtime is  28.0  sec  & The Number of Generations Passed is  547 ...... Fitness value of the best solution = 14.14494465886815\n",
      "Demand is 1742 MWe  and the Availability Criteria is : 1\n",
      "Parameters of the best solution : [0 6 2]\n",
      "Fitness value of the best solution = 13.902058696775573\n",
      "\n",
      " The optimization program runtime is  27.0  sec  & The Number of Generations Passed is  524 ...... Fitness value of the best solution = 13.902058696775573\n",
      "Demand is 2188 MWe  and the Availability Criteria is : 0.95\n",
      "Parameters of the best solution : [ 2  0 16]\n",
      "Fitness value of the best solution = 14.295586271631601\n",
      "\n",
      " The optimization program runtime is  29.0  sec  & The Number of Generations Passed is  518 ...... Fitness value of the best solution = 14.295586271631601\n",
      "Demand is 2188 MWe  and the Availability Criteria is : 0.96\n",
      "Parameters of the best solution : [2 1 0]\n",
      "Fitness value of the best solution = 14.209887538228246\n",
      "\n",
      " The optimization program runtime is  31.0  sec  & The Number of Generations Passed is  566 ...... Fitness value of the best solution = 14.209887538228246\n",
      "Demand is 2188 MWe  and the Availability Criteria is : 0.97\n",
      "Parameters of the best solution : [2 1 0]\n",
      "Fitness value of the best solution = 14.209887538228246\n",
      "\n",
      " The optimization program runtime is  33.0  sec  & The Number of Generations Passed is  610 ...... Fitness value of the best solution = 14.209887538228246\n",
      "Demand is 2188 MWe  and the Availability Criteria is : 0.98\n",
      "Parameters of the best solution : [2 1 0]\n",
      "Fitness value of the best solution = 14.209887538228246\n",
      "\n",
      " The optimization program runtime is  30.0  sec  & The Number of Generations Passed is  541 ...... Fitness value of the best solution = 14.209887538228246\n",
      "Demand is 2188 MWe  and the Availability Criteria is : 0.99\n",
      "Parameters of the best solution : [2 1 0]\n",
      "Fitness value of the best solution = 14.209887538228246\n",
      "\n",
      " The optimization program runtime is  31.0  sec  & The Number of Generations Passed is  553 ...... Fitness value of the best solution = 14.209887538228246\n",
      "Demand is 2188 MWe  and the Availability Criteria is : 1\n",
      "Parameters of the best solution : [2 1 0]\n",
      "Fitness value of the best solution = 14.209887538228246\n",
      "\n",
      " The optimization program runtime is  35.0  sec  & The Number of Generations Passed is  648 ...... Fitness value of the best solution = 14.209887538228246\n",
      "Demand is 2620 MWe  and the Availability Criteria is : 0.95\n",
      "Parameters of the best solution : [0 9 0]\n",
      "Fitness value of the best solution = 14.715875942697986\n",
      "\n",
      " The optimization program runtime is  43.0  sec  & The Number of Generations Passed is  662 ...... Fitness value of the best solution = 14.715875942697986\n",
      "Demand is 2620 MWe  and the Availability Criteria is : 0.96\n",
      "Parameters of the best solution : [0 9 0]\n",
      "Fitness value of the best solution = 14.715875942697986\n",
      "\n",
      " The optimization program runtime is  34.0  sec  & The Number of Generations Passed is  524 ...... Fitness value of the best solution = 14.715875942697986\n",
      "Demand is 2620 MWe  and the Availability Criteria is : 0.97\n",
      "Parameters of the best solution : [0 9 0]\n",
      "Fitness value of the best solution = 14.715875942697986\n",
      "\n",
      " The optimization program runtime is  44.0  sec  & The Number of Generations Passed is  690 ...... Fitness value of the best solution = 14.715875942697986\n",
      "Demand is 2620 MWe  and the Availability Criteria is : 0.98\n",
      "Parameters of the best solution : [0 9 0]\n",
      "Fitness value of the best solution = 14.715875942697986\n",
      "\n",
      " The optimization program runtime is  47.0  sec  & The Number of Generations Passed is  711 ...... Fitness value of the best solution = 14.715875942697986\n",
      "Demand is 2620 MWe  and the Availability Criteria is : 0.99\n",
      "Parameters of the best solution : [0 9 0]\n",
      "Fitness value of the best solution = 14.715875942697986\n",
      "\n",
      " The optimization program runtime is  51.0  sec  & The Number of Generations Passed is  675 ...... Fitness value of the best solution = 14.715875942697986\n",
      "Demand is 2620 MWe  and the Availability Criteria is : 1\n",
      "Parameters of the best solution : [0 9 3]\n",
      "Fitness value of the best solution = 14.512647489888423\n",
      "\n",
      " The optimization program runtime is  43.0  sec  & The Number of Generations Passed is  591 ...... Fitness value of the best solution = 14.512647489888423\n"
     ]
    }
   ],
   "source": [
    "power_list = [1000, 300 ,10] # we can decrease the number of solution if needed\n",
    "levelization_period_years = 40\n",
    "interest_rate = 0.06\n",
    "\n",
    "best_solss = []\n",
    "for Demand in [1310 , 1742, 2188 , 2620]:\n",
    "    for capacity_factor_criteria_1 in [0.95 , 0.96, 0.97, 0.98, 0.99, 1]: \n",
    "        print(f'Demand is {Demand} MWe', f' and the Availability Criteria is : {capacity_factor_criteria_1}' )\n",
    "        optimization = optimize_lcoe( power_list ,  int(40*365/7), Demand, 0.06, capacity_factor_criteria_1)\n",
    "        best_sol = optimization[0]\n",
    "        best_solss.append(best_sol) # 1 1 0 ,    0 4 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Demand_list = [1310 , 1742, 2188 , 2620]\n",
    "Longer_list = [item for item in Demand_list for _ in range(6)]\n",
    "Longer_list_arr = np.array(Longer_list )\n",
    "lcoess = []\n",
    "CFSS = []\n",
    "for i in range(len(best_solss)):\n",
    "    ss = best_solss[i]\n",
    "    finals = level_cost_of_energy_reactor_mix_starting_from_BOAK_simplified_thermal(Longer_list[i], interest_rate, ss, power_list, levelization_period_years)\n",
    "    lcoess.append(finals[0])\n",
    "    CFSS.append(finals[1])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_75ac6_level0_col0\" class=\"col_heading level0 col0\" >sol0</th>\n",
       "      <th id=\"T_75ac6_level0_col1\" class=\"col_heading level0 col1\" >sol1</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td id=\"T_75ac6_row17_col0\" class=\"data row17 col0\" >2</td>\n",
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       "      <td id=\"T_75ac6_row18_col0\" class=\"data row18 col0\" >0</td>\n",
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       "      <td id=\"T_75ac6_row18_col2\" class=\"data row18 col2\" >0</td>\n",
       "      <td id=\"T_75ac6_row18_col3\" class=\"data row18 col3\" >0.000000</td>\n",
       "      <td id=\"T_75ac6_row18_col4\" class=\"data row18 col4\" >103.053435</td>\n",
       "      <td id=\"T_75ac6_row18_col5\" class=\"data row18 col5\" >0.000000</td>\n",
       "      <td id=\"T_75ac6_row18_col6\" class=\"data row18 col6\" >0.990898</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_75ac6_row19_col0\" class=\"data row19 col0\" >0</td>\n",
       "      <td id=\"T_75ac6_row19_col1\" class=\"data row19 col1\" >9</td>\n",
       "      <td id=\"T_75ac6_row19_col2\" class=\"data row19 col2\" >0</td>\n",
       "      <td id=\"T_75ac6_row19_col3\" class=\"data row19 col3\" >0.000000</td>\n",
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       "      <td id=\"T_75ac6_row19_col5\" class=\"data row19 col5\" >0.000000</td>\n",
       "      <td id=\"T_75ac6_row19_col6\" class=\"data row19 col6\" >0.990898</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_75ac6_row20_col0\" class=\"data row20 col0\" >0</td>\n",
       "      <td id=\"T_75ac6_row20_col1\" class=\"data row20 col1\" >9</td>\n",
       "      <td id=\"T_75ac6_row20_col2\" class=\"data row20 col2\" >0</td>\n",
       "      <td id=\"T_75ac6_row20_col3\" class=\"data row20 col3\" >0.000000</td>\n",
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       "      <td id=\"T_75ac6_row20_col5\" class=\"data row20 col5\" >0.000000</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_75ac6_row21_col0\" class=\"data row21 col0\" >0</td>\n",
       "      <td id=\"T_75ac6_row21_col1\" class=\"data row21 col1\" >9</td>\n",
       "      <td id=\"T_75ac6_row21_col2\" class=\"data row21 col2\" >0</td>\n",
       "      <td id=\"T_75ac6_row21_col3\" class=\"data row21 col3\" >0.000000</td>\n",
       "      <td id=\"T_75ac6_row21_col4\" class=\"data row21 col4\" >103.053435</td>\n",
       "      <td id=\"T_75ac6_row21_col5\" class=\"data row21 col5\" >0.000000</td>\n",
       "      <td id=\"T_75ac6_row21_col6\" class=\"data row21 col6\" >0.990898</td>\n",
       "      <td id=\"T_75ac6_row21_col7\" class=\"data row21 col7\" >67.612148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_75ac6_row22_col0\" class=\"data row22 col0\" >0</td>\n",
       "      <td id=\"T_75ac6_row22_col1\" class=\"data row22 col1\" >9</td>\n",
       "      <td id=\"T_75ac6_row22_col2\" class=\"data row22 col2\" >0</td>\n",
       "      <td id=\"T_75ac6_row22_col3\" class=\"data row22 col3\" >0.000000</td>\n",
       "      <td id=\"T_75ac6_row22_col4\" class=\"data row22 col4\" >103.053435</td>\n",
       "      <td id=\"T_75ac6_row22_col5\" class=\"data row22 col5\" >0.000000</td>\n",
       "      <td id=\"T_75ac6_row22_col6\" class=\"data row22 col6\" >0.990898</td>\n",
       "      <td id=\"T_75ac6_row22_col7\" class=\"data row22 col7\" >67.612148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_75ac6_row23_col0\" class=\"data row23 col0\" >0</td>\n",
       "      <td id=\"T_75ac6_row23_col1\" class=\"data row23 col1\" >9</td>\n",
       "      <td id=\"T_75ac6_row23_col2\" class=\"data row23 col2\" >3</td>\n",
       "      <td id=\"T_75ac6_row23_col3\" class=\"data row23 col3\" >0.000000</td>\n",
       "      <td id=\"T_75ac6_row23_col4\" class=\"data row23 col4\" >103.053435</td>\n",
       "      <td id=\"T_75ac6_row23_col5\" class=\"data row23 col5\" >1.145038</td>\n",
       "      <td id=\"T_75ac6_row23_col6\" class=\"data row23 col6\" >1.002087</td>\n",
       "      <td id=\"T_75ac6_row23_col7\" class=\"data row23 col7\" >68.560575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1134e3650>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "df['sol0']= np.vstack(best_solss)[:,0]\n",
    "df['sol1']= np.vstack(best_solss)[:,1]\n",
    "df['sol2']= np.vstack(best_solss)[:,2]\n",
    "\n",
    "df['sol0 perent']= 100*pd.Series(np.divide(np.vstack(best_solss)[:, 0]* power_list[0], Longer_list_arr))\n",
    "df['sol1 percent']= 100*pd.Series(np.divide(np.vstack(best_solss)[:, 1]* power_list[1], Longer_list_arr))\n",
    "df['sol2 percent']= 100*pd.Series(np.divide(np.vstack(best_solss)[:, 2]* power_list[2], Longer_list_arr))\n",
    "df['CF']=pd.Series(CFSS)\n",
    "df['LCOE']=pd.Series(lcoess)\n",
    "\n",
    "\n",
    "df.style.hide(axis=\"index\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 2\n"
     ]
    }
   ],
   "source": [
    "def initial_pop_reactors(power_list, sol_per_pop, possible_solutions):\n",
    "    \"\"\"\n",
    "    This function initializes a population of potential solutions for the reactor mix optimization problem.\n",
    "\n",
    "    Parameters:\n",
    "    - power_list (list): A list of possible reactor capacities.\n",
    "    - sol_per_pop (int): The number of potential solutions per population.\n",
    "    - possible_solutions (list): A list of upper limits for each reactor capacity.\n",
    "\n",
    "    Returns:\n",
    "    - pop (list): A list of potential solutions, where each sub-list represents a solution and contains integers representing the number of each reactor type.\n",
    "    \"\"\"\n",
    "    print(sol_per_pop, len(power_list))\n",
    "    pop = np.zeros((sol_per_pop, len(power_list)))\n",
    "   \n",
    "    for i in range(len(power_list)):\n",
    "        temp = np.random.randint(0, max(possible_solutions[i]) + 1, size=sol_per_pop).tolist()\n",
    "        pop[:, i] = temp\n",
    "    return pop.tolist()\n",
    "\n",
    "\n",
    "def gene_upper_limit(power_list, demand): # limiting the maximum number of each type of reactors\n",
    "\n",
    "    upper_limit_list = []\n",
    " \n",
    "    for i in range(len(power_list )):\n",
    "        \n",
    "        # I use the multiplier 1.05 because of the ratio between refueling duration and operational lifetime is will nnot be higher than 1.05\n",
    "        upper_limit =range(int(np.ceil(1.05*demand/power_list[i]))+2) ## TODO check the +1\n",
    "        upper_limit_list.append(upper_limit )\n",
    "\n",
    "    return  upper_limit_list\n",
    "\n",
    "\n",
    "\n",
    "demand = 310\n",
    "power_list = [300, 10]\n",
    "sol_per_pop = 5\n",
    "possible_solutions = gene_upper_limit(power_list, demand) # gene space\n",
    "possible_solutions\n",
    "initial_pop  = initial_pop_reactors(power_list,  sol_per_pop, possible_solutions)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LCOE Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def LCOE_similar_or_mixed(power_list, demand, potential_solution, interest_rate , levelization_period_weeks):\n",
    "    if np.count_nonzero(potential_solution )  == 0:\n",
    "        print(\"ERROR!!!\")\n",
    "        \n",
    "    elif np.count_nonzero(potential_solution )  == 0:\n",
    "        print(\"ERROR!!!\")    \n",
    "         \n",
    "    elif np.count_nonzero(potential_solution ) >1: # mixed reactors:\n",
    "        power_list_modified = [power_list [i] for i in range(len(potential_solution)) if potential_solution[i] != 0]\n",
    "        Num_of_each_reactor_type_modified = [x for x in potential_solution if x != 0]\n",
    "\n",
    "\n",
    "        long_list_power = repeat_elements(power_list_modified, Num_of_each_reactor_type_modified)\n",
    "\n",
    "\n",
    "\n",
    "        capacity_factor_results =   capacity_factor_weeks_approach_mix_reactors(long_list_power  ,levelization_period_weeks, demand)\n",
    "        MWh_generated_per_year_per_demand_list = capacity_factor_results[4]\n",
    "        MWh_excess_per_year_list =     capacity_factor_results[5]\n",
    "        Tot_OM__cost_per_year_list =  capacity_factor_results[6]\n",
    "\n",
    "        capacity_factor_t_min = min(capacity_factor_results[1])\n",
    "        output_lcoe = level_cost_of_energy_reactor_mix_starting_from_BOAK( interest_rate, power_list_modified, Num_of_each_reactor_type_modified,\\\n",
    "                    MWh_generated_per_year_per_demand_list, MWh_excess_per_year_list, 0,\\\n",
    "                        Tot_OM__cost_per_year_list)\n",
    "        return output_lcoe, capacity_factor_t_min\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Schedule Generation :  1 [  0   3   6   9  12  15  19  22  25  28  31  35  38  41  44  47  50  54\n",
      "  57  60  63  66  70  73  76  79  82  85  89  92  95  98 101 105 108 111\n",
      " 114 117 120 124 127 130 133 136 140 143 146 149 152 155 159 162 165 168\n",
      " 171 175] 0.003115264797507788\n",
      "1 [[  0   3   6 ... 168 171 175]\n",
      " [  0   3   6 ... 168 171 175]\n",
      " [  0   3   6 ... 168 171 175]\n",
      " ...\n",
      " [  0   3   6 ... 168 171 175]\n",
      " [  0   3   6 ... 168 171 175]\n",
      " [  0   3   6 ... 168 171 175]]\n",
      "Schedule Generation :  2 [  0   3   6   9  12  15  19  22  25  28  31  35  38  41  44  47  50  54\n",
      "  57  60  63  66  70  73  76  79  82  85  88  91  95  98 101 105 108 111\n",
      " 114 117 120 124 127 130 133 136 140 143 146 149 152 155 159 162 165 168\n",
      " 171 175] 0.003115264797507788\n",
      "2 [[  0   3   6 ... 168 171 175]\n",
      " [  0   3   6 ... 168 171 175]\n",
      " [  0   3   6 ... 168 171 175]\n",
      " ...\n",
      " [  0   2   6 ... 168 171 175]\n",
      " [  0   3   5 ... 168 171 175]\n",
      " [  0   3   6 ... 168 171 175]]\n",
      "Schedule Generation :  3 [  0   3   6   9  12  15  19  22  25  28  31  35  38  40  44  47  50  54\n",
      "  57  60  63  65  70  73  76  79  82  85  89  92  95  98 101 105 108 111\n",
      " 114 117 120 123 127 130 133 136 140 143 146 149 152 154 159 162 165 168\n",
      " 171 175] 0.003115264797507788\n",
      "3 [[  0   3   6 ... 168 171 175]\n",
      " [  0   3   5 ... 168 171 175]\n",
      " [  0   2   6 ... 168 171 175]\n",
      " ...\n",
      " [  0   2   6 ... 168 171 175]\n",
      " [  0   3   6 ... 168 171 175]\n",
      " [  0   3   6 ... 168 171 175]]\n",
      "Schedule Generation :  4 [  0   3   6   9  12  15  19  22  25  28  31  35  38  41  44  47  50  54\n",
      "  57  60  62  66  70  73  76  78  82  84  89  92  95  98 101 105 108 111\n",
      " 114 117 120 124 127 130 133 135 140 143 146 149 151 155 159 162 164 168\n",
      " 171 175] 0.003115264797507788\n",
      "4 [[  0   3   6 ... 168 171 175]\n",
      " [  0   3   6 ... 168 171 175]\n",
      " [  0   2   6 ... 168 171 175]\n",
      " ...\n",
      " [  0   3   6 ... 168 171 175]\n",
      " [  0   3   6 ... 168 171 175]\n",
      " [  0   3   6 ... 168 170 175]]\n",
      "Schedule Generation :  5 [  0   3   6   9  12  15  19  22  25  28  31  35  37  41  44  47  50  53\n",
      "  57  60  63  66  70  73  76  79  82  85  89  92  95  98 100 105 108 111\n",
      " 114 117 120 124 127 130 132 136 140 143 145 149 152 155 159 161 165 168\n",
      " 170 175] 0.003115264797507788\n",
      "5 [[  0   3   6 ... 168 170 175]\n",
      " [  0   3   6 ... 168 171 175]\n",
      " [  0   3   6 ... 168 171 175]\n",
      " ...\n",
      " [  0   3   6 ... 167 171 175]\n",
      " [  0   2   5 ... 168 170 175]\n",
      " [  0   3   6 ... 168 171 175]]\n",
      "Nu of Gs passed is 5\n",
      "\n",
      " The difference between prediction and desired output is 320 : 2000 vs. 1680 Total MW\n",
      "\n",
      "It takes 3.36 years to startup up all the reactors\n",
      "\n",
      "\n",
      "\n",
      " The schedule optimizatation ends\n"
     ]
    }
   ],
   "source": [
    "demand = 1500\n",
    "potential_solution = [1, 5, 50]\n",
    "power_list = [1000, 300, 10]\n",
    "interest_rate = 0.06\n",
    "levelization_period_weeks = int(40*365/7)\n",
    "results = LCOE_similar_or_mixed(power_list, demand, potential_solution, interest_rate , levelization_period_weeks)"
   ]
  }
 ],
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