cookie_example_unfinished.py

# So the database can be saved in the location from which
# the command is called.
import os
curr_dir = os.path.dirname(__file__)

# Simulation metadata goes here
# database_filename = 'bakery_model_02.sqlite'  # where the database will be written
scenario_name = 'test_kitchen'
version = '01'
folder = 'pygen_test'
start_year = 2020  # the first year optimized by the model
end_year = 2021  # the last year optimized by the model
N_years = 2  # the number of years optimized by the model
N_seasons = 4  # the number of "seasons" in the model
N_hours = 24  # the number of hours in a day
reserve_margin = {'MyBakery':0.02}  # fraction of excess capacity to ensure reliability
discount_rate = 0.001 # The discount rate applied globally.
database_filename = f'{folder}/bakery_{scenario_name}_{version}.sqlite'  # where the database will be written


# Need Demands first
from products import NH3_DEM

NH3_DEM.add_demand(region='MyBakery',
                       init_demand=4800, # in units of COOKIES_DEM
                       start_year=start_year,
                       end_year=end_year,
                       N_years=N_years,
                       growth_rate=0.00,
                       growth_method='linear')



"""
If the desire for cookies changes throughout the year you can specify
``COOKIES_DEM.set_distribution(...)``
"""

from pantry import *
from appliances import *
from pygenesys.commodity.resource import ethos
from store import *

elc_cost = 0.1161 # cents per kwh
oven_power = 3  # kw
mixer_power = 0.325  # kw
fridge_power = 0.5  # kw


import numpy as np
n_points=24
x = np.linspace(0,24,n_points)
w = np.sin(8*x*np.pi/180)
y = np.cos(8*x*np.pi/180)
z = np.cos(8*x*np.pi/180+0.5)
demand = np.array([w,w,w,w]).flatten()
demand = demand/demand.sum()
oven_cf = np.array([y,y,y,y]).flatten()
mixer_cf = np.array([z,z,z,z]).flatten()
demand[demand<0] = 0
oven_cf[oven_cf<0] = 0
mixer_cf[mixer_cf<0] = 0

MIXER_ELC.add_regional_data(region='MyBakery',
                            input_comm=[ELC, H2],
                            output_comm=NH3,
                            efficiency=[0.25, 2], # units of X to produce 1 lb of dough
                            tech_lifetime=25,
                            capacity_factor_tech=mixer_cf,
                            cost_invest=300, # dollars per mixer
                            cost_fixed=2, # spend $2/year on cleaning supplies
                            cost_variable=mixer_power*elc_cost, # cost of dough plus tax
                            min_capacity={2020:1}
                            )


cost_of_dozen_eggs = 0.4  # 42 cents
cost_of_pound_flour = 0.5  # 50 cents
cost_of_pound_sugar = 0.6 # 59 cents
cost_of_two_sticks_butter = 2 # 2 dollars, two sticks = 1 cup


IMP_H2.add_regional_data(region='MyBakery',
                          input_comm=ethos,
                          output_comm=H2,
                          efficiency=1.00,
                          tech_lifetime=1000,
                          cost_variable=cost_of_dozen_eggs/12,
                          )

IMP_ELC.add_regional_data(region='MyBakery',
                          input_comm=ethos,
                          output_comm=ELC,
                          efficiency=1.00,
                          tech_lifetime=1000,
                          cost_variable=cost_of_dozen_eggs/12,
                          )


STORE.add_regional_data(region='MyBakery',
                        input_comm=NH3,
                        output_comm=NH3_DEM,
                        efficiency=1.0,
                        tech_lifetime=1000)


# this STAYS at the bottom
demands_list = [NH3_DEM]
resources_list = [H2, NH3, ELC, ethos]
emissions_list = [] # empty

if __name__ =='__main__':
    pass