if goodIO == 1:
cur.execute(
'''SELECT Area, GD2016, ED2016, Age FROM Stores Where id= {vn1}'''.format(
vn1=id_store))
Index = cur.fetchall()
if not Index:
pass
else:
SurfaceArea = np.array([elt[0] for elt in Index])
Gas = np.array([elt[1] for elt in Index])
Ele = np.array([elt[2] for elt in Index])
Store_age = np.array([elt[3] for elt in Index])
solution = pb.CHPproblem(id_store).SimpleOpti5NPV(mod = [1.195,1,1,1], ECA_value = 0.26, table_string = 'Utility_Prices_Aitor _NoGasCCL')
payback.append(solution[4][1])
carbon_savings.append(solution[5][2])
Capex.append(solution[4][5])
cum_disc_cashflow.append(solution[4][4])
CHP_size.extend(list(map(int, re.findall('\d+', solution[1]))))
financial_savings.append(solution[4][0])
h2p.append(Gas[0]/Ele[0])
Area.append(SurfaceArea[0])
Ele_demand.append(Ele[0])
Gas_demand.append(Gas[0])
Age.append(Store_age[0])
MAC = -1*np.array(cum_disc_cashflow)/abs(np.array(carbon_savings))
#Inputs =======================================================================
PV_pb.gas_price = p_gas_mod * init_p_gas
#calculate solution, extract opex savings, carbon savings and electricity production
PV_solution = PV_pb.SimulatePVonAllRoof(1, n_panels)
PV_opex = PV_solution[1]
PV_Carbon = PV_solution[4]
PV_prod = PV_solution[6]
CHP_capex_array = []
CHP_size_array = []
for tech_id in range(1, 21):
cur.execute('''SELECT * FROM Technologies WHERE id=?''', (tech_id, ))
dummy = cur.fetchall()
CHP_tech_size = (list(map(int, re.findall('\d+', dummy[0][1]))))
CHP_tech_price = (dummy[0][2])
CHP_pb = BBC.CHPproblem(id_store)
CHP_pb.store.d_ele = abs(init_d_ele - PV_prod)
CHP_solution = CHP_pb.SimpleOpti5NPV(tech_range=[tech_id],
mod=[p_elec_mod, p_gas_mod, 1, 1],
ECA_value=0.26)
CHP_opex = CHP_solution[4][0]
CHP_Carbon = CHP_solution[5][2]
PV_array.append(n_panels)
CHP_array.extend(CHP_tech_size)
Capex_array.append(CHP_tech_price + PV_capex)
OPEX_array.append(CHP_opex + PV_opex)
Carbon_array.append((PV_Carbon + CHP_Carbon))
Cum_disc_cash_flow_CHP = []
Carbon_PV =[]
Carbon_CHP =[]
Cum_disc_cash_flow_south=[]
Carbon_PV_south=[]
Cum_disc_cash_flow_central=[]
Carbon_PV_central=[]
Cum_disc_cash_flow_north=[]
Carbon_PV_north=[]
for store_id in Store_id_range:
a = pb.PVproblem(store_id)
a.elec_price = a.store.p_ele*11.9/9.87
[best_tech, opti_savings, opti_capex,opti_ele_prod,opti_panel,opti_carbon,Cum_disc_cash_flow] = a.OptiPVpanels()
b=BBC.CHPproblem(store_id)
b.store.p_ele =b.store.p_ele*11.9/9.87
[opti_tech, opti_tech_name, opti_CHPQI, opti_part_load, financials, carbon, year_BAU_cost] =b.SimpleOpti5NPV(ECA_value = 0.26)
Area = a.store.area
Cum_disc_cash_flow_PV.append(Cum_disc_cash_flow)
Cum_disc_cash_flow_CHP.append(financials[-2])
Carbon_PV.append(opti_carbon)
Carbon_CHP.append(carbon[2])
if Area <25000:
Cum_disc_cash_flow_PV_cat_1.append(Cum_disc_cash_flow)
Carbon_PV_1.append(opti_carbon)
Cum_disc_cash_flow_CHP_cat_1.append(financials[-2])
Carbon_CHP_1.append(carbon[2])
PV_mod = np.power(1+capex_reduction_PV, np.linspace(year_start,year_stop, time_window+1)[0:-1] - year_start)
cf_mod = np.power(1+cf_decrease, np.linspace(year_start,year_stop, time_window+1)[0:-1] - year_start)
BAU_op_cost = []
BAU_carbon = []
time = []
store_range = []
for n in range(0,time_window):
print('Time window:%d' %year[n])
for store_id in Store_id_range[:stores]:
print('store:%d' %store_id)
time.append(year[n])
store_range.append(store_id)
solution = BBC.CHPproblem(store_id).SimpleOpti5NPV(tech_range=[1],mod=[p_elec_mod[n],p_gas_mod[n],1,1],ECA_value = 0.26)
BAU_op_cost.append(solution[6])
BAU_carbon.append(solution[5][0]*cf_mod[n])
workbook=xlsxwriter.Workbook(scenario_names[scen]+' BAU.xlsx')
worksheet=workbook.add_worksheet()
headers = ['time','store','BAU Cost','BAU carbon']
row = 0
col = 0
for h in headers:
worksheet.write(row,col,h)
col +=1
import re
import matplotlib.pyplot as plt
biometh_CF= 0.00039546
gas_CF = 0.18416
ele_CF = 0.35156
database_path = "Sainsburys.sqlite"
conn = sqlite3.connect(database_path)
cur = conn.cursor()
conn.commit()
cur.execute('''SELECT * FROM Technologies''')
dummy_CHP = cur.fetchall()
sol=BBC.CHPproblem(51)
BAU_gas = []
BAU_ele =[]
biomethane_usage = []
BAU_carbon = []
savings_range =[]
CHP_tech_size_range =[]
for tech in range(1,21):
if tech ==20:
CHP_tech_size = [0]
savings = 0
else:
CHP_tech_size =(list(map(int, re.findall('\d+', dummy_CHP[tech][1]))))
savings = sol.SimpleOpti5NPV(tech_range=[tech])[5][0]-(sol.SimpleOpti5NPV(tech_range=[tech])[5][3]*gas_CF/1000+sol.SimpleOpti5NPV(tech_range=[tech])[5][4]*ele_CF/1000)
CHP_tech_size_range.append(CHP_tech_size)
# -*- coding: utf-8 -*- """ Created on Tue Jan 30 13:04:55 2018 @author: nl211 """ import sqlite3 import Common.classStore as st import Common.classTech as tc import Solvers.classCHPProblemnew as pb import numpy as np import matplotlib.pyplot as plt import datetime import pandas as pd store_index = 507 #Leicester North p_id = pb.CHPproblem(store_index) #default_initial_time = datetime.datetime(2016,1,1) #default_final_time = datetime.datetime(2017,1,1) #time_start= int((default_initial_time-datetime.datetime(1970,1,1)).total_seconds()/60/30) #time_stop= int((default_final_time-datetime.datetime(1970,1,1)).total_seconds()/60/30) p_id.store.getWeatherData(p_id.time_start, p_id.time_stop) plt.plot(p_id.store.irr)