def test_set_log_level(self):
# We assign a handler to the Calliope logger on loading calliope
assert calliope._logger.hasHandlers() is True
assert len(calliope._logger.handlers) == 1
for level in ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL']:
calliope.set_log_level(level)
assert calliope._logger.level == getattr(logging, level)
# We have a custom level 'SOLVER' at level 19
calliope.set_log_level('SOLVER')
assert calliope._logger.level == 19
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 1 11:12:58 2020
@author: Mohammad Amin Tahavori
"""
import time
start = time.time()
import calliope
import pickle
try:
calliope.set_log_level('INFO')
except:
calliope.set_log_verbosity('INFO')
model = calliope.Model('model.yaml')
model.run()
end=time.time()
print(str(end-start))
#%%
import cal_graph as CG
amin=CG.C_Graph(model=model,ex_path=r'Graph_inputs.xlsx',unit='kW')
# with open('cof_4n', 'wb') as config_dictionary_file:
# pickle.dump(amin,config_dictionary_file)
#%%
amin.system_pie(kind='absolute',unit='GWh',v_round=1,title_font=40)
#%%
amin.node_pie(kind='absolute',unit='GWh',v_round=1,title_font=40)
#%%
amin.node_pie(kind='absolute',unit='GWh',v_round=1,title_font=40,rational='consumption')
def calliope_x_GISEle(time_span, c, cost_km_line, project_life):
# setting the path for calliope
_PATHS = {'GISEle': os.path.join(os.path.dirname(__file__), '..', 'Input', '7_sizing_calliope')}
# calling and running the Model
calliope.set_log_level('INFO')
model = calliope.Model(os.path.join(_PATHS['GISEle'], 'model.yaml'))
print('ciao')
model.run()
print("Model runned")
#os.chdir(r'..//Output//Sizing_calliope')
os.chdir(r'Output//Sizing_calliope')
'Saving results'
model.to_csv("GISEle_results_" + str(c))
model.plot.summary(to_file='gisele' + str(c) + '.html')
'Extracting all technologies'
os.chdir("GISEle_results_" + str(c))
inputs_techs = pd.read_csv('inputs_colors.csv', header=None, index_col=0)
technologies = inputs_techs.index.values
technologies = technologies[technologies != 'Demand']
print(technologies)
'Finding lifetimes'
title = 'Lifetime [years]'
inputs_lifetime = pd.read_csv('inputs_lifetime.csv', header=None, index_col=1)
lifetimes = pd.DataFrame(index=technologies, columns=[title])
for tech_given in inputs_lifetime.index.values:
if tech_given in technologies:
life = inputs_lifetime.loc[str(tech_given), 2]
lifetimes.at[tech_given, title] = life
print(lifetimes)
'Finding which of the technologies is being used in the results and their corresponding capacities'
title = 'Capacity [kW]'
results_energy_cap = pd.read_csv('results_energy_cap.csv', header=None, index_col=1)
capacities = pd.DataFrame(index=technologies, columns=[title])
for tech_given in inputs_lifetime.index.values:
if tech_given in technologies:
cap = results_energy_cap.loc[str(tech_given), 2]
capacities.at[tech_given, title] = cap
print(capacities)
'Finding which of the STORAGE technologies is being used in the results and their corresponding capacities'
title = 'Capacity [kWh]'
results_storage_cap = pd.read_csv('results_storage_cap.csv', header=None, index_col=1)
storage_cap = []
storage_used = []
for tech_used in results_storage_cap.index.values:
if tech_used in technologies:
co_sto = float(results_storage_cap.loc[str(tech_used), 2])
storage_cap.append(round(co_sto, 2))
storage_used.append(tech_used)
sto_capacities = pd.DataFrame(index=storage_used, data=storage_cap, columns=[title])
print(sto_capacities)
'Creating the costs table for all components over the time'
costs_rows = []
for tech in technologies:
costs_rows.append("CAPEX_" + str(tech))
costs_rows.append("OPEX_" + str(tech))
undiscounted_costs = pd.DataFrame(columns=time_span, index=costs_rows)
print(undiscounted_costs)
'Extracting specific costs €/kW'
title = 'Specific cost [€/kW]'
inputs_cost_energy_cap = pd.read_csv('inputs_cost_energy_cap.csv', header=None, index_col=1)
specific_costs = pd.DataFrame(index=technologies, columns=[title])
for tech_used in inputs_cost_energy_cap.index.values:
if tech_used in technologies:
spec_cost = inputs_cost_energy_cap.loc[str(tech_used), 3]
specific_costs.at[tech_used, title] = spec_cost
print(specific_costs)
'Extracting specific costs €/kWh for storage only'
title = 'Specific cost [€/kWh]'
inputs_cost_storage_cap = pd.read_csv('inputs_cost_storage_cap.csv', header=None, index_col=1)
specific_cost_storage = []
storage_techs = []
# ----------------------------------------------------
'Evaluating OPEX'
os.chdir(r'..//..//..//Input//5_components')
om_costs = pd.read_csv('om_costs.csv', index_col=0, header=0)
print(om_costs)
os.chdir(r'..//..//Output//Sizing_calliope')
# ----------------------------------------------------
for tech_used in inputs_cost_storage_cap.index.values:
if tech_used in technologies:
co_sto = float(inputs_cost_storage_cap.loc[str(tech_used), 3])
specific_cost_storage.append(round(co_sto, 2))
storage_techs.append(tech_used)
specific_costs_storage = pd.DataFrame(index=storage_techs, data=specific_cost_storage, columns=[title])
print(specific_costs_storage)
# ----------------------------------------------------------------
grid_resume = pd.read_csv('grid_resume.csv', header=0, index_col=0)
resume = pd.concat([lifetimes, capacities, sto_capacities, specific_costs,
specific_costs_storage, om_costs['%_as_fraction/year'], om_costs['€/year']], axis=1, sort=False)
'Only for lines'
resume = resume.fillna(0)
resume['investment_cost_per kW [€]'] = resume['Capacity [kW]'] * resume['Specific cost [€/kW]']
resume['investment_cost_per kWh [€]'] = resume['Capacity [kWh]'] * resume['Specific cost [€/kWh]']
resume['total investment_cost [€]'] = resume['investment_cost_per kWh [€]'] + resume['investment_cost_per kW [€]']
resume.at['MV_1', 'total investment_cost [€]'] = grid_resume.loc[c, 'Grid_Cost']
resume['total_om_costs_as_fraction [€/year]'] = resume['total investment_cost [€]'] * resume[
'%_as_fraction/year'] / 100
print(resume)
resume.to_csv('resume.csv')
'Inserting CAPEX'
for capex in undiscounted_costs.index.values:
for techs in technologies:
if techs in capex:
if 'CAPEX' in capex:
investment_cost = resume.at[techs, 'total investment_cost [€]']
undiscounted_costs.at[capex, 0] = investment_cost
if resume.at[techs, 'Lifetime [years]'] != 0 and resume.at[
techs, 'Lifetime [years]'] < project_life:
n_replacements = int(project_life / resume.at[techs, 'Lifetime [years]'] - 0.5)
for n in range(1, n_replacements + 1):
undiscounted_costs.at[capex, n * resume.at[techs, 'Lifetime [years]']] = investment_cost
############ residual_value =
'Inserting OPEX'
for opex in undiscounted_costs.index.values:
for techs in technologies:
if techs in opex:
if 'OPEX' in opex:
om_cost_as_fraction = resume.loc[techs, 'total_om_costs_as_fraction [€/year]']
om_cost_fix = om_costs.loc[techs, '€/year']
total_om_cost = om_cost_as_fraction + om_cost_fix
for y in range(1, project_life):
undiscounted_costs.at[opex, y] = total_om_cost
undiscounted_costs = undiscounted_costs.fillna(0)
print(undiscounted_costs)
# discount_rate = float(input("What's the discount rate?: [%]"))
discount_rate = 10 / 100
discount_factors = []
for t in time_span:
disc = 1 / ((1 + discount_rate) ** t)
discount_factors.append(disc)
discounted_costs = undiscounted_costs * discount_factors
undiscounted_costs.to_csv('undiscounted_costs' + str(c) + '.csv')
discounted_costs.to_csv('discounted_costs' + str(c) + '.csv')
total_undiscounted_cost = undiscounted_costs.values.sum()
return total_undiscounted_cost
os.chdir(r'..//Load')