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setup_solve_model.py
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setup_solve_model.py
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"""
oemof application for research project quarree100.
Copyright (c) 2018 Quarree100 AB-3 Project-Team
SPDX-License-Identifier: GPL-3.0-or-later
"""
from oemof.tools import logger
from oemof.tools import economics
import oemof.solph as solph
import oemof.outputlib as outputlib
import logging
import pandas as pd
def nodes_from_excel(filename):
xls = pd.ExcelFile(filename)
nodes_data = {'buses': xls.parse('Buses'),
'commodity_sources': xls.parse('Sources'),
'sources_series': xls.parse('Sources_series'),
'demand': xls.parse('Demand'),
'transformers_siso': xls.parse('Transformer_siso'),
'transformers_sido': xls.parse('Transformer_sido'),
'storages': xls.parse('Storages'),
'timeseries': xls.parse('Timeseries'),
'general': xls.parse('General')
}
# set datetime index
nodes_data['timeseries'].set_index('timestamp', inplace=True)
nodes_data['timeseries'].index = pd.to_datetime(
nodes_data['timeseries'].index)
print('Data from Excel file {} imported.'
.format(filename))
return nodes_data
def create_nodes(nd=None):
"""Create nodes (oemof objects) from node dict
Parameters
----------
nd : :obj:`dict`
Nodes data
Returns
-------
nodes : `obj`:dict of :class:`nodes <oemof.network.Node>`
"""
if not nd:
raise ValueError('No nodes data provided.')
nodes = []
# Create Bus objects from buses table
busd = {}
for i, b in nd['buses'].iterrows():
if b['active']:
bus = solph.Bus(label=b['label'])
nodes.append(bus)
busd[b['label']] = bus
if b['excess']:
nodes.append(
solph.Sink(label=b['label'] + '_excess',
inputs={busd[b['label']]: solph.Flow(
variable_costs=b['excess costs'])})
)
if b['shortage']:
nodes.append(
solph.Source(label=b['label'] + '_shortage',
outputs={busd[b['label']]: solph.Flow(
variable_costs=b['shortage costs'])})
)
# Create Source objects from table 'Sources'
for i, cs in nd['commodity_sources'].iterrows():
if cs['active']:
nodes.append(
solph.Source(label=cs['label'],
outputs={busd[cs['to']]: solph.Flow(
variable_costs=cs['variable costs'],
emission=cs['emissions'])})
)
# Create Source objects with fixed time series from 'renewables' table
for i, ss in nd['sources_series'].iterrows():
if ss['active']:
# set static outflow values
outflow_args = {'nominal_value': ss['scalingfactor'],
'fixed': True}
# get time series for node and parameter
for col in nd['timeseries'].columns.values:
if col.split('.')[0] == ss['label']:
outflow_args[col.split('.')[1]] = nd['timeseries'][col]
# create
nodes.append(
solph.Source(label=ss['label'],
outputs={
busd[ss['to']]: solph.Flow(**outflow_args)})
)
# Create Sink objects with fixed time series from 'demand' table
for i, de in nd['demand'].iterrows():
if de['active']:
# set static inflow values
inflow_args = {'nominal_value': de['scalingfactor'],
'fixed': de['fixed']}
# get time series for node and parameter
for col in nd['timeseries'].columns.values:
if col.split('.')[0] == de['label']:
inflow_args[col.split('.')[1]] = nd['timeseries'][col]
# create
nodes.append(
solph.Sink(label=de['label'],
inputs={
busd[de['from']]: solph.Flow(**inflow_args)})
)
# Create Transformer objects from 'transformers' table
for i, t in nd['transformers_siso'].iterrows():
if t['active']:
# calculation epc
epc_t = economics.annuity(
capex=t['capex'], n=t['n'],
wacc=nd['general']['interest rate'][0]) * \
nd['general']['timesteps'][0] / 8760
# create
nodes.append(
solph.Transformer(
label=t['label'],
inputs={busd[t['from']]: solph.Flow()},
outputs={busd[t['to']]: solph.Flow(
variable_costs=t['variable costs'],
emissions=['emissions'],
investment=solph.Investment(
ep_costs=epc_t))},
conversion_factors={busd[t['to']]: t['efficiency']})
)
for i, tdo in nd['transformers_sido'].iterrows():
if tdo['active']:
# calculation epc
epc_tdo = economics.annuity(
capex=tdo['capex'], n=tdo['n'],
wacc=nd['general']['interest rate'][0]) *\
nd['general']['timesteps'][0] / 8760
# create
nodes.append(
solph.Transformer(
label=tdo['label'],
inputs={busd[tdo['from']]: solph.Flow()},
outputs={busd[tdo['to_1']]: solph.Flow(
investment=solph.Investment(ep_costs=epc_tdo)),
busd[tdo['to_2']]: solph.Flow()
},
conversion_factors={
busd[tdo['to_1']]: tdo['efficiency_1'],
busd[tdo['to_2']]: tdo['efficiency_2']
})
)
for i, s in nd['storages'].iterrows():
if s['active']:
# calculate epc
epc_s = economics.annuity(
capex=s['capex'], n=s['n'],
wacc=nd['general']['interest rate'][0]) * \
nd['general']['timesteps'][0] / 8760
# create Storages
nodes.append(
solph.components.GenericStorage(
label=s['label'],
inputs={busd[s['bus']]: solph.Flow()},
outputs={busd[s['bus']]: solph.Flow()},
capacity_loss=s['capacity_loss'],
invest_relation_input_capacity=s[
'invest_relation_input_capacity'],
invest_relation_output_capacity=s[
'invest_relation_output_capacity'],
inflow_conversion_factor=s['inflow_conversion_factor'],
outflow_conversion_factor=s['outflow_conversion_factor'],
investment=solph.Investment(ep_costs=epc_s)))
return nodes
def setup_es(excel_nodes=None):
# Initialise the Energy System
logger.define_logging()
logging.info('Initialize the energy system')
number_timesteps = excel_nodes['general']['timesteps'][0]
date_time_index = pd.date_range('1/1/2016',
periods=number_timesteps,
freq='H')
energysystem = solph.EnergySystem(timeindex=date_time_index)
logging.info('Create oemof objects')
# create nodes from Excel sheet data with create_nodes function
my_nodes = create_nodes(nd=excel_nodes)
# add nodes and flows to energy system
energysystem.add(*my_nodes)
print('Energysystem has been created')
print("*********************************************************")
print("The following objects have been created from excel sheet:")
for n in energysystem.nodes:
oobj =\
str(type(n)).replace("<class 'oemof.solph.", "").replace("'>", "")
print(oobj + ':', n.label)
print("*********************************************************")
return energysystem
def solve_es(energysystem=None, excel_nodes=None):
# Optimise the energy system
logging.info('Optimise the energy system')
# initialise the operational model
om = solph.Model(energysystem)
# Global CONSTRAINTS: CO2 Limit
solph.constraints.emission_limit(
om, flows=None, limit=excel_nodes['general']['emission limit'][0])
logging.info('Solve the optimization problem')
# if tee_switch is true solver messages will be displayed
om.solve(solver='cbc', solve_kwargs={'tee': True})
logging.info('Store the energy system with the results.')
# processing results
result = outputlib.processing.results(om)
return result
def create_comp_lists(es=None):
l_buses = []
l_storages = []
l_transformer = []
for n in es.nodes:
type_name = str(type(n)).replace("<class 'oemof.solph.", "").replace("'>", "")
if type_name == "network.Bus":
l_buses.append(n.label)
if type_name == "network.Transformer":
l_transformer.append(n.label)
if type_name == "components.GenericStorage":
l_storages.append(n.label)
comp_dict = {'buses': l_buses,
'transformer': l_transformer,
'storages': l_storages
}
return comp_dict
# # df_invest_ges = pd.DataFrame([[
# # p_chp_gas, p_chp_H2, p_electrolysis_pem, p_boiler_gas, p_heatpump_el,
# # c_storage_elec, c_storage_heat, c_storgae_H2
# # ]],
# # columns=['p_chp_gas', 'p_chp_H2', 'p_electrolysis_pem',
# # 'p_boiler_gas', 'p_heatpump_el', 'c_storage_elec',
# # 'c_storage_heat', 'c_storgae_H2'])
#
# # the result_gesamt df is exported in excel
# path_to_results = os.path.join(os.path.expanduser("~"),
# cfg.get('paths', 'results'))
# # filename = 'results.xlsx'
# # with pd.ExcelWriter(os.path.join(path_to_results, filename)) as xls:
# # df_ges.to_excel(xls, sheet_name='Timeseries')
# # df_invest_ges.to_excel(xls, sheet_name='Invest')
#