def run_example(config):
# misc.
datetime_index = pd.date_range(config['date_from'],
config['date_to'],
freq='60min')
# model creation and solving
logging.info('Starting optimization')
es = EnergySystem(timeindex=datetime_index)
NodesFromCSV(file_nodes_flows=os.path.join(config['scenario_path'],
config['nodes_flows']),
file_nodes_flows_sequences=os.path.join(
config['scenario_path'], config['nodes_flows_sequences']),
delimiter=',')
om = OperationalModel(es)
om.receive_duals()
om.solve(solver=config['solver'], solve_kwargs={'tee': config['verbose']})
logging.info('Done! \n Check the results')
# create pandas dataframe with results
results = ResultsDataFrame(energy_system=es)
rdict = {'objective': es.results.objective, 'time_series': results}
return rdict
def run_example(config):
# misc.
datetime_index = pd.date_range(config['date_from'],
config['date_to'],
freq='60min')
# model creation and solving
logging.info('Starting optimization')
es = EnergySystem(timeindex=datetime_index)
NodesFromCSV(file_nodes_flows=os.path.join(
config['scenario_path'],
config['nodes_flows']),
file_nodes_flows_sequences=os.path.join(
config['scenario_path'],
config['nodes_flows_sequences']),
delimiter=',')
om = OperationalModel(es)
om.receive_duals()
om.solve(solver=config['solver'], solve_kwargs={'tee': config['verbose']})
logging.info('Done! \n Check the results')
# create pandas dataframe with results
results = ResultsDataFrame(energy_system=es)
rdict = {
'objective': es.results.objective,
'time_series': results
}
return rdict
def run_investment_example(solver='cbc', verbose=True, nologg=False):
if not nologg:
logger.define_logging()
# %% model creation and solving
date_from = '2050-01-01 00:00:00'
date_to = '2050-01-01 23:00:00'
datetime_index = pd.date_range(date_from, date_to, freq='60min')
es = EnergySystem(timeindex=datetime_index)
data_path = os.path.join(os.path.dirname(__file__), 'data')
NodesFromCSV(file_nodes_flows=os.path.join(data_path, 'nodes_flows.csv'),
file_nodes_flows_sequences=os.path.join(
data_path, 'nodes_flows_seq.csv'),
delimiter=',')
stopwatch()
om = OperationalModel(es)
logging.info('OM creation time: ' + stopwatch())
om.receive_duals()
om.solve(solver=solver, solve_kwargs={'tee': verbose})
logging.info('Optimization time: ' + stopwatch())
results = ResultsDataFrame(energy_system=es)
results_path = os.path.join(os.path.expanduser("~"), 'csv_invest')
if not os.path.isdir(results_path):
os.mkdir(results_path)
results.to_csv(os.path.join(results_path, 'results.csv'))
logging.info("The results can be found in {0}".format(results_path))
logging.info("Read the documentation (outputlib) to learn how" +
" to process the results.")
logging.info("Or search the web to learn how to handle a MultiIndex" +
"DataFrame with pandas.")
logging.info('Done!')
def run_example(config):
# creation of an hourly datetime_index
datetime_index = pd.date_range(config['date_from'],
config['date_to'],
freq='60min')
# model creation and solving
logging.info('Starting optimization')
# initialisation of the energy system
es = EnergySystem(timeindex=datetime_index)
# adding all nodes and flows to the energy system
# (data taken from csv-file)
NodesFromCSV(file_nodes_flows=os.path.join(config['scenario_path'],
config['nodes_flows']),
file_nodes_flows_sequences=os.path.join(
config['scenario_path'], config['nodes_flows_sequences']),
delimiter=',')
# creation of a least cost model from the energy system
om = OperationalModel(es)
om.receive_duals()
# solving the linear problem using the given solver
om.solve(solver=config['solver'], solve_kwargs={'tee': config['verbose']})
logging.info("Done!")
# create pandas dataframe with results
results = ResultsDataFrame(energy_system=es)
# write results for selected busses to single csv files
results.bus_balance_to_csv(bus_labels=['R1_bus_el', 'R2_bus_el'],
output_path=config['results_path'])
logging.info("The results can be found in {0}".format(
config['results_path']))
logging.info("Read the documentation (outputlib) to learn how" +
" to process the results.")
rdict = {'objective': es.results.objective, 'time_series': results}
return rdict
def simulate(es=None, **arguments):
"""Creates the optimization model, solves it and writes back results to
energy system object
Parameters
----------
es : :class:`oemof.solph.network.EnergySystem` object
Energy system holding nodes, grouping functions and other important
information.
**arguments : key word arguments
Arguments passed from command line
"""
om = OperationalModel(es)
logging.info('OM creation time: ' + stopwatch())
om.receive_duals()
om.solve(solver=arguments['--solver'], solve_kwargs={'tee': True})
logging.info('Optimization time: ' + stopwatch())
return om
if read_only:
logging.info("Reading scenario tables.")
else:
create_objects_from_dataframe_collection()
logging.info("Creating nodes.")
de21.create_nodes()
logging.info("Creating OperationalModel")
om = OperationalModel(de21)
logging.info('OM created. Starting optimisation using {0}'.format(solver))
om.receive_duals()
om.solve(solver=solver, solve_kwargs={'tee': True})
logging.info('Optimisation done.')
results = ResultsDataFrame(energy_system=de21)
if not os.path.isdir('results'):
os.mkdir('results')
date = '2017_03_21'
file_name = ('scenario_' + de21.name + date + '_' + 'results_complete.csv')
results_path = 'results'
results.to_csv(os.path.join(results_path, file_name))
