def output_results(self, final=False):
(best_gene, best_gene_data) = self.algorithm.get_final()
if len(best_gene) > 0:
# Protect against an exception before there are any params
results = self.evaluate_results(best_gene)
if 'demand' in self.dispatch_order:
ts_demand = results['other']['demand']['ts_demand']
else:
ts_demand = self.data.get_timeseries('ts_demand')
# and print out the text strings, accompanied by the costs
strings = results['gen_desc']
costs = results['cost']
total_cost = 0.0
for gen in results['cost'].iterkeys():
info = strings[gen]
cost = costs[gen]
total_cost += cost
logger.info(gen + ' ($M {:.2f}) : '.format(cost) + info)
logger.info('Total cost ($M): {:.2f}'.format(total_cost))
else:
results = None
pickle_dict = {}
pickle_dict['best_gene_data'] = best_gene_data
pickle_dict['best_gene'] = best_gene
full_conf = self.get_full_config()
mureiloutput.clean_config_for_pickle(full_conf)
pickle_dict['config'] = full_conf
pickle_dict['best_results'] = results
pickle_dict['ts_demand'] = ts_demand
if self.config['do_plots']:
mureiloutput.plot_timeseries(results['output'], ts_demand, final)
output_file = self.config['output_file']
mureiloutput.pickle_out(pickle_dict, output_file)
return results
def output_results(self, final=False):
(best_gene, best_gene_data) = self.algorithm.get_final()
if len(best_gene) > 0:
# Protect against an exception before there are any params
results = self.evaluate_results(best_gene)
if "demand" in self.dispatch_order:
ts_demand = results["other"]["demand"]["ts_demand"]
else:
ts_demand = self.data.get_timeseries("ts_demand")
# and print out the text strings, accompanied by the costs
strings = results["gen_desc"]
costs = results["cost"]
total_cost = 0.0
for gen in results["cost"].iterkeys():
info = strings[gen]
cost = costs[gen]
total_cost += cost
logger.info(gen + " ($M {:.2f}) : ".format(cost) + info)
logger.info("Total cost ($M): {:.2f}".format(total_cost))
else:
results = None
pickle_dict = {}
pickle_dict["best_gene_data"] = best_gene_data
pickle_dict["best_gene"] = best_gene
full_conf = self.get_full_config()
mureiloutput.clean_config_for_pickle(full_conf)
pickle_dict["config"] = full_conf
pickle_dict["best_results"] = results
pickle_dict["ts_demand"] = ts_demand
if self.config["do_plots"]:
mureiloutput.plot_timeseries(results["output"], ts_demand, final)
output_file = self.config["output_file"]
mureiloutput.pickle_out(pickle_dict, output_file)
return results
def output_results(self, final=False, iteration=0):
(best_params, opt_data) = self.algorithm.get_final()
if len(best_params) > 0:
# Protect against an exception before there are any params
results = self.evaluate_results(best_params)
logger.info(
'======================================================')
logger.info(
'Total cost ($M): {:.2f}, including carbon (MT): {:.2f}, terminal value ($M): {:.2f}'
.format(results['totals']['cost'],
results['totals']['carbon'] * 1e-6,
results['totals']['terminal_value']))
logger.info(
'======================================================')
ts_demand = {}
# Now iterate across the periods, and then across the generators
for period in self.run_periods:
period_results = results['periods'][period]
logger.info(
'------------------------------------------------------')
logger.info('PERIOD ' + str(period) + ':')
logger.info(
'------------------------------------------------------')
logger.info(
'Period cost ($M): {:.2f}, carbon (MT): {:.2f}'.format(
period_results['totals']['cost'],
period_results['totals']['carbon'] * 1e-6))
if 'demand' in self.dispatch_order:
ts_demand[period] = period_results['generators']['demand'][
'other']['ts_demand']
else:
ts_demand[period] = self.data.get_timeseries('ts_demand')
period_results['totals']['demand'] = (
numpy.sum(ts_demand[period]) *
self.global_config['time_scale_up_mult'] *
self.global_config['timestep_hrs'])
logger.info('Period total demand (GWh): {:.2f}'.format(
period_results['totals']['demand'] / 1000))
for gen_type, value in period_results['generators'].iteritems(
):
gen_string = value['desc_string']
gen_cost = value['cost']
gen_supply = value['total_supply_period']
logger.info(gen_type +
' ($M {:.2f}, GWh {:.2f}) : '.format(
gen_cost, gen_supply / 1000) + gen_string)
logger.info(
'======================================================')
pickle_dict = {}
pickle_dict['opt_data'] = opt_data
pickle_dict['best_params'] = best_params
full_conf = self.get_full_config()
mureiloutput.clean_config_for_pickle(full_conf)
pickle_dict['config'] = full_conf
pickle_dict['best_results'] = results
pickle_dict['ts_demand'] = ts_demand
if self.config['do_plots']:
for period in self.run_periods:
plot_data = {}
for gen_type, value in results['periods'][period][
'generators'].iteritems():
plot_data[gen_type] = value['aggregate_supply']
this_final = final and (period
== self.config['run_periods'][-1])
mureiloutput.plot_timeseries(
plot_data,
ts_demand[period],
this_final,
plot_title=(str(period) + ' at iteration ' +
str(iteration)))
output_file = self.config['output_file']
mureiloutput.pickle_out(pickle_dict, output_file)
else:
results = None
return results
def output_results(self, final=False, iteration=0):
(best_params, opt_data) = self.algorithm.get_final()
if len(best_params) > 0:
# Protect against an exception before there are any params
results = self.evaluate_results(best_params)
if 'dispatch_fail' in results:
logger.info('======================================================')
logger.info('Dispatch failed.')
logger.info('======================================================')
else:
logger.info('======================================================')
logger.info('Total cost ($M): {:.2f}, including carbon (MT): {:.2f}, terminal value ($M): {:.2f}'.format(
results['totals']['cost'], results['totals']['carbon'] * 1e-6, results['totals']['terminal_value']))
logger.info('======================================================')
ts_demand = {}
# Now iterate across the periods, and then across the generators
for period in self.run_periods:
period_results = results['periods'][period]
logger.info('------------------------------------------------------')
logger.info('PERIOD ' + str(period) + ':')
logger.info('------------------------------------------------------')
logger.info('Period cost ($M): {:.2f}, carbon (MT): {:.2f}'.format(
period_results['totals']['cost'],
period_results['totals']['carbon'] * 1e-6))
for gen_type, value in period_results['generators'].iteritems():
gen_string = value['desc_string']
gen_cost = value['cost']
gen_supply = value['total_supply_period']
logger.info(gen_type + ' ($M {:.2f}, GWh {:.2f}) : '.format(
gen_cost, gen_supply / 1000) + gen_string)
logger.info('Total connection cost: $M {:.2f}'.format(
period_results['transmission']['connection_cost_total']))
logger.info('Total system demand: GWh {:.2f}'.format(
period_results['totals']['demand'] / 1000))
logger.info('Total unserved energy: GWh {:.2f}'.format(
period_results['demand']['unserved_energy_total'] / 1000))
logger.info('======================================================')
pickle_dict = {}
pickle_dict['opt_data'] = opt_data
pickle_dict['best_params'] = best_params
full_conf = self.get_full_config()
mureiloutput.clean_config_for_pickle(full_conf)
pickle_dict['config'] = full_conf
pickle_dict['best_results'] = results
if self.config['do_plots']:
if 'dispatch_fail' not in results:
for period in self.run_periods:
plot_data = {}
for gen_type, value in results['periods'][period]['generators'].iteritems():
plot_data[gen_type] = value['aggregate_supply']
ts_demand = results['periods'][period]['demand']['aggregate_ts']
this_final = final and (period == self.config['run_periods'][-1])
mureiloutput.plot_timeseries(plot_data,
ts_demand, this_final, plot_title=(
str(period) + ' at iteration ' + str(iteration)))
output_file = self.config['output_file']
mureiloutput.pickle_out(pickle_dict, output_file)
else:
def output_results(self, final=False, iteration=0):
(best_params, opt_data) = self.algorithm.get_final()
if len(best_params) > 0:
# Protect against an exception before there are any params
results = self.evaluate_results(best_params)
if 'dispatch_fail' in results:
logger.info(
'======================================================')
logger.info('Dispatch failed.')
logger.info(
'======================================================')
else:
logger.info(
'======================================================')
logger.info(
'Total cost ($M): {:.2f}, including carbon (MT): {:.2f}, terminal value ($M): {:.2f}'
.format(results['totals']['cost'],
results['totals']['carbon'] * 1e-6,
results['totals']['terminal_value']))
logger.info(
'======================================================')
ts_demand = {}
# Now iterate across the periods, and then across the generators
for period in self.run_periods:
period_results = results['periods'][period]
logger.info(
'------------------------------------------------------'
)
logger.info('PERIOD ' + str(period) + ':')
logger.info(
'------------------------------------------------------'
)
logger.info(
'Period cost ($M): {:.2f}, carbon (MT): {:.2f}'.format(
period_results['totals']['cost'],
period_results['totals']['carbon'] * 1e-6))
for gen_type, value in period_results[
'generators'].iteritems():
gen_string = value['desc_string']
gen_cost = value['cost']
gen_supply = value['total_supply_period']
logger.info(gen_type +
' ($M {:.2f}, GWh {:.2f}) : '.format(
gen_cost, gen_supply / 1000) +
gen_string)
logger.info('Total connection cost: $M {:.2f}'.format(
period_results['transmission']
['connection_cost_total']))
logger.info('Total system demand: GWh {:.2f}'.format(
period_results['totals']['demand'] / 1000))
logger.info('Total unserved energy: GWh {:.2f}'.format(
period_results['demand']['unserved_energy_total'] /
1000))
logger.info(
'======================================================')
pickle_dict = {}
pickle_dict['opt_data'] = opt_data
pickle_dict['best_params'] = best_params
full_conf = self.get_full_config()
mureiloutput.clean_config_for_pickle(full_conf)
pickle_dict['config'] = full_conf
pickle_dict['best_results'] = results
if self.config['do_plots']:
if 'dispatch_fail' not in results:
for period in self.run_periods:
plot_data = {}
for gen_type, value in results['periods'][period][
'generators'].iteritems():
plot_data[gen_type] = value['aggregate_supply']
ts_demand = results['periods'][period]['demand'][
'aggregate_ts']
this_final = final and (
period == self.config['run_periods'][-1])
mureiloutput.plot_timeseries(
plot_data,
ts_demand,
this_final,
plot_title=(str(period) + ' at iteration ' +
str(iteration)))
output_file = self.config['output_file']
mureiloutput.pickle_out(pickle_dict, output_file)
else:
results = None
return results
def output_results(self, final=False, iteration=0):
(best_params, opt_data) = self.algorithm.get_final()
if len(best_params) > 0:
# Protect against an exception before there are any params
results = self.evaluate_results(best_params)
logger.info('======================================================')
logger.info('Total cost ($M): {:.2f}, including carbon (MT): {:.2f}, terminal value ($M): {:.2f}'.format(
results['totals']['cost'], results['totals']['carbon'] * 1e-6, results['totals']['terminal_value']))
logger.info('======================================================')
ts_demand = {}
# Now iterate across the periods, and then across the generators
for period in self.run_periods:
period_results = results['periods'][period]
logger.info('------------------------------------------------------')
logger.info('PERIOD ' + str(period) + ':')
logger.info('------------------------------------------------------')
logger.info('Period cost ($M): {:.2f}, carbon (MT): {:.2f}'.format(
period_results['totals']['cost'],
period_results['totals']['carbon'] * 1e-6))
if 'demand' in self.dispatch_order:
ts_demand[period] = period_results['generators']['demand']['other']['ts_demand']
else:
ts_demand[period] = self.data.get_timeseries('ts_demand')
period_results['totals']['demand'] = (numpy.sum(ts_demand[period]) *
self.global_config['time_scale_up_mult'] * self.global_config['timestep_hrs'])
logger.info('Period total demand (GWh): {:.2f}'.format(
period_results['totals']['demand'] / 1000))
for gen_type, value in period_results['generators'].iteritems():
gen_string = value['desc_string']
gen_cost = value['cost']
gen_supply = value['total_supply_period']
logger.info(gen_type + ' ($M {:.2f}, GWh {:.2f}) : '.format(
gen_cost, gen_supply / 1000) + gen_string)
logger.info('======================================================')
pickle_dict = {}
pickle_dict['opt_data'] = opt_data
pickle_dict['best_params'] = best_params
full_conf = self.get_full_config()
mureiloutput.clean_config_for_pickle(full_conf)
pickle_dict['config'] = full_conf
pickle_dict['best_results'] = results
pickle_dict['ts_demand'] = ts_demand
if self.config['do_plots']:
for period in self.run_periods:
plot_data = {}
for gen_type, value in results['periods'][period]['generators'].iteritems():
plot_data[gen_type] = value['aggregate_supply']
this_final = final and (period == self.config['run_periods'][-1])
mureiloutput.plot_timeseries(plot_data,
ts_demand[period], this_final, plot_title=(
str(period) + ' at iteration ' + str(iteration)))
output_file = self.config['output_file']
mureiloutput.pickle_out(pickle_dict, output_file)
else:
results = None
