diff --git a/.gitignore b/.gitignore index b7fe82f5..977fd1f0 100644 --- a/.gitignore +++ b/.gitignore @@ -168,4 +168,7 @@ data/timeseries/* !data/timeseries/50fa074e-9279-4b3f-bead-7762fe1643f3.csv !data/timeseries/141a6e6c-e47f-4363-85ed-8d40b525ac77.csv !data/timeseries/f97df022-1dc7-4f59-bb15-3e0d94f4ba9d.csv -!data/timeseries/dac8030c-c9b5-48db-8550-cc78312ddabb.csv \ No newline at end of file +!data/timeseries/dac8030c-c9b5-48db-8550-cc78312ddabb.csv +calliope_app/scripts/fixtures/parameters.xlsx +calliope_app/scripts/fixtures/template_loc_tech_params.xlsx +calliope_app/scripts/fixtures/template_tech_params.xlsx diff --git a/README.md b/README.md index ead2d7e2..ef11c1d3 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,8 @@ -# NREL Engage: A [Calliope](https://github.com/calliope-project/calliope) Web Environment for Collaborative Energy Systems Modeling and Planning +# NLR Engage: A [Calliope](https://github.com/calliope-project/calliope) Web Environment for Collaborative Energy Systems Modeling and Planning -Engage is a free, open-access energy system planning tool that allows multiple users/stakeholders to develop and collaborate on capacity expansion models. The tool, developed at the National Renewable Energy Laboratory (NREL), provides a collaborative and easy-to-use interface built on Calliope, a multi-scale energy systems modeling framework. +Engage is a free, open-access energy system planning tool that allows multiple users/stakeholders to develop and collaborate on capacity expansion models. The tool, developed at the National Laboratory of the Rockies (NLR), provides a collaborative and easy-to-use interface built on Calliope, a multi-scale energy systems modeling framework. -- Visit the NREL hosted webtool at https://engage.nrel.gov/ +- Visit the NLR hosted webtool at https://engage.nrel.gov/ ## Requirements diff --git a/calliope_app/api/calliope_utils.py b/calliope_app/api/calliope_utils.py index 1ab3e3c3..7b0482b0 100644 --- a/calliope_app/api/calliope_utils.py +++ b/calliope_app/api/calliope_utils.py @@ -3,19 +3,20 @@ interfacing with Calliope. """ +import calendar +import copy +import json +import logging import os -import yaml import shutil -from calliope import Model as CalliopeModel + +import calliope import pandas as pd -import json -import copy -import calendar +import yaml +from calliope import Model as CalliopeModel from api.models.configuration import Scenario_Param, Scenario_Loc_Tech, \ - Location, Tech_Param, Loc_Tech_Param, Loc_Tech, Scenario, Carrier -from api.models.outputs import Run -import logging + Location, Tech_Param, Loc_Tech_Param, Loc_Tech, Scenario logger = logging.getLogger(__name__) @@ -36,7 +37,7 @@ def get_model_yaml_set(run, scenario_id, year): # NOTE: deprecated run parameter in the database if unique_param == "run.objective_options": continue - + if unique_param not in unique_params: # If parameter hasn't been set, add to Return List unique_params.append(unique_param) @@ -164,7 +165,7 @@ def get_loc_techs_yaml_set(scenario_id, year): value = float(param.value) / 100 else: value = param.value - + param_list = [parent_type, location, 'techs', param.loc_tech.technology.calliope_name]+\ unique_param.split('.') @@ -173,7 +174,7 @@ def get_loc_techs_yaml_set(scenario_id, year): def get_carriers_yaml_set(scenario_id): model = Scenario.objects.get(id=scenario_id).model - + carriers_yaml_set = {} for carrier in model.carriers.all(): carriers_yaml_set[carrier.name] = {'rate':carrier.rate_unit,'quantity':carrier.quantity_unit} @@ -229,6 +230,11 @@ def run_basic(model_path, logger): model = CalliopeModel(config=model_path) logger.info(model.info()) logger.info(model._model_data.coords.get("techs_non_transmission", [])) + + # NOTE: with log_to_console=True, the model run would get hanging if error happened. + if model.run_config['solver'] == 'appsi_highs': + model.run_config['solver_options'] = {'log_to_console': False} + model.run() _write_outputs(model, model_path) return model.results.termination_condition @@ -241,6 +247,11 @@ def run_clustered(model_path, idx, logger): _set_subset_time(model_path) _set_capacities(model_path) model = CalliopeModel(config=model_path) + + # NOTE: with log_to_console=True, the model run would get hanging if error happened. + if model.run_config['solver'] == 'appsi_highs': + model.run_config['solver_options'] = {'log_to_console': False} + model.run() _write_outputs(model, model_path) if model.results.termination_condition != 'optimal': @@ -272,7 +283,7 @@ def run_clustered(model_path, idx, logger): def _set_clustering(model_path, on=False, k=14): # Read with open(model_path) as file: - model_yaml = yaml.load(file, Loader=yaml.FullLoader) + model_yaml = yaml.safe_load(file) # Update if on is True: time = {} @@ -292,7 +303,7 @@ def _set_clustering(model_path, on=False, k=14): def _set_subset_time(model_path, start_time=None, end_time=None): # Read with open(model_path) as file: - model_yaml = yaml.load(file, Loader=yaml.FullLoader) + model_yaml = yaml.safe_load(file) # Update if start_time is not None: subset_time = [start_time, end_time] @@ -309,7 +320,7 @@ def _set_capacities(model_path, ignore_techs=[], # ---- UPDATE MODEL REFERENCE # Read with open(model_path) as file: - model_yaml = yaml.load(file, Loader=yaml.FullLoader) + model_yaml = yaml.safe_load(file) # Update Model Settings if capacity is None: model_yaml['import'] = ['techs.yaml', 'locations.yaml'] @@ -324,7 +335,7 @@ def _set_capacities(model_path, ignore_techs=[], # Read locations_path = model_path.replace('model.yaml', 'locations.yaml') with open(locations_path) as file: - locations_yaml = yaml.load(file, Loader=yaml.FullLoader) + locations_yaml = yaml.safe_load(file) # Update Locations Settings for loc, loc_data in locations_yaml['locations'].items(): if 'techs' not in loc_data: @@ -441,9 +452,9 @@ def _yaml_outputs(model_path, outputs_dir): results_var = {'energy_cap':'results_energy_cap.csv','storage_cap':'results_storage_cap.csv'} inputs_dir = os.path.join(base_path, 'inputs') - model = yaml.load(open(os.path.join(inputs_dir,'model.yaml')), Loader=yaml.FullLoader) - model.update(yaml.load(open(os.path.join(inputs_dir,'locations.yaml')), Loader=yaml.FullLoader)) - model.update(yaml.load(open(os.path.join(inputs_dir,'techs.yaml')), Loader=yaml.FullLoader)) + model = yaml.safe_load(open(os.path.join(inputs_dir,'model.yaml'),'r')) + model.update(yaml.safe_load(open(os.path.join(inputs_dir,'locations.yaml'),'r'))) + model.update(yaml.safe_load(open(os.path.join(inputs_dir,'techs.yaml'),'r'))) has_outputs = False for v in results_var.keys(): @@ -482,127 +493,148 @@ def _yaml_outputs(model_path, outputs_dir): yaml.dump(model, open(os.path.join(outputs_dir,'model_results.yaml'),'w+'), default_flow_style=False) def apply_gradient(old_inputs,old_results,new_inputs,old_year,new_year,logger): - old_model = yaml.safe_load(open(old_results+'/model_results.yaml')) + old_model = yaml.safe_load(open(os.path.join(old_results,'model_results.yaml'),'r')) - new_techs = yaml.safe_load(open(new_inputs+'/techs.yaml','r')) - new_loctechs = yaml.safe_load(open(new_inputs+'/locations.yaml','r')) - new_model = yaml.safe_load(open(new_inputs+'/model.yaml','r')) + new_techs = yaml.safe_load(open(os.path.join(new_inputs,'techs.yaml'),'r')) + new_loctechs = yaml.safe_load(open(os.path.join(new_inputs,'locations.yaml'),'r')) + new_model = yaml.safe_load(open(os.path.join(new_inputs,'model.yaml'),'r')) - built_tech_names = [] + built_tech_names = {} built_techs = {} built_loc_techs = {} - for l in old_model['locations']: - if 'techs' in old_model['locations'][l]: - for t in old_model['locations'][l]['techs']: - old_tech = old_model['techs'][t] - new_tech = new_techs['techs'][t] - new_loc_tech = new_loctechs['locations'][l]['techs'][t] - loc_tech = old_model['locations'][l]['techs'][t] - if ('energy_cap_max' in loc_tech.get('constraints',{}) or 'storage_cap_max' in loc_tech.get('constraints',{})) or\ - ('energy_cap_max' in old_tech.get('constraints',{}) or 'storage_cap_max' in old_tech.get('constraints',{})): - if loc_tech.get('results',{'energy_cap_equals':0}).get('energy_cap_equals',0) != 0 or\ - loc_tech.get('results',{'storage_cap_equals':0}).get('storage_cap_equals',0) != 0: - loc_tech_b = copy.deepcopy(loc_tech) - built_tech_names.append(t) - - if 'constraints' in loc_tech_b: - [loc_tech_b['constraints'].pop(c) for c in ['energy_cap_max', 'storage_cap_max'] if c in loc_tech_b['constraints']] - else: - loc_tech_b['constraints'] = {} - if 'energy_cap_equals' in loc_tech['results']: - loc_tech_b['constraints']['energy_cap_equals'] = loc_tech['results']['energy_cap_equals'] - if 'storage_cap_equals' in loc_tech['results']: - loc_tech_b['constraints']['storage_cap_equals'] = loc_tech['results']['storage_cap_equals'] - cost_classes = [c for c in loc_tech_b.keys() if 'costs.' in c] - for cost in cost_classes: - [loc_tech_b[cost].pop(c) for c in ['energy_cap','interest_rate','storage_cap'] if c in loc_tech_b[cost]] - loc_tech_b.pop('results') - - if new_loc_tech and 'constraints' in new_loc_tech: - new_energy_cap_min = new_loc_tech['constraints'].get('energy_cap_min',new_tech.get('constraints',{}).get('energy_cap_min',0)) - new_energy_cap_max = new_loc_tech['constraints'].get('energy_cap_max',new_tech.get('constraints',{}).get('energy_cap_max',0)) - new_storage_cap_min = new_loc_tech['constraints'].get('storage_cap_min',new_tech.get('constraints',{}).get('storage_cap_min',0)) - new_storage_cap_max = new_loc_tech['constraints'].get('storage_cap_max',new_tech.get('constraints',{}).get('storage_cap_max',0)) - else: - new_energy_cap_min = new_tech.get('constraints',{}).get('energy_cap_min',0) - new_energy_cap_max = new_tech.get('constraints',{}).get('energy_cap_max',0) - new_storage_cap_min = new_tech.get('constraints',{}).get('storage_cap_min',0) - new_storage_cap_max = new_tech.get('constraints',{}).get('storage_cap_max',0) + for loc_type in ['locations','links']: + for l in old_model[loc_type]: + if 'techs' in old_model[loc_type][l]: + for t in old_model[loc_type][l]['techs']: + old_tech = old_model['techs'][t] + if t not in new_techs['techs']: + continue + new_tech = new_techs['techs'][t] + new_loc_tech = new_loctechs[loc_type][l]['techs'][t] + loc_tech = old_model[loc_type][l]['techs'][t] + if ('energy_cap_max' in loc_tech.get('constraints',{}) or 'storage_cap_max' in loc_tech.get('constraints',{})) or\ + ('energy_cap_max' in old_tech.get('constraints',{}) or 'storage_cap_max' in old_tech.get('constraints',{})): + if loc_tech.get('results',{'energy_cap_equals':0}).get('energy_cap_equals',0) != 0 or\ + loc_tech.get('results',{'storage_cap_equals':0}).get('storage_cap_equals',0) != 0: + loc_tech_b = copy.deepcopy(loc_tech) + + # Record built techs and the total systemwide capacity of those techs to use with energy_cap_max_systemwide + if t in built_tech_names: + built_tech_names[t] += loc_tech.get('results',{'energy_cap_equals':0}).get('energy_cap_equals',0) + else: + built_tech_names[t] = loc_tech.get('results',{'energy_cap_equals':0}).get('energy_cap_equals',0) - if new_loc_tech == None: + if 'constraints' in loc_tech_b: + [loc_tech_b['constraints'].pop(c) for c in ['energy_cap_max', 'storage_cap_max'] if c in loc_tech_b['constraints']] + else: + loc_tech_b['constraints'] = {} + if 'energy_cap_equals' in loc_tech['results']: + loc_tech_b['constraints']['energy_cap_equals'] = loc_tech['results']['energy_cap_equals'] + if 'storage_cap_equals' in loc_tech['results']: + loc_tech_b['constraints']['storage_cap_equals'] = loc_tech['results']['storage_cap_equals'] + if 'energy_cap_per_storage_cap_equals' in loc_tech_b['constraints']: + loc_tech_b['constraints'].pop('energy_cap_per_storage_cap_equals') + cost_classes = [c for c in loc_tech_b.keys() if 'costs.' in c] + for cost in cost_classes: + [loc_tech_b[cost].pop(c) for c in ['energy_cap','interest_rate','storage_cap'] if c in loc_tech_b[cost]] + loc_tech_b.pop('results') + + if new_loc_tech and 'constraints' in new_loc_tech: + new_energy_cap_min = new_loc_tech['constraints'].get('energy_cap_min',new_tech.get('constraints',{}).get('energy_cap_min',0)) + new_energy_cap_max = new_loc_tech['constraints'].get('energy_cap_max',new_tech.get('constraints',{}).get('energy_cap_max',0)) + new_storage_cap_min = new_loc_tech['constraints'].get('storage_cap_min',new_tech.get('constraints',{}).get('storage_cap_min',0)) + new_storage_cap_max = new_loc_tech['constraints'].get('storage_cap_max',new_tech.get('constraints',{}).get('storage_cap_max',0)) + else: + new_energy_cap_min = new_tech.get('constraints',{}).get('energy_cap_min',0) + new_energy_cap_max = new_tech.get('constraints',{}).get('energy_cap_max',0) + new_storage_cap_min = new_tech.get('constraints',{}).get('storage_cap_min',0) + new_storage_cap_max = new_tech.get('constraints',{}).get('storage_cap_max',0) + + if new_loc_tech == None: new_loc_tech = {} - if 'constraints' not in new_loc_tech: + if 'constraints' not in new_loc_tech: new_loc_tech['constraints'] = {} - if new_energy_cap_min > 0 and new_energy_cap_min-loc_tech['results']['energy_cap_equals'] > 0: - new_loc_tech['constraints']['energy_cap_min'] = new_energy_cap_min-loc_tech['results']['energy_cap_equals'] - if new_loc_tech['constraints']['energy_cap_min'] < 0: - new_loc_tech['constraints']['energy_cap_min'] = 0 - if new_energy_cap_max != 'inf' and new_energy_cap_max > 0: - new_loc_tech['constraints']['energy_cap_max'] = new_energy_cap_max-loc_tech['results']['energy_cap_equals'] - if new_loc_tech['constraints']['energy_cap_max'] < 0: - new_loc_tech['constraints']['energy_cap_max'] = 0 - if new_storage_cap_min > 0 and new_storage_cap_min-loc_tech['results']['storage_cap_equals'] > 0: - new_loc_tech['constraints']['storage_cap_min'] = new_storage_cap_min-loc_tech['results']['storage_cap_equals'] - if new_loc_tech['constraints']['storage_cap_min'] < 0: - new_loc_tech['constraints']['storage_cap_min'] = 0 - if new_storage_cap_max != 'inf' and new_storage_cap_max > 0: - new_loc_tech['constraints']['storage_cap_max'] = new_storage_cap_max-loc_tech['results']['storage_cap_equals'] - if new_loc_tech['constraints']['storage_cap_max'] < 0: - new_loc_tech['constraints']['storage_cap_max'] = 0 - - new_loctechs['locations'][l]['techs'][t] = new_loc_tech - for x in loc_tech_b: - for y in loc_tech_b[x].keys(): - # Copy over timeseries files for old techs, updating year to match new year - if 'file=' in str(loc_tech_b[x][y]): - filename=loc_tech_b[x][y].replace('file=','').replace('.csv:value','') - ts_df = pd.read_csv(old_inputs+'/'+filename+'.csv') - ts_df['Unnamed: 0'] = pd.to_datetime(ts_df['Unnamed: 0']) - freq = pd.infer_freq(ts_df['Unnamed: 0']) - if not calendar.isleap(new_year): - feb_29_mask = (ts_df['Unnamed: 0'].dt.month == 2) & (ts_df['Unnamed: 0'].dt.day == 29) - ts_df = ts_df[~feb_29_mask] - ts_df.index = ts_df['Unnamed: 0'].apply(lambda x: x.replace(year=new_year)) - ts_df.drop(columns=['Unnamed: 0'], inplace=True) - elif not calendar.isleap(old_year): - ts_df.index = ts_df['Unnamed: 0'].apply(lambda x: x.replace(year=new_year)) - ts_df.drop(columns=['Unnamed: 0'], inplace=True) - idx = pd.date_range(ts_df.index.min(),ts_df.index.max(),freq=freq) - ts_df = ts_df.reindex(idx, fill_value=0) - - # Leap Year Handling (Fill w/ Feb 28th) - feb_28_mask = (ts_df.index.month == 2) & (ts_df.index.day == 28) - feb_29_mask = (ts_df.index.month == 2) & (ts_df.index.day == 29) - feb_28 = ts_df.loc[feb_28_mask, 'value'].values - feb_29 = ts_df.loc[feb_29_mask, 'value'].values - if ((len(feb_29) > 0) & (len(feb_28) > 0)): - ts_df.loc[feb_29_mask, 'value'] = feb_28 - else: - ts_df.index = ts_df['Unnamed: 0'].apply(lambda x: x.replace(year=new_year)) - ts_df.drop(columns=['Unnamed: 0'], inplace=True) - ts_df.index.name = None - ts_df.to_csv(os.path.join(new_inputs,filename+'-'+str(old_year)+'.csv'),index=True) - loc_tech_b[x][y] = 'file='+filename+'-'+str(old_year)+'.csv:value' - - if l not in built_loc_techs: - built_loc_techs[l] = {} - built_loc_techs[l][t+'_'+str(old_year)] = loc_tech_b - - new_loctechs['locations'][l]['techs'][t+'_'+str(old_year)] = loc_tech_b - for t in built_tech_names: + if new_energy_cap_min > 0 and new_energy_cap_min-loc_tech['results']['energy_cap_equals'] > 0: + new_loc_tech['constraints']['energy_cap_min'] = new_energy_cap_min-loc_tech['results']['energy_cap_equals'] + if new_loc_tech['constraints']['energy_cap_min'] < 0: + new_loc_tech['constraints']['energy_cap_min'] = 0 + if new_energy_cap_max != 'inf' and new_energy_cap_max > 0: + new_loc_tech['constraints']['energy_cap_max'] = new_energy_cap_max-loc_tech['results']['energy_cap_equals'] + if new_loc_tech['constraints']['energy_cap_max'] < 0: + new_loc_tech['constraints']['energy_cap_max'] = 0 + if new_storage_cap_min > 0 and new_storage_cap_min-loc_tech['results']['storage_cap_equals'] > 0: + new_loc_tech['constraints']['storage_cap_min'] = new_storage_cap_min-loc_tech['results']['storage_cap_equals'] + if new_loc_tech['constraints']['storage_cap_min'] < 0: + new_loc_tech['constraints']['storage_cap_min'] = 0 + if new_storage_cap_max != 'inf' and new_storage_cap_max > 0: + new_loc_tech['constraints']['storage_cap_max'] = new_storage_cap_max-loc_tech['results']['storage_cap_equals'] + if new_loc_tech['constraints']['storage_cap_max'] < 0: + new_loc_tech['constraints']['storage_cap_max'] = 0 + + new_loctechs[loc_type][l]['techs'][t] = new_loc_tech + for x in loc_tech_b: + for y in loc_tech_b[x].keys(): + try: + # Copy over timeseries files for old techs, updating year to match new year + if 'file=' in loc_tech_b[x][y]: + filename=loc_tech_b[x][y].replace('file=','').replace('.csv:value','') + ts_df = pd.read_csv(old_inputs+'/'+filename+'.csv') + ts_df['Unnamed: 0'] = pd.to_datetime(ts_df['Unnamed: 0']) + freq = pd.infer_freq(ts_df['Unnamed: 0']) + if not calendar.isleap(new_year): + feb_29_mask = (ts_df['Unnamed: 0'].dt.month == 2) & (ts_df['Unnamed: 0'].dt.day == 29) + ts_df = ts_df[~feb_29_mask] + ts_df.index = ts_df['Unnamed: 0'].apply(lambda x: x.replace(year=new_year)) + ts_df.drop(columns=['Unnamed: 0'], inplace=True) + elif not calendar.isleap(old_year): + ts_df.index = ts_df['Unnamed: 0'].apply(lambda x: x.replace(year=new_year)) + ts_df.drop(columns=['Unnamed: 0'], inplace=True) + idx = pd.date_range(ts_df.index.min(),ts_df.index.max(),freq=freq) + ts_df = ts_df.reindex(idx, fill_value=0) + + # Leap Year Handling (Fill w/ Feb 28th) + feb_28_mask = (ts_df.index.month == 2) & (ts_df.index.day == 28) + feb_29_mask = (ts_df.index.month == 2) & (ts_df.index.day == 29) + feb_28 = ts_df.loc[feb_28_mask, 'value'].values + feb_29 = ts_df.loc[feb_29_mask, 'value'].values + if ((len(feb_29) > 0) & (len(feb_28) > 0)): + ts_df.loc[feb_29_mask, 'value'] = feb_28 + else: + ts_df.index = ts_df['Unnamed: 0'].apply(lambda x: x.replace(year=new_year)) + ts_df.drop(columns=['Unnamed: 0'], inplace=True) + ts_df.index.name = None + ts_df.to_csv(os.path.join(new_inputs,filename+'-'+str(old_year)+'.csv'),index=True) + loc_tech_b[x][y] = 'file='+filename+'-'+str(old_year)+'.csv:value' + except TypeError: + continue + + if l not in built_loc_techs: + built_loc_techs[l] = {} + built_loc_techs[l][t+'_'+str(old_year)] = loc_tech_b + + new_loctechs[loc_type][l]['techs'][t+'_'+str(old_year)] = loc_tech_b + + for t in built_tech_names.keys(): tech = old_model['techs'][t] - tech_b = copy.deepcopy(tech) + + # Handle systemwide energy cap gradient + if 'constraints' in new_techs['techs'][t]: + if 'energy_cap_max_systemwide' in new_techs['techs'][t]['constraints']: + new_techs['techs'][t]['constraints']['energy_cap_max_systemwide'] = max([new_techs['techs'][t]['constraints']['energy_cap_max_systemwide']-built_tech_names[t],0]) + if 'energy_cap_equals_systemwide' in new_techs['techs'][t]['constraints']: + new_techs['techs'][t]['constraints']['energy_cap_max_systemwide'] = max([new_techs['techs'][t]['constraints']['energy_cap_equals_systemwide']-built_tech_names[t],0]) + if 'constraints' in tech_b: - [tech_b['constraints'].pop(c) for c in ['energy_cap_max', 'storage_cap_max'] if c in tech_b['constraints']] + [tech_b['constraints'].pop(c) for c in ['energy_cap_max', 'storage_cap_max','energy_cap_per_storage_cap_equals'] if c in tech_b['constraints']] cost_classes = [c for c in tech_b.keys() if 'costs.' in c] for cost in cost_classes: [tech_b[cost].pop(c) for c in ['energy_cap','interest_rate','storage_cap'] if c in tech_b[cost]] if len(tech_b[cost].keys()) == 0: tech_b.pop(cost) - + tech_b['essentials']['name'] += ' '+str(old_year) for x in tech_b: @@ -657,11 +689,11 @@ def apply_gradient(old_inputs,old_results,new_inputs,old_year,new_year,logger): if t in c.get('techs_rhs',[]) and t+'_'+str(old_year) not in c.get('techs',[]): new_model['group_constraints'][g]['techs_rhs'].append(t+'_'+str(old_year)) - with open(new_inputs+'/techs.yaml','w') as outfile: + with open(os.path.join(new_inputs,'techs.yaml'),'w') as outfile: yaml.dump(new_techs,outfile,default_flow_style=False) - with open(new_inputs+'/locations.yaml','w') as outfile: + with open(os.path.join(new_inputs,'locations.yaml'),'w') as outfile: yaml.dump(new_loctechs,outfile,default_flow_style=False) - with open(new_inputs+'/model.yaml', 'w') as outfile: - yaml.dump(new_model,outfile,default_flow_style=False) \ No newline at end of file + with open(os.path.join(new_inputs,'model.yaml'), 'w') as outfile: + yaml.dump(new_model,outfile,default_flow_style=False) diff --git a/calliope_app/api/models/engage.py b/calliope_app/api/models/engage.py index 822998a0..3c65c62b 100644 --- a/calliope_app/api/models/engage.py +++ b/calliope_app/api/models/engage.py @@ -84,7 +84,7 @@ def register(http_host, email, password, first_name, last_name, organization): message = render_to_string("registration/user_activation.txt", context) try: send_mail( - subject="NREL ENGAGE Registration", + subject="NLR ENGAGE Registration", message=message, from_email=settings.AWS_SES_FROM_EMAIL, recipient_list=[email] diff --git a/calliope_app/api/tasks.py b/calliope_app/api/tasks.py index 20861574..6ab809c1 100644 --- a/calliope_app/api/tasks.py +++ b/calliope_app/api/tasks.py @@ -469,7 +469,8 @@ def on_failure(self, exc, task_id, args, kwargs, einfo): timeout_message = "Run Timeout! TimeLimit=%s seconds." % self.time_limit exc = timeout_message if str(exc) == "SoftTimeLimitExceeded()" else exc - logger.error("{!r}".format(str(exc))) + logger.exception(exc) + #logger.error("{!r}".format(str(exc))) # Add pretty error to the log for key in self.clean_msg_dict: diff --git a/calliope_app/api/views/outputs.py b/calliope_app/api/views/outputs.py index c6dbd9df..2c921b91 100644 --- a/calliope_app/api/views/outputs.py +++ b/calliope_app/api/views/outputs.py @@ -443,7 +443,7 @@ def delete_run(request): @csrf_protect def publish_run(request): """ - Publish a scenario run to Cambium (https://cambium.nrel.gov/) + Publish a scenario run to the Scenario Viewer (https://scenarioviewer.nrel.gov/) Parameters: model_uuid (uuid): required diff --git a/calliope_app/calliope-files/backend/run.py b/calliope_app/calliope-files/backend/run.py index 0a70e0c1..4ee335d9 100644 --- a/calliope_app/calliope-files/backend/run.py +++ b/calliope_app/calliope-files/backend/run.py @@ -152,10 +152,10 @@ def run_plan( # NOTE: pyomo==6.7.0 opt does not have name attribute # Disable this for use 'appsi_highs' solver, which does not have 'persistent' in name. - # if "persistent" in opt.name and persistent is True: - # results.attrs["objective_function_value"] = opt.get_model_attr("ObjVal") - # else: - results.attrs["objective_function_value"] = backend_model.obj() + if "appsi_highs" not in solver and "persistent" in opt.name and persistent is True: + results.attrs["objective_function_value"] = opt.get_model_attr("ObjVal") + else: + results.attrs["objective_function_value"] = backend_model.obj() else: results = xr.Dataset(attrs={"termination_condition": termination}) diff --git a/calliope_app/calliope_app/settings/base.py b/calliope_app/calliope_app/settings/base.py index b607ec46..43e71888 100644 --- a/calliope_app/calliope_app/settings/base.py +++ b/calliope_app/calliope_app/settings/base.py @@ -148,7 +148,7 @@ os.path.join(ROOT_DIR, 'locale'), ) -## NREL API Key +## NLR API Key NREL_API_EMAIL = env.str("NREL_API_EMAIL", "") NREL_API_KEY = env.str("NREL_API_KEY", "") diff --git a/calliope_app/calliope_app/urls.py b/calliope_app/calliope_app/urls.py index 778e6dac..da00a76d 100644 --- a/calliope_app/calliope_app/urls.py +++ b/calliope_app/calliope_app/urls.py @@ -20,7 +20,7 @@ from django.conf.urls.i18n import i18n_patterns from django.conf.urls.static import static -admin.site.site_header = 'NREL\'s Calliope Admin' +admin.site.site_header = 'NLR\'s Calliope Admin' urlpatterns = [ path('i18n/', include('django.conf.urls.i18n')), diff --git a/calliope_app/client/static/images/nlr.webp b/calliope_app/client/static/images/nlr.webp new file mode 100644 index 00000000..9603c7c3 Binary files /dev/null and b/calliope_app/client/static/images/nlr.webp differ diff --git a/calliope_app/client/static/js/scenarios.js b/calliope_app/client/static/js/scenarios.js index 2dad7329..d98a166d 100644 --- a/calliope_app/client/static/js/scenarios.js +++ b/calliope_app/client/static/js/scenarios.js @@ -786,11 +786,11 @@ function activate_scenario_settings() { }); $('#settings_weights_import_data').on('click', function() { - dialogObj["monetary"] = $("#monetary").val(); - dialogObj["co2"] = $("#co2").val(); - dialogObj["ch4"] = $("#ch4").val(); - dialogObj["n2o"] = $("#n2o").val(); - dialogObj["co2e"] = $("#co2e").val(); + dialogObj["monetary"] = !isNaN(parseFloat($("#monetary").val())) ? parseFloat($("#monetary").val()) : $("#monetary").val(); + dialogObj["co2"] = !isNaN(parseFloat($("#co2").val())) ? parseFloat($("#co2").val()) : $("#co2").val(); + dialogObj["ch4"] = !isNaN(parseFloat($("#ch4").val())) ? parseFloat($("#ch4").val()) : $("#ch4").val(); + dialogObj["n2o"] = !isNaN(parseFloat($("#n2o").val())) ? parseFloat($("#n2o").val()) : $("#n2o").val(); + dialogObj["co2e"] = !isNaN(parseFloat($("#co2e").val())) ? parseFloat($("#co2e").val()) : $("#co2e").val(); $('textarea[name="edit' + dialogInputId + '"]').text(JSON.stringify(dialogObj, undefined, 2)); $('#scenario_weights_json_form').hide(); diff --git a/calliope_app/client/templates/add_run.html b/calliope_app/client/templates/add_run.html index 5c5975e4..4c670a69 100644 --- a/calliope_app/client/templates/add_run.html +++ b/calliope_app/client/templates/add_run.html @@ -89,7 +89,7 @@
{% trans "Run Options" %}:
{% trans "Enable Clustered Run:" %} - +
diff --git a/calliope_app/client/templates/base.html b/calliope_app/client/templates/base.html index 25b9d740..13724297 100644 --- a/calliope_app/client/templates/base.html +++ b/calliope_app/client/templates/base.html @@ -119,7 +119,7 @@ {% endif %} -
      
+
      
 @@ -238,7 +238,7 @@
{% trans "Project Assistance" %}:  - Tom Harris + James Morris
diff --git a/calliope_app/client/templates/locations.html b/calliope_app/client/templates/locations.html index dc5dd119..5bcf8ab7 100644 --- a/calliope_app/client/templates/locations.html +++ b/calliope_app/client/templates/locations.html @@ -4,7 +4,7 @@ {% load i18n %} {% block head %} -NREL Engage | {{ model.name }} | Locations +NLR Engage | {{ model.name }} | Locations