def get_initial_model(self, options: Options, num_time_steps: int,
minutes_per_timestep: int) -> EgretModel:
''' Get a model ready to be populated with data
Returns
-------
A model object populated with static system information, such as
buses and generators, and with time series arrays that are large
enough to hold num_time_steps entries.
Initial values in time time series do not have meaning.
'''
# Get data for the first simulation day
first_day_model = self._get_forecast_by_date(self._first_day)
# Copy it, making sure we've got the right number of time periods
data = _recurse_copy_with_time_series_length(first_day_model.data,
num_time_steps)
new_model = EgretModel(data)
new_model.data['system']['time_keys'] = list(
str(i) for i in range(1, num_time_steps + 1))
new_model.data['system'][
'time_period_length_minutes'] = minutes_per_timestep
return new_model
def _get_initial_model(self, options: Options, num_time_steps: int,
minutes_per_timestep: int) -> EgretModel:
''' Get a model ready to be populated with data
Returns
-------
A model object populated with static system information, such as
buses and generators, and with time series arrays that are large
enough to hold num_time_steps entries.
Initial values in time time series do not have meaning.
'''
data = copy.deepcopy(self._initial_model)
data['system']['time_period_length_minutes'] = minutes_per_timestep
data['system']['time_keys'] = [
str(i) for i in range(1, num_time_steps + 1)
]
def _ensure_timeseries_allocated(d):
for k, v in d.items():
if isinstance(v, dict):
if 'data_type' in v and v['data_type'] == 'time_series':
v['values'] = [None] * num_time_steps
else:
_ensure_timeseries_allocated(v)
_ensure_timeseries_allocated(data)
return EgretModel(data)
def _get_initial_model(self, sim_type:str, options:Options, num_time_steps:int, minutes_per_timestep:int) -> EgretModel:
''' Get a model ready to be populated with data
Returns
-------
A model object populated with static system information, such as
buses and generators, and with time series arrays that are large
enough to hold num_time_steps entries.
Initial values in time time series do not have meaning.
'''
data = self._cache.get_new_skeleton()
data['system']['time_period_length_minutes'] = minutes_per_timestep
data['system']['time_keys'] = [str(i) for i in range(1,num_time_steps+1)]
md = EgretModel(data)
self._ensure_forecastable_storage(sim_type, num_time_steps, md)
return md
def _get_egret_model_for_date(
self, requested_date: date, dat_filename: str,
cache_dict: Dict[date, EgretModel]) -> EgretModel:
''' Get data for a specific calendar day.
Implements the common logic of _get_actuals_by_date and _get_forecast_by_date.
'''
# Return cached model, if we have it
if requested_date in cache_dict:
return cache_dict[requested_date]
# Otherwise read the requested data and store it in the cache
date_str = str(requested_date)
path_to_dat = os.path.join(self._instance_directory_name, date_str,
dat_filename)
day_pyomo = self._uc_model_template.create_instance(path_to_dat)
day_dict = create_model_data_dict_params(day_pyomo, True)
day_model = EgretModel(day_dict)
cache_dict[requested_date] = day_model
return day_model
def _get_egret_model_for_date(
self, requested_date: date, dat_filename: str,
cache_dict: Dict[date, EgretModel]) -> EgretModel:
''' Get data for a specific calendar day.
Implements the common logic of _get_actuals_by_date and _get_forecast_by_date.
'''
# Return cached model, if we have it
if requested_date in cache_dict:
return cache_dict[requested_date]
# Otherwise read the requested data and store it in the cache
date_str = str(requested_date)
path_to_dat = os.path.join(self._instance_directory_name, date_str,
dat_filename)
# if requested day does not exist, use the last day's data instead
if not os.path.exists(path_to_dat):
# Pull it from the cache, if present
if self._final_day in cache_dict:
day_model = cache_dict[self._final_day]
cache_dict[requested_date] = day_model
return day_model
# Or set the dat path to the final day, if it's not in the cache
else:
date_str = str(self._final_day)
path_to_dat = os.path.join(self._instance_directory_name,
date_str, dat_filename)
day_pyomo = self._uc_model_template.create_instance(path_to_dat)
day_dict = create_model_data_dict_params(day_pyomo, True)
day_model = EgretModel(day_dict)
cache_dict[requested_date] = day_model
return day_model