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Logical flow
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@@ -9,6 +9,7 @@ | |
| :template: my_class.rst | ||
| osier.DispatchModel | ||
| osier.LogicDispatchModel | ||
| osier.CapacityExpansion | ||
| ``` | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,151 @@ | ||
| from osier import OsierModel | ||
| from osier.utils import get_tech_names | ||
| from copy import deepcopy | ||
| from unyt import unyt_array, MW | ||
| import pandas as pd | ||
| import numpy as np | ||
| import warnings | ||
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| LARGE_NUMBER = 1e20 | ||
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| class LogicDispatchModel(OsierModel): | ||
| """ | ||
| The :class:`LogicDispatchModel` class creates and solves a basic dispatch | ||
| model from the perspective of a "grid operator." | ||
| Parameters | ||
| ---------- | ||
| technology_list : list of :class:`osier.Technology` | ||
| The list of :class:`Technology` objects to dispatch -- i.e. decide | ||
| how much energy each technology should produce. | ||
| net_demand : list, :class:`numpy.ndarray`, :class:`unyt.array.unyt_array`, :class:`pandas.DataFrame` | ||
| The remaining energy demand to be fulfilled by the technologies in | ||
| :attr:`technology_list`. The `values` of an object passed as | ||
| `net_demand` are used to create a supply constraint. See | ||
| :attr:`oversupply` and :attr:`undersupply`. | ||
| If a :class:`pandas.DataFrame` is passed, :mod:`osier` will try | ||
| inferring a `time_delta` from the dataframe index. Otherwise, the | ||
| :attr:`time_delta` must be passed or the default is used. | ||
| time_delta : str, :class:`unyt.unyt_quantity`, float, int | ||
| Specifies the amount of time between two time slices. The default is | ||
| one hour. Can be overridden by specifying a unit with the value. For | ||
| example: | ||
| >>> time_delta = "5 minutes" | ||
| >>> from unyt import days | ||
| >>> time_delta = 30*days | ||
| would both work. | ||
| power_units : str, :class:`unyt.unit_object` | ||
| Specifies the units for the power demand. The default is :attr:`MW`. | ||
| Can be overridden by specifying a unit with the value. | ||
| verbosity : Optional, int | ||
| Sets the logging level for the simulation. Accepts `logging.LEVEL` | ||
| or integer where LEVEL is {10:DEBUG, 20:INFO, 30:WARNING, 40:ERROR, 50:CRITICAL}. | ||
| curtailment : boolean | ||
| Indicates if the model should enable a curtailment option. | ||
| allow_blackout : boolean | ||
| If True, a "reliability" technology is added to the model that will | ||
| fulfill the mismatch in supply and demand. This reliability technology | ||
| has a variable cost of 1e4 $/MWh. The value must be higher than the | ||
| variable cost of any other technology to prevent a pathological | ||
| preference for blackouts. Default is False. | ||
| Attributes | ||
| ---------- | ||
| objective : float | ||
| The result of the model's objective function. Only instantiated | ||
| after :meth:`DispatchModel.solve()` is called. | ||
| technology_list : list of :class:`osier.Technology` | ||
| A _sorted_ list of technologies. | ||
| original_order : list of str | ||
| A list of technology names to preserve the intended order of the | ||
| technologies. | ||
| """ | ||
| def __init__(self, | ||
| technology_list, | ||
| net_demand, | ||
| allow_blackout=False, | ||
| curtailment=True, | ||
| verbosity=50, | ||
| *args, **kwargs): | ||
| super().__init__(technology_list=technology_list, | ||
| net_demand=net_demand, | ||
| *args, **kwargs) | ||
| self.technology_list = technology_list | ||
| self.technology_list.sort() | ||
| self.original_order = get_tech_names(technology_list) | ||
| self.cost_history = np.zeros(len(net_demand)) | ||
| self.covered_demand = None | ||
| self.objective = None | ||
| self.results = None | ||
| self.verbosity = verbosity | ||
| self.allow_blackout = allow_blackout | ||
| self.curtailment = curtailment | ||
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| def _reset_all(self): | ||
| for t in self.technology_list: | ||
| t.reset_history() | ||
| return | ||
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| def _format_results(self): | ||
| data = {} | ||
| for t in self.technology_list: | ||
| data[f"{t.technology_name}"] = unyt_array( | ||
| t.power_history).to_ndarray() | ||
| if t.technology_type == 'storage': | ||
| data[f"{t.technology_name}_level"] = unyt_array( | ||
| t.storage_history).to_ndarray() | ||
| data[f"{t.technology_name}_charge"] = unyt_array( | ||
| t.charge_history).to_ndarray() | ||
| data["Curtailment"] = np.array( | ||
| [v if v <= 0 else 0 for v in self.covered_demand]) | ||
| data["Shortfall"] = np.array( | ||
| [v if v > 0 else 0 for v in self.covered_demand]) | ||
| self.results = pd.DataFrame(data) | ||
| return | ||
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| def _calculate_objective(self): | ||
| self.objective = sum(np.array(t.power_history).sum() | ||
| * t.variable_cost.to_value() | ||
| for t in self.technology_list) | ||
| return | ||
|
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| def solve(self): | ||
| """ | ||
| This function executes the model solve with a rule-based approach. | ||
| """ | ||
| self.covered_demand = self.net_demand.copy() | ||
| self._reset_all() | ||
| try: | ||
| for i, v in enumerate(self.covered_demand): | ||
| for t in self.technology_list: | ||
| power_out = t.power_output(v, time_delta=self.time_delta) | ||
| v -= power_out | ||
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| self.covered_demand[i] = v | ||
| if not self.allow_blackout and (v > 0): | ||
| if self.verbosity <= 20: | ||
| print('solve failed -- unmet demand') | ||
| raise ValueError | ||
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| if not self.curtailment and (v < 0): | ||
| if self.verbosity <= 20: | ||
| print( | ||
| ('solve failed -- ' | ||
| 'too much overproduction ' | ||
| '(no curtailment allowed)')) | ||
| raise ValueError | ||
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| self._format_results() | ||
| self._calculate_objective() | ||
| except ValueError: | ||
| if self.verbosity <= 30: | ||
| warnings.warn( | ||
| (f"Infeasible or no solution." | ||
| f"Objective set to {LARGE_NUMBER}") | ||
| ) | ||
| self.objective = LARGE_NUMBER | ||
|
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| return |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,80 @@ | ||
| import pandas as pd | ||
| import numpy as np | ||
| from unyt import unyt_array, hr, MW, kW, GW | ||
| import itertools as it | ||
| from osier import Technology | ||
| from osier.technology import _validate_quantity, _validate_unit | ||
| from osier.utils import synchronize_units | ||
| import warnings | ||
| import logging | ||
|
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| _freq_opts = {'D': 'day', | ||
| 'H': 'hour', | ||
| 'S': 'second', | ||
| 'T': 'minute'} | ||
|
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| class OsierModel(): | ||
| """ | ||
| A class for instantiating energy models in Osier. | ||
| """ | ||
|
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| def __init__(self, | ||
| technology_list, | ||
| net_demand, | ||
| time_delta=None, | ||
| power_units=MW, | ||
| **kwargs): | ||
| self.net_demand = net_demand | ||
| self.time_delta = time_delta | ||
| self.results = None | ||
| self.objective = None | ||
|
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| if isinstance(net_demand, unyt_array): | ||
| self.power_units = net_demand.units | ||
| elif isinstance(net_demand, (np.ndarray, list)): | ||
| self.power_units = power_units | ||
| self.net_demand = np.array(self.net_demand) * self.power_units | ||
| elif isinstance(net_demand, pd.core.series.Series): | ||
| self.power_units = power_units | ||
| self.net_demand = np.array(self.net_demand) * self.power_units | ||
| else: | ||
| self.power_units = power_units | ||
|
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| self.technology_list = synchronize_units( | ||
| technology_list, | ||
| unit_power=self.power_units, | ||
| unit_time=self.time_delta.units) | ||
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| @property | ||
| def time_delta(self): | ||
| return self._time_delta | ||
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| @time_delta.setter | ||
| def time_delta(self, value): | ||
| if value: | ||
| valid_quantity = _validate_quantity(value, dimension='time') | ||
| self._time_delta = valid_quantity | ||
| else: | ||
| if isinstance(self.net_demand, pd.DataFrame): | ||
| try: | ||
| freq_list = list(self.net_demand.index.inferred_freq) | ||
| freq_key = freq_list[-1] | ||
| try: | ||
| value = float(freq_list[0]) | ||
| except ValueError: | ||
| warnings.warn((f"Could not convert value " | ||
| f"{freq_list[0]} to float. " | ||
| "Setting to 1.0."), | ||
| UserWarning) | ||
| value = 1.0 | ||
| self._time_delta = _validate_quantity( | ||
| f"{value} {_freq_opts[freq_key]}", dimension='time') | ||
| except KeyError: | ||
| warnings.warn( | ||
| (f"Could not infer time delta with freq {freq_key} " | ||
| "from pandas dataframe. Setting delta to 1 hour."), | ||
| UserWarning) | ||
| self._time_delta = 1 * hr | ||
| else: | ||
| self._time_delta = 1 * hr |
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