hyveopt.single_bus_pv_el_battery_det package

Submodules

hyveopt.single_bus_pv_el_battery_det.single_bus_pv_el_battery_det module

class hyveopt.single_bus_pv_el_battery_det.single_bus_pv_el_battery_det.PlanningTimeConfig(planning_horizon_start: Timestamp, planning_horizon_end: Timestamp, scenarios_per_year: int, scenario_resolution: str, scenario_subsampling: str)[source]

Bases: SerializableDataClass

Configuration class for defining time-related parameters in a planning horizon.

Parameters:

  • planning_horizon_start (str or pandas.Timestamp) – The start date of the planning horizon. If a string is provided, it will be converted to a pandas.Timestamp.

  • planning_horizon_end (str or pandas.Timestamp) – The end date of the planning horizon. If a string is provided, it will be converted to a pandas.Timestamp.

  • scenarios_per_year (int) – Number of representative scenarios to generate per year.

  • scenario_resolution (str) – Temporal resolution of each scenario. For example, ‘1D’ represents daily scenarios, ‘1W’ represents weekly scenarios, etc.

  • scenario_subsampling (str) – Subsampling frequency used for dynamic modeling purposes, such as battery simulation within the defined scenario. For example, ‘1h’ for hourly subsampling.

Raises:

ValueError – If planning_horizon_end is earlier than or equal to planning_horizon_start.

Examples

Create a time configuration for a planning horizon from 2025 to 2065 with daily scenarios and hourly subsampling:

```python time_config = TimeConfig(

planning_horizon_start=’2025-01-01’, planning_horizon_end=’2065-12-31’, scenarios_per_year=12, scenario_resolution=’1D’, scenario_subsampling=’1h’

)

property planning_horizon_duration: Timedelta
planning_horizon_end: Timestamp
planning_horizon_start: Timestamp
property range: DatetimeIndex

Returns the range of dates from planning_horizon_start to planning_horizon_end at the resolution specified by scenario_resolution.

Returns:

pd.DatetimeIndex – A range of dates between planning_horizon_start and planning_horizon_end using the frequency defined in scenario_resolution.

Examples

>>> time_config = TimeConfig(
...     planning_horizon_start='2025-01-01',
...     planning_horizon_end='2025-12-31',
...     scenarios_per_year=12,
...     scenario_resolution='1D',
...     scenario_subsampling='1h'
... )
>>> time_config.range
DatetimeIndex(['2025-01-01', '2025-01-02', ..., '2025-12-31'], dtype='datetime64[ns]', freq='D')
property scenario_duration: Timedelta
scenario_resolution: str
scenario_subsampling: str
scenarios_per_year: int
class hyveopt.single_bus_pv_el_battery_det.single_bus_pv_el_battery_det.SCIPSolution(el_size: float, pv_size: float, bat_size: float, bat_pin: pandas.core.frame.DataFrame, bat_pout: pandas.core.frame.DataFrame, bat_soc: pandas.core.frame.DataFrame, pv_gen: pandas.core.frame.DataFrame, el_gen: pandas.core.frame.DataFrame)[source]

Bases: object

bat_pin: DataFrame
bat_pout: DataFrame
bat_size: float
bat_soc: DataFrame
el_gen: DataFrame
el_size: float
print_report()[source]
pv_gen: DataFrame
pv_size: float
class hyveopt.single_bus_pv_el_battery_det.single_bus_pv_el_battery_det.SerializableDataClass[source]

Bases: object

class hyveopt.single_bus_pv_el_battery_det.single_bus_pv_el_battery_det.SingleBusDesignData(plant_template: hyveopt.pdata.PlantTemplate, tsdata: shilps.components.DataTimeSeries, time_config: hyveopt.single_bus_pv_el_battery_det.single_bus_pv_el_battery_det.PlanningTimeConfig, budget_inv_M: float = 100.0)[source]

Bases: SerializableDataClass

budget_inv_M: float
static generate_tsdata(plant_template: PlantTemplate, time_config: PlanningTimeConfig) → DataTimeSeries[source]
property n_years
plant_template: PlantTemplate
property sce_default
time_config: PlanningTimeConfig
tsdata: DataTimeSeries
property years

Returns a sorted list of years in the time series data.

class hyveopt.single_bus_pv_el_battery_det.single_bus_pv_el_battery_det.SingleBusDesignModel(pdata: SingleBusDesignData)[source]

Bases: object

AMPL_MODEL = '/home/jp/Dropbox/Projects/event_simulator/hyveopt/src/hyveopt/single_bus_pv_el_battery_det/single_bus_pv_el_battery_det.mod'
class SolverParams(mip_solver: str = 'highs')[source]

Bases: object

mip_solver: str = 'highs'
build_scip_model()[source]
depr_rate_sce(scenario: int) → float[source]

Returns the depreciation rate for a given scenario.

Parameters:

scenario (int) – The scenario index.

Returns:

float – The depreciation rate for the scenario.

depr_rate_year(year: int) → float[source]

Returns the depreciation rate for a given year.

get_scenarios_per_year()[source]
parse_glpsol_output()[source]
plot_daily_profiles(solution: SCIPSolution)[source]
plot_solution(solution: SCIPSolution)[source]

Plots key variables (PV generation, Electrolyzer generation, Battery SOC) for each scenario, with the x-axis representing hours.

report(solution=None)[source]
retrieve_scip_solution() → SCIPSolution[source]
scenario2year(scenario: int) → int[source]
solve()[source]
hyveopt.single_bus_pv_el_battery_det.single_bus_pv_el_battery_det.generate_scenario_time_ranges(time_config: PlanningTimeConfig) → Dict[Tuple[int, int], DatetimeIndex][source]

Generate a set of time ranges indexed by a tuple (year, scenario), where each time range corresponds to a randomly selected time period within the year, using the scenario resolution and scenario subsampling frequency from the TimeConfig.

Parameters:

time_config (TimeConfig) – The configuration that defines the planning horizon, scenario resolution, and subsampling frequency.

Returns:

Dict[Tuple[int, int], pd.DatetimeIndex] – A dictionary where the keys are tuples (year, scenario), and the values are the corresponding time ranges with subsampling frequency.

Module contents