annular.satellite_model.multi_profile_strategy

Classes

MultiProfileBiddingStrategy

A 24-hour bidding strategy to bid using multiple profiles based on multiple scenarios.

Module Contents

class annular.satellite_model.multi_profile_strategy.MultiProfileBiddingStrategy(demands_path: pathlib.Path | str, forecasts_path: pathlib.Path | str, carrier_prices_path: pathlib.Path | str, cronian_config_path: pathlib.Path | str, tariff_folder: pathlib.Path | str | None = None, tariff_categories: dict[str, str | int] | None = None, floor_price: int = 0, horizon_size: int = 48, cronian_storage_model: str = 'simple', forecast_scaling_factor: float = 1.5, **kwargs)

Bases: annular.satellite_model.satellite_model.SatelliteModel

A 24-hour bidding strategy to bid using multiple profiles based on multiple scenarios.

See also

annular.tariffs.TariffManager

Parameters:

• demands_path – Path to csv file with demand values per timestamp, with different flexibility as separate columns named ‘flex+N’.

• forecasts_path – Path to csv file containing the electricity price forecast based on which multiple forecast scenarios will be generated by adjusting with the forecast_scaling_factor.

• carrier_prices_path – Path to csv file containing price timeseries for any energy carrier other than electricity such as methane, hydrogen, etc.

• cronian_config_path – Path to the Cronian configuration describing the prosumer’s demand and assets for building its optimization model.

• tariff_folder – Path to the folder with tariff data; passed to a TariffManager.

• tariff_categories – Dictionary that indicates the tariff category the satellite is subject to. Keys are categories, and values are the levels in that category.

• floor_price – Minimum price to bid at, defaults to 0.

• horizon_size – Full length of the horizon to use for an optimization iteration, i.e., bidding window + look ahead period, in number of snapshots.

• forecast_scaling_factor – Factor to scale up/down the electricity price forecast from the base scenario to create multiple forecast scenarios.

• cronian_storage_model – Type of storage model (simple or complex) to use in the optimization model if prosumer has storage assets.

• kwargs – Any other keyword arguments are passed to the initialization of the base class.

cur_timestamp_idx = 0

floor_price = 0

horizon_size = 48

forecast_scaling_factor = 1.5

storage_model = 'simple'

demands = None

base_forecast = None

carrier_prices = None

manager

max_electricity_withdrawal_so_far = 0.0

property bidding_window: pandas.Index

Returns current bidding window as a pandas Index of timestamps.

determine_bids() → pandas.DataFrame

Determine bids based on multiple forecast price scenarios.

Different forecast price scenarios are made as variation of the given electricity_price_forecast. An optimization is run based on each of these scenarios, to determine the corresponding demand profile.

The set of these profiles is submitted in a single exclusive group, all bid for at ceiling price.

Returns:

A collection of bids covering the current bidding window.

meet_demand(market_price: numpy.ndarray | None, demand_met: numpy.ndarray | None) → None

Process the amount of demand that was met.

Solve the cronian model again, now with the electric power fixed to the values given from the central market. Record dispatch of assets and update the state of charge for any storage assets.

Parameters:

• market_price – Price of electricity as provided per timestep.

• demand_met – Amount of demand that was met at the market price per timestep.

_update_internal_state(horizon: pandas.Index) → None

Store intermediate values to carry over to the next rolling horizon.

Specifically, these values are the energy levels of storage assets if any.

Parameters:

horizon – Current optimization horizon.

_get_horizon(length: int = None) → pandas.Index

Select the relevant horizon index values at the current timestamp index.

Parameters:

length – length of the desired horizon in number of timestamps. Uses self.horizon_size when None is given.

Returns:

Pandas Index object of the selected horizon values.

static make_forecast_scenarios(base_forecast: pandas.DataFrame, forecast_scaling_factor: float) → dict[str, pandas.DataFrame]

Create different scenarios from a base forecast.

The different scenarios are created by either scaling the base forecast according to some profile, or by scaling a fixed profile according to a numerical property of the base forecast.

The following scenarios are created:

• scale_up: base_forecast_values * forecast_scaling_factor

• scale_down: base_forecast_values / forecast_scaling_factor

• scale_up_increasing: base_forecast_values * linear_increase

• scale_down_increasing: base_forecast_values * linear_decrease

• peak_mid: base_forecast_values * peak_shape_scaling

• valley_mid: base_forecast_values * valley_shape_scaling

• day_period_boost: base_forecast_values * boost_day_period

• morning_evening_boost: base_forecast_values * boost_morning_evening

• base_min_day_period_boost: base_forecast_min * boost_day_period

• base_min_morning_evening_boost: base_forecast_min * boost_morning_evening

• base_max_day_period_boost: base_forecast_max * boost_day_period

• base_max_morning_evening_boost: base_forecast_max * boost_morning_evening

Parameters:

• base_forecast – Electricity price forecast used as a base for generating different scenarios of electricity price variations.

• forecast_scaling_factor – Factor to scale the base forecast to create different scenarios.

Returns:

Electricity price forecast scenarios.

Raises:

ValueError – If the length of the forecast is not 24.

static plot_forecast_scenarios(forecast_scenarios: dict[str, pandas.DataFrame], output_path) → None

Plot the electricity price forecast scenarios.

Parameters:

• forecast_scenarios – Dictionary of forecast scenarios.

• output_path – Path where the plot will be saved.

determine_power_profile(horizon: pandas.Index, scenario_forecast: pandas.DataFrame) → list[float]

Determine electricity consumption/generation profile based on a given scenario.

Parameters:

• horizon – Time horizon.

• scenario_forecast – DataFrame containing forecasted electricity prices.

• model – Abstract pyomo optimization model.

Returns:

Optimized electricity power profile for the given forecast price.

_make_bids(profiles: dict[str, list[float]]) → pandas.DataFrame

Create a bids table to submit given profiles as an exclusive group.

Actual bid entries are only made for timestamps with non-zero demand.

Parameters:

profiles – dictionary of lists of demand values for each timestamp.

Returns:

pd.DataFrame table of the constructed bids.

_create_model(horizon: pandas.Index) → pyomo.environ.AbstractModel

Create a model instance using cronian.

Parameters:

• horizon – Timestamps spanning the optimization horizon currently of interest.

• e_price – Forecasted electricity price.

Returns:

Abstract model for this satellite in the current optimization horizon.

_add_cost_objective(model: pyomo.environ.AbstractModel) → pyomo.environ.AbstractModel

Create a cost minimization or revenue maximization objective.

Depending on the sign of the electric_power variable, the objective minimizes electricity consumption cost or maximizes revenues from electricity generated locally. It also minimizes the cost of consuming other externally priced energy carriers such as methane, biomass, etc.

Parameters:

model – Pyomo model to add the cost objective to.

Returns:

The given model object, now with cost_objective added.

_define_max_electricity_withdrawal(model: pyomo.environ.AbstractModel) → pyomo.environ.AbstractModel

Define maximum electricity withdrawal for modeling capacity tariffs.

This function defines the maximum electricity withdrawal in the current optimization horizon to which a capacity tariff charge is applied. Directly computing max withdrawal as max (withdrawals in current horizon, previous maximum withdrawal) will lead to a nonlinear optimization problem due to the max() operator. We linearize this by introducing a variable representing the maximum power withdrawal and enforcing the following constraints on it: (1) It must be greater than or equal to the instantaneous withdrawals in the current horizon. (2) It must be greater than or equal to the recorded maximum withdrawal from previous horizons.

Parameters:

model – Pyomo model to add the constraint to.

Returns:

The given model object, now with capacity_tariff_constraint added.

_add_contracted_capacity_limit_constraint(model: pyomo.environ.AbstractModel) → pyomo.environ.AbstractModel

Constrain max electricity withdrawal based on contracted transport limit.

NOTE: We assume that the unit of the contracted_transport_limit is MW.

Parameters:

model – Pyomo model to add the constraint to.

Returns:

The given model object, now with max_withdrawal_in_previous_constraint added.