hydesign.ems.ems_hifi_dems

Attributes

dic

Classes

ems	Energy management optimization model

Functions

ems_cplex(parameter_dict, simulation_dict[, BM_model, ...])

Module Contents

class hydesign.ems.ems_hifi_dems.ems(parameter_dict, simulation_dict, market_fn, N_time, life_y=25, intervals_per_hour=4, BM_model=False, RD_model=True)

Bases: openmdao.api.ExplicitComponent

Energy management optimization model The energy management system optimization model consists in maximizing the revenue generated by the plant over a period of time, including a possible penalty for not meeting the requirement of energy generation during peak hours over the period. It also assigns a cost for rapid fluctuations of the battery in order to slow down its degradation. The EMS type can be either a CPLEX optimization or a rule-based ems (Faster but not as optimal).

Parameters:

• wind_t (WPP power time series [MW])

• solar_t (PVP power time series [MW])

• price_t (Electricity price time series)

• b_P (Battery power capacity [MW])

• b_E (Battery energy storage capacity [MW])

• G_MW (Grid capacity [MW])

• battery_depth_of_discharge (battery depth of discharge)

• peak_hr_quantile (Quantile of price time series to define peak price hours (above this quantile))

• cost_of_battery_P_fluct_in_peak_price_ratio (cost of battery power fluctuations computed as a peak price ratio)

• n_full_power_hours_expected_per_day_at_peak_price (Penalty occurs if number of full power hours expected per day at peak price are not reached)

Returns:

• wind_t_ext (WPP power time series)

• solar_t_ext (PVP power time series)

• price_t_ext (Electricity price time series)

• hpp_t (HPP power time series)

• hpp_curt_t (HPP curtailed power time series)

• b_t (Battery charge/discharge power time series)

• b_E_SOC_t (Battery energy SOC time series)

• penalty_t (Penalty for not reaching expected energy productin at peak hours)

parameter_dict

simulation_dict

market_fn

intervals_per_hour = 4

life_y = 25

life_h = 219000

life_intervals = 876000

yearly_intervals = 35040

BM_model = False

RD_model = True

N_time

setup()

Declare inputs and outputs.

Available attributes:

name pathname comm options

setup_partials()

Declare partials.

This is meant to be overridden by component classes. All partials should be declared here since this is called after all size/shape information is known for all variables.

compute_partials(inputs, partials)

Compute sub-jacobian parts. The model is assumed to be in an unscaled state.

Parameters:

• inputs (Vector) – Unscaled, dimensional input variables read via inputs[key].

• partials (Jacobian) – Sub-jac components written to partials[output_name, input_name]..

• discrete_inputs (dict or None) – If not None, dict containing discrete input values.

compute(inputs, outputs)

Compute outputs given inputs. The model is assumed to be in an unscaled state.

An inherited component may choose to either override this function or to define a compute_primal function.

Parameters:

• inputs (Vector) – Unscaled, dimensional input variables read via inputs[key].

• outputs (Vector) – Unscaled, dimensional output variables read via outputs[key].

• discrete_inputs (dict-like or None) – If not None, dict-like object containing discrete input values.

• discrete_outputs (dict-like or None) – If not None, dict-like object containing discrete output values.

hydesign.ems.ems_hifi_dems.ems_cplex(parameter_dict, simulation_dict, BM_model=False, RD_model=False)

hydesign.ems.ems_hifi_dems.dic