Battery Storage

Contents

Battery Storage#

Stateful Battery Storage#

Battery Storage class based on PySAM’s BatteryStateful Model

Battery Model#

class hopp.simulation.technologies.battery.battery.Battery(site, config, config_name='StandaloneBatterySingleOwner')#

Bases: PowerSource

Battery storage class based on PySAM’s BatteryStateful Model.

Parameters:

  • site (SiteInfo) – Site information

  • config (BatteryConfig) – Battery configuration

  • config_name (str)

site: SiteInfo#
config: BatteryConfig#
config_name: str#
outputs: BatteryOutputs#
module_specs = {'capacity': 400, 'surface_area': 30}#
setup_system_model()#

Executes Stateful Battery setup

property system_capacity_voltage: tuple#

Battery energy capacity [kWh] and voltage [VDC]

property system_capacity_kwh: float#

Battery energy capacity [kWh]

property system_capacity_kw: float#

Battery power rating [kW]

property system_voltage_volts: float#

Battery bank voltage [VDC]

property system_mass: float#

Battery bank mass [kg]

property footprint_area: float#

Battery bank footprint area [m^2]

property energy_capital_cost: float | int#

The capital cost per unit of energy capacity [$/kWh] for battery storage technology

property power_capital_cost: float | int#

The capital cost per unit of power capacity [$/kW] for battery storage technology

property chemistry: str#

Battery chemistry type

setup_performance_model()#

Executes Stateful Battery setup

simulate_with_dispatch(n_periods, sim_start_time=None)#

Step through dispatch solution for battery and simulate battery

Parameters:

  • n_periods (int) – Number of hours to simulate [hrs]

  • sim_start_time (int | None) – Start hour of simulation horizon

simulate_power(time_step=None)#

Runs battery simulate and stores values if time step is provided

Parameters:

time_step – (optional) if provided outputs are stored, o.w. they are not stored.

update_battery_stored_values(time_step)#

Stores Stateful battery.outputs at time step provided.

Parameters:

time_step – time step where outputs will be stored.

validate_replacement_inputs(project_life)#

Checks that the battery replacement part of the model has the required inputs and that they are formatted correctly.

batt_bank_replacement is a required array of length (project_life + 1), where year 0 is “financial year 0” and is prior to system operation If the battery replacements are to follow a schedule (batt_replacement_option == 2), the batt_replacement_schedule_percent is required. This array is of length (project_life), where year 0 is the first year of system operation.

__init__(site, config, config_name='StandaloneBatterySingleOwner')#

Method generated by attrs for class Battery.

Parameters:
Return type:

None

_get_model_dict()#

Convenience method that wraps the attrs.asdict method. Returns the object’s parameters as a dictionary.

Returns:

dict – The provided or default, if no input provided, model settings as a dictionary.

Return type:

dict

as_dict()#

Creates a JSON and YAML friendly dictionary that can be save for future reloading. This dictionary will contain only Python types that can later be converted to their proper Turbine formats.

Returns:

dict – All key, vaue pais required for class recreation.

Return type:

dict

assign(input_dict)#

Sets input variables in the PowerSource class or any of its subclasses (system or financial models)

Parameters:

input_dict (dict)

property benefit_cost_ratio: float#

Benefit cost ratio [-] = Benefits / Costs

Benefits include (using present values):

  1. PPA, capacity payment, and curtailment revenues

  2. Federal, state, utility, and other production-based incentive income

  3. Salvage value

Costs: uses the present value of annual costs

calc_capacity_credit_percent(interconnect_kw)#

Calculates the capacity credit (value) using the last simulated year’s max feasible generation profile.

Parameters:

interconnect_kw (float) – Interconnection limit [kW]

Returns:

capacity value [%]

Return type:

float

calc_nominal_capacity(interconnect_kw)#

Calculates the nominal AC net system capacity based on specific technology.

Parameters:

interconnect_kw (float) – Interconnection limit [kW]

Returns:

system’s nominal AC net capacity [kW]

property capacity_credit_percent: float#

Capacity credit (eligible portion of nameplate) [%]

property capacity_factor: float#

System capacity factor [%]

property capacity_payment: list#

Capacity payment revenue [$]

property capacity_price: list#

Capacity payment price [$/MW]

property construction_financing_cost: float#
copy()#
Returns:

new instance

property cost_installed: float#

Net capital cost [$]

property debt_payment: tuple#

Debt total payment [$]

property degradation: tuple#

Annual energy degradation [%/year]

property dispatch#

Dispatch object

property dispatch_factors: tuple#

Time-series dispatch factors normalized by PPA price [-]

property energy_purchases: tuple#

Energy purchases from grid [$]

property energy_sales: tuple#

PPA revenue gross [$]

property energy_value: tuple#

PPA revenue net [$]

export()#
Returns:

dictionary of variables for system and financial

property federal_depreciation_total: tuple#

Total federal tax depreciation [$]

property federal_taxes: tuple#

Federal tax benefit (liability) [$]

classmethod from_dict(data)#

Maps a data dictionary to an attr-defined class.

TODO: Add an error to ensure that either none or all the parameters are passed in

Parameters:

data (dict) – dict The data dictionary to be mapped.

Returns:

cls

The attr-defined class.

property gen_max_feasible: list#

Maximum feasible generation profile that could have occurred (year 1)

classmethod get_model_defaults()#

Produces a dictionary of the keyword arguments and their defaults.

Returns:

Dict[str, Any] – Dictionary of keyword argument: default.

Return type:

Dict[str, Any]

static import_financial_model(financial_model, system_model, config_name)#
initialize_financial_values()#

These values are provided as default values from PySAM but should be customized by user

Debt, Reserve Account and Construction Financing Costs are initialized to 0 Federal Bonus Depreciation also initialized to 0

property insurance_expense: tuple#

Insurance expense [$]

property internal_rate_of_return: float#

Internal rate of return (after-tax) [%]

property levelized_cost_of_energy_nominal: float#

Levelized cost (nominal) [cents/kWh]

property levelized_cost_of_energy_real: float#

Levelized cost (real) [cents/kWh]

property logger#
property net_present_value: float#

After-tax cumulative NPV [$]

property om_capacity#

Capacity-based O&M amount [$/kWcap]

property om_capacity_expense#

O&M capacity-based expense [$]

property om_fixed#

Fixed O&M annual amount [$/year]

property om_fixed_expense#

O&M fixed expense [$]

property om_production#

Production-based O&M amount [$/Mwh]

property om_total_expense#

Total operating expenses [$]

property om_variable#

Production-based O&M amount [$/kWh] For battery: production-based System Costs amount [$/kWh-discharged]

Type:

For non-battery technologies

property om_variable_expense#

O&M production-based expense [$]

plot(figure=None, axes=None, color='b', site_border_color='k', site_alpha=0.95, linewidth=4.0)#
property ppa_price: tuple#

PPA price [$/kWh]

set_overnight_capital_cost(energy_capital_cost, power_capital_cost)#

Set overnight capital costs [$/kW].

This method calculates and sets the overnight capital cost based on the given energy and power capital costs.

Parameters:

  • energy_capital_cost (float) – The capital cost per unit of energy capacity [$/kWh].

  • power_capital_cost (float) – The capital cost per unit of power capacity [$/kW].

Returns:

None – This method does not return any value. The calculated overnight capital cost is stored internally.

Note

The overnight capital cost is calculated using the formula: overnight_capital_cost = (energy_capital_cost * hours) + power_capital_cost where hours is the ratio of energy capacity to power capacity.

Example

>>> set_overnight_capital_cost(1500, 500)
simulate(interconnect_kw, project_life=25, lifetime_sim=False)#

Run the system and financial model

Parameters:

  • project_life (int) – int, Number of year in the analysis period (execepted project lifetime) [years]

  • lifetime_sim – bool, For simulation modules which support simulating each year of the project_life, whether or not to do so; otherwise the first year data is repeated

  • interconnect_kw (float)

property system_nameplate_mw: float#

System nameplate [MW]

property tax_incentives: list#

The sum of Federal and State PTC and ITC tax incentives [$]

property total_installed_cost: float#

Installed cost [$]

property total_revenue: list#

Total revenue [$]

value(var_name, var_value=None)#

Gets or Sets a variable value within either the system or financial PySAM models. Method looks in system model first. If unsuccessful, then it looks in the financial model.

Note

If system and financial models contain a variable with the same name, only the system model variable will be set.

value(var_name) Gets variable value

value(var_name, var_value) Sets variable value

Parameters:

  • var_name (str) – PySAM variable name

  • var_value – (optional) PySAM variable value

Returns:

Variable value (when getter)

calculate_total_installed_cost(energy_capital_cost, power_capital_cost)#
Parameters:

  • energy_capital_cost (float)

  • power_capital_cost (float)

Return type:

float

simulate_financials(interconnect_kw, project_life, cap_cred_avail_storage=True)#

Sets-up and simulates financial model for the battery

Parameters:

  • interconnect_kw (float) – Interconnection limit [kW]

  • project_life (int) – Analysis period [years]

  • cap_cred_avail_storage (bool) – Base capacity credit on available storage (True), otherwise use only dispatched generation (False)

calc_gen_max_feasible_kwh(interconnect_kw, use_avail_storage=True)#

Calculates the maximum feasible capacity (generation profile) that could have occurred.

Parameters:

  • interconnect_kw – Interconnection limit [kW]

  • use_avail_storage (bool) – Base capacity credit on available storage (True), otherwise use only dispatched generation (False)

Returns:

Maximum feasible capacity [kWh]

Return type:

List[float]

property generation_profile: Sequence#

System power generated [kW]

property replacement_costs: Sequence#

Battery replacement cost [$]

property annual_energy_kwh: float#

Annual energy [kWh]

Battery Configuration#

class hopp.simulation.technologies.battery.battery.BatteryConfig(system_capacity_kwh, system_capacity_kw, system_model_source='pysam', chemistry='LFPGraphite', tracking=True, minimum_SOC=10, maximum_SOC=90, initial_SOC=10, fin_model=None, name='Battery')#

Bases: BaseClass

Configuration class for Battery.

Parameters:

  • tracking (bool) – default True -> Battery

  • system_capacity_kwh (float) – Battery energy capacity [kWh]

  • system_capacity_kw (float) – Battery rated power capacity [kW]

  • system_model_source (str) – software source for the system model, can by ‘pysam’ or ‘hopp’

  • chemistry (str) –

    Battery chemistry option

    PySAM options:

    • ”LFPGraphite” (default)

    • ”LMOLTO”

    • ”LeadAcid”

    • ”NMCGraphite”

    HOPP options:

    • ”LDES” generic long-duration energy storage

  • minimum_SOC (float) – Minimum state of charge [%]

  • maximum_SOC (float) – Maximum state of charge [%]

  • initial_SOC (float) – Initial state of charge [%]

  • fin_model (str | dict | Singleowner | CustomFinancialModel | None) – Financial model. Can be any of the following: - a dict representing a CustomFinancialModel - an object representing a CustomFinancialModel or a Singleowner.Singleowner instance

  • name (str)

system_capacity_kwh: float#
system_capacity_kw: float#
system_model_source: str#
chemistry: str#
tracking: bool#
minimum_SOC: float#
maximum_SOC: float#
initial_SOC: float#
fin_model: str | dict | Singleowner | CustomFinancialModel | None#
name: str#
__init__(system_capacity_kwh, system_capacity_kw, system_model_source='pysam', chemistry='LFPGraphite', tracking=True, minimum_SOC=10, maximum_SOC=90, initial_SOC=10, fin_model=None, name='Battery')#

Method generated by attrs for class BatteryConfig.

Parameters:

  • system_capacity_kwh (float)

  • system_capacity_kw (float)

  • system_model_source (str)

  • chemistry (str)

  • tracking (bool)

  • minimum_SOC (float)

  • maximum_SOC (float)

  • initial_SOC (float)

  • fin_model (str | dict | Singleowner | CustomFinancialModel | None)

  • name (str)

Return type:

None

_get_model_dict()#

Convenience method that wraps the attrs.asdict method. Returns the object’s parameters as a dictionary.

Returns:

dict – The provided or default, if no input provided, model settings as a dictionary.

Return type:

dict

as_dict()#

Creates a JSON and YAML friendly dictionary that can be save for future reloading. This dictionary will contain only Python types that can later be converted to their proper Turbine formats.

Returns:

dict – All key, vaue pais required for class recreation.

Return type:

dict

classmethod from_dict(data)#

Maps a data dictionary to an attr-defined class.

TODO: Add an error to ensure that either none or all the parameters are passed in

Parameters:

data (dict) – dict The data dictionary to be mapped.

Returns:

cls

The attr-defined class.

classmethod get_model_defaults()#

Produces a dictionary of the keyword arguments and their defaults.

Returns:

Dict[str, Any] – Dictionary of keyword argument: default.

Return type:

Dict[str, Any]

property logger#

Battery Outputs#

class hopp.simulation.technologies.battery.battery.BatteryOutputs(n_timesteps, n_periods_per_day)#

Bases: object

I: Sequence#
P: Sequence#
Q: Sequence#
SOC: Sequence#
T_batt: Sequence#
gen: Sequence#
n_cycles: Sequence#
dispatch_I: List[float]#
dispatch_P: List[float]#
dispatch_SOC: List[float]#
__init__(n_timesteps, n_periods_per_day)#

Class for storing stateful battery and dispatch outputs.

dispatch_lifecycles_per_day: List[int | None]#
The following outputs are simulated from the BatteryStateful model, an entry per timestep:

I: current [A] P: power [kW] Q: capacity [Ah] SOC: state-of-charge [%] T_batt: temperature [C] gen: same as P n_cycles: number of rainflow cycles elapsed since start of simulation [1]

The next outputs, an entry per timestep, are from the HOPP dispatch model, which are then passed to the simulation:

dispatch_I: current [A], only applicable to battery dispatch models with current modeled dispatch_P: power [mW] dispatch_SOC: state-of-charge [%]

This output has a different length, one entry per day:

dispatch_lifecycles_per_day: number of cycles per day

export()#

Stateless Battery Storage#

Battery Storage class with no system model for tracking the state of the battery.

Stateless Battery Model#

class hopp.simulation.technologies.battery.battery_stateless.BatteryStateless(site, config)#

Bases: PowerSource

Battery Storage class with no system model for tracking the state of the battery The state variables are pulled directly from the BatteryDispatch pyomo model. Therefore, this battery model is compatible only with dispatch methods that use pyomo such as:

  • ‘simple’: SimpleBatteryDispatch

  • ‘convex_LV’: ConvexLinearVoltageBatteryDispatch

  • ‘non_convex_LV’: NonConvexLinearVoltageBatteryDispatch

Parameters:
site: SiteInfo#
config: BatteryStatelessConfig#
minimum_SOC: float#
maximum_SOC: float#
initial_SOC: float#
simulate_with_dispatch(n_periods, sim_start_time=None)#

Step through dispatch solution for battery to collect outputs

Parameters:

  • n_periods (int) – Number of hours to simulate [hrs]

  • sim_start_time (int | None) – Start hour of simulation horizon

simulate_power(time_step=None)#

Runs battery simulate and stores values if time step is provided

Parameters:

time_step – (optional) if provided outputs are stored, o.w. they are not stored.

validate_replacement_inputs(project_life)#

Checks that the battery replacement part of the model has the required inputs and that they are formatted correctly.

batt_bank_replacement is a required array of length (project_life + 1), where year 0 is “financial year 0” and is prior to system operation If the battery replacements are to follow a schedule (batt_replacement_option == 2), the batt_replacement_schedule_percent is required. This array is of length (project_life), where year 0 is the first year of system operation.

export()#

Return all the battery system configuration in a dictionary for the financial model

simulate_financials(interconnect_kw, project_life)#

Sets-up and simulates financial model for the battery

Parameters:

  • interconnect_kw (float) – Interconnection limit [kW]

  • project_life (int) – Analysis period [years]

property system_capacity_kwh: float#

Battery energy capacity [kWh]

property system_capacity_kw: float#

Battery power rating [kW]

__init__(site, config)#

Method generated by attrs for class BatteryStateless.

Parameters:
Return type:

None

_get_model_dict()#

Convenience method that wraps the attrs.asdict method. Returns the object’s parameters as a dictionary.

Returns:

dict – The provided or default, if no input provided, model settings as a dictionary.

Return type:

dict

as_dict()#

Creates a JSON and YAML friendly dictionary that can be save for future reloading. This dictionary will contain only Python types that can later be converted to their proper Turbine formats.

Returns:

dict – All key, vaue pais required for class recreation.

Return type:

dict

assign(input_dict)#

Sets input variables in the PowerSource class or any of its subclasses (system or financial models)

Parameters:

input_dict (dict)

property benefit_cost_ratio: float#

Benefit cost ratio [-] = Benefits / Costs

Benefits include (using present values):

  1. PPA, capacity payment, and curtailment revenues

  2. Federal, state, utility, and other production-based incentive income

  3. Salvage value

Costs: uses the present value of annual costs

calc_capacity_credit_percent(interconnect_kw)#

Calculates the capacity credit (value) using the last simulated year’s max feasible generation profile.

Parameters:

interconnect_kw (float) – Interconnection limit [kW]

Returns:

capacity value [%]

Return type:

float

calc_gen_max_feasible_kwh(interconnect_kw)#

Calculates the maximum feasible generation profile that could have occurred (year 1)

Parameters:

interconnect_kw (float) – Interconnection limit [kW]

Returns:

maximum feasible generation [kWh]

Return type:

list

calc_nominal_capacity(interconnect_kw)#

Calculates the nominal AC net system capacity based on specific technology.

Parameters:

interconnect_kw (float) – Interconnection limit [kW]

Returns:

system’s nominal AC net capacity [kW]

calculate_total_installed_cost(cost)#
Parameters:

cost (float)

Return type:

float

property capacity_credit_percent: float#

Capacity credit (eligible portion of nameplate) [%]

property capacity_payment: list#

Capacity payment revenue [$]

property capacity_price: list#

Capacity payment price [$/MW]

property construction_financing_cost: float#
copy()#
Returns:

new instance

property cost_installed: float#

Net capital cost [$]

property debt_payment: tuple#

Debt total payment [$]

property degradation: tuple#

Annual energy degradation [%/year]

property dispatch#

Dispatch object

property dispatch_factors: tuple#

Time-series dispatch factors normalized by PPA price [-]

property energy_purchases: tuple#

Energy purchases from grid [$]

property energy_sales: tuple#

PPA revenue gross [$]

property energy_value: tuple#

PPA revenue net [$]

property federal_depreciation_total: tuple#

Total federal tax depreciation [$]

property federal_taxes: tuple#

Federal tax benefit (liability) [$]

classmethod from_dict(data)#

Maps a data dictionary to an attr-defined class.

TODO: Add an error to ensure that either none or all the parameters are passed in

Parameters:

data (dict) – dict The data dictionary to be mapped.

Returns:

cls

The attr-defined class.

property gen_max_feasible: list#

Maximum feasible generation profile that could have occurred (year 1)

classmethod get_model_defaults()#

Produces a dictionary of the keyword arguments and their defaults.

Returns:

Dict[str, Any] – Dictionary of keyword argument: default.

Return type:

Dict[str, Any]

static import_financial_model(financial_model, system_model, config_name)#
initialize_financial_values()#

These values are provided as default values from PySAM but should be customized by user

Debt, Reserve Account and Construction Financing Costs are initialized to 0 Federal Bonus Depreciation also initialized to 0

property insurance_expense: tuple#

Insurance expense [$]

property internal_rate_of_return: float#

Internal rate of return (after-tax) [%]

property levelized_cost_of_energy_nominal: float#

Levelized cost (nominal) [cents/kWh]

property levelized_cost_of_energy_real: float#

Levelized cost (real) [cents/kWh]

property logger#
property net_present_value: float#

After-tax cumulative NPV [$]

property om_capacity#

Capacity-based O&M amount [$/kWcap]

property om_capacity_expense#

O&M capacity-based expense [$]

property om_fixed#

Fixed O&M annual amount [$/year]

property om_fixed_expense#

O&M fixed expense [$]

property om_production#

Production-based O&M amount [$/Mwh]

property om_total_expense#

Total operating expenses [$]

property om_variable#

Production-based O&M amount [$/kWh] For battery: production-based System Costs amount [$/kWh-discharged]

Type:

For non-battery technologies

property om_variable_expense#

O&M production-based expense [$]

plot(figure=None, axes=None, color='b', site_border_color='k', site_alpha=0.95, linewidth=4.0)#
property ppa_price: tuple#

PPA price [$/kWh]

set_overnight_capital_cost(overnight_capital_cost)#

Set overnight capital costs [$/kW].

setup_performance_model()#

Sets up performance model to before simulating power production. Required by specific technologies

simulate(interconnect_kw, project_life=25, lifetime_sim=False)#

Run the system and financial model

Parameters:

  • project_life (int) – int, Number of year in the analysis period (execepted project lifetime) [years]

  • lifetime_sim – bool, For simulation modules which support simulating each year of the project_life, whether or not to do so; otherwise the first year data is repeated

  • interconnect_kw (float)

property system_nameplate_mw: float#

System nameplate [MW]

property tax_incentives: list#

The sum of Federal and State PTC and ITC tax incentives [$]

property total_installed_cost: float#

Installed cost [$]

property total_revenue: list#

Total revenue [$]

value(var_name, var_value=None)#

Gets or Sets a variable value within either the system or financial PySAM models. Method looks in system model first. If unsuccessful, then it looks in the financial model.

Note

If system and financial models contain a variable with the same name, only the system model variable will be set.

value(var_name) Gets variable value

value(var_name, var_value) Sets variable value

Parameters:

  • var_name (str) – PySAM variable name

  • var_value – (optional) PySAM variable value

Returns:

Variable value (when getter)

property nominal_energy: float#

Battery energy capacity [kWh]

property capacity_factor#

System capacity factor [%]

property generation_profile: Sequence#

System power generated [kW]

property annual_energy_kwh: float#

Annual energy [kWh]

property SOC: float#
property lifecycles: List[int | None]#

Stateless Battery Configuration#

class hopp.simulation.technologies.battery.battery_stateless.BatteryStatelessConfig(system_capacity_kwh, system_capacity_kw, tracking=False, minimum_SOC=10, maximum_SOC=90, initial_SOC=10, fin_model=None, name='BatteryStateless')#

Bases: BaseClass

Configuration class for BatteryStateless.

Converts nested dicts into relevant financial configurations.

Parameters:

  • tracking (bool) – default False -> BatteryStateless

  • system_capacity_kwh (float) – Battery energy capacity [kWh]

  • system_capacity_kw (float) – Battery rated power capacity [kW]

  • minimum_SOC (float) – Minimum state of charge [%]

  • maximum_SOC (float) – Maximum state of charge [%]

  • initial_SOC (float) – Initial state of charge [%]

  • fin_model (str | dict | Singleowner | CustomFinancialModel | None) –

    Financial model. Can be any of the following:

    • a dict representing a CustomFinancialModel

    • an object representing a CustomFinancialModel instance

  • name (str)

system_capacity_kwh: float#
system_capacity_kw: float#
tracking: bool#
minimum_SOC: float#
maximum_SOC: float#
initial_SOC: float#
fin_model: str | dict | Singleowner | CustomFinancialModel | None#
name: str#
__init__(system_capacity_kwh, system_capacity_kw, tracking=False, minimum_SOC=10, maximum_SOC=90, initial_SOC=10, fin_model=None, name='BatteryStateless')#

Method generated by attrs for class BatteryStatelessConfig.

Parameters:

  • system_capacity_kwh (float)

  • system_capacity_kw (float)

  • tracking (bool)

  • minimum_SOC (float)

  • maximum_SOC (float)

  • initial_SOC (float)

  • fin_model (str | dict | Singleowner | CustomFinancialModel | None)

  • name (str)

Return type:

None

_get_model_dict()#

Convenience method that wraps the attrs.asdict method. Returns the object’s parameters as a dictionary.

Returns:

dict – The provided or default, if no input provided, model settings as a dictionary.

Return type:

dict

as_dict()#

Creates a JSON and YAML friendly dictionary that can be save for future reloading. This dictionary will contain only Python types that can later be converted to their proper Turbine formats.

Returns:

dict – All key, vaue pais required for class recreation.

Return type:

dict

classmethod from_dict(data)#

Maps a data dictionary to an attr-defined class.

TODO: Add an error to ensure that either none or all the parameters are passed in

Parameters:

data (dict) – dict The data dictionary to be mapped.

Returns:

cls

The attr-defined class.

classmethod get_model_defaults()#

Produces a dictionary of the keyword arguments and their defaults.

Returns:

Dict[str, Any] – Dictionary of keyword argument: default.

Return type:

Dict[str, Any]

property logger#

Stateless Battery Outputs#

class hopp.simulation.technologies.battery.battery_stateless.BatteryStatelessOutputs(n_timesteps, n_periods_per_day)#

Bases: object

__init__(n_timesteps, n_periods_per_day)#

Class for storing battery.outputs.

I: List[float]#
P: List[float]#
SOC: List[float]#
lifecycles_per_day: List[int | None]#
The following outputs are from the HOPP dispatch model, an entry per timestep:

I: current [A], only applicable to battery dispatch models with current modeled P: power [kW] SOC: state-of-charge [%]

This output has a different length, one entry per day:

lifecycles_per_day: number of cycles per day

export()#
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