NYGrid class

Class documentation

class nygrid.nygrid.NYGrid(grid_prop: Dict[str, DataFrame], start_datetime: str | Timestamp, end_datetime: str | Timestamp, verbose: bool = False)[source]

Bases: object

Class for running the NYGrid model.

Basic attributes
grid_prop: dict

Grid properties.

start_datetime: pandas.Timestamp

Start datetime of the simulation.

end_datetime: pandas.Timestamp

End datetime of the simulation.

verbose: bool

If True, print out the information of the simulation.

ppc: dict

PyPower case dictionary.

ppc_int: dict

PyPower case dictionary with internal indexing.

baseMVA: float

Base MVA.

bus: numpy.ndarray

Bus matrix.

gen: numpy.ndarray

Generator matrix.

branch: numpy.ndarray

Branch matrix.

gencost: numpy.ndarray

Generator cost matrix.

PTDF: numpy.ndarray

Power Transfer Distribution Factors.

Attributes for dimension and indexing
NG: int

Number of generators.

NB: int

Number of buses.

NBR: int

Number of branches.

NL: int

Number of loads.

gen_bus: numpy.ndarray

Generator’s bus location.

B: numpy.ndarray

Full B matrix.

Bf: numpy.ndarray

Full Bf matrix.

gen_map: numpy.ndarray

Mapping from generator to bus.

gen_i2e: numpy.ndarray

Index for converting external to internal indexing.

gen_idx_non_cvt: numpy.ndarray

Index of non-converter generators.

dcline_idx_f: numpy.ndarray

Index of DC line converted generators at FROM bus.

dcline_idx_t: numpy.ndarray

Index of DC line converted generators at TO bus.

esr_idx: numpy.ndarray

Index of ESR converted generators.

vre_idx: numpy.ndarray

Index of VRE converted generators.

gen_idx_offline: numpy.ndarray

Index of offline generators.

NG_offline: int

Number of offline generators.

gen_idx_avail: numpy.ndarray

Index of available generators.

NG_avail: int

Number of available generators.

gen_idx_mustrun: numpy.ndarray

Index of must run generators.

NG_mustrun: int

Number of must run generators.

Attributes for constraints
min_up_time: numpy.ndarray

Generator min up time.

min_down_time: numpy.ndarray

Generator min down time.

load_map: numpy.ndarray

Mapping from load to bus.

load_bus: numpy.ndarray

Load bus location.

load_pu: numpy.ndarray

Load in p.u.

br_max: numpy.ndarray

Line flow upper limit in p.u.

br_min: numpy.ndarray

Line flow lower limit in p.u.

if_map: numpy.ndarray

Interface mapping.

if_lims: numpy.ndarray

Interface limits.

if_br_dir: numpy.ndarray

Interface branch direction.

if_br_idx: numpy.ndarray

Interface branch index.

if_lims_max: numpy.ndarray

Interface limits maximum.

if_lims_min: numpy.ndarray

Interface limits minimum.

NIF: int

Number of interfaces.

gen_hist: numpy.ndarray

Historical generation data.

gen_max: numpy.ndarray

Generator upper operating limit in p.u.

gen_min: numpy.ndarray

Generator lower operating limit in p.u.

ramp_up: numpy.ndarray

Generator ramp up limit in p.u./hour.

ramp_down: numpy.ndarray

Generator ramp down limit in p.u./hour.

gencost_0: numpy.ndarray

Generator linear cost intercept in p.u.

gencost_1: numpy.ndarray

Generator linear cost slope in p.u.

gencost_startup: numpy.ndarray

Generator startup cost in p.u.

gencost_shutdown: numpy.ndarray

Generator shutdown cost in p.u.

gen_init: numpy.ndarray

Generator initial condition.

gen_init_cmt: numpy.ndarray

Generator initial unit commitment.

gen_last_startup_hour: numpy.ndarray

Generator last startup hour.

gen_last_shutdown_hour: numpy.ndarray

Generator last shutdown hour.

esr_crg_max: numpy.ndarray

ESR charging power upper limit in p.u.

esr_dis_max: numpy.ndarray

ESR discharging power upper limit in p.u.

esr_crg_eff: numpy.ndarray

ESR charging efficiency.

esr_dis_eff: numpy.ndarray

ESR discharging efficiency.

esr_soc_max: numpy.ndarray

ESR SOC upper limit in p.u.

esr_soc_min: numpy.ndarray

ESR SOC lower limit in p.u.

esr_init: numpy.ndarray

ESR SOC initial condition in p.u.

esr_target: numpy.ndarray

ESR SOC target condition in p.u.

esrcost_crg: numpy.ndarray

ESR charging cost.

esrcost_dis: numpy.ndarray

ESR discharging cost.

Attributes for optimization
UsePTDF: bool

Use Power Transfer Distribution Factors (PTDF) for linear shift factor.

solver: str

Solver name. Default is ‘gurobi’.

NoPowerBalanceViolation: bool

No power balance violation. Default is False.

NoRampViolation: bool

No ramp violation. Default is False.

PenaltyForOverGeneration: float

Penalty for over generation. Default is 1_500 $/MWh.

PenaltyForLoadShed: float

Penalty for load shed. Default is 5_000 $/MWh.

PenaltyForRampViolation: float

Penalty for ramp violation. Default is 11_000 $/MW.

PenaltyForMinTimeViolation: float

Penalty for minimum time violation. Default is 1_000 $/MWh.

PenaltyForNumberCommitViolation: float

Penalty for number of commitment violation. Default is 10_000 $/hour.

NoReserveViolation: bool

No reserve violation. Default is False.

PenaltyForReserveViolation: float

Penalty for reserve violation. Default is 1_300 $/MW.

NoImplicitReserveCascading: bool

No implicit reserve cascading. Default is False.

OfflineReserveNotFromOnline: bool

Offline reserve not from online. Default is False.

NoPowerflowViolation: bool

No power flow violation. Default is False.

HvdcHurdleCost: float

HVDC hurdle cost. Default is 0.10 $/MWh. Not used.

PenaltyForBranchMwViolation: float

Penalty for branch MW violation. Default is 1_000 $/MWh.

PenaltyForInterfaceMWViolation: float

Penalty for interface MW violation. Default is 1_000 $/MWh.

MaxPhaseAngleDifference: float

Maximum phase angle difference. Default is 1.5.

PenaltyForESRPowerViolation: float

Penalty for ESR power violation. Default is 8_000 $/MWh.

PenaltyForESRSOCLimitViolation: float

Penalty for ESR SOC limit violation. Default is 8_000 $/MWh.

PenaltyForESRSOCTargetViolation: float

Penalty for ESR SOC target violation. Default is 5_000 $/MWh.

check_input_dim() → None[source]

Check the dimensions of the input data.

Return type:

None

create_dc_opf() → None[source]

Create a multi-period DC OPF problem.

Return type:

None

get_last_gen(model_multi_opf: ConcreteModel) → ndarray[source]

Get generator power generation at the last simulation. Used to create initial condition for the next simulation.

get_results_dc_opf() → Dict[str, DataFrame][source]

Get results for a multi-period OPF problem.

Returns:

a dict of pandas DataFrames, including:

  1. Generator power generation.

  2. Bus phase angle.

  3. Branch power flow.

  4. Interface flow.

  5. Bus locational marginal price (LMP).

  6. Total cost.

Return type:

results (dict)

relax_external_branch_lim()[source]

Relax external branch flow limit to 999.99.

Return type:

None

set_esr_init_data(esr_init: ndarray | None) → None[source]

Get ESR initial condition.

Parameters:

(numpy.ndarray) (esr_init) –

set_gen_cost_sch(gen_cost0_sch: DataFrame, gen_cost1_sch: DataFrame) → None[source]

Set generator cost data from generation cost profile.

Parameters:

  • gen_cost0_sch (pandas.DataFrame) – Generator cost intercept profile of thermal generators.

  • gen_cost1_sch (pandas.DataFrame) – Generator cost slope profile of thermal generators.

Return type:

None

set_gen_cost_shutdown_sch(gen_cost_shutdown: DataFrame) → None[source]

Set generator shutdown cost data from shutdown cost profile.

Parameters:

gen_shutdown_cost (pandas.DataFrame) – Generator shutdown cost profile of thermal generators.

Return type:

None

set_gen_cost_startup_sch(gen_cost_startup: DataFrame) → None[source]

Set generator startup cost data from startup cost profile.

Parameters:

gen_startup_cost (pandas.DataFrame) – Generator startup cost profile of thermal generators.

Return type:

None

set_gen_init_cmt_data(gen_init_cmt: ndarray | None) → None[source]

Get generator initial condition.

Parameters:

(numpy.ndarray) (gen_init) – unit commitment condition

set_gen_init_data(gen_init: ndarray | None) → None[source]

Get generator initial condition.

Parameters:

(numpy.ndarray) (gen_init) –

set_gen_last_shutdown_data(gen_last_shutdown_hour: ndarray | None) → None[source]

Get generator past shutdown records.

Parameters:

(numpy.ndarray) (gen_past_shutdown_hour) – unit commitment shutdown record.

set_gen_last_startup_data(gen_last_startup_hour: ndarray | None) → None[source]

Get generator past startup records.

Parameters:

(numpy.ndarray) (gen_past_startup_hour) – unit commitment startup record.

set_gen_max_sch(gen_max_sch: DataFrame) → None[source]

Set generator upper operating limit data from generation capacity profile.

Parameters:

gen_max_sch (pandas.DataFrame) – Generator upper operating limit profile of thermal generators.

Return type:

None

set_gen_min_sch(gen_min_sch: DataFrame) → None[source]

Set generator lower operating limit data from generation capacity profile.

Parameters:

gen_min_sch (pandas.DataFrame) – Generator lower operating limit profile of thermal generators.

Return type:

None

set_gen_mw_sch(gen_mw_sch: DataFrame) → None[source]

Set generator schedule data from generation profile.

Parameters:

gen_mw_sch (pandas.DataFrame) – Generation profile of thermal generators.

Return type:

None

set_gen_ramp_sch(gen_ramp_sch: DataFrame, interval: str = '30min') → None[source]

Set generator ramp rate limit data from ramp rate profile.

Parameters:

  • gen_ramp_sch (pandas.DataFrame) – Generator ramp rate limit profile of thermal generators.

  • interval (str) – Time interval of the ramp rate profile. Default is 30min.

Return type:

None

set_load_sch(load_sch: DataFrame) → None[source]

Set load schedule data from load profile.

Parameters:

load_sch (pandas.DataFrame) – Load profile of the network.

Return type:

None

set_options(options: Dict[str, int | float]) → None[source]

Set solver options and penalty parameters.

Parameters:

options (dict) – Solver options and penalty parameters.

Return type:

None

set_vre_max_sch(vre_max_sch: DataFrame) → None[source]

Set VRE upper operating limit data from generation capacity profile.

Parameters:

vre_max_sch (pandas.DataFrame) – VRE upper operating limit profile of thermal generators.

Return type:

None

show_model_dim() → None[source]

Show model dimensions.

solve_dc_opf(solver_options: Dict[str, int | float] | None = None) → None[source]

Solve a multi-period DC OPF problem.

Parameters:

solver_options (dict) – Solver options.

Return type:

None

Helper functions

nygrid.nygrid.check_status(results: SolverResults) → bool[source]

Check the status of a Pyomo model.

Parameters:

results (pyomo.opt.results.results_.SolverResults) – Pyomo model results.

Returns:

status – True if the model is solved successfully.

Return type:

bool

nygrid.nygrid.convert_dcline_2_gen(ppc: Dict[str, ndarray], dcline_prop: ndarray | DataFrame | None = None) → Tuple[Dict[str, ndarray], int][source]

Convert DC lines to generators and add their parameters in the PyPower matrices. For each DC line, add two injectors: one at FROM bus and another at TO bus. The injection of them are linked in the optimization.

Parameters:

  • ppc (dict) – PyPower case dictionary.

  • dcline_prop (numpy.ndarray or pandas.DataFrame) – DC line properties.

Returns:

  • ppc_dc (dict) – PyPower case dictionary with DC lines converted to generators.

  • num_dcline (int) – Number of DC lines. ppc (dict): PyPower case dictionary.

nygrid.nygrid.convert_esr_2_gen(ppc: Dict[str, ndarray], esr_prop: ndarray | DataFrame | None = None) → Tuple[Dict[str, ndarray], int][source]

Convert ESR to generators and add their parameters in the PyPower matrices. For each ESR, add one injector to represent the combined injection of the ESR. Positive injection is discharging and negative injection is charging.

Parameters:

  • ppc (dict) – PyPower case dictionary.

  • esr_prop (numpy.ndarray or pandas.DataFrame) – ESR properties.

Returns:

  • ppc_esr (dict) – PyPower case dictionary with ESR converted to generators.

  • num_esr (int) – Number of ESR.

nygrid.nygrid.convert_vre_2_gen(ppc: Dict[str, ndarray], vre_prop: ndarray | DataFrame | None = None) → Tuple[Dict[str, ndarray], int][source]

Convert renewable generators to generators and add their parameters in the PyPower matrices.

Parameters:

  • ppc (dict) – PyPower case dictionary.

  • vre_prop (numpy.ndarray or pandas.DataFrame) – VRE properties.

Returns:

  • ppc_vre (dict) – PyPower case dictionary with VRE converted to generators.

  • num_vre (int) – Number of VRE.