Time Series (doc in progress)

The documentation of this section is in progress. It is rather incomplete for the moment, and only expose the most basic features.

If you are interested in collaborating to improve this section, let us know.

Goal

This class aims to make faster (and easier) the computations of the current flows (measured in Amps) at a certain side of a powerline / transformer when the topology is not modified.

It can be used as:

from lightsim2grid import TimeSerie
import grid2op
from lightsim2grid.lightSimBackend import LightSimBackend

env_name = ...
env = grid2op.make(env_name, backend=LightSimBackend())

time_series = TimeSerie(env)
Vs = time_series.compute_V(scenario_id=..., seed=...)
As = time_series.compute_A()  # will contain the flows, in amps at each step (rows) for each powerline (column)

For now this relies on grid2op, but we could imagine a version of this class that can read to / from other data sources (for now please use the more basic lightsim2grid.timeSerie.Computers for such purpose)

Importantly, this method is around 13x faster than simulating “do nothing” (or “one change then nothing”) with grid2op (see section Benchmarks )

Note

A more detailed example is given in the examples\time_serie.py file from the lightsim2grid package.

Warning

Topology and injections

The topology is taken from the initial provided grid and cannot be changed when evaluating a given “time serie”.

Then, the call to time_series.compute_V(scenario_id=…, seed=…) will only read the injections (productions and loads) from grid2op to compute the voltages.

Note

As this class calls a long c++ function, it is possible to use the python Threading module to achieve high efficient parrallelism. An example is provided in the examples\computers_with_grid2op_multithreading.py file.

Benchmarks

Here are some benchmarks made with:

system: Linux 5.11.0-40-generic
OS: ubuntu 20.04
processor: Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz
python version: 3.8.10.final.0 (64 bit)
numpy version: 1.18.5
pandas version: 1.1.4
pandapower version: 2.7.0
lightsim2grid version: 0.6.0
grid2op version: 1.6.4

Where lightsim2grid has been installed from source with all optimization enabled.

This benchmark is available by running, from the root of the lightsim2grid repository:

cd examples
python3 time_serie.py

For this setting the outputs are:

For environment: l2rpn_neurips_2020_track2
Total time spent in "computer" to solve everything: 0.05s (12277 pf / s), 0.08 ms / pf)
    - time to pre process the injections: 0.00s
    - time to perform powerflows: 0.05s (12697 pf / s, 0.08 ms / pf)
In addition, it took 0.00 s to retrieve the current from the complex voltages (in total 11681.3 pf /s, 0.09 ms / pf)

Comparison with raw grid2op timings
It took grid2op (with lightsim2grid): 0.66s to perform the same computation
    This is a 13.4 speed up from TimeSerie over raw grid2op (lightsim2grid)
It took grid2op (with pandapower): 14.94s to perform the same computation
    This is a 302.9 speed up from TimeSerie over raw grid2op (pandapower)
All results match !

In this case then, the TimeSerie module is more than 15 times faster than raw grid2op.

Detailed usage

Classes:

`Computers`	Allows the computation of time series, that is, the same grid topology is used while the active / reactive power injected at each buse vary.
`TimeSerie`(grid2op_env)	This helper class, that only works with grid2op when using a LightSimBackend allows to compute the flows (at the origin side of the powerline / transformers).

class lightsim2grid.timeSerie.Computers

Allows the computation of time series, that is, the same grid topology is used while the active / reactive power injected at each buse vary. The grid topology is fixed, the injections vary.

This is a “raw” c++ class, for an easier to use interface, please refer to the python documentation of the lightsim2grid.timeSerie.TimeSerie class.

Methods:

`amps_computation_time`(self)	Time spent in computing the flows (in amps) after the voltages have been computed at each nodes
`available_solvers`(self)	Return the list of solver available on the current lightsim2grid installation.
`change_solver`(self, arg0)	This function allows to control which solver is used during the powerflow.
`compute_Vs`(self, arg0, arg1, arg2, arg3, ...)	Compute the voltages (at each bus of the grid model) for some time series of injections (productions, loads, storage units, etc.)
`compute_flows`(self)	Retrieve the flows (in amps, at the origin of each powerlines / high voltage size of each transformers.
`compute_power_flows`(self)	Retrieve the active flows (in MW, at the origin of each powerlines / high voltage size of each transformers.
`get_flows`(self)	Get the current flows (in kA) at the origin side / high voltage side of each transformers / powerlines.
`get_power_flows`(self)	Get the active flows (in MW) at the origin side / high voltage side of each transformers / powerlines.
`get_sbuses`(self)	Ge the complex power injected at each (solver id) bus of the powergrid.
`get_solver_type`(self)	Return the type of the solver currently used.
`get_status`(self)	Status of the solvers (1: success, 0: failure).
`get_voltages`(self)	Get the complex voltage angles at each bus of the powergrid.
`nb_solved`(self)	Total number of powerflows solved.
`preprocessing_time`(self)	Time spent in pre processing the data (this involves, but is not limited to the computation of the Sbus)
`solver_time`(self)	Total time spent only in solving the powerflows (excluding pre processing the data, post processing them, initializing everything etc.)
`total_time`(self)	Total time spent in solving the powerflows, pre processing the data, post processing them, initializing everything etc.

amps_computation_time(self: lightsim2grid_cpp.Computers) → float

Time spent in computing the flows (in amps) after the voltages have been computed at each nodes

It is given in seconds (float).

available_solvers(self: lightsim2grid_cpp.Computers) → List[lightsim2grid_cpp.SolverType]

Return the list of solver available on the current lightsim2grid installation.

This is a list of lightsim2grid.solver.SolverType.

change_solver(self: lightsim2grid_cpp.Computers, arg0: lightsim2grid_cpp.SolverType) → None

This function allows to control which solver is used during the powerflow. See the section Even more advanced usage for more information about them.

See also

lightsim2grid.solver.SolverType for a list of the available solver (NB: some solvers might not be available on all platform)

Note

If the solver type entered is a DC solver (eg from lightsim2grid.solver.SolverType, DC, KLUDC or NICSLUDC), it will change the _dc_solver otherwise the regular _solver is modified.

Examples

from lightsim2grid.solver import SolverType
# init the grid model
from lightsim2grid.gridmodel import init
pp_net = ...  # any pandapower grid
lightsim_grid_model = init(pp_net)  # some warnings might be issued as well as some warnings

# change the solver used for the powerflow
# to use internally a solver based on Newton Raphson algorithme using Eigen sparse LU
lightsim_grid_model.change_solver(SolverType.SparseLUSolver)

compute_Vs(self: lightsim2grid_cpp.Computers, arg0: numpy.ndarray[numpy.float64[m, n], flags.c_contiguous], arg1: numpy.ndarray[numpy.float64[m, n], flags.c_contiguous], arg2: numpy.ndarray[numpy.float64[m, n], flags.c_contiguous], arg3: numpy.ndarray[numpy.float64[m, n], flags.c_contiguous], arg4: numpy.ndarray[numpy.complex128[m, 1]], arg5: int, arg6: float) → int

Compute the voltages (at each bus of the grid model) for some time series of injections (productions, loads, storage units, etc.)

Note

This function must be called before lightsim2grid.timeSerie.Computers.compute_flows() and lightsim2grid.timeSerie.Computers.get_flows(), lightsim2grid.timeSerie.Computers.get_voltages() or lightsim2grid.timeSerie.Computers.get_sbuses().

Note

During this computation, the GIL is released, allowing easier parrallel computation

Parameters

gen_p (numy.ndarray, float) – Active generation for each generators. Its counts as many column as the number of generators on the grid and as many rows as the number of steps to compute.
sgen_p (numy.ndarray, float) – Active generation for each static generator. Its counts as many column as the number of static generators on the grid and as many rows as the number of steps to compute.
load_p (numy.ndarray, float) – Active consumption for each loads. Its counts as many column as the number of loads on the grid and as many rows as the number of steps to compute.
load_q (numy.ndarray, float) – Reactive consumption for each loads. Its counts as many column as the number of loads on the grid and as many rows as the number of steps to compute.
Vinit (numy.ndarray, complex) – First voltage at each bus of the grid model (including the disconnected buses)
max_iter (int) – Total number of iteration (>0 integer)
tol (float) – Solver tolerance (> 0. float)

Returns

status – The status of the computation. 1 means “success”: all powerflows were computed sucessfully, 0 means there were some errors and that the computation stopped after a certain number of steps.

Return type

int

compute_flows(self: lightsim2grid_cpp.Computers) → numpy.ndarray[numpy.float64[m, n], flags.c_contiguous]: Retrieve the flows (in amps, at the origin of each powerlines / high voltage size of each transformers.

Warning

This function must be called after lightsim2grid.timeSerie.Computers.compute_Vs() has been called.

Note

This function must be called before lightsim2grid.timeSerie.Computers.get_flows()

Note

During this computation, the GIL is released, allowing easier parrallel computation

compute_power_flows(self: lightsim2grid_cpp.Computers) → numpy.ndarray[numpy.float64[m, n], flags.c_contiguous]: Retrieve the active flows (in MW, at the origin of each powerlines / high voltage size of each transformers.

Warning

This function must be called after lightsim2grid.timeSerie.Computers.compute_Vs() has been called.

Note

This function must be called before lightsim2grid.timeSerie.Computers.get_flows()

Note

During this computation, the GIL is released, allowing easier parrallel computation

get_flows(self: lightsim2grid_cpp.Computers) → numpy.ndarray[numpy.float64[m, n], flags.c_contiguous]

Get the current flows (in kA) at the origin side / high voltage side of each transformers / powerlines.

Each rows correspond to a time step, each column to a powerline / transformer

Warning

This function must be called after lightsim2grid.timeSerie.Computers.compute_flows() has been called. (compute_flows also requires that lightsim2grid.timeSerie.Computers.compute_Vs() has been caleed)

Returns: As – The flows (in kA) at the origin side / high voltage side of each transformers / powerlines.
Return type: numpy.ndarry (matrix)

get_power_flows(self: lightsim2grid_cpp.Computers) → numpy.ndarray[numpy.float64[m, n], flags.c_contiguous]

Get the active flows (in MW) at the origin side / high voltage side of each transformers / powerlines.

Each rows correspond to a time step, each column to a powerline / transformer

Warning

This function must be called after lightsim2grid.timeSerie.Computers.compute_power_flows() has been called. (compute_flows also requires that lightsim2grid.timeSerie.Computers.compute_Vs() has been caleed)

Returns: As – The flows (in kA) at the origin side / high voltage side of each transformers / powerlines.
Return type: numpy.ndarry (matrix)

get_sbuses(self: lightsim2grid_cpp.Computers) → numpy.ndarray[numpy.complex128[m, n], flags.c_contiguous]

Ge the complex power injected at each (solver id) bus of the powergrid. Results are given in pair unit. We do not recommend to use it as it uses the solver id and NOT the powergrid bus id (you can refer to lightsim2grid.gridmodel.GridModel.id_me_to_ac_solver() and lightsim2grid.gridmodel.GridModel.id_ac_solver_to_me() for more information)

Each rows correspond to a time step, each column to a bus (bus are identified by their solver id !)

Warning

This function must be called after lightsim2grid.timeSerie.Computers.compute_Vs().

Returns: Sbuses – The complex power injected at each bus (pair unit, load sign convention)
Return type: numpy.ndarry (matrix)

get_solver_type(self: lightsim2grid_cpp.Computers) → lightsim2grid_cpp.SolverType

Return the type of the solver currently used.

This is equivalent to the get_type of the lightsim2grid.solver.AnySolver.get_type() of the solver used.

get_status(self: lightsim2grid_cpp.Computers) → int: Status of the solvers (1: success, 0: failure).

Note

Even if the solver failed at some point, some results might still be available (but not totally).

get_voltages(self: lightsim2grid_cpp.Computers) → numpy.ndarray[numpy.complex128[m, n], flags.c_contiguous]

Get the complex voltage angles at each bus of the powergrid.

Each rows correspond to a time step, each column to a bus.

Warning

This function must be called after lightsim2grid.timeSerie.Computers.compute_Vs().

Returns: Vs – The complex voltage angles at each bus of the powergrid.
Return type: numpy.ndarry (matrix)

nb_solved(self: lightsim2grid_cpp.Computers) → int: Total number of powerflows solved.

preprocessing_time(self: lightsim2grid_cpp.Computers) → float

Time spent in pre processing the data (this involves, but is not limited to the computation of the Sbus)

It is given in seconds (float).

solver_time(self: lightsim2grid_cpp.Computers) → float

Total time spent only in solving the powerflows (excluding pre processing the data, post processing them, initializing everything etc.)

It is given in seconds (float).

total_time(self: lightsim2grid_cpp.Computers) → float

Total time spent in solving the powerflows, pre processing the data, post processing them, initializing everything etc.

It is given in seconds (float).

class lightsim2grid.timeSerie.TimeSerie(grid2op_env)[source]

This helper class, that only works with grid2op when using a LightSimBackend allows to compute the flows (at the origin side of the powerline / transformers). It is roughly equivalent to the grid2op code:

import grid2op
import numpy as np
from grid2op.Parameters import Parameters
from lightsim2grid.LightSimBackend import LightSimBackend

env_name = ...
param = Parameters()
param.NO_OVERFLOW_DISCONNECTION = True

env = grid2op.make(env_name, param=param, backend=LightSimBackend())

done = False
obs = env.reset()
nb_step = obs.max_step
Vs = np.zeros((nb_step, 2 * env.n_sub), dtype=complex)
As = np.zeros((nb_step, env.n_line), dtype=float)
while not done:
    obs, reward, done, info = env.step(env.action_space())
    Vs[i, :env.n_sub] = env.backend.V
    As[i] = obs.a_or

Compare to the previous code, it avoid all grid2op code and can be more than 15 times faster (on the case 118).

It also allows to use python threading module, as the c++ computation can be done in different python threads (the GIL is not locked during the c++ computation).

Examples

It can be used as:

from lightsim2grid import TimeSerie
import grid2op
from lightsim2grid.LightSimBackend import LightSimBackend

env_name = ...
env = grid2op.make(env_name, param=param, backend=LightSimBackend())

time_series = TimeSerie(env)
Vs = time_series.compute_V(scenario_id=..., seed=...)
As = time_series.compute_A()

Methods:

`compute_A`()	This function returns the current flows (in Amps, A) at the origin (for powerline) / high voltage (for transformer) side
`compute_P`()	This function returns the active power flows (in MW) at the origin (for powerline) / high voltage (for transformer) side
`compute_V`([scenario_id, seed, v_init, ...])	This function allows to retrieve the complex voltage at each bus of the grid for each step.
`compute_V_from_inj`(prod_p, load_p, load_q[, ...])	This function allows to compute the voltages, at each bus given a list of productions and loads.
`get_injections`([scenario_id, seed])	This function allows to retrieve the injection of the given scenario, for the given seed from the grid2op internal environment.

compute_A()[source]

This function returns the current flows (in Amps, A) at the origin (for powerline) / high voltage (for transformer) side

It does not recompute the voltages at each buses, it uses the information get from compute_V and This is why you must call compute_V(…) first !

compute_P()[source]

This function returns the active power flows (in MW) at the origin (for powerline) / high voltage (for transformer) side

It does not recompute the voltages at each buses, it uses the information get from compute_V and This is why you must call compute_V(…) first !

compute_V(scenario_id=None, seed=None, v_init=None, ignore_errors=False)[source]: This function allows to retrieve the complex voltage at each bus of the grid for each step.

Warning

Topology fixed = no maintenance, no attacks, etc.

As the topology is fixed, this class does not allow to simulate the effect of maintenance or attacks !

compute_V_from_inj(prod_p, load_p, load_q, v_init=None, ignore_errors=False)[source]

This function allows to compute the voltages, at each bus given a list of productions and loads.

We do not recommend to use it directly, as the order of the load or generators might vary !

get_injections(scenario_id=None, seed=None)[source]: This function allows to retrieve the injection of the given scenario, for the given seed from the grid2op internal environment.