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README.md

LightSim2Grid

Provide a fast backend for grid2op using c++ KLU and Eigen librairies. Its primary goal is to serve as a fast backend for the grid2op platform, used primarily as a testbed platform for sequential decision making in the world of power system.

See the Disclaimer to have a more detailed view on what is and what is not this package. For example this package should not be used for detailed power system computations or simulations.

Usage

Once installed (don't forget, if you used the optional virtual env above you need to load it with source venv/bin/activate) you can use it as any python package.

1. As a grid2op backend (preferred method)

This functionality requires you to have grid2op installed, with at least version 0.7.0. You can install it with

pip install grid2op>=0.7.0

Then you can use a LightSimBackend instead of the default PandapowerBackend this way:

import grid2op
from lightsim2grid import LightSimBackend
backend = LightSimBackend()
env = grid2op.make(backend=backend)
# do regular computation as you would with grid2op

And you are good to go.

2. replacement of pandapower "newtonpf" method (advanced method)

It is also possible to use directly the "solver" part of lightsim2grid.

Suppose you somehow get:

  • Ybus the admittance matrix of your powersystem given by pandapower
  • V0 the (complex) voltage vector at each bus given by pandapower
  • Sbus the (complex) power absorb at each bus as given by pandapower
  • ppci a ppc internal pandapower test case
  • pv list of PV buses
  • pq list of PQ buses
  • options list of pandapower "options"

You can define replace the newtonpf function of pandapower.pandapower.newtonpf function with the following piece of code:

from lightsim2grid.newtonpf import newtonpf
V, converged, iterations, J = newtonpf(Ybus, V, Sbus, pv, pq, ppci, options)

This function uses the KLU algorithm and a c++ implementation of a Newton solver for speed.

Since version 0.5.3, lightsim2grid is can be installed like most python packages, with a call to: python -m pip install lightsim2grid

It includes faster grid2op backend and the SuiteSparse faster KLU solver, even on windows. This is definitely the easiest method to install lightsim2grid on your system and have it running without too much issues.

Note though that these packages have been compiled on a different platform that the one you are using. You might still get some benefit (in terms of performances) to install it from your on your machine.

Installation (from source, for more advanced user)

You need to:

  • clone this repository and get the code of Eigen (mandatory for compilation) and SparseSuite (optional)
  • (optional) compile a piece of SparseSuite
  • (optional) [experimental] retrieve and get a proper license for the NICSLU linear solver (see https://github.com/chenxm1986/nicslu)
  • install the package

Important note

This package relies on the excellent pybind11 package to integrate c++ code into python easily.

So to install lightsim2grid you need pybind11 and its requirement, which include a working compiler: for example (as of writing) gcc (default on ubuntu, version >= 4.8), clang (default on MacOS, version >= 5.0.0) or Microsoft visual studio (Microsoft Visual Studio 2015 Update 3 or newer).

This readme does not cover the install of such compilers. Please refer to the documentation of pybind11 for more information. Do not hesitate to write github issues if you encounter a problem in installing such compiler (nb on windows you have to install visual studio, on linux of MacOs you might already have a working compiler installed).

1. Retrieve the sources

First, you can download it with git with:

git clone https://github.com/BDonnot/lightsim2grid.git
cd lightsim2grid
# it is recommended to do a python virtual environment
python -m virtualenv venv  # optional
source venv/bin/activate  # optional

# retrieve the code of SparseSuite and Eigen (dependencies, mandatory)
git submodule init
git submodule update

(optional) Compilation of SuiteSparse

SuiteSparse comes with the faster KLU linear solver.

Since version 0.3.0 this requirement has been removed. This entails that on linux / macos you can still benefit from the faster KLU solver. You can still benefit from the speed up of lightsim (versus the default PandaPowerBackend) but this speed up will be less than if you manage to compile SuiteSparse (see the subsection Benchmark for more information).

NB in both cases the algorithm to compute the powerflow is exactly the same. It is a Newton-Raphson based method. But to carry out this algorithm, one need to solver some linear equations. The only difference in the two version (with KLU and without) is that the linear equation solver is different. Up to the double float precision, both results (with and without KLU) should match.

We only detail the compilation on a system using "make" (so most likely GNU-Linux and MacOS). If you don't feel comfortable with this, either you can ignore it, or you have also the possibility to use the provided a docker version. See the next section Installation Using Docker for more information.

(optional) option A. Compilation of SuiteSparse using "make"

This is the easiest method to compile SuiteSparse on your system but unfortunately it only works on OS where "make" is available (eg Linux or MacOS) but this will not work on Windows... The compilation on windows is covered in the next paragraph (optional) option B. Compilation of SuiteSparse using "cmake"

Anyway, in this case, it's super easy. Just do:

# compile static libraries of SparseSuite
make

And you are good to go. Nothing more.

(optional) option B. Compilation of SuiteSparse using "cmake"

This works on most platform including MacOS, Linux and Windows.

It requires to install the free cmake program and to do a bit more works than for other system. This is why we only recommend to use it on Windows.

The main steps (for windows, somme commands needs to be adapted on linux / macos) are:

  1. cd build_cmake
  2. py generate_c_files.py
  3. mkdir build and cd there: cd build
  4. cmake -DCMAKE_INSTALL_PREFIX=..\built -DCMAKE_BUILD_TYPE=Release ..
  5. cmake --build . --config Release
  6. cmake --build . --config Release --target install

For more information, feel free to read the dedicated README.

(optional) Include NICSLU linear solver (experimental)

Another linear solver that can be used with lighsim2grid is the "NICSLU" linear solver that might, in some cases, be even faster than the KLU linear solver. This can lead to more speed up if using lighsim2grid.

To use it, you need to:

  1. retrieve the sources (only available as a freeware) from https://github.com/chenxm1986/nicslu and save it on your machine. Say you clone this github repository in NICSLU_GIT (eg NICSLU_GIT="/home/user/Documents/nicslu/"). Also note that you need to check that your usage is compliant with their license !
  2. define the "PATH_NICSLU" environment variable before compiling lightsim2grid, on linux you can do export PATH_NICSLU=NICSLU_GIT/nicsluDATE (for example export PATH_NICSLU=/home/user/Documents/nicslu/nicslu202103 if you cloned the repository as the example of step 1) and use the version of nicslu compiled by the author on March 2021 [version distributed at time of writing the readme] )

And this is it. Lightsim will be able to use this linear solver.

Be carefull though, you require a license file in order to use it. As of now, the best way is to copy paste the license file at the same location that the one you execute python from (ie you need to copy paste it each time). We will try to find another solution.

2. Installation of the python package

Now you simply need to install the lightsim2grid package this way, like any python package:

# install the dependency
pip install -U pybind11
# compile and install the python package
pip install -U .

And you are done :-)

Benchmark

In this section we will expose some brief benchmarks about the use of lightsim2grid in the grid2op settings. The code to run these benchmarks are given with this package int the benchmark folder.

All of them has been run on a computer with the following configuration: Configuration:

  • system: Linux 5.8.0-63-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: 2.6.0
  • grid2op version: 1.6.3
  • lightsim2grid version: 0.5.4

The code to reproduce the benchmark on your machine are given, once cd into the benchmarks directory:

cd benchmarks  # cd in the lightsim2grid benchmarks directory if not already
python3 benchmark_solvers.py --name l2rpn_case14_sandbox --no_test --number 1000
python3 benchmark_solvers.py --name l2rpn_neurips_2020_track2_small --no_test --number 1000

(results may vary depending on the hard drive, the ram etc. )

(to run these benchmarks, some data will automatically be downloaded, this requires an internet access)

(we remind that these simulations correspond to simulation on one core of the CPU. Of course it is possible to make use of all the available cores, which would increase the number of steps that can be performed per second)

We compare 6 different solvers:

  • PP: PandaPowerBackend (default grid2op backend) which is the reference in our benchmarks (uses the numba acceleration). It is our reference solver.
  • LS+GS (LightSimBackend+Gauss Seidel): the grid2op backend based on lightsim2grid that uses the "Gauss Seidel" solver to compute the powerflows It is implemented in GaussSeidelSolver.
  • LS+GS S (LightSimBackend+Gauss Seidel Synchronous): the grid2op backend based on lightsim2grid that uses a variant of the "Gauss Seidel" method to compute the powerflows. It is implemented in GaussSeidelSynchSolver.
  • LS+SLU (Newton Raphson+SparseLU): the grid2op backend based on lightsim2grid that uses the "Newton Raphson" algorithm coupled with the linear solver "SparseLU" from the Eigen c++ library (available on all platform) and is implemented in SparseLUSolver.
  • LS+KLU (Newton Raphson+KLU): he grid2op backend based on lightsim2grid that uses the "Newton Raphson" algorithm coupled with the linear solver "KLU" from the SuiteSparse c package implemented in KLUSolver.
  • LS+NICSLU (Newton Raphson+NICSLU): he grid2op backend based on lightsim2grid that uses the "Newton Raphson" algorithm coupled with the linear solver "NICSLU" implemented in NICSLUSolver. [NB NICSLU is a free software but not open source, in order to use it with lightsim2grid, you need to check section (optional) Include NICSLU linear solver (experimental) It is required to install lightsim2grid from source for such solver]

First on an environment based on the IEEE case14 grid:

case14_sandbox grid2op speed (it/s) grid2op 'backend.runpf' time (ms) solver powerflow time (ms)
PP 60.8 12.8 4.91
LS+GS 800 0.458 0.334
LS+GS S 790 0.47 0.347
LS+SLU 992 0.214 0.0886
LS+KLU 1030 0.177 0.0507
LS+NICSLU 1020 0.178 0.0513

From a grid2op perspective, lightsim2grid allows to compute up to ~1000 steps each second on the case 14 and "only" 61 for the default PandaPower Backend, leading to a speed up of ~16 in this case (lightsim2grid is ~16 times faster than Pandapower). For such a small environment, there is no sensible difference in using KLU linear solver compared to using the SparseLU solver of Eigen (1030 vs 992 iterations on the reported runs, might slightly vary across runs). KLU and NICSLU achieve almost identical performances.

Then on an environment based on the IEEE case 118:

neurips_2020_track2 grid2op speed (it/s) grid2op 'backend.runpf' time (ms) solver powerflow time (ms)
PP 38.6 13.6 5.67
LS+GS 5.39 184 184
LS+GS S 31.8 30.5 30.2
LS+SLU 533 1.06 0.791
LS+KLU 706 0.602 0.329
LS+NICSLU 704 0.603 0.33

For an environment based on the IEEE 118, the speed up in using lightsim + KLU (LS+KLU) is ~18 time faster than using the default PandaPower backend. The speed up of lightsim + SparseLU is a bit lower, but it is still ~10 times faster than using the default backend [the LS+KLU solver is ~2-3 times faster than the LS+SLU solver (0.33 ms per powerflow for L2+KLU compared to 0.79 ms for LS+SLU), but it only translates to LS+KLU providing ~30-40% more iterations per second in the total program (706 vs 533) mainly because grid2op itself takes some times to modify the grid and performs all the check it does.] For this testcase once again there is no noticeable difference between NICSLU and KLU.

If we look now only at the time to compute one powerflow (and don't take into account the time to load the data, to initialize the solver, to modify the grid, read back the results, to perform the other update in the grid2op environment etc.) we can notice that it takes on average (over 1000 different states) approximately 0.33ms to compute a powerflow with the LightSimBackend (if using the KLU linear solver) compared to the 6.6 ms when using the PandaPowerBackend (speed up of ~18 times)

NB pandapower performances heavily depends on the pandas version used, we used here a version of pandas which we found gave the best performances on our machine.

Philosophy

Lightsim2grid aims at providing a somewhat efficient (in terms of computation speed) backend targeting the grid2op platform.

It provides a c++ api, compatible with grid2op that is able to compute flows (and voltages and reactive power) from a given grid. This grid can be modified according to grid2op mechanism (see more information in the official grid2Op documentation ).

This code do not aim at providing state of the art solver in term of performances nor in terms of realism in the modeling of power system elements (eg loads, generators, powerlines, transformers, etc.).

Lightsim2grid codebase is "organized" in 4 different parts:

  1. modify the elements (eg disconnecting a powerline or changing the voltage magnitude setpoint of a generator, or any other action made possible by grid2op)
  2. generate the Ybus (sparse) complex admitance matrix and Sbus complex injection vector from the state of the powergrid (eg physical properties of each elements, which elements are in service, which power is produce at each generators and consumed at each loads, what is the grid topology etc.)
  3. solving for the complex voltage V (and part of the Sbus vector) the equation V.(Ybus.V)* = Sbus with the "standard" "powerflow constraints" (eg the voltage magnitude of V is set at given components, and on other it's the imaginary part of Sbus)
  4. computes the active power, reactive power, flow on powerllines etc. from the V and Sbus complex vectors computed at step 3).

For now some basic "solver" (eg the program that performs points 3. above) are available, based on the Gauss Seidel or the Newton-Raphson methods to perform "powerflows".

Nothing prevents any other "solver" to be used with lightsim2grid and thus with grid2op. For this, you simply need to implement, in c++ a "lightsim2grid solver" which mainly consists in defining a function:

bool compute_pf(const Eigen::SparseMatrix<cplx_type> & Ybus,  // the admittance matrix
                CplxVect & V,  // store the results of the powerflow and the Vinit !
                const CplxVect & Sbus,  // the injection vector
                const Eigen::VectorXi & pv,  // (might be ignored) index of the components of Sbus should be computed
                const Eigen::VectorXi & pq,  // (might be ignored) index of the components of |V| should be computed
                int max_iter,  // maximum number of iteration (might be ignored)
                real_type tol  // solver tolerance 
                );

The types used are:

  • real_type: double => type representing the real number
  • cplx_type : std::complex<real_type> => type representing the complex number
  • CplxVect : Eigen::Matrix<cplx_type, Eigen::Dynamic, 1> => type representing a vector of complex elements
  • Eigen::VectorXi => represents a vector of integer
  • Eigen::SparseMatrix<cplx_type> => represents a sparse matrix

See for example BaseNRSolver for the implementation of a Newton Raphson solver (and its derived classes KLUSolver and SparseLUSolver that uses different routine to implement this algorithm) for examples on how to implement a solver.

Any contribution in this area is more than welcome.

NB It is not mandatory to "embed" all the code of the solver in lightsim2grid. Thanks to different customization, it is perfectly possible to install a given "lightsim solver" only if certain conditions are met. For example, on windows based machine, the SuiteSparse library cannot be easily compiled, and the KLUSolver is then not available.

It would be totally fine if some "lightsim2grid" solvers are available only if some packages are installed on the machine for example.

Installation (using docker)

In this section we cover the use of docker with grid2op.

1. Install docker

First, you need to install docker. You can consult the docker on windows if you use a windows like operating system, if you are using MacOs you can consult docker on Mac. The installation of docker on linux depends on your linux distribution, we will not list them all here.

2. Get the lightsim2grid image

Once done, you can simply "install" the lightsim2grid image with:

docker pull bdonnot/lightsim2grid:latest

This step should be done only once (unless you delete the image) it will download approximately 4 or 5GB from the internet. The lightsim2grid image contains lightsim and grid2op python packages (as well as their dependencies), equivalent of what would be installed if you typed:

pip install -U grid2op[optional] pybind11
# and do steps detailed in section "Installation (from source)"
# that we will not repeat

3. Run a code on this container

You can skip this section if you know how to use docker. We will present here "the simplest way" to use. This is NOT a tutorial on docker, and you can find better use of this technology on the docker website.

For this tutorial, we suppose you have a script named my_script.py located in the directory (complete path) DIR_PATH (e.g. on windows you can have DIR_PATH looking like "c:\User\MyName\L2RPNCompeitionCode" or on Linux DIR_PATH will look like "/home/MyName/L2RPNCompeitionCode", this path is your choice, you can name it the way you like)

3.1) Start a docker container

You first need to start a docker container and tell docker that the container can access your local files with the following command:

docker run -t -d -p 8888:8888 --name lightsim_container -v DIR_PATH:/L2RPNCompeitionCode -w /L2RPNCompeitionCode bdonnot/lightsim2grid

More information on this command in the official docker documentation

After this call you can check everything went smoothly with by invoking:

docker ps

And the results should look like:

CONTAINER ID        IMAGE                   COMMAND             CREATED             STATUS              PORTS               NAMES
89750964ca55        bdonnot/lightsim2grid   "python3"           5 seconds ago       Up 4 seconds        80/tcp              lightsim_container

NB I insist, DIR_PATH should be replaced by the path on which you are working, see again the introduction of this section for more information, in the example above this can look like:

docker run -t -d -p 8888:8888 --name lightsim_container -v /home/MyName/L2RPNCompeitionCode:/L2RPNCompeitionCode -w /L2RPNCompeitionCode bdonnot/lightsim2grid

3.2) Execute your code on this container

Once everything is set-up you can execute anything you want on this container. Note that doing so, the execution of the code will be totally independant of your system. Only the things located in DIR_PATH will be visible by your script, only the python package installed in the container will be usable, only the python interpreter of the containter (python 3.6 at time of writing) will be usable etc.

docker exec lightsim_container python my_script.py

Of course, the "my_script.py" should save its output somewhere on the hard drive.

If you rather want to execute a python REPL (read-eval-print loop), corresponding to the "interactive python interpreter", you can run this command:

docker exec -it lightsim_container python

We also added the possibility to run jupyter notebook from this container. To do so, you can run the command:

docker exec -it lightsim_container jupyter notebook --port=8888 --no-browser --ip='*' --allow-root

More information is provided in the official documentation of docker exec.

3.3) Disclaimer

Usually, docker run as root on your machine, be careful, you can do irreversible things with it. "A great power comes with a great responsibility".

Also, we recall that we presented a really short introduction to docker and its possibility. We have not implied that this was enough, nor explain (on purpose, to make this short) any of the commands. We strongly encourage you to have a look for yourself.

We want to recall the paragraph 7. Limitation of Liability under which lightsim2grid, and this "tutorial" is distributed:

Under no circumstances and under no legal theory, whether tort (including negligence), contract, or otherwise, shall any Contributor, or anyone who distributes Covered Software as permitted above, be liable to You for any direct, indirect, special, incidental, or consequential damages of any character including, without limitation, damages for lost profits, loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses, even if such party shall have been informed of the possibility of such damages.

4) Clean-up

Once you are done with your experiments, you can stop the docker container:

docker container stop lightsim_container

This will free all the CPU / GPU resources that this container will use. If you want to start it again, for another experiment for example, just use the command:

docker container start lightsim_container

This will allow you to run another batch of dcoker exec (see 3.2) Execute your code on this container) without having to re run the container.

If you want to go a step further, you can also delete the container with the command:

docker container rm lightsim_container

This will remove the container, and all your code executed there, the history of commands etc. If you want to use lightsim2grid with docker again you will have to go through section 3. Run a code on this container all over again.

And if you also want to remove the image, you can do:

docker rmi bdonnot/lightsim2grid 

NB this last command will completely erase the lightsim2grid image from your machine. This means that if you want to use it again, you will have to download it again (see section 2. Get the lightsim2grid image)

Finally, you can see the official documentation in case you need to uninstall docker completely from your system.

Miscellaneous

Local testing

And some official tests, to make sure the solver returns the same results as pandapower are performed in "lightsim2grid/tests"

cd lightsim2grid/tests
python -m unittest discover

This tests ensure that the results given by this simulator are consistent with the one given by pandapower when using the Newton-Raphson algorithm, with a single slack bus, without enforcing q limits on the generators etc.

NB to run these tests you need to install grid2op from source otherwise all the test of the LightSim2gridBackend will fail. In order to do so you can do:

git clone https://github.com/rte-france/Grid2Op.git
cd Grid2Op
pip3 install -U -e .
cd ..

Tests performed automatically

Some tests are performed automatically on standard platform each time modifications are made in the lightsim2grid code.

These tests include, for now, compilation on gcc (version 8, 9, 10 and 11) and clang (version 10, 11 and 12).

NB Older version of clang are not tested regularly, but lightsim2grid used to work on these versions.

Known issues

Storage units

There are discrepency in the handling of storage units, when the are not asked to produce / consume anything (setpoint is 0.) between pandapower and lightsim2grid only in the case where the storage unit is alone on its bus.

Pandapower does not detect it and the episode can continue. On the other side, lightsim2grid detects it and raise an error because in that case the grid is not connex anymore (which is the desired behaviour).

Compilation issue

On the clang compiler (default one on MacOS computer) it is sometime require to downgrade the pybind11 version to 2.6.2 to install the package.

You can downgrade pybind11 with: python -m pip install -U pybind11==2.6.2