.. _benchmark-grid-size: Benchmarks (grid size) ====================== In this paragraph 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](./benchmarks) folder. TODO DOC in progress If you are interested in other type of benchmarks, let us know ! TL;DR ------- In summary, lightsim2grid (when using KLU linear solver) perfomances are: ================ =============== ================== ===================== ==================== ============================== grid size (nb bus) time (recycling) time (no recycling) time (`TimeSerie`) time (`ContingencyAnalysis`) ================ =============== ================== ===================== ==================== ============================== case14 14 0.0130073 0.0343035 0.00457648 0.00986142 case118 118 0.0727236 0.209858 0.031906 0.0466558 case_illinois200 200 0.151148 0.363769 0.0633617 0.102013 case300 300 0.264309 0.59775 0.129651 0.174466 case1354pegase 1354 1.50257 2.7327 0.826231 1.05762 case1888rte 1888 2.37322 3.92464 1.06475 1.37497 case2848rte 2848 3.7093 6.13028 1.62492 2.19232 case2869pegase 2869 3.6351 6.42046 1.92647 2.3468 case3120sp 3120 4.13678 6.85112 1.52145 2.32498 case6495rte 6495 11.4654 17.3329 4.96104 5.75883 case6515rte 6515 13.1227 18.832 4.77071 5.86091 case9241pegase 9241 16.9394 27.053 8.11946 9.34644 ================ =============== ================== ===================== ==================== ============================== All timings reported above are in milliseconds (ms) for one powerflow (in all cases lots of powerflow are carried out, up to a thousands and the timings here are averaged accross all the powerflows performed) For detailed explanation about each column as well as the hardware used, please refer to the section below, but in summary: - benchmark were run on python 3.12 with a laptop (see section :ref:`bench_grid_size_hardware` and page :ref:`benchmark-deep-dive` for more information about the exact definition of the timers ): - `time (recycling)` indicates the average time it took to run 1 powerflow (with consecutive run of 288 powerflows) while allowing lighsim2grid to re use some basic previous computation from one powerflow to another. This is the most consommations usecase in grid2op for example (default behaviour). See :ref:`bench_grid_size_glop` for more information - `time (no recycling)` indicates the same average time as aboved but lightsim2grid is forced to restart the computation from scratch each time, as if it was a completely different grid on a completely different computers. See :ref:`bench_grid_size_glop` for more information. - `time (TimeSerie)` reports the time it takes to run one powerflow using the lightsim2grid `TimeSerie` module, were everything is in c++ and some care has been taken to improve the performance (reuse of as many things as possible, carefull memory allocation, etc.). See :ref:`bench_grid_size_ts` for more information. - `time (ContingencyAnalysis)` reports the time it takes to run one powerflow using the lightsim2grid `ContingencyAnalysis` module, were everything is in c++ and some care has been taken to improve the performance (reuse of as many things as possible, carefull memory allocation, etc.). See :ref:`bench_grid_size_ca` for more information. **NB** on this settings, as opposed to the others, the grid production / generations stay the same, but the grid topology changes by the connection and disconnection of powerlines. .. _bench_grid_size_hardware: Using a grid2op environment ---------------------------- In this section we perform some benchmark of a `do nothing` agent to test the raw performance of lightsim2grid on different grid sizes varying from the ieee case 14 grid (14 buses) up to the pegase 9241 grid (case9241 from pandapower counting 9241 buses). All of them has been run on a computer with a the following characteristics: - date: 2025-01-09 11:59 CET - system: Linux 6.5.0-1024-oem - OS: ubuntu 22.04 - processor: 13th Gen Intel(R) Core(TM) i7-13700H - python version: 3.9.21.final.0 (64 bit) - numpy version: 1.26.4 - pandas version: 2.2.3 - pandapower version: 2.14.10 - grid2op version: 1.10.5 - lightsim2grid version: 0.10.0 - lightsim2grid extra information: - klu_solver_available: True - nicslu_solver_available: True - cktso_solver_available: True - compiled_march_native: True - compiled_o3_optim: True Solver used for linear algebra: NR single (KLU) To run the benchmark `cd` in the [benchmark](./benchmarks) folder and type: .. code-block:: bash python benchmark_grid_size.py (results may vary depending on the hard drive, the ram etc. and are presented here for illustration only) (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) .. _bench_grid_size_glop: Computation time using grid2op ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This benchmark in doing repeat calls to `env.step(do_nothing)` (usually 288 or 1000) for a given environment build on a grid coming from data available in pandapower. Then we compare different measurments: - `avg step duration (ms)` is the average time it takes to perform the `grid2op.step`. It is given in milliseconds (ms). It takes into account the time to read the data, to feed the data to the underlying c++ model, to run the powerflow and to read back the data from the c++ model. - `time [DC + AC] (ms / pf)` is the time it takes to perform the entire powerflow, which consists in first providing an initial guess (DC approximation) and then to compute the powerflow. As compared to the above timings, it only take into account the time to run the powerflow. This "time to run the powerflow" can be at this stage decomposed in: - converting the provided data into valid matrix / vector to run a DC powerflow - computing a DC powerflow (used to initialize the AC powerflow) - converting again the provided data into valid matrix / vector to run an AC powerflow - computint the AC Powerflow - post processing the internal data (which includes *eg* the flows on the lines in amps, the reactive value produced / absorbed by each generator etc.) - `time in 'solver' (ms / pf)` gives the time it takes to only perform the AC powerflow: - converting the provided data into valid matrix / vector to run an AC powerflow - computing the AC Powerflow - post processing the internal data (which includes *eg* the flows on the lines in amps, the reactive value produced / absorbed by each generator etc.) - `time in 'algo' (ms / pf)` gives the time spent in the algorithm that computes the AC powerflow only .. warning:: For more information about what is actually done and the wordings used in this section, you can consult the page :ref:`benchmark-deep-dive` The results are given in two tables: - the first one corresponds to the default settings were lightsim2grid is allowed to "recycle" previous results, which is the default in grid2op and lightsim2grid. This corresponds to a generic grid2op usecase. - the second one is the same run for the same environment, but this time lightsim2grid recreate everything from scratch each time, the "recycling" is deactivated. The main impact on "recycling" is that, when activated (default), lightsim2grid can skip some of its internal computation, especially in the steps: - "converting the provided data into valid matrix / vector to run a DC powerflow" - "converting again the provided data into valid matrix / vector to run an AC powerflow" - also the computation of the DC and AC powerflows can be a little bit faster (depending on the linear solver used) The "no recycling" strategy is closer to a situation were you would simulate different powerflows on different cores or even on different computers and cannot share the internal state of the solvers (for example). It can also represent a situation were you would run powerflows for vastly different grids one after the other. Results using grid2op.steps (288 consecutive steps, only measuring 'dc pf [init] + ac pf') (recyling allowed, default) ================ =============== ======================== ========================== ================ ============================ ========================== grid size (nb bus) avg step duration (ms) time [DC + AC] (ms / pf) speed (pf / s) time in 'solver' (ms / pf) time in 'algo' (ms / pf) ================ =============== ======================== ========================== ================ ============================ ========================== case14 14 0.350895 0.0245781 40686.6 0.0130073 0.0105687 case118 118 0.438527 0.0921714 10849.4 0.0727236 0.0640808 case_illinois200 200 0.531842 0.177022 5649 0.151148 0.139818 case300 300 0.692534 0.298294 3352.4 0.264309 0.247054 case1354pegase 1354 2.54428 1.61281 620.037 1.50257 1.42742 case1888rte 1888 3.1374 2.50807 398.713 2.37322 2.27984 case2848rte 2848 4.66414 3.90836 255.862 3.7093 3.56542 case2869pegase 2869 5.45635 3.87341 258.171 3.6351 3.4594 case3120sp 3120 5.16431 4.37043 228.81 4.13678 3.99066 case6495rte 6495 13.3672 11.9835 83.4479 11.4654 11.1138 case6515rte 6515 15.0186 13.6416 73.305 13.1227 12.7565 case9241pegase 9241 22.7308 17.9356 55.755 16.9394 16.294 ================ =============== ======================== ========================== ================ ============================ ========================== Results using grid2op.steps (288 consecutive steps, only measuring 'dc pf [init] + ac pf') (**no recycling allowed**, non default) ================ =============== ======================== ========================== ================ ============================ ========================== grid name size (nb bus) avg step duration (ms) time [DC + AC] (ms / pf) speed (pf / s) time in 'solver' (ms / pf) time in 'algo' (ms / pf) ================ =============== ======================== ========================== ================ ============================ ========================== case14 14 0.394679 0.0589122 16974.4 0.0343035 0.028186 case118 118 0.66635 0.292747 3415.92 0.209858 0.187849 case_illinois200 200 0.851082 0.476794 2097.34 0.363769 0.336049 case300 300 1.17444 0.764839 1307.46 0.59775 0.554902 case1354pegase 1354 4.3213 3.37901 295.945 2.7327 2.52633 case1888rte 1888 5.38228 4.7376 211.077 3.92464 3.68506 case2848rte 2848 8.18769 7.40336 135.074 6.13028 5.75152 case2869pegase 2869 9.52221 7.92512 126.181 6.42046 5.94842 case3120sp 3120 9.07648 8.25089 121.199 6.85112 6.4863 case6495rte 6495 21.8053 20.3641 49.1061 17.3329 16.4422 case6515rte 6515 23.2821 21.8367 45.7945 18.832 17.9478 case9241pegase 9241 37.3876 32.5509 30.7211 27.053 25.3161 ================ =============== ======================== ========================== ================ ============================ ========================== .. _bench_grid_size_ts: Computation time using the lightsim2grid `TimeSerie` module ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ As opposed to the experiment above, the `TimeSerie` lightsim2grid module allows to perform sequential computation of varying productions and loads with the exact same grid topology. This does not rely on grid2op and is coded in "pure c++" still using one single CPU core. It should be faster than the timings reported on the above sequence because: - the loop is made in c++ instead of python - the code has been optimize to run faster and "recycle" as many things as possible: the matrices representing the grid is computed only once, it is factorized only once, conversion from the internal solver representation to MW, MVAr and A is done in a vectorized way etc. This rapidity has a cost, it is much less flexible. With the grid2op framework an "agent" can do a lot of different actions (even though "do nothing" was used for the benchmark). Here on the other hand, only a "*do nothing*" action can be performed (and without emulation of any kind of protections). The column `time (ms / pf)` can be compared with the column `time [DC + AC] (ms / pf)` of the table in the previous benchmark. ================ =============== ================ ================ grid size (nb bus) time (ms / pf) speed (pf / s) ================ =============== ================ ================ case14 14 0.00457648 218508 case118 118 0.031906 31342.1 case_illinois200 200 0.0633617 15782.4 case300 300 0.129651 7713.03 case1354pegase 1354 0.826231 1210.32 case1888rte 1888 1.06475 939.19 case2848rte 2848 1.62492 615.415 case2869pegase 2869 1.92647 519.085 case3120sp 3120 1.52145 657.267 case6495rte 6495 4.96104 201.571 case6515rte 6515 4.77071 209.613 case9241pegase 9241 8.11946 123.161 ================ =============== ================ ================ .. _bench_grid_size_ca: Computation time using the lightsim2grid `ContingencyAnalysis` module ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ As opposed to the benchmarks reported in the previous two sections, this benchmark is focused on the `ContingencyAnalysis` lightsim2grid module. A "contingency analysis" is often carried out in power system. The objective is to assess whether or not the current grid state is safe if one (or more) powerline would be disconnected. It uses the same productions / consommations for each computation. Each time it disconnects one or more powerlines, run the powerflow and then stores the results. For this benchmark we focus on disconnecting only one powerline (though lightsim2grid offers the possibility to disconnect as many as you want) with a limit on 1000 contingency simulated (even for grid were there would be more than 1000 powerlines / trafos to disconnect we limit the computation to only 1000). ================ =============== =================== =================== grid size (nb bus) time (ms / cont.) speed (cont. / s) ================ =============== =================== =================== case14 14 0.00986142 101405 case118 118 0.0466558 21433.6 case_illinois200 200 0.102013 9802.67 case300 300 0.174466 5731.77 case1354pegase 1354 1.05762 945.523 case1888rte 1888 1.37497 727.287 case2848rte 2848 2.19232 456.137 case2869pegase 2869 2.3468 426.112 case3120sp 3120 2.32498 430.111 case6495rte 6495 5.75883 173.646 case6515rte 6515 5.86091 170.622 case9241pegase 9241 9.34644 106.993 ================ =============== =================== ===================