From 24c8298420b9b5303f6bbe38f8f65c9b7d1d341b Mon Sep 17 00:00:00 2001 From: jinningwang Date: Sun, 24 Nov 2024 17:20:46 -0500 Subject: [PATCH 1/4] Typo --- docs/source/release-notes.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/release-notes.rst b/docs/source/release-notes.rst index c1e222f0..10e4f0e6 100644 --- a/docs/source/release-notes.rst +++ b/docs/source/release-notes.rst @@ -197,7 +197,7 @@ v0.8.2 (2024-01-30) ------------------- - Improve examples -- Add report module and export_csv for results export +- Add module ``report`` and func ``RoutineBase.export_csv`` for results export v0.8.1 (2024-01-20) ------------------- From e184f8ad85eb4cdb29b70584e0a7613078357a4c Mon Sep 17 00:00:00 2001 From: jinningwang Date: Sun, 24 Nov 2024 17:25:51 -0500 Subject: [PATCH 2/4] Typo --- docs/source/release-notes.rst | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/source/release-notes.rst b/docs/source/release-notes.rst index 10e4f0e6..15fc5797 100644 --- a/docs/source/release-notes.rst +++ b/docs/source/release-notes.rst @@ -116,7 +116,7 @@ Outage Distribution Factors". - Add a loss factor in ``RTED.dc2ac`` - Add ``DCOPF.dc2ac`` - Fix OModel parse status to ensure no_parsed params can be updated -- Fix and rerun ex2 +- Fix and rerun ``ex2`` - Format ``Routine.get`` return type to be consistent with input idx type - Remove unused ``Routine.prepare`` - Refactor ``MatProcessor`` to separate matrix building @@ -124,12 +124,12 @@ Outage Distribution Factors". - Add ``build_ptdf``, ``build_lodf``, and ``build_otdf`` - Fix ``Routine.get`` to support pd.Series type idx input - Reserve ``exec_time`` after ``dc2ac`` -- Adjust kloss to fix ex2 +- Adjust kloss to fix ``ex2`` v0.9.5 (2024-03-25) ------------------- -- Add more plots in demo_AGC +- Add more plots in ``demo_AGC`` - Improve line rating adjustment - Adjust static import sequence in ``models.__init__.py`` - Adjust pjm5bus case line rate_a @@ -143,18 +143,18 @@ v0.9.4 (2024-03-16) - Add Var ``pi`` and ExpressionCalc ``pic`` to store the dual of constraint power balance - Add Param ``M`` and ``D`` to model ``REGCV1`` -- Add CPS1 score calculation in demo_AGC +- Add CPS1 score calculation in ``demo_AGC`` v0.9.3 (2024-03-06) ------------------- -- Major improvemets on demo_AGC +- Major improvemets on ``demo_AGC`` - Bug fix in ``RTED.dc2ac`` v0.9.2 (2024-03-04) ------------------- -- Add demo_AGC to demonstrate detailed SFR study +- Add ``demo_AGC`` to demonstrate detailed secondary frequency regulation study - Add ``ExpressionCalc`` to handle post-solving calculation - Rename ``type='eq'`` to ``is_eq=False`` in ``Constraint`` to avoid overriding built-in attribute - Several formatting improvements @@ -162,7 +162,7 @@ v0.9.2 (2024-03-04) v0.9.1 (2024-03-02) ------------------- -- Change sphinx extension myst_nb to nbsphinx for math rendering in ex8 +- Change sphinx extension myst_nb to nbsphinx for math rendering in ``ex8`` - Improve ``symprocessor`` to include routine config - Add config to Routine reference - Fix symbol processor issue with power operator @@ -170,7 +170,7 @@ v0.9.1 (2024-03-02) v0.9.0 (2024-02-27) ------------------- -- Add ex8 to demonstrate customize existing formulations via API +- Add ``ex8`` for formulation customization via API - Improve Development documentation - Fix ``addService``, ``addVars`` - Rename ``RoutineModel`` to ``RoutineBase`` for better naming From 7ff9a721873977d6d3b64e0d443682beef364a69 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Sun, 24 Nov 2024 17:52:23 -0500 Subject: [PATCH 3/4] Rerun examples --- examples/demonstration/demo_AGC.ipynb | 112 ++++--- examples/demonstration/demo_ESD1.ipynb | 46 +-- examples/demonstration/demo_debug.ipynb | 33 +- examples/ex1.ipynb | 139 ++++---- examples/ex2.ipynb | 402 ++++++++++++++++-------- examples/ex3.ipynb | 89 +++--- examples/ex4.ipynb | 54 ++-- examples/ex5.ipynb | 180 ++++++++--- examples/ex6.ipynb | 40 ++- examples/ex7.ipynb | 68 ++-- examples/ex8.ipynb | 54 ++-- 11 files changed, 732 insertions(+), 485 deletions(-) diff --git a/examples/demonstration/demo_AGC.ipynb b/examples/demonstration/demo_AGC.ipynb index 8bfb6da8..f661a213 100644 --- a/examples/demonstration/demo_AGC.ipynb +++ b/examples/demonstration/demo_AGC.ipynb @@ -84,9 +84,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last run time: 2024-06-18 20:13:59\n", + "Last run time: 2024-11-24 17:50:01\n", "andes:1.9.2\n", - "ams:0.9.8\n" + "ams:0.9.12\n" ] } ], @@ -204,9 +204,11 @@ "output_type": "stream", "text": [ "Following PFlow models in addfile will be overwritten: , , , , , , \n", - "/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/ams/interop/andes.py:933: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", - " ssa_key0 = ssa_key0.fillna(value=False)\n", - "AMS system 0x103da75e0 is linked to the ANDES system 0x103f00520.\n" + "AMS system 0x169825970 is linked to the ANDES system 0x16bd2b380.\n", + "Parsing OModel for \n", + "Building system matrices\n", + "Evaluating OModel for \n", + "Finalizing OModel for \n" ] } ], @@ -315,7 +317,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -324,7 +326,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -410,14 +412,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/06/z8ws9b2d733f7h6yc5qpn22w0000gn/T/ipykernel_41267/1576960110.py:70: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", + "/var/folders/__/n5kx_m_s0tbg6n5qd7rh51700000gn/T/ipykernel_94953/3863718312.py:70: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " maptab = sp.dyn.link.copy().fillna(False)\n" ] } @@ -538,15 +540,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ + "Building system matrices\n", " reinit OModel due to non-parametric change.\n", - " solved as optimal in 0.0143 seconds, converged in 10 iterations with CLARABEL.\n" + "Parsing OModel for \n", + "Evaluating OModel for \n", + "Finalizing OModel for \n", + " solved as optimal in 0.0121 seconds, converged in 10 iterations with CLARABEL.\n", + "Parsing OModel for \n", + "Evaluating OModel for \n", + "Finalizing OModel for \n" ] }, { @@ -561,7 +570,23 @@ "name": "stderr", "output_type": "stream", "text": [ - " converted to AC.\n" + "Parsing OModel for \n", + " converted to AC.\n", + "GENROU (xl <= xd2) out of typical upper limit.\n", + "\n", + " idx | values | limit\n", + "-----------+--------+------\n", + " GENROU_1 | 0.012 | 0.001\n", + " GENROU_2 | 0.042 | 0.036\n", + " GENROU_3 | 0.036 | 0.003\n", + " GENROU_4 | 0.025 | 0.001\n", + " GENROU_5 | 0.050 | 0.001\n", + " GENROU_7 | 0.031 | 0.002\n", + " GENROU_8 | 0.029 | 0.006\n", + " GENROU_9 | 0.018 | 0.001\n", + " GENROU_10 | 0.003 | 0.000\n", + "\n", + "\n" ] }, { @@ -581,8 +606,11 @@ "name": "stderr", "output_type": "stream", "text": [ + "Building system matrices\n", " reinit OModel due to non-parametric change.\n", - " solved as optimal in 0.0144 seconds, converged in 10 iterations with CLARABEL.\n" + "Evaluating OModel for \n", + "Finalizing OModel for \n", + " solved as optimal in 0.0125 seconds, converged in 10 iterations with CLARABEL.\n" ] }, { @@ -598,6 +626,7 @@ "name": "stderr", "output_type": "stream", "text": [ + "Parsing OModel for \n", " converted to AC.\n" ] }, @@ -617,8 +646,11 @@ "name": "stderr", "output_type": "stream", "text": [ + "Building system matrices\n", " reinit OModel due to non-parametric change.\n", - " solved as optimal in 0.0145 seconds, converged in 10 iterations with CLARABEL.\n" + "Evaluating OModel for \n", + "Finalizing OModel for \n", + " solved as optimal in 0.0122 seconds, converged in 10 iterations with CLARABEL.\n" ] }, { @@ -635,6 +667,7 @@ "name": "stderr", "output_type": "stream", "text": [ + "Parsing OModel for \n", " converted to AC.\n" ] }, @@ -890,32 +923,32 @@ "text": [ "Dispatch generation\n", " n_rted Slack_39 PV_38 PV_37 PV_36 PV_35 \\\n", - "0 0.0 386.970706 383.193196 380.621215 380.820996 382.533818 \n", + "0 0.0 386.970702 383.193201 380.621217 380.820996 382.533818 \n", "1 1.0 379.935352 376.269537 373.771156 373.934512 375.594240 \n", "2 2.0 380.257507 376.586538 374.084835 374.249842 375.911982 \n", "\n", " PV_34 PV_33 PV_32 PV_31 PV_30 \n", - "0 367.937512 379.728939 385.417789 387.455489 385.588323 \n", + "0 367.937512 379.728940 385.417787 387.455488 385.588320 \n", "1 361.574196 372.866231 378.381743 380.365045 378.582243 \n", "2 361.865859 373.180473 378.703844 380.689629 378.903045 \n", "Dispatch RegUp\n", " n_rted Slack_39 PV_38 PV_37 PV_36 PV_35 PV_34 \\\n", - "0 0.0 1.293631 0.835879 0.440582 0.262976 0.576169 0.262661 \n", + "0 0.0 1.287971 0.825139 0.442992 0.267389 0.574634 0.266773 \n", "1 1.0 1.297501 0.836450 0.441245 0.263458 0.576924 0.263087 \n", "2 2.0 1.295520 0.837502 0.441919 0.264108 0.577644 0.263742 \n", "\n", " PV_33 PV_32 PV_31 PV_30 \n", - "0 0.263037 0.441208 0.513050 0.698096 \n", + "0 0.267681 0.442814 0.513461 0.698435 \n", "1 0.263505 0.441893 0.513794 0.698779 \n", "2 0.264153 0.442587 0.514523 0.699629 \n", "Dispatch RegDn\n", " n_rted Slack_39 PV_38 PV_37 PV_36 PV_35 PV_34 \\\n", - "0 0.0 1.073637 0.873002 0.475992 0.261143 0.626160 0.261050 \n", + "0 0.0 1.061255 0.886641 0.453684 0.265824 0.633451 0.258944 \n", "1 1.0 1.068877 0.876098 0.477181 0.262271 0.627822 0.260922 \n", "2 2.0 1.077700 0.874332 0.476962 0.262233 0.627325 0.260933 \n", "\n", " PV_33 PV_32 PV_31 PV_30 \n", - "0 0.261698 0.465901 0.540887 0.747821 \n", + "0 0.272305 0.479759 0.546196 0.729230 \n", "1 0.262731 0.467584 0.542867 0.750283 \n", "2 0.262696 0.467352 0.542516 0.749276 \n" ] @@ -949,16 +982,16 @@ "output_type": "stream", "text": [ "AGC milage in MWh:\n", - "Slack_39 0.192372\n", - "PV_38 0.018817\n", - "PV_37 0.014045\n", - "PV_36 0.013969\n", - "PV_35 0.014014\n", - "PV_34 0.012150\n", - "PV_33 0.015034\n", - "PV_32 0.013771\n", - "PV_31 0.014140\n", - "PV_30 0.014826\n", + "Slack_39 0.192374\n", + "PV_38 0.018822\n", + "PV_37 0.014038\n", + "PV_36 0.013970\n", + "PV_35 0.014016\n", + "PV_34 0.012151\n", + "PV_33 0.015037\n", + "PV_32 0.013776\n", + "PV_31 0.014142\n", + "PV_30 0.014817\n", "dtype: float64\n" ] } @@ -990,7 +1023,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPS1 score: 100.00368881757518\n" + "CPS1 score: 100.00360714360514\n" ] } ], @@ -1045,7 +1078,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 19, @@ -1054,7 +1087,7 @@ }, { "data": { - "image/png": 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", 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", 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", + "image/png": 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", "text/plain": [ "
" ] @@ -1179,7 +1212,7 @@ ], "metadata": { "kernelspec": { - "display_name": "ams", + "display_name": "amsre", "language": "python", "name": "python3" }, @@ -1193,14 +1226,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.0" }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "d2b3bf80176349caa68dc4a3c77bd06eaade8abc678330f7d1c813c53380e5d2" - } - } + "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 diff --git a/examples/demonstration/demo_ESD1.ipynb b/examples/demonstration/demo_ESD1.ipynb index 81ce8715..684fa696 100644 --- a/examples/demonstration/demo_ESD1.ipynb +++ b/examples/demonstration/demo_ESD1.ipynb @@ -40,8 +40,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last run time: 2024-06-18 20:15:02\n", - "ams:0.9.8\n" + "Last run time: 2024-11-24 17:51:59\n", + "ams:0.9.12\n" ] } ], @@ -76,10 +76,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "Parsing input file \"/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/ams/cases/5bus/pjm5bus_uced_esd1.xlsx\"...\n", - "Input file parsed in 0.1104 seconds.\n", + "Working directory: \"/Users/jinningwang/work/ams/examples/demonstration\"\n", + "Parsing input file \"/Users/jinningwang/work/miniconda3/envs/amsre/lib/python3.12/site-packages/ams/cases/5bus/pjm5bus_uced_esd1.xlsx\"...\n", + "Input file parsed in 0.0237 seconds.\n", "Zero line rates detacted in rate_b, rate_c, adjusted to 999.\n", - "System set up in 0.0027 seconds.\n" + "System set up in 0.0020 seconds.\n" ] } ], @@ -218,8 +219,12 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0205 seconds.\n", - " solved as optimal in 0.0260 seconds, converged in -1 iteration with SCIP.\n" + "Building system matrices\n", + "Parsing OModel for \n", + "Evaluating OModel for \n", + "Finalizing OModel for \n", + " initialized in 0.0148 seconds.\n", + " solved as optimal in 0.0380 seconds, converged in -1 iteration with SCIP.\n" ] }, { @@ -315,8 +320,11 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0268 seconds.\n", - " solved as optimal in 0.0457 seconds, converged in -1 iteration with SCIP.\n" + "Parsing OModel for \n", + "Evaluating OModel for \n", + "Finalizing OModel for \n", + " initialized in 0.0196 seconds.\n", + " solved as optimal in 0.0857 seconds, converged in -1 iteration with SCIP.\n" ] }, { @@ -377,9 +385,12 @@ "output_type": "stream", "text": [ "All generators are online at initial, make initial guess for commitment.\n", - "Turn off StaticGen ['PV_1'] as initial commitment guess.\n", - " initialized in 0.0289 seconds.\n", - " solved as optimal in 0.0850 seconds, converged in -1 iteration with SCIP.\n" + "As initial commitment guess, turn off StaticGen: PV_1\n", + "Parsing OModel for \n", + "Evaluating OModel for \n", + "Finalizing OModel for \n", + " initialized in 0.0379 seconds.\n", + " solved as optimal in 0.0646 seconds, converged in -1 iteration with SCIP.\n" ] }, { @@ -433,7 +444,7 @@ ], "metadata": { "kernelspec": { - "display_name": "ams", + "display_name": "amsre", "language": "python", "name": "python3" }, @@ -447,14 +458,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.0" }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "d2b3bf80176349caa68dc4a3c77bd06eaade8abc678330f7d1c813c53380e5d2" - } - } + "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 diff --git a/examples/demonstration/demo_debug.ipynb b/examples/demonstration/demo_debug.ipynb index 6f144393..4df916e3 100644 --- a/examples/demonstration/demo_debug.ipynb +++ b/examples/demonstration/demo_debug.ipynb @@ -15,15 +15,7 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generating code for 1 models on 12 processes.\n" - ] - } - ], + "outputs": [], "source": [ "import ams" ] @@ -36,7 +28,7 @@ { "data": { "text/plain": [ - "'0.9.12rc1.post5.dev0+gc3478b86'" + "'0.9.12'" ] }, "execution_count": 2, @@ -73,15 +65,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "Parsing input file \"/Users/jinningwang/work/ams/ams/cases/matpower/case14.m\"...\n", - "Input file parsed in 0.0042 seconds.\n", + "Working directory: \"/Users/jinningwang/work/ams/examples/demonstration\"\n", + "Parsing input file \"/Users/jinningwang/work/miniconda3/envs/amsre/lib/python3.12/site-packages/ams/cases/matpower/case14.m\"...\n", + "CASE14 Power flow data for IEEE 14 bus test case.\n", + "Input file parsed in 0.0034 seconds.\n", "Zero line rates detacted in rate_a, rate_b, rate_c, adjusted to 999.\n", "System set up in 0.0020 seconds.\n" ] } ], "source": [ - "sp = ams.load(ams.get_case('matpower/case14.m'))" + "sp = ams.load(ams.get_case('matpower/case14.m'),\n", + " no_output=True)" ] }, { @@ -97,8 +92,8 @@ "Parsing OModel for \n", "Evaluating OModel for \n", "Finalizing OModel for \n", - " initialized in 0.0104 seconds.\n", - " solved as optimal in 0.0084 seconds, converged in 11 iterations with CLARABEL.\n" + " initialized in 0.0305 seconds.\n", + " solved as optimal in 0.0113 seconds, converged in 11 iterations with CLARABEL.\n" ] }, { @@ -276,8 +271,8 @@ "text": [ "Disabled constraints: pglb, pgub\n", "Finalizing OModel for \n", - " initialized in 0.0012 seconds.\n", - " solved as optimal in 0.0066 seconds, converged in 9 iterations with CLARABEL.\n" + " initialized in 0.0013 seconds.\n", + " solved as optimal in 0.0055 seconds, converged in 9 iterations with CLARABEL.\n" ] }, { @@ -365,7 +360,7 @@ ], "metadata": { "kernelspec": { - "display_name": "ams", + "display_name": "amsre", "language": "python", "name": "python3" }, @@ -379,7 +374,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/examples/ex1.ipynb b/examples/ex1.ipynb index b114b680..20accc68 100644 --- a/examples/ex1.ipynb +++ b/examples/ex1.ipynb @@ -51,8 +51,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last run time: 2024-06-18 20:15:21\n", - "ams:0.9.8\n" + "Last run time: 2024-11-24 17:46:14\n", + "ams:0.9.12\n" ] } ], @@ -126,8 +126,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "Parsing input file \"/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/ams/cases/5bus/pjm5bus_uced.xlsx\"...\n", - "Input file parsed in 0.1224 seconds.\n", + "Working directory: \"/Users/jinningwang/work/ams/examples\"\n", + "Parsing input file \"/Users/jinningwang/work/miniconda3/envs/amsre/lib/python3.12/site-packages/ams/cases/5bus/pjm5bus_uced.xlsx\"...\n", + "Input file parsed in 0.0231 seconds.\n", "Zero line rates detacted in rate_b, rate_c, adjusted to 999.\n", "System set up in 0.0022 seconds.\n" ] @@ -163,35 +164,36 @@ { "data": { "text/plain": [ - "OrderedDict([('Summary', Summary (3 devices) at 0x103780700),\n", - " ('Bus', Bus (5 devices) at 0x1226d8e50),\n", - " ('PQ', PQ (3 devices) at 0x1226ee6d0),\n", - " ('Slack', Slack (1 device) at 0x1226fd340),\n", - " ('PV', PV (3 devices) at 0x122717250),\n", - " ('Shunt', Shunt (0 devices) at 0x122717cd0),\n", - " ('Line', Line (7 devices) at 0x1227211c0),\n", - " ('PVD1', PVD1 (0 devices) at 0x12272d970),\n", - " ('ESD1', ESD1 (0 devices) at 0x12273e130),\n", - " ('EV1', EV1 (0 devices) at 0x12273e6a0),\n", - " ('EV2', EV2 (0 devices) at 0x12273ecd0),\n", - " ('REGCA1', REGCA1 (0 devices) at 0x12274c1f0),\n", - " ('REGCV1', REGCV1 (0 devices) at 0x12274c6a0),\n", - " ('REGCV2', REGCV2 (0 devices) at 0x12274cee0),\n", - " ('Area', Area (3 devices) at 0x1227584c0),\n", - " ('Region', Region (2 devices) at 0x122758c40),\n", - " ('SFR', SFR (2 devices) at 0x122767430),\n", - " ('SR', SR (2 devices) at 0x122767a90),\n", - " ('NSR', NSR (2 devices) at 0x122767eb0),\n", - " ('VSGR', VSGR (0 devices) at 0x122775310),\n", - " ('GCost', GCost (4 devices) at 0x122775760),\n", - " ('SFRCost', SFRCost (4 devices) at 0x122775df0),\n", - " ('SRCost', SRCost (4 devices) at 0x1227833d0),\n", - " ('NSRCost', NSRCost (4 devices) at 0x1227837f0),\n", - " ('VSGCost', VSGCost (0 devices) at 0x122783c10),\n", - " ('DCost', DCost (3 devices) at 0x122783f10),\n", - " ('TimeSlot', TimeSlot (0 devices) at 0x12278e4c0),\n", - " ('EDTSlot', EDTSlot (24 devices) at 0x12278ef40),\n", - " ('UCTSlot', UCTSlot (24 devices) at 0x12279a3a0)])" + "OrderedDict([('Summary', Summary (3 devices) at 0x1177b4aa0),\n", + " ('Bus', Bus (5 devices) at 0x30dfb8260),\n", + " ('PQ', PQ (3 devices) at 0x30dfbbdd0),\n", + " ('Slack', Slack (1 device) at 0x30ecaa0c0),\n", + " ('PV', PV (3 devices) at 0x30f8544a0),\n", + " ('Shunt', Shunt (0 devices) at 0x30efc8890),\n", + " ('Line', Line (7 devices) at 0x30df930e0),\n", + " ('Jumper', Jumper (0 devices) at 0x30f855ca0),\n", + " ('PVD1', PVD1 (0 devices) at 0x30e3116a0),\n", + " ('ESD1', ESD1 (0 devices) at 0x30f8568a0),\n", + " ('EV1', EV1 (0 devices) at 0x30ef49370),\n", + " ('EV2', EV2 (0 devices) at 0x30f857350),\n", + " ('REGCA1', REGCA1 (0 devices) at 0x30f857770),\n", + " ('REGCV1', REGCV1 (0 devices) at 0x30f857ef0),\n", + " ('REGCV2', REGCV2 (0 devices) at 0x30f8945f0),\n", + " ('Area', Area (3 devices) at 0x30f894ad0),\n", + " ('Region', Region (2 devices) at 0x30f894fe0),\n", + " ('SFR', SFR (2 devices) at 0x30dfd0cb0),\n", + " ('SR', SR (2 devices) at 0x30c561670),\n", + " ('NSR', NSR (2 devices) at 0x30e9b8bf0),\n", + " ('VSGR', VSGR (0 devices) at 0x30f8962d0),\n", + " ('GCost', GCost (4 devices) at 0x30f896660),\n", + " ('SFRCost', SFRCost (4 devices) at 0x30f897050),\n", + " ('SRCost', SRCost (4 devices) at 0x30f8974d0),\n", + " ('NSRCost', NSRCost (4 devices) at 0x30efa8e00),\n", + " ('VSGCost', VSGCost (0 devices) at 0x30eca8e30),\n", + " ('DCost', DCost (3 devices) at 0x30ed80e60),\n", + " ('TimeSlot', TimeSlot (0 devices) at 0x30f8c0350),\n", + " ('EDTSlot', EDTSlot (24 devices) at 0x30f8c0980),\n", + " ('UCTSlot', UCTSlot (24 devices) at 0x30df28ec0)])" ] }, "execution_count": 5, @@ -344,23 +346,25 @@ { "data": { "text/plain": [ - "OrderedDict([('DCPF', DCPF at 0x1226d8dc0),\n", - " ('PFlow', PFlow at 0x12279afd0),\n", - " ('CPF', CPF at 0x1227a96d0),\n", - " ('ACOPF', ACOPF at 0x1227a9d30),\n", - " ('DCOPF', DCOPF at 0x1227bd640),\n", - " ('ED', ED at 0x1227d5610),\n", - " ('EDDG', EDDG at 0x122803a60),\n", - " ('EDES', EDES at 0x1228276d0),\n", - " ('RTED', RTED at 0x12284bdf0),\n", - " ('RTEDDG', RTEDDG at 0x12284beb0),\n", - " ('RTEDES', RTEDES at 0x1228829a0),\n", - " ('RTEDVIS', RTEDVIS at 0x1228a5940),\n", - " ('UC', UC at 0x1259417f0),\n", - " ('UCDG', UCDG at 0x125d0dca0),\n", - " ('UCES', UCES at 0x125d2fdf0),\n", - " ('DOPF', DOPF at 0x125d66a30),\n", - " ('DOPFVIS', DOPFVIS at 0x125d7bf10)])" + "OrderedDict([('DCPF', DCPF at 0x1177b48f0),\n", + " ('PFlow', PFlow at 0x30f8e0e60),\n", + " ('CPF', CPF at 0x30f8e1850),\n", + " ('ACOPF', ACOPF at 0x30f917e30),\n", + " ('DCOPF', DCOPF at 0x30df2b6e0),\n", + " ('ED', ED at 0x30dfba270),\n", + " ('EDDG', EDDG at 0x30f9656a0),\n", + " ('EDES', EDES at 0x30f9672c0),\n", + " ('RTED', RTED at 0x30f9b18e0),\n", + " ('RTEDDG', RTEDDG at 0x30f9b2d20),\n", + " ('RTEDES', RTEDES at 0x30f9e8380),\n", + " ('RTEDVIS', RTEDVIS at 0x30f9ea2a0),\n", + " ('UC', UC at 0x30f9ebad0),\n", + " ('UCDG', UCDG at 0x30fa26810),\n", + " ('UCES', UCES at 0x30fa5c920),\n", + " ('DOPF', DOPF at 0x30fa5f4a0),\n", + " ('DOPFVIS', DOPFVIS at 0x30fa8ce30),\n", + " ('PFlow0', PFlow0 at 0x30fa8dca0),\n", + " ('DCPF0', DCPF0 at 0x30fa8e450)])" ] }, "execution_count": 7, @@ -397,7 +401,11 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0127 seconds.\n" + "Building system matrices\n", + "Parsing OModel for \n", + "Evaluating OModel for \n", + "Finalizing OModel for \n", + " initialized in 0.0107 seconds.\n" ] }, { @@ -428,13 +436,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['CLARABEL', 'ECOS', 'ECOS_BB', 'GUROBI', 'SCIP', 'SCIPY', 'SCS']" + "['CLARABEL', 'ECOS', 'ECOS_BB', 'OSQP', 'SCIP', 'SCIPY', 'SCS']" ] }, "execution_count": 9, @@ -455,7 +463,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0133 seconds, converged in 10 iterations with CLARABEL.\n" + " solved as optimal in 0.0105 seconds, converged in 10 iterations with CLARABEL.\n" ] }, { @@ -573,8 +581,6 @@ "text/plain": [ "OrderedDict([('pg', Var: StaticGen.pg),\n", " ('aBus', Var: Bus.aBus),\n", - " ('pi', Var: Bus.pi),\n", - " ('plf', Var: Line.plf),\n", " ('pru', Var: StaticGen.pru),\n", " ('prd', Var: StaticGen.prd)])" ] @@ -603,7 +609,7 @@ { "data": { "text/plain": [ - "0.1953750000267141" + "0.19537500002212937" ] }, "execution_count": 15, @@ -631,10 +637,10 @@ { "data": { "text/plain": [ - "OrderedDict([('pglb', Constraint: pglb [ON]),\n", - " ('pgub', Constraint: pgub [ON]),\n", + "OrderedDict([('pb', Constraint: pb [ON]),\n", " ('sba', Constraint: sbus [ON]),\n", - " ('pb', Constraint: pb [ON]),\n", + " ('pglb', Constraint: pglb [ON]),\n", + " ('pgub', Constraint: pgub [ON]),\n", " ('plflb', Constraint: plflb [ON]),\n", " ('plfub', Constraint: plfub [ON]),\n", " ('alflb', Constraint: alflb [ON]),\n", @@ -671,7 +677,7 @@ { "data": { "text/plain": [ - "array([-997. , -996.9, -993.8, -998.3])" + "array([ -997. , -997.9 , -997.0349, -1002.9651])" ] }, "execution_count": 17, @@ -686,7 +692,7 @@ ], "metadata": { "kernelspec": { - "display_name": "ams", + "display_name": "amsre", "language": "python", "name": "python3" }, @@ -700,14 +706,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.0" }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "d2b3bf80176349caa68dc4a3c77bd06eaade8abc678330f7d1c813c53380e5d2" - } - } + "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 diff --git a/examples/ex2.ipynb b/examples/ex2.ipynb index 7979d9f5..ba885972 100644 --- a/examples/ex2.ipynb +++ b/examples/ex2.ipynb @@ -36,8 +36,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last run time: 2024-06-18 20:16:41\n", - "ams:0.9.8\n" + "Last run time: 2024-11-24 17:46:29\n", + "ams:0.9.12\n" ] } ], @@ -81,10 +81,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "Parsing input file \"/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/ams/cases/5bus/pjm5bus_uced.xlsx\"...\n", - "Input file parsed in 0.1065 seconds.\n", + "Working directory: \"/Users/jinningwang/work/ams/examples\"\n", + "Parsing input file \"/Users/jinningwang/work/miniconda3/envs/amsre/lib/python3.12/site-packages/ams/cases/5bus/pjm5bus_uced.xlsx\"...\n", + "Input file parsed in 0.0218 seconds.\n", "Zero line rates detacted in rate_b, rate_c, adjusted to 999.\n", - "System set up in 0.0025 seconds.\n" + "System set up in 0.0018 seconds.\n" ] } ], @@ -269,8 +270,12 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0130 seconds.\n", - " solved as optimal in 0.0120 seconds, converged in 10 iterations with CLARABEL.\n" + "Building system matrices\n", + "Parsing OModel for \n", + "Evaluating OModel for \n", + "Finalizing OModel for \n", + " initialized in 0.0097 seconds.\n", + " solved as optimal in 0.0095 seconds, converged in 10 iterations with CLARABEL.\n" ] }, { @@ -358,20 +363,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sp.PQ.alter(src='p0', idx=['PQ_1', 'PQ_2'], value=[3.2, 3.2])" ] @@ -421,7 +415,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0030 seconds, converged in 10 iterations with CLARABEL.\n" + " solved as optimal in 0.0014 seconds, converged in 10 iterations with CLARABEL.\n" ] }, { @@ -447,7 +441,7 @@ { "data": { "text/plain": [ - "array([2. , 2.1 , 5.19999999, 1.10000001])" + "array([2. , 2.09999999, 5.19999999, 1.10000002])" ] }, "execution_count": 13, @@ -476,7 +470,7 @@ { "data": { "text/plain": [ - "StaticLoad (3 devices) at 0x142804dc0" + "StaticLoad (3 devices) at 0x16b03adb0" ] }, "execution_count": 14, @@ -695,7 +689,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0019 seconds, converged in 10 iterations with CLARABEL.\n" + " solved as optimal in 0.0018 seconds, converged in 10 iterations with CLARABEL.\n" ] }, { @@ -793,7 +787,20 @@ " p0\n", " q0\n", " pmax\n", - " ...\n", + " pmin\n", + " qmax\n", + " qmin\n", + " v0\n", + " vmax\n", + " vmin\n", + " ra\n", + " xs\n", + " ctrl\n", + " uf\n", + " Pc1\n", + " Pc2\n", + " Qc1min\n", + " Qc1max\n", " Qc2min\n", " Qc2max\n", " Ragc\n", @@ -828,6 +835,19 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -843,7 +863,20 @@ " 1.0000\n", " 0.0\n", " 2.1\n", - " ...\n", + " 0.2\n", + " 0.300\n", + " -0.300\n", + " 1.0\n", + " 1.4\n", + " 0.6\n", + " 0.01\n", + " 0.3\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " 0.0\n", " 0.0\n", " 999.0\n", @@ -867,7 +900,20 @@ " 3.2349\n", " 0.0\n", " 5.2\n", - " ...\n", + " 0.5\n", + " 1.275\n", + " -1.275\n", + " 1.0\n", + " 1.4\n", + " 0.6\n", + " 0.01\n", + " 0.3\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " 0.0\n", " 0.0\n", " 999.0\n", @@ -891,7 +937,20 @@ " 4.6651\n", " 0.0\n", " 6.0\n", - " ...\n", + " 0.6\n", + " 4.500\n", + " -4.500\n", + " 1.0\n", + " 1.4\n", + " 0.6\n", + " 0.01\n", + " 0.3\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " 0.0\n", " 0.0\n", " 999.0\n", @@ -905,23 +964,26 @@ " \n", " \n", "\n", - "

3 rows × 33 columns

\n", "" ], "text/plain": [ - " idx u name Sn Vn bus busr p0 q0 pmax ... \\\n", - "uid ... \n", - "0 PV_1 1.0 Alta 100.0 230.0 Bus_1 None 1.0000 0.0 2.1 ... \n", - "1 PV_3 1.0 Solitude 100.0 230.0 Bus_3 None 3.2349 0.0 5.2 ... \n", - "2 PV_5 1.0 Brighton 100.0 230.0 Bus_5 None 4.6651 0.0 6.0 ... \n", + " idx u name Sn Vn bus busr p0 q0 pmax pmin \\\n", + "uid \n", + "0 PV_1 1.0 Alta 100.0 230.0 Bus_1 None 1.0000 0.0 2.1 0.2 \n", + "1 PV_3 1.0 Solitude 100.0 230.0 Bus_3 None 3.2349 0.0 5.2 0.5 \n", + "2 PV_5 1.0 Brighton 100.0 230.0 Bus_5 None 4.6651 0.0 6.0 0.6 \n", "\n", - " Qc2min Qc2max Ragc R10 R30 Rq apf pg0 td1 td2 \n", - "uid \n", - "0 0.0 0.0 999.0 999.0 999.0 999.0 0.0 0.0 0.5 0.0 \n", - "1 0.0 0.0 999.0 999.0 999.0 999.0 0.0 0.0 0.5 0.0 \n", - "2 0.0 0.0 999.0 999.0 999.0 999.0 0.0 0.0 0.5 0.0 \n", + " qmax qmin v0 vmax vmin ra xs ctrl uf Pc1 Pc2 Qc1min \\\n", + "uid \n", + "0 0.300 -0.300 1.0 1.4 0.6 0.01 0.3 1.0 0.0 0.0 0.0 0.0 \n", + "1 1.275 -1.275 1.0 1.4 0.6 0.01 0.3 1.0 0.0 0.0 0.0 0.0 \n", + "2 4.500 -4.500 1.0 1.4 0.6 0.01 0.3 1.0 0.0 0.0 0.0 0.0 \n", "\n", - "[3 rows x 33 columns]" + " Qc1max Qc2min Qc2max Ragc R10 R30 Rq apf pg0 td1 td2 \n", + "uid \n", + "0 0.0 0.0 0.0 999.0 999.0 999.0 999.0 0.0 0.0 0.5 0.0 \n", + "1 0.0 0.0 0.0 999.0 999.0 999.0 999.0 0.0 0.0 0.5 0.0 \n", + "2 0.0 0.0 0.0 999.0 999.0 999.0 999.0 0.0 0.0 0.5 0.0 " ] }, "execution_count": 21, @@ -942,7 +1004,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -981,7 +1043,10 @@ "name": "stderr", "output_type": "stream", "text": [ - " reinit OModel due to non-parametric change.\n" + "Building system matrices\n", + " reinit OModel due to non-parametric change.\n", + "Evaluating OModel for \n", + "Finalizing OModel for \n" ] }, { @@ -1015,7 +1080,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0126 seconds, converged in 9 iterations with CLARABEL.\n" + " solved as optimal in 0.0099 seconds, converged in 9 iterations with CLARABEL.\n" ] }, { @@ -1110,7 +1175,14 @@ " Vn1\n", " Vn2\n", " r\n", - " ...\n", + " x\n", + " b\n", + " g\n", + " b1\n", + " g1\n", + " b2\n", + " g2\n", + " trans\n", " tap\n", " phi\n", " rate_a\n", @@ -1145,6 +1217,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1160,7 +1239,14 @@ " 230.0\n", " 230.0\n", " 0.00281\n", - " ...\n", + " 0.0281\n", + " 0.00712\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", " 4.0\n", @@ -1184,7 +1270,14 @@ " 230.0\n", " 230.0\n", " 0.00304\n", - " ...\n", + " 0.0304\n", + " 0.00658\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", " 2.0\n", @@ -1208,7 +1301,14 @@ " 230.0\n", " 230.0\n", " 0.00064\n", - " ...\n", + " 0.0064\n", + " 0.03126\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", " 2.0\n", @@ -1232,7 +1332,14 @@ " 230.0\n", " 230.0\n", " 0.00108\n", - " ...\n", + " 0.0108\n", + " 0.01852\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", " 2.0\n", @@ -1256,7 +1363,14 @@ " 230.0\n", " 230.0\n", " 0.00297\n", - " ...\n", + " 0.0297\n", + " 0.00674\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", " 2.0\n", @@ -1280,7 +1394,14 @@ " 230.0\n", " 230.0\n", " 0.00297\n", - " ...\n", + " 0.0297\n", + " 0.00674\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", " 2.4\n", @@ -1304,7 +1425,14 @@ " 230.0\n", " 230.0\n", " 0.00281\n", - " ...\n", + " 0.0281\n", + " 0.00712\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", " 4.0\n", @@ -1318,7 +1446,6 @@ " \n", " \n", "\n", - "

7 rows × 28 columns

\n", "" ], "text/plain": [ @@ -1332,27 +1459,25 @@ "5 Line_5 1.0 Line DE Bus_4 Bus_5 100.0 60.0 230.0 230.0 0.00297 \n", "6 Line_6 1.0 Line AB2 Bus_1 Bus_2 100.0 60.0 230.0 230.0 0.00281 \n", "\n", - " ... tap phi rate_a rate_b rate_c owner xcoord ycoord amin \\\n", - "uid ... \n", - "0 ... 1.0 0.0 4.0 999.0 999.0 None None None -6.283185 \n", - "1 ... 1.0 0.0 2.0 999.0 999.0 None None None -6.283185 \n", - "2 ... 1.0 0.0 2.0 999.0 999.0 None None None -6.283185 \n", - "3 ... 1.0 0.0 2.0 999.0 999.0 None None None -6.283185 \n", - "4 ... 1.0 0.0 2.0 999.0 999.0 None None None -6.283185 \n", - "5 ... 1.0 0.0 2.4 999.0 999.0 None None None -6.283185 \n", - "6 ... 1.0 0.0 4.0 999.0 999.0 None None None -6.283185 \n", + " x b g b1 g1 b2 g2 trans tap phi rate_a \\\n", + "uid \n", + "0 0.0281 0.00712 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 4.0 \n", + "1 0.0304 0.00658 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 2.0 \n", + "2 0.0064 0.03126 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 2.0 \n", + "3 0.0108 0.01852 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 2.0 \n", + "4 0.0297 0.00674 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 2.0 \n", + "5 0.0297 0.00674 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 2.4 \n", + "6 0.0281 0.00712 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 4.0 \n", "\n", - " amax \n", - "uid \n", - "0 6.283185 \n", - "1 6.283185 \n", - "2 6.283185 \n", - "3 6.283185 \n", - "4 6.283185 \n", - "5 6.283185 \n", - "6 6.283185 \n", - "\n", - "[7 rows x 28 columns]" + " rate_b rate_c owner xcoord ycoord amin amax \n", + "uid \n", + "0 999.0 999.0 None None None -6.283185 6.283185 \n", + "1 999.0 999.0 None None None -6.283185 6.283185 \n", + "2 999.0 999.0 None None None -6.283185 6.283185 \n", + "3 999.0 999.0 None None None -6.283185 6.283185 \n", + "4 999.0 999.0 None None None -6.283185 6.283185 \n", + "5 999.0 999.0 None None None -6.283185 6.283185 \n", + "6 999.0 999.0 None None None -6.283185 6.283185 " ] }, "execution_count": 26, @@ -1375,20 +1500,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sp.Line.alter(src='u', idx='Line_1', value=0)" ] @@ -1402,7 +1516,10 @@ "name": "stderr", "output_type": "stream", "text": [ - " reinit OModel due to non-parametric change.\n" + "Building system matrices\n", + " reinit OModel due to non-parametric change.\n", + "Evaluating OModel for \n", + "Finalizing OModel for \n" ] }, { @@ -1429,7 +1546,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0122 seconds, converged in 9 iterations with CLARABEL.\n" + " solved as optimal in 0.0102 seconds, converged in 9 iterations with CLARABEL.\n" ] }, { @@ -1499,10 +1616,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "Parsing input file \"/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/ams/cases/5bus/pjm5bus_uced.xlsx\"...\n", - "Input file parsed in 0.0398 seconds.\n", + "Working directory: \"/Users/jinningwang/work/ams/examples\"\n", + "Parsing input file \"/Users/jinningwang/work/miniconda3/envs/amsre/lib/python3.12/site-packages/ams/cases/5bus/pjm5bus_uced.xlsx\"...\n", + "Input file parsed in 0.0215 seconds.\n", "Zero line rates detacted in rate_b, rate_c, adjusted to 999.\n", - "System set up in 0.0027 seconds.\n" + "System set up in 0.0019 seconds.\n" ] } ], @@ -1521,7 +1639,11 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0115 seconds.\n" + "Building system matrices\n", + "Parsing OModel for \n", + "Evaluating OModel for \n", + "Finalizing OModel for \n", + " initialized in 0.0085 seconds.\n" ] }, { @@ -1541,7 +1663,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1595,10 +1717,10 @@ { "data": { "text/plain": [ - "OrderedDict([('pglb', Constraint: pglb [ON]),\n", - " ('pgub', Constraint: pgub [ON]),\n", + "OrderedDict([('pb', Constraint: pb [ON]),\n", " ('sba', Constraint: sbus [ON]),\n", - " ('pb', Constraint: pb [ON]),\n", + " ('pglb', Constraint: pglb [ON]),\n", + " ('pgub', Constraint: pgub [ON]),\n", " ('plflb', Constraint: plflb [ON]),\n", " ('plfub', Constraint: plfub [ON]),\n", " ('alflb', Constraint: alflb [ON]),\n", @@ -1636,7 +1758,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0721 seconds, converged in 10 iterations with CLARABEL.\n" + " solved as optimal in 0.0098 seconds, converged in 10 iterations with CLARABEL.\n" ] }, { @@ -1723,10 +1845,10 @@ { "data": { "text/plain": [ - "OrderedDict([('pglb', Constraint: pglb [ON]),\n", - " ('pgub', Constraint: pgub [ON]),\n", + "OrderedDict([('pb', Constraint: pb [ON]),\n", " ('sba', Constraint: sbus [ON]),\n", - " ('pb', Constraint: pb [ON]),\n", + " ('pglb', Constraint: pglb [ON]),\n", + " ('pgub', Constraint: pgub [ON]),\n", " ('plflb', Constraint: plflb [OFF]),\n", " ('plfub', Constraint: plfub [OFF]),\n", " ('alflb', Constraint: alflb [ON]),\n", @@ -1758,8 +1880,9 @@ "output_type": "stream", "text": [ "Disabled constraints: plflb, plfub\n", - " initialized in 0.0029 seconds.\n", - " solved as optimal in 0.0120 seconds, converged in 9 iterations with CLARABEL.\n" + "Finalizing OModel for \n", + " initialized in 0.0011 seconds.\n", + " solved as optimal in 0.0078 seconds, converged in 9 iterations with CLARABEL.\n" ] }, { @@ -1846,10 +1969,10 @@ { "data": { "text/plain": [ - "OrderedDict([('pglb', Constraint: pglb [ON]),\n", - " ('pgub', Constraint: pgub [ON]),\n", + "OrderedDict([('pb', Constraint: pb [ON]),\n", " ('sba', Constraint: sbus [ON]),\n", - " ('pb', Constraint: pb [ON]),\n", + " ('pglb', Constraint: pglb [ON]),\n", + " ('pgub', Constraint: pgub [ON]),\n", " ('plflb', Constraint: plflb [ON]),\n", " ('plfub', Constraint: plfub [ON]),\n", " ('alflb', Constraint: alflb [ON]),\n", @@ -1880,8 +2003,9 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0005 seconds.\n", - " solved as optimal in 0.0224 seconds, converged in 10 iterations with CLARABEL.\n" + "Finalizing OModel for \n", + " initialized in 0.0009 seconds.\n", + " solved as optimal in 0.0083 seconds, converged in 10 iterations with CLARABEL.\n" ] }, { @@ -1962,7 +2086,9 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0026 seconds.\n" + "Disabled constraints: plflb, plfub, rgu, rgd\n", + "Finalizing OModel for \n", + " initialized in 0.0380 seconds.\n" ] }, { @@ -1988,20 +2114,20 @@ { "data": { "text/plain": [ - "OrderedDict([('pglb', Constraint: pglb [ON]),\n", - " ('pgub', Constraint: pgub [ON]),\n", + "OrderedDict([('pb', Constraint: pb [ON]),\n", " ('sba', Constraint: sbus [ON]),\n", - " ('pb', Constraint: pb [ON]),\n", - " ('plflb', Constraint: plflb [ON]),\n", - " ('plfub', Constraint: plfub [ON]),\n", + " ('pglb', Constraint: pglb [ON]),\n", + " ('pgub', Constraint: pgub [ON]),\n", + " ('plflb', Constraint: plflb [OFF]),\n", + " ('plfub', Constraint: plfub [OFF]),\n", " ('alflb', Constraint: alflb [ON]),\n", " ('alfub', Constraint: alfub [ON]),\n", " ('rbu', Constraint: rbu [ON]),\n", " ('rbd', Constraint: rbd [ON]),\n", " ('rru', Constraint: rru [ON]),\n", " ('rrd', Constraint: rrd [ON]),\n", - " ('rgu', Constraint: rgu [ON]),\n", - " ('rgd', Constraint: rgd [ON])])" + " ('rgu', Constraint: rgu [OFF]),\n", + " ('rgd', Constraint: rgd [OFF])])" ] }, "execution_count": 48, @@ -2036,10 +2162,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "Parsing input file \"/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/ams/cases/5bus/pjm5bus_uced.xlsx\"...\n", - "Input file parsed in 0.0340 seconds.\n", + "Working directory: \"/Users/jinningwang/work/ams/examples\"\n", + "Parsing input file \"/Users/jinningwang/work/miniconda3/envs/amsre/lib/python3.12/site-packages/ams/cases/5bus/pjm5bus_uced.xlsx\"...\n", + "Input file parsed in 0.0226 seconds.\n", "Zero line rates detacted in rate_b, rate_c, adjusted to 999.\n", - "System set up in 0.0024 seconds.\n" + "System set up in 0.0018 seconds.\n" ] } ], @@ -2064,7 +2191,7 @@ { "data": { "text/plain": [ - "OrderedDict([('t', 0.08333333333333333)])" + "OrderedDict({'t': 0.08333333333333333})" ] }, "execution_count": 50, @@ -2085,8 +2212,12 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0109 seconds.\n", - " solved as optimal in 0.0118 seconds, converged in 10 iterations with CLARABEL.\n" + "Building system matrices\n", + "Parsing OModel for \n", + "Evaluating OModel for \n", + "Finalizing OModel for \n", + " initialized in 0.0096 seconds.\n", + " solved as optimal in 0.0103 seconds, converged in 10 iterations with CLARABEL.\n" ] }, { @@ -2112,7 +2243,7 @@ { "data": { "text/plain": [ - "0.1953750000267141" + "0.19537500002212937" ] }, "execution_count": 52, @@ -2158,7 +2289,10 @@ "name": "stderr", "output_type": "stream", "text": [ - " reinit OModel due to non-parametric change.\n" + "Building system matrices\n", + " reinit OModel due to non-parametric change.\n", + "Evaluating OModel for \n", + "Finalizing OModel for \n" ] }, { @@ -2185,7 +2319,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0602 seconds, converged in 325 iterations with SCS.\n" + " solved as optimal in 0.0103 seconds, converged in 325 iterations with SCS.\n" ] }, { @@ -2218,7 +2352,7 @@ { "data": { "text/plain": [ - "2.3444999975679632" + "2.3445000011377894" ] }, "execution_count": 56, @@ -2293,8 +2427,11 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0075 seconds.\n", - " solved as optimal in 0.0079 seconds, converged in 225 iterations with SCS.\n" + "Parsing OModel for \n", + "Evaluating OModel for \n", + "Finalizing OModel for \n", + " initialized in 0.0050 seconds.\n", + " solved as optimal in 0.0061 seconds, converged in 225 iterations with SCS.\n" ] }, { @@ -2327,7 +2464,7 @@ { "data": { "text/plain": [ - "2.3445000000033347" + "2.344500000003334" ] }, "execution_count": 60, @@ -2342,7 +2479,7 @@ ], "metadata": { "kernelspec": { - "display_name": "ams", + "display_name": "amsre", "language": "python", "name": "python3" }, @@ -2356,14 +2493,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.0" }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "d2b3bf80176349caa68dc4a3c77bd06eaade8abc678330f7d1c813c53380e5d2" - } - } + "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 diff --git a/examples/ex3.ipynb b/examples/ex3.ipynb index 58f3931d..8c8aba9d 100644 --- a/examples/ex3.ipynb +++ b/examples/ex3.ipynb @@ -36,8 +36,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last run time: 2024-06-18 20:16:54\n", - "ams:0.9.8\n" + "Last run time: 2024-11-24 17:46:40\n", + "ams:0.9.12\n" ] } ], @@ -72,10 +72,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "Parsing input file \"/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/ams/cases/5bus/pjm5bus_uced.xlsx\"...\n", - "Input file parsed in 0.1222 seconds.\n", + "Working directory: \"/Users/jinningwang/work/ams/examples\"\n", + "Parsing input file \"/Users/jinningwang/work/miniconda3/envs/amsre/lib/python3.12/site-packages/ams/cases/5bus/pjm5bus_uced.xlsx\"...\n", + "Input file parsed in 0.0411 seconds.\n", "Zero line rates detacted in rate_b, rate_c, adjusted to 999.\n", - "System set up in 0.0027 seconds.\n" + "System set up in 0.0039 seconds.\n" ] } ], @@ -107,6 +108,7 @@ " Group | Models \n", "-------------+-------------------------------\n", " ACLine | Line \n", + " ACShort | Jumper \n", " ACTopology | Bus \n", " Collection | Area, Region \n", " Cost | GCost, SFRCost, VSGCost, DCost\n", @@ -153,7 +155,7 @@ " DCED | DCOPF, ED, EDDG, EDES, RTED, RTEDDG, RTEDES, RTEDVIS\n", " DCUC | UC, UCDG, UCES \n", " DED | DOPF, DOPFVIS \n", - " PF | DCPF, PFlow, CPF \n", + " PF | DCPF, PFlow, CPF, PFlow0, DCPF0 \n", "\n", "\n" ] @@ -214,14 +216,22 @@ "----\n", " $ \n", "\n", + "Expressions\n", + "\n", + " Name | Description | Unit\n", + "-------+----------------+-----\n", + " plf | Line flow | p.u.\n", + " pmaxe | Effective pmax | p.u.\n", + " pmine | Effective pmin | p.u.\n", + "\n", "Constraints\n", "\n", " Name | Description \n", "-------+----------------------------------\n", + " pb | power balance \n", + " sbus | align slack bus angle \n", " pglb | pg min \n", " pgub | pg max \n", - " sbus | align slack bus angle \n", - " pb | power balance \n", " plflb | line flow lower bound \n", " plfub | line flow upper bound \n", " alflb | line angle difference lower bound\n", @@ -233,32 +243,29 @@ " rgu | Gen ramping up \n", " rgd | Gen ramping down \n", "\n", - "Expressions\n", + "Vars\n", "\n", - "Name | Variable | Description \n", - "------+----------+----------------------\n", - " plfc | plf | plf calculation \n", - " pic | pi | dual of Constraint pb\n", + "Name | Description | Unit | Properties\n", + "------+-------------------+------+-----------\n", + " pg | Gen active power | p.u. | \n", + " aBus | Bus voltage angle | rad | \n", + " pru | RegUp reserve | p.u. | nonneg \n", + " prd | RegDn reserve | p.u. | nonneg \n", "\n", - "Vars\n", + "ExpressionCalcs\n", "\n", - "Name | Description | Unit | Properties\n", - "------+-------------------+--------+-----------\n", - " pg | Gen active power | p.u. | \n", - " aBus | Bus voltage angle | rad | \n", - " pi | nodal price | $/p.u. | \n", - " plf | Line flow | p.u. | \n", - " pru | RegUp reserve | p.u. | nonneg \n", - " prd | RegDn reserve | p.u. | nonneg \n", + "Name | Description | Unit \n", + "-----+-------------------+-------\n", + " pi | LMP, dual of | $/p.u.\n", "\n", "Services\n", "\n", " Name | Description | Type \n", "--------+--------------------------------------+----------\n", + " csb | select slack bus | VarSelect\n", " ctrle | Effective Gen controllability | NumOpDual\n", " nctrl | Effective Gen uncontrollability | NumOp \n", " nctrle | Effective Gen uncontrollability | NumOpDual\n", - " csb | select slack bus | VarSelect\n", " gs | Sum Gen vars vector in shape of zone | ZonalSum \n", " ds | Sum pd vector in shape of zone | ZonalSum \n", " pdz | zonal total load | NumOpDual\n", @@ -269,20 +276,11 @@ "\n", " Name | Description | Unit \n", "---------+-------------------------------------------+-----------\n", - " c2 | Gen cost coefficient 2 | $/(p.u.^2)\n", - " c1 | Gen cost coefficient 1 | $/(p.u.) \n", - " c0 | Gen cost coefficient 0 | $ \n", " ug | Gen connection status | \n", - " ctrl | Gen controllability | \n", - " pmax | Gen maximum active power | p.u. \n", - " pmin | Gen minimum active power | p.u. \n", - " p0 | Gen initial active power | p.u. \n", + " pg0 | Gen initial active power | p.u. \n", + " gsh | shunt conductance | \n", " buss | Bus slack | \n", " pd | active demand | p.u. \n", - " rate_a | long-term flow limit | p.u. \n", - " amax | max line angle difference | \n", - " amin | min line angle difference | \n", - " gsh | shunt conductance | \n", " Cg | Gen connection matrix | \n", " Cl | Load connection matrix | \n", " CftT | Transpose of line connection matrix | \n", @@ -291,6 +289,16 @@ " Bf | Bf matrix | \n", " Pbusinj | Bus power injection vector | \n", " Pfinj | Line power injection vector | \n", + " c2 | Gen cost coefficient 2 | $/(p.u.^2)\n", + " c1 | Gen cost coefficient 1 | $/(p.u.) \n", + " c0 | Gen cost coefficient 0 | $ \n", + " ctrl | Gen controllability | \n", + " pmax | Gen maximum active power | p.u. \n", + " pmin | Gen minimum active power | p.u. \n", + " ul | Line connection status | \n", + " rate_a | long-term flow limit | p.u. \n", + " amax | max line angle difference | \n", + " amin | min line angle difference | \n", " zg | Gen zone | \n", " zd | Load zone | \n", " R10 | 10-min ramp rate | p.u./h \n", @@ -346,7 +354,7 @@ "DCED: DCOPF, ED, RTED\n", "DCUC: UC\n", "DED: DOPF\n", - "PF: DCPF, PFlow, CPF\n" + "PF: DCPF, PFlow, CPF, PFlow0, DCPF0\n" ] } ], @@ -357,7 +365,7 @@ ], "metadata": { "kernelspec": { - "display_name": "ams", + "display_name": "amsre", "language": "python", "name": "python3" }, @@ -371,14 +379,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.0" }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "d2b3bf80176349caa68dc4a3c77bd06eaade8abc678330f7d1c813c53380e5d2" - } - } + "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 diff --git a/examples/ex4.ipynb b/examples/ex4.ipynb index 068af702..d8823dfa 100644 --- a/examples/ex4.ipynb +++ b/examples/ex4.ipynb @@ -45,8 +45,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last run time: 2024-06-18 20:17:03\n", - "ams:0.9.8\n" + "Last run time: 2024-11-24 17:46:50\n", + "ams:0.9.12\n" ] } ], @@ -88,9 +88,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "Parsing input file \"/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/ams/cases/ieee14/ieee14_uced.xlsx\"...\n", - "Input file parsed in 0.1504 seconds.\n", - "System set up in 0.0025 seconds.\n", + "Working directory: \"/Users/jinningwang/work/ams/examples\"\n", + "Parsing input file \"/Users/jinningwang/work/miniconda3/envs/amsre/lib/python3.12/site-packages/ams/cases/ieee14/ieee14_uced.xlsx\"...\n", + "Input file parsed in 0.0259 seconds.\n", + "System set up in 0.0016 seconds.\n", "-> Systen size:\n", "Base: 100 MVA; Frequency: 60 Hz\n", "14 Buses; 20 Lines; 5 Static Generators\n", @@ -100,7 +101,7 @@ "DCED: DCOPF, ED, RTED\n", "DCUC: UC\n", "DED: DOPF\n", - "PF: DCPF, PFlow, CPF\n" + "PF: DCPF, PFlow, CPF, PFlow0, DCPF0\n" ] } ], @@ -128,16 +129,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "Parsing input file \"/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/ams/cases/ieee14/ieee14.json\"...\n", - "Input file parsed in 0.0030 seconds.\n", + "Working directory: \"/Users/jinningwang/work/ams/examples\"\n", + "Parsing input file \"/Users/jinningwang/work/miniconda3/envs/amsre/lib/python3.12/site-packages/ams/cases/ieee14/ieee14.json\"...\n", + "Input file parsed in 0.0013 seconds.\n", "Zero line rates detacted in rate_c, adjusted to 999.\n", - "System set up in 0.0024 seconds.\n", + "System set up in 0.0018 seconds.\n", "-> Systen size:\n", "Base: 100 MVA; Frequency: 60 Hz\n", "14 Buses; 20 Lines; 5 Static Generators\n", "Active load: 2.24 p.u.; Reactive load: 0.95 p.u.\n", "-> Data check results:\n", - "PF: DCPF, PFlow, CPF\n" + "PF: DCPF, PFlow, CPF, PFlow0, DCPF0\n" ] } ], @@ -165,10 +167,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "Parsing input file \"/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/ams/cases/matpower/case14.m\"...\n", - "Input file parsed in 0.0046 seconds.\n", + "Working directory: \"/Users/jinningwang/work/ams/examples\"\n", + "Parsing input file \"/Users/jinningwang/work/miniconda3/envs/amsre/lib/python3.12/site-packages/ams/cases/matpower/case14.m\"...\n", + "CASE14 Power flow data for IEEE 14 bus test case.\n", + "Input file parsed in 0.0028 seconds.\n", "Zero line rates detacted in rate_a, rate_b, rate_c, adjusted to 999.\n", - "System set up in 0.0026 seconds.\n", + "System set up in 0.0016 seconds.\n", "-> Systen size:\n", "Base: 100.0 MVA; Frequency: 60 Hz\n", "14 Buses; 20 Lines; 5 Static Generators\n", @@ -177,7 +181,7 @@ "ACED: ACOPF\n", "DCED: DCOPF\n", "DED: DOPF\n", - "PF: DCPF, PFlow, CPF\n" + "PF: DCPF, PFlow, CPF, PFlow0, DCPF0\n" ] } ], @@ -219,16 +223,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "Parsing input file \"/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/ams/cases/ieee14/ieee14.raw\"...\n", - "Input file parsed in 0.0080 seconds.\n", + "Working directory: \"/Users/jinningwang/work/ams/examples\"\n", + "Parsing input file \"/Users/jinningwang/work/miniconda3/envs/amsre/lib/python3.12/site-packages/ams/cases/ieee14/ieee14.raw\"...\n", + " IEEE 14 BUS TEST CASE\n", + " 03/06/14 CONTO 100.0 1962 W\n", + "Input file parsed in 0.0047 seconds.\n", "Zero line rates detacted in rate_c, adjusted to 999.\n", - "System set up in 0.0025 seconds.\n", + "System set up in 0.0017 seconds.\n", "-> Systen size:\n", "Base: 100.0 MVA; Frequency: 60.0 Hz\n", "14 Buses; 20 Lines; 5 Static Generators\n", "Active load: 2.24 p.u.; Reactive load: 0.95 p.u.\n", "-> Data check results:\n", - "PF: DCPF, PFlow, CPF\n" + "PF: DCPF, PFlow, CPF, PFlow0, DCPF0\n" ] } ], @@ -301,7 +308,7 @@ ], "metadata": { "kernelspec": { - "display_name": "ams", + "display_name": "amsre", "language": "python", "name": "python3" }, @@ -315,14 +322,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.0" }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "d2b3bf80176349caa68dc4a3c77bd06eaade8abc678330f7d1c813c53380e5d2" - } - } + "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 diff --git a/examples/ex5.ipynb b/examples/ex5.ipynb index c55792f4..b0cbc679 100644 --- a/examples/ex5.ipynb +++ b/examples/ex5.ipynb @@ -40,9 +40,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last run time: 2024-06-18 20:17:19\n", + "Last run time: 2024-11-24 17:47:02\n", "andes:1.9.2\n", - "ams:0.9.8\n" + "ams:0.9.12\n" ] } ], @@ -79,9 +79,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "Parsing input file \"/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/ams/cases/ieee14/ieee14_uced.xlsx\"...\n", - "Input file parsed in 0.1177 seconds.\n", - "System set up in 0.0017 seconds.\n" + "Working directory: \"/Users/jinningwang/work/ams/examples\"\n", + "Parsing input file \"/Users/jinningwang/work/miniconda3/envs/amsre/lib/python3.12/site-packages/ams/cases/ieee14/ieee14_uced.xlsx\"...\n", + "Input file parsed in 0.0261 seconds.\n", + "System set up in 0.0016 seconds.\n" ] } ], @@ -100,7 +101,11 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0572 seconds.\n" + "Building system matrices\n", + "Parsing OModel for \n", + "Evaluating OModel for \n", + "Finalizing OModel for \n", + " initialized in 0.0106 seconds.\n" ] }, { @@ -127,7 +132,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0157 seconds, converged in 10 iterations with CLARABEL.\n" + " solved as optimal in 0.0115 seconds, converged in 10 iterations with CLARABEL.\n" ] }, { @@ -170,19 +175,66 @@ "name": "stderr", "output_type": "stream", "text": [ - "Parsing additional file \"/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/andes/cases/ieee14/ieee14_full.xlsx\"...\n", + "> Reloaded generated Python code of module \"pycode\".\n", + "Generated code for is stale.\n", + "Numerical code generation (rapid incremental mode) started...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating code for 3 models on 12 processes.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Saved generated pycode to \"/Users/jinningwang/.andes/pycode\"\n", + "> Reloaded generated Python code of module \"pycode\".\n", + "Generated numerical code for 3 models in 0.4299 seconds.\n", + "Parsing additional file \"/Users/jinningwang/work/miniconda3/envs/amsre/lib/python3.12/site-packages/andes/cases/ieee14/ieee14_full.xlsx\"...\n", "Following PFlow models in addfile will be overwritten: , , , , , , \n", - "Addfile parsed in 0.0474 seconds.\n", - "System converted to ANDES in 0.1402 seconds.\n", - "/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/ams/interop/andes.py:933: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", - " ssa_key0 = ssa_key0.fillna(value=False)\n", - "AMS system 0x107bf5640 is linked to the ANDES system 0x1591bc310.\n", - " initialized in 0.0014 seconds.\n", - " 0: |F(x)| = 0.4665790376\n", - " 1: |F(x)| = 0.01697226536\n", - " 2: |F(x)| = 3.214367637e-05\n", - " 3: |F(x)| = 1.533567526e-10\n", - " solved in 0.0102 seconds, converged in 3 iterations with PYPOWER-Newton.\n", + "Addfile parsed in 0.0278 seconds.\n", + "System converted to ANDES in 0.5277 seconds.\n", + "AMS system 0x32344ab70 is linked to the ANDES system 0x32344a060.\n", + "System internal structure set up in 0.0179 seconds.\n", + "> Reloaded generated Python code of module \"pycode\".\n", + "System internal structure set up in 0.0140 seconds.\n", + "Parsing OModel for \n", + "Evaluating OModel for \n", + "Finalizing OModel for \n", + "-> System connectivity check results:\n", + " No islanded bus detected.\n", + " System is interconnected.\n", + " Each island has a slack bus correctly defined and enabled.\n", + "\n", + "-> Power flow calculation\n", + " Numba: Off\n", + " Sparse solver: KLU\n", + " Solution method: NR method\n", + "Power flow initialized in 0.0020 seconds.\n", + "0: |F(x)| = 0.7340879087\n", + "1: |F(x)| = 0.01697227038\n", + "2: |F(x)| = 3.214367857e-05\n", + "3: |F(x)| = 1.533653204e-10\n", + "Converged in 4 iterations in 0.0027 seconds.\n", + "-> System connectivity check results:\n", + " No islanded bus detected.\n", + " System is interconnected.\n", + " Each island has a slack bus correctly defined and enabled.\n", + "\n", + "-> Power flow calculation\n", + " Numba: Off\n", + " Sparse solver: KLU\n", + " Solution method: NR method\n", + "Power flow initialized in 0.0019 seconds.\n", + "0: |F(x)| = 0.7340879087\n", + "1: |F(x)| = 0.01697227038\n", + "2: |F(x)| = 3.214367857e-05\n", + "3: |F(x)| = 1.533653204e-10\n", + "Converged in 4 iterations in 0.0023 seconds.\n", "Power flow results are consistent.\n" ] } @@ -223,13 +275,13 @@ "\n", "to_andes(setup=True, addfile=None, **kwargs) method of ams.system.System instance\n", " Convert the AMS system to an ANDES system.\n", - " \n", + "\n", " A preferred dynamic system file to be added has following features:\n", " 1. The file contains both power flow and dynamic models.\n", " 2. The file can run in ANDES natively.\n", " 3. Power flow models are in the same shape as the AMS system.\n", " 4. Dynamic models, if any, are in the same shape as the AMS system.\n", - " \n", + "\n", " Parameters\n", " ----------\n", " setup : bool, optional\n", @@ -238,12 +290,12 @@ " The additional file to be converted to ANDES dynamic mdoels.\n", " **kwargs : dict\n", " Keyword arguments to be passed to `andes.system.System`.\n", - " \n", + "\n", " Returns\n", " -------\n", " andes : andes.system.System\n", " The converted ANDES system.\n", - " \n", + "\n", " Examples\n", " --------\n", " >>> import ams\n", @@ -413,8 +465,12 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0037 seconds.\n", - " solved in 0.1872 seconds, converged in 12 iterations with PYPOWER-PIPS.\n", + "Parsing OModel for \n", + "Evaluating OModel for \n", + "Finalizing OModel for \n", + " initialized in 0.0032 seconds.\n", + " solved in 0.1401 seconds, converged in 12 iterations with PYPOWER-PIPS.\n", + "Parsing OModel for \n", " converted to AC.\n" ] }, @@ -471,7 +527,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Send results to ANDES <0x1591bc310>...\n", + "Send results to ANDES <0x32344a060>...\n", "*Send to StaticGen.v0\n", "Send to Bus.v0\n", "Send to StaticGen.u\n", @@ -511,20 +567,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sa.TGOV1.alter(src='VMAX', idx=sa.TGOV1.idx.v, value=100*np.ones(sa.TGOV1.n))\n", "sa.TGOV1.alter(src='VMIN', idx=sa.TGOV1.idx.v, value=np.zeros(sa.TGOV1.n))" @@ -542,6 +587,27 @@ "execution_count": 14, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "-> System connectivity check results:\n", + " No islanded bus detected.\n", + " System is interconnected.\n", + " Each island has a slack bus correctly defined and enabled.\n", + "\n", + "-> Power flow calculation\n", + " Numba: Off\n", + " Sparse solver: KLU\n", + " Solution method: NR method\n", + "Power flow initialized in 0.0080 seconds.\n", + "0: |F(x)| = 0.7743935696\n", + "1: |F(x)| = 0.01847784692\n", + "2: |F(x)| = 3.493405927e-05\n", + "3: |F(x)| = 1.193747323e-10\n", + "Converged in 4 iterations in 0.0125 seconds.\n" + ] + }, { "data": { "text/plain": [ @@ -568,7 +634,16 @@ "cell_type": "code", "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initialization for dynamics completed in 0.0198 seconds.\n", + "Initialization was successful.\n" + ] + } + ], "source": [ "_ = sa.TDS.init()" ] @@ -585,6 +660,18 @@ "execution_count": 16, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "-> Time Domain Simulation Summary:\n", + "Sparse Solver: KLU\n", + "Simulation time: 0.0-20.0 s.\n", + "Fixed step size: h=33.33 ms. Shrink if not converged.\n", + "Simulation to t=20.00 sec completed in 0.2986 seconds.\n" + ] + }, { "data": { "text/plain": [ @@ -671,7 +758,7 @@ { "data": { "text/plain": [ - "array([1.79492221, 0.48428986, 0.01000094, 0.02000094, 0.01000095])" + "array([1.79503641, 0.48417982, 0.01000094, 0.02000094, 0.01000095])" ] }, "execution_count": 19, @@ -686,7 +773,7 @@ ], "metadata": { "kernelspec": { - "display_name": "ams", + "display_name": "amsre", "language": "python", "name": "python3" }, @@ -700,14 +787,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.0" }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "d2b3bf80176349caa68dc4a3c77bd06eaade8abc678330f7d1c813c53380e5d2" - } - } + "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 diff --git a/examples/ex6.ipynb b/examples/ex6.ipynb index b150d398..737a41d5 100644 --- a/examples/ex6.ipynb +++ b/examples/ex6.ipynb @@ -37,8 +37,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last run time: 2024-06-18 20:17:27\n", - "ams:0.9.8\n" + "Last run time: 2024-11-24 17:47:13\n", + "ams:0.9.12\n" ] } ], @@ -74,10 +74,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "Parsing input file \"/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/ams/cases/5bus/pjm5bus_demo.xlsx\"...\n", - "Input file parsed in 0.0933 seconds.\n", + "Working directory: \"/Users/jinningwang/work/ams/examples\"\n", + "Parsing input file \"/Users/jinningwang/work/miniconda3/envs/amsre/lib/python3.12/site-packages/ams/cases/5bus/pjm5bus_demo.xlsx\"...\n", + "Input file parsed in 0.0222 seconds.\n", "Zero line rates detacted in rate_b, rate_c, adjusted to 999.\n", - "System set up in 0.0021 seconds.\n" + "System set up in 0.0019 seconds.\n" ] } ], @@ -378,9 +379,9 @@ { "data": { "text/plain": [ - "OrderedDict([('TimeSlot', TimeSlot (0 devices) at 0x1531d24c0),\n", - " ('EDTSlot', EDTSlot (6 devices) at 0x1531d2f40),\n", - " ('UCTSlot', UCTSlot (6 devices) at 0x1531de3a0)])" + "OrderedDict([('TimeSlot', TimeSlot (0 devices) at 0x15b8bc530),\n", + " ('EDTSlot', EDTSlot (6 devices) at 0x15b8bcb30),\n", + " ('UCTSlot', UCTSlot (6 devices) at 0x15b8bcec0)])" ] }, "execution_count": 7, @@ -560,7 +561,11 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0195 seconds.\n" + "Building system matrices\n", + "Parsing OModel for \n", + "Evaluating OModel for \n", + "Finalizing OModel for \n", + " initialized in 0.0138 seconds.\n" ] }, { @@ -587,7 +592,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0203 seconds, converged in 10 iterations with CLARABEL.\n" + " solved as optimal in 0.0139 seconds, converged in 9 iterations with CLARABEL.\n" ] }, { @@ -622,8 +627,6 @@ "text/plain": [ "OrderedDict([('pg', Var: StaticGen.pg),\n", " ('aBus', Var: Bus.aBus),\n", - " ('pi', Var: Bus.pi),\n", - " ('plf', Var: Line.plf),\n", " ('pru', Var: StaticGen.pru),\n", " ('prd', Var: StaticGen.prd),\n", " ('prs', Var: StaticGen.prs)])" @@ -684,7 +687,7 @@ { "data": { "text/plain": [ - "2.0999999997419287" + "2.0999999983486095" ] }, "execution_count": 14, @@ -727,7 +730,7 @@ ], "metadata": { "kernelspec": { - "display_name": "ams", + "display_name": "amsre", "language": "python", "name": "python3" }, @@ -741,14 +744,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.0" }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "d2b3bf80176349caa68dc4a3c77bd06eaade8abc678330f7d1c813c53380e5d2" - } - } + "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 diff --git a/examples/ex7.ipynb b/examples/ex7.ipynb index c6651094..9dee337d 100644 --- a/examples/ex7.ipynb +++ b/examples/ex7.ipynb @@ -42,8 +42,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last run time: 2024-06-18 20:17:35\n", - "ams:0.9.8\n" + "Last run time: 2024-11-24 17:47:25\n", + "ams:0.9.12\n" ] } ], @@ -78,10 +78,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "Parsing input file \"/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/ams/cases/5bus/pjm5bus_demo.xlsx\"...\n", - "Input file parsed in 0.0926 seconds.\n", + "Working directory: \"/Users/jinningwang/work/ams/examples\"\n", + "Parsing input file \"/Users/jinningwang/work/miniconda3/envs/amsre/lib/python3.12/site-packages/ams/cases/5bus/pjm5bus_demo.xlsx\"...\n", + "Input file parsed in 0.0218 seconds.\n", "Zero line rates detacted in rate_b, rate_c, adjusted to 999.\n", - "System set up in 0.0027 seconds.\n" + "System set up in 0.0019 seconds.\n" ] } ], @@ -100,8 +101,12 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0073 seconds.\n", - " solved as optimal in 0.0112 seconds, converged in 8 iterations with CLARABEL.\n" + "Building system matrices\n", + "Parsing OModel for \n", + "Evaluating OModel for \n", + "Finalizing OModel for \n", + " initialized in 0.0081 seconds.\n", + " solved as optimal in 0.0064 seconds, converged in 8 iterations with CLARABEL.\n" ] }, { @@ -144,7 +149,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Report saved to \"pjm5bus_demo_out.txt\" in 0.0011 seconds.\n" + "Report saved to \"pjm5bus_demo_out.txt\" in 0.0012 seconds.\n" ] }, { @@ -178,12 +183,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "AMS 0.9.8\n", + "AMS 0.9.12\n", "Copyright (C) 2023-2024 Jinning Wang\n", "\n", "AMS comes with ABSOLUTELY NO WARRANTY\n", - "Case file: /Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/ams/cases/5bus/pjm5bus_demo.xlsx\n", - "Report time: 06/18/2024 08:17:36 PM\n", + "Case file: /Users/jinningwang/work/miniconda3/envs/amsre/lib/python3.12/site-packages/ams/cases/5bus/pjm5bus_demo.xlsx\n", + "Report time: 11/24/2024 05:47:25 PM\n", "\n", "\n", "========== System Statistics ==========\n", @@ -203,24 +208,13 @@ "Load 10\n", "\n", "Bus DATA:\n", - " Name aBus (rad) pi ($/p.u.)\n", + " Name aBus (rad)\n", "\n", - "Bus_1 A 0.02086 0.4\n", - "Bus_2 B 0.001022 0.4\n", - "Bus_3 C 0.018174 0.4\n", - "Bus_4 D -0 0.4\n", - "Bus_5 E 0.020847 0.4\n", - "\n", - "Line DATA:\n", - " Name plf (p.u.)\n", - "\n", - "Line_0 Line AB 0.70595\n", - "Line_1 Line AD 0.68617\n", - "Line_2 Line AE 0.001925\n", - "Line_3 Line BC -1.5881\n", - "Line_4 Line CD 0.61191\n", - "Line_5 Line DE -0.70193\n", - "Line_6 Line AB2 0.70595\n", + "Bus_1 A 0.02086\n", + "Bus_2 B 0.001022\n", + "Bus_3 C 0.018174\n", + "Bus_4 D -0\n", + "Bus_5 E 0.020847\n", "\n", "StaticGen DATA:\n", " Name pg (p.u.)\n", @@ -266,8 +260,11 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0608 seconds.\n", - " solved as optimal in 0.0182 seconds, converged in 10 iterations with CLARABEL.\n" + "Parsing OModel for \n", + "Evaluating OModel for \n", + "Finalizing OModel for \n", + " initialized in 0.0281 seconds.\n", + " solved as optimal in 0.0136 seconds, converged in 9 iterations with CLARABEL.\n" ] }, { @@ -496,7 +493,7 @@ ], "metadata": { "kernelspec": { - "display_name": "ams", + "display_name": "amsre", "language": "python", "name": "python3" }, @@ -510,14 +507,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.0" }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "d2b3bf80176349caa68dc4a3c77bd06eaade8abc678330f7d1c813c53380e5d2" - } - } + "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 diff --git a/examples/ex8.ipynb b/examples/ex8.ipynb index b3e84e6e..4ff75c84 100644 --- a/examples/ex8.ipynb +++ b/examples/ex8.ipynb @@ -39,8 +39,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last run time: 2024-06-18 20:17:45\n", - "ams:0.9.8\n" + "Last run time: 2024-11-24 17:47:36\n", + "ams:0.9.12\n" ] } ], @@ -75,10 +75,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "Parsing input file \"/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/ams/cases/5bus/pjm5bus_demo.xlsx\"...\n", - "Input file parsed in 0.0920 seconds.\n", + "Working directory: \"/Users/jinningwang/work/ams/examples\"\n", + "Parsing input file \"/Users/jinningwang/work/miniconda3/envs/amsre/lib/python3.12/site-packages/ams/cases/5bus/pjm5bus_demo.xlsx\"...\n", + "Input file parsed in 0.0220 seconds.\n", "Zero line rates detacted in rate_b, rate_c, adjusted to 999.\n", - "System set up in 0.0021 seconds.\n" + "System set up in 0.0020 seconds.\n" ] } ], @@ -105,7 +106,11 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0080 seconds.\n" + "Building system matrices\n", + "Parsing OModel for \n", + "Evaluating OModel for \n", + "Finalizing OModel for \n", + " initialized in 0.0082 seconds.\n" ] }, { @@ -431,7 +436,10 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0101 seconds.\n" + "Parsing OModel for \n", + "Evaluating OModel for \n", + "Finalizing OModel for \n", + " initialized in 0.0059 seconds.\n" ] }, { @@ -465,7 +473,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0102 seconds, converged in 8 iterations with CLARABEL.\n" + " solved as optimal in 0.0076 seconds, converged in 8 iterations with CLARABEL.\n" ] }, { @@ -538,7 +546,7 @@ { "data": { "text/plain": [ - "5.337040792868603" + "5.337040792868601" ] }, "execution_count": 19, @@ -566,10 +574,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "Parsing input file \"/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/ams/cases/5bus/pjm5bus_demo.xlsx\"...\n", - "Input file parsed in 0.0342 seconds.\n", + "Working directory: \"/Users/jinningwang/work/ams/examples\"\n", + "Parsing input file \"/Users/jinningwang/work/miniconda3/envs/amsre/lib/python3.12/site-packages/ams/cases/5bus/pjm5bus_demo.xlsx\"...\n", + "Input file parsed in 0.0682 seconds.\n", "Zero line rates detacted in rate_b, rate_c, adjusted to 999.\n", - "System set up in 0.0021 seconds.\n" + "System set up in 0.0020 seconds.\n" ] } ], @@ -588,8 +597,12 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0058 seconds.\n", - " solved as optimal in 0.0083 seconds, converged in 8 iterations with CLARABEL.\n" + "Building system matrices\n", + "Parsing OModel for \n", + "Evaluating OModel for \n", + "Finalizing OModel for \n", + " initialized in 0.0067 seconds.\n", + " solved as optimal in 0.0057 seconds, converged in 8 iterations with CLARABEL.\n" ] }, { @@ -642,7 +655,7 @@ { "data": { "text/plain": [ - "2.3445000001347758" + "2.3445000001347767" ] }, "execution_count": 23, @@ -657,7 +670,7 @@ ], "metadata": { "kernelspec": { - "display_name": "ams", + "display_name": "amsre", "language": "python", "name": "python3" }, @@ -671,14 +684,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.0" }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "d2b3bf80176349caa68dc4a3c77bd06eaade8abc678330f7d1c813c53380e5d2" - } - } + "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 From 0c9e0701ad1fd2defdf24761ad71e9c80e8819d6 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Sun, 24 Nov 2024 17:56:41 -0500 Subject: [PATCH 4/4] Add a note with SCIP import error --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 524856d8..3776f649 100644 --- a/README.md +++ b/README.md @@ -67,6 +67,7 @@ Use the following resources to get involved. - Version **0.9.9** has known issues and has been yanked from PyPI - `kvxopt` is recommended to install via `conda` as sometimes ``pip`` struggles to set the correct path for compiled libraries - `cvxpy` versions **below 1.5** are incompatible with `numpy` versions **2.0 and above** +- If solver `SCIP` run into import error, try to reinstall its Python interface by running `pip install pyscipopt --no-binary scip` AMS is released as ``ltbams`` on PyPI and conda-forge. Install from PyPI using pip: