From 83753cdeb5b8939fb8b8759dff2a67b0a3298337 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Sun, 21 Apr 2024 17:04:14 -0400 Subject: [PATCH 01/44] Refactor tesst_mats to combine connectivity matrices test --- tests/test_mats.py | 20 ++++++-------------- 1 file changed, 6 insertions(+), 14 deletions(-) diff --git a/tests/test_mats.py b/tests/test_mats.py index 7087a3ff..74367ab0 100644 --- a/tests/test_mats.py +++ b/tests/test_mats.py @@ -40,37 +40,29 @@ def test_MParam(self): # check if `v` is 1D-array self.assertEqual(one_vec.v.shape, (self.ss.Bus.n,)) - def test_cg(self): + def test_c(self): """ - Test `Cg`. + Test connectivity matrices. """ + # Test `Cg` self.assertIsInstance(self.mats.Cg._v, (c_sparse, l_sparse)) self.assertIsInstance(self.mats.Cg.v, np.ndarray) self.assertEqual(self.mats.Cg._v.max(), 1) np.testing.assert_equal(self.mats.Cg._v.sum(axis=0), np.ones((1, self.ng))) - def test_cl(self): - """ - Test `Cl`. - """ + # Test `Cl` self.assertIsInstance(self.mats.Cl._v, (c_sparse, l_sparse)) self.assertIsInstance(self.mats.Cl.v, np.ndarray) self.assertEqual(self.mats.Cl._v.max(), 1) np.testing.assert_equal(self.mats.Cl._v.sum(axis=0), np.ones((1, self.nD))) - def test_csh(self): - """ - Test `Csh`. - """ + # Test `Csh` self.assertIsInstance(self.mats.Csh._v, (c_sparse, l_sparse)) self.assertIsInstance(self.mats.Csh.v, np.ndarray) self.assertEqual(self.mats.Csh._v.max(), 1) np.testing.assert_equal(self.mats.Csh._v.sum(axis=0), np.ones((1, self.nsh))) - def test_cft(self): - """ - Test `Cft`. - """ + # Test `Cft` self.assertIsInstance(self.mats.Cft._v, (c_sparse, l_sparse)) self.assertIsInstance(self.mats.Cft.v, np.ndarray) self.assertEqual(self.mats.Cft._v.max(), 1) From b1f1c0b78a2644988d4c46bb5cdc6201f09a1e30 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Sun, 21 Apr 2024 17:06:58 -0400 Subject: [PATCH 02/44] Update release notes --- docs/source/release-notes.rst | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/docs/source/release-notes.rst b/docs/source/release-notes.rst index dc24316e..2f3fd7b6 100644 --- a/docs/source/release-notes.rst +++ b/docs/source/release-notes.rst @@ -9,7 +9,7 @@ The APIs before v3.0.0 are in beta and may change without prior notice. Pre-v1.0.0 ========== -v0.9.6 (2024-xx-xx) +v0.9.6 (2024-04-21) ------------------- This patch release refactor and improve `MatProcessor`, where it support PTDF, LODF, @@ -28,6 +28,9 @@ Outage Distribution Factors". - Refactor `MatProcessor` to separate matrix building - Add Var `plf` in `DCPF`, `PFlow`, and `ACOPF` to store the line flow - 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 v0.9.5 (2024-03-25) ------------------- From 641c290cbf60b1951f81855924abddd34ded6450 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Sun, 21 Apr 2024 17:33:03 -0400 Subject: [PATCH 03/44] Rerun examples with v0.9.6 --- examples/demonstration/demo_AGC.ipynb | 46 ++++++--- examples/demonstration/demo_ESD1.ipynb | 20 ++-- examples/ex1.ipynb | 98 +++++++++---------- examples/ex2.ipynb | 64 ++++++------ examples/ex3.ipynb | 6 +- examples/ex4.ipynb | 18 ++-- examples/ex5.ipynb | 39 +++++--- examples/ex6.ipynb | 20 ++-- examples/ex7.ipynb | 22 ++--- examples/ex8.ipynb | 22 ++--- .../verification/ams_dcopf_verification.ipynb | 18 ++-- 11 files changed, 203 insertions(+), 170 deletions(-) diff --git a/examples/demonstration/demo_AGC.ipynb b/examples/demonstration/demo_AGC.ipynb index d72656ef..08d80642 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: 2024-04-21 16:53:49\n", - "andes:1.9.1.post46+g65e10e02\n", - "ams:0.9.5.post60.dev0+gc5f79b1\n" + "Last run time: 2024-04-21 17:31:10\n", + "andes:1.9.1\n", + "ams:0.9.6\n" ] } ], @@ -199,12 +199,21 @@ "execution_count": 10, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating code for 2 models on 8 processes.\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ "Following PFlow models in addfile will be overwritten: , , , , , , \n", - "AMS system 0x108668a30 is linked to the ANDES system 0x15387a460.\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 0x1440dab80 is linked to the ANDES system 0x106363c10.\n" ] } ], @@ -313,7 +322,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -322,7 +331,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -410,7 +419,16 @@ "cell_type": "code", "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/06/z8ws9b2d733f7h6yc5qpn22w0000gn/T/ipykernel_10319/1576960110.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", + " maptab = sp.dyn.link.copy().fillna(False)\n" + ] + } + ], "source": [ "# --- time constants ---\n", "total_time = 610\n", @@ -535,7 +553,7 @@ "output_type": "stream", "text": [ " reinit OModel due to non-parametric change.\n", - " solved as optimal in 0.0271 seconds, converged in 12 iterations with ECOS.\n" + " solved as optimal in 0.0295 seconds, converged in 12 iterations with ECOS.\n" ] }, { @@ -571,7 +589,7 @@ "output_type": "stream", "text": [ " reinit OModel due to non-parametric change.\n", - " solved as optimal in 0.0167 seconds, converged in 11 iterations with ECOS.\n" + " solved as optimal in 0.0158 seconds, converged in 11 iterations with ECOS.\n" ] }, { @@ -607,7 +625,7 @@ "output_type": "stream", "text": [ " reinit OModel due to non-parametric change.\n", - " solved as optimal in 0.0168 seconds, converged in 12 iterations with ECOS.\n" + " solved as optimal in 0.0155 seconds, converged in 12 iterations with ECOS.\n" ] }, { @@ -1003,7 +1021,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPS1 score: 99.99774731877633\n" + "CPS1 score: 99.99774733891043\n" ] } ], @@ -1058,7 +1076,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 19, @@ -1067,7 +1085,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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", + "image/png": 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", "text/plain": [ "
" ] diff --git a/examples/demonstration/demo_ESD1.ipynb b/examples/demonstration/demo_ESD1.ipynb index ff95e0c2..24927edc 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: 2024-03-25 22:18:00\n", - "ams:0.9.5\n" + "Last run time: 2024-04-21 17:32:43\n", + "ams:0.9.6\n" ] } ], @@ -77,9 +77,9 @@ "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 0.1323 seconds.\n", + "Input file parsed in 0.1406 seconds.\n", "Zero line rates detacted in rate_b, rate_c, adjusted to 999.\n", - "System set up in 0.0066 seconds.\n" + "System set up in 0.0019 seconds.\n" ] } ], @@ -218,8 +218,8 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0232 seconds.\n", - " solved as optimal in 0.0638 seconds, converged in -1 iteration with SCIP.\n" + " initialized in 0.0198 seconds.\n", + " solved as optimal in 0.0547 seconds, converged in -1 iteration with SCIP.\n" ] }, { @@ -315,8 +315,8 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0313 seconds.\n", - " solved as optimal in 0.0564 seconds, converged in -1 iteration with SCIP.\n" + " initialized in 0.0321 seconds.\n", + " solved as optimal in 0.0529 seconds, converged in -1 iteration with SCIP.\n" ] }, { @@ -378,8 +378,8 @@ "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 0.0343 seconds.\n", - " solved as optimal in 0.1160 seconds, converged in -1 iteration with SCIP.\n" + " initialized in 0.0369 seconds.\n", + " solved as optimal in 0.1760 seconds, converged in -1 iteration with SCIP.\n" ] }, { diff --git a/examples/ex1.ipynb b/examples/ex1.ipynb index b2a1ff5a..e0924c4f 100644 --- a/examples/ex1.ipynb +++ b/examples/ex1.ipynb @@ -51,8 +51,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last run time: 2024-03-25 22:18:12\n", - "ams:0.9.5\n" + "Last run time: 2024-04-21 17:29:32\n", + "ams:0.9.6\n" ] } ], @@ -127,9 +127,9 @@ "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 0.0957 seconds.\n", + "Input file parsed in 0.1118 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 0.0020 seconds.\n" ] } ], @@ -163,33 +163,33 @@ { "data": { "text/plain": [ - "OrderedDict([('Summary', Summary (3 devices) at 0x2832a0df0),\n", - " ('Bus', Bus (5 devices) at 0x2833420d0),\n", - " ('PQ', PQ (3 devices) at 0x2833428b0),\n", - " ('Slack', Slack (1 device) at 0x283353520),\n", - " ('PV', PV (3 devices) at 0x283368400),\n", - " ('Shunt', Shunt (0 devices) at 0x283368e50),\n", - " ('Line', Line (7 devices) at 0x283373340),\n", - " ('PVD1', PVD1 (0 devices) at 0x28337ea00),\n", - " ('ESD1', ESD1 (0 devices) at 0x283391070),\n", - " ('REGCA1', REGCA1 (0 devices) at 0x2833915e0),\n", - " ('REGCV1', REGCV1 (0 devices) at 0x283391be0),\n", - " ('REGCV2', REGCV2 (0 devices) at 0x28339d460),\n", - " ('Area', Area (3 devices) at 0x28339da00),\n", - " ('Region', Region (2 devices) at 0x2833a81c0),\n", - " ('SFR', SFR (2 devices) at 0x2833a8970),\n", - " ('SR', SR (2 devices) at 0x2833a8fd0),\n", - " ('NSR', NSR (2 devices) at 0x2833b9430),\n", - " ('VSGR', VSGR (0 devices) at 0x2833b9850),\n", - " ('GCost', GCost (4 devices) at 0x2833b9ca0),\n", - " ('SFRCost', SFRCost (4 devices) at 0x2833c4370),\n", - " ('SRCost', SRCost (4 devices) at 0x2833c4910),\n", - " ('NSRCost', NSRCost (4 devices) at 0x2833c4d30),\n", - " ('VSGCost', VSGCost (0 devices) at 0x2833d1190),\n", - " ('DCost', DCost (3 devices) at 0x2833d1490),\n", - " ('TimeSlot', TimeSlot (0 devices) at 0x2833d1a00),\n", - " ('EDTSlot', EDTSlot (24 devices) at 0x2833db4c0),\n", - " ('UCTSlot', UCTSlot (24 devices) at 0x2833db8e0)])" + "OrderedDict([('Summary', Summary (3 devices) at 0x1271e9970),\n", + " ('Bus', Bus (5 devices) at 0x2920881c0),\n", + " ('PQ', PQ (3 devices) at 0x292088940),\n", + " ('Slack', Slack (1 device) at 0x29209d5b0),\n", + " ('PV', PV (3 devices) at 0x2920ad490),\n", + " ('Shunt', Shunt (0 devices) at 0x2920adee0),\n", + " ('Line', Line (7 devices) at 0x2920bb3d0),\n", + " ('PVD1', PVD1 (0 devices) at 0x2920c9ac0),\n", + " ('ESD1', ESD1 (0 devices) at 0x2920db160),\n", + " ('REGCA1', REGCA1 (0 devices) at 0x2920db6d0),\n", + " ('REGCV1', REGCV1 (0 devices) at 0x2920dbcd0),\n", + " ('REGCV2', REGCV2 (0 devices) at 0x2920e8550),\n", + " ('Area', Area (3 devices) at 0x2920e8af0),\n", + " ('Region', Region (2 devices) at 0x2920f02b0),\n", + " ('SFR', SFR (2 devices) at 0x2920f0a60),\n", + " ('SR', SR (2 devices) at 0x292207100),\n", + " ('NSR', NSR (2 devices) at 0x292207520),\n", + " ('VSGR', VSGR (0 devices) at 0x292207940),\n", + " ('GCost', GCost (4 devices) at 0x292207d90),\n", + " ('SFRCost', SFRCost (4 devices) at 0x292211460),\n", + " ('SRCost', SRCost (4 devices) at 0x292211a00),\n", + " ('NSRCost', NSRCost (4 devices) at 0x292211e20),\n", + " ('VSGCost', VSGCost (0 devices) at 0x29221e280),\n", + " ('DCost', DCost (3 devices) at 0x29221e580),\n", + " ('TimeSlot', TimeSlot (0 devices) at 0x29221eaf0),\n", + " ('EDTSlot', EDTSlot (24 devices) at 0x2922295b0),\n", + " ('UCTSlot', UCTSlot (24 devices) at 0x2922299d0)])" ] }, "execution_count": 5, @@ -342,23 +342,23 @@ { "data": { "text/plain": [ - "OrderedDict([('DCPF', DCPF at 0x2832a0a30),\n", - " ('PFlow', PFlow at 0x2833e7520),\n", - " ('CPF', CPF at 0x2833e7b80),\n", - " ('ACOPF', ACOPF at 0x2833fd1c0),\n", - " ('DCOPF', DCOPF at 0x2833fdac0),\n", - " ('ED', ED at 0x283417a90),\n", - " ('EDDG', EDDG at 0x283446eb0),\n", - " ('EDES', EDES at 0x28346bb20),\n", - " ('RTED', RTED at 0x28349f280),\n", - " ('RTEDDG', RTEDDG at 0x28349f340),\n", - " ('RTEDES', RTEDES at 0x2834c4df0),\n", - " ('RTEDVIS', RTEDVIS at 0x2834e9d90),\n", - " ('UC', UC at 0x28350c730),\n", - " ('UCDG', UCDG at 0x295eb55e0),\n", - " ('UCES', UCES at 0x295ed9730),\n", - " ('DOPF', DOPF at 0x2960ff370),\n", - " ('DOPFVIS', DOPFVIS at 0x296112850)])" + "OrderedDict([('DCPF', DCPF at 0x2920880d0),\n", + " ('PFlow', PFlow at 0x292239640),\n", + " ('CPF', CPF at 0x292239d00),\n", + " ('ACOPF', ACOPF at 0x29224c3a0),\n", + " ('DCOPF', DCOPF at 0x29224cc70),\n", + " ('ED', ED at 0x29226cc40),\n", + " ('EDDG', EDDG at 0x2922a4100),\n", + " ('EDES', EDES at 0x2922b8d30),\n", + " ('RTED', RTED at 0x2922ef490),\n", + " ('RTEDDG', RTEDDG at 0x2922ef550),\n", + " ('RTEDES', RTEDES at 0x292327040),\n", + " ('RTEDVIS', RTEDVIS at 0x29233bfa0),\n", + " ('UC', UC at 0x29235c940),\n", + " ('UCDG', UCDG at 0x2960517f0),\n", + " ('UCES', UCES at 0x296075940),\n", + " ('DOPF', DOPF at 0x2960ae580),\n", + " ('DOPFVIS', DOPFVIS at 0x2960c0a60)])" ] }, "execution_count": 7, @@ -453,7 +453,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0158 seconds, converged in 12 iterations with ECOS.\n" + " solved as optimal in 0.0165 seconds, converged in 12 iterations with ECOS.\n" ] }, { diff --git a/examples/ex2.ipynb b/examples/ex2.ipynb index aae19277..35912233 100644 --- a/examples/ex2.ipynb +++ b/examples/ex2.ipynb @@ -36,8 +36,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last run time: 2024-04-19 22:43:40\n", - "ams:0.9.5.post32.dev0+g886abba\n" + "Last run time: 2024-04-21 17:29:40\n", + "ams:0.9.6\n" ] } ], @@ -81,10 +81,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "Parsing input file \"/Users/jinningwang/Documents/work/ams/ams/cases/5bus/pjm5bus_uced.xlsx\"...\n", - "Input file parsed in 0.1113 seconds.\n", + "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 0.0945 seconds.\n", "Zero line rates detacted in rate_b, rate_c, adjusted to 999.\n", - "System set up in 0.0030 seconds.\n" + "System set up in 0.0022 seconds.\n" ] } ], @@ -269,8 +269,8 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0188 seconds.\n", - " solved as optimal in 0.0211 seconds, converged in 12 iterations with ECOS.\n" + " initialized in 0.0137 seconds.\n", + " solved as optimal in 0.0152 seconds, converged in 12 iterations with ECOS.\n" ] }, { @@ -423,7 +423,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0025 seconds, converged in 12 iterations with ECOS.\n" + " solved as optimal in 0.0017 seconds, converged in 12 iterations with ECOS.\n" ] }, { @@ -478,7 +478,7 @@ { "data": { "text/plain": [ - "StaticLoad (3 devices) at 0x156740520" + "StaticLoad (3 devices) at 0x17f154850" ] }, "execution_count": 14, @@ -697,7 +697,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0045 seconds, converged in 10 iterations with ECOS.\n" + " solved as optimal in 0.0018 seconds, converged in 10 iterations with ECOS.\n" ] }, { @@ -1014,7 +1014,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0193 seconds, converged in 10 iterations with ECOS.\n" + " solved as optimal in 0.0143 seconds, converged in 10 iterations with ECOS.\n" ] }, { @@ -1428,7 +1428,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0162 seconds, converged in 10 iterations with ECOS.\n" + " solved as optimal in 0.0149 seconds, converged in 10 iterations with ECOS.\n" ] }, { @@ -1498,10 +1498,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "Parsing input file \"/Users/jinningwang/Documents/work/ams/ams/cases/5bus/pjm5bus_uced.xlsx\"...\n", - "Input file parsed in 0.0461 seconds.\n", + "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 0.0397 seconds.\n", "Zero line rates detacted in rate_b, rate_c, adjusted to 999.\n", - "System set up in 0.0039 seconds.\n" + "System set up in 0.0031 seconds.\n" ] } ], @@ -1520,7 +1520,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0123 seconds.\n" + " initialized in 0.0169 seconds.\n" ] }, { @@ -1635,7 +1635,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0187 seconds, converged in 11 iterations with ECOS.\n" + " solved as optimal in 0.0154 seconds, converged in 11 iterations with ECOS.\n" ] }, { @@ -1757,8 +1757,8 @@ "output_type": "stream", "text": [ "Disabled constraints: plflb, plfub\n", - " initialized in 0.0014 seconds.\n", - " solved as optimal in 0.0139 seconds, converged in 11 iterations with ECOS.\n" + " initialized in 0.0013 seconds.\n", + " solved as optimal in 0.0122 seconds, converged in 11 iterations with ECOS.\n" ] }, { @@ -1879,8 +1879,8 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0007 seconds.\n", - " solved as optimal in 0.0150 seconds, converged in 11 iterations with ECOS.\n" + " initialized in 0.0008 seconds.\n", + " solved as optimal in 0.0128 seconds, converged in 11 iterations with ECOS.\n" ] }, { @@ -1961,7 +1961,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0020 seconds.\n" + " initialized in 0.0017 seconds.\n" ] }, { @@ -2035,10 +2035,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "Parsing input file \"/Users/jinningwang/Documents/work/ams/ams/cases/5bus/pjm5bus_uced.xlsx\"...\n", - "Input file parsed in 0.1139 seconds.\n", + "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 0.0401 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 0.0030 seconds.\n" ] } ], @@ -2084,8 +2084,8 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0110 seconds.\n", - " solved as optimal in 0.0158 seconds, converged in 12 iterations with ECOS.\n" + " initialized in 0.0119 seconds.\n", + " solved as optimal in 0.0144 seconds, converged in 12 iterations with ECOS.\n" ] }, { @@ -2184,7 +2184,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0184 seconds, converged in 325 iterations with SCS.\n" + " solved as optimal in 0.0164 seconds, converged in 325 iterations with SCS.\n" ] }, { @@ -2217,7 +2217,7 @@ { "data": { "text/plain": [ - "2.3444999975709524" + "2.3444999975679632" ] }, "execution_count": 56, @@ -2292,8 +2292,8 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0057 seconds.\n", - " solved as optimal in 0.0089 seconds, converged in 225 iterations with SCS.\n" + " initialized in 0.0058 seconds.\n", + " solved as optimal in 0.0091 seconds, converged in 225 iterations with SCS.\n" ] }, { @@ -2326,7 +2326,7 @@ { "data": { "text/plain": [ - "2.344500000003336" + "2.3445000000033347" ] }, "execution_count": 60, diff --git a/examples/ex3.ipynb b/examples/ex3.ipynb index 593f3280..0d608d23 100644 --- a/examples/ex3.ipynb +++ b/examples/ex3.ipynb @@ -36,8 +36,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last run time: 2024-03-25 22:18:40\n", - "ams:0.9.5\n" + "Last run time: 2024-04-21 17:29:49\n", + "ams:0.9.6\n" ] } ], @@ -73,7 +73,7 @@ "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 0.1046 seconds.\n", + "Input file parsed in 0.0897 seconds.\n", "Zero line rates detacted in rate_b, rate_c, adjusted to 999.\n", "System set up in 0.0019 seconds.\n" ] diff --git a/examples/ex4.ipynb b/examples/ex4.ipynb index 32cd7d1a..eee14ffc 100644 --- a/examples/ex4.ipynb +++ b/examples/ex4.ipynb @@ -45,8 +45,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last run time: 2024-03-25 22:19:07\n", - "ams:0.9.5\n" + "Last run time: 2024-04-21 17:29:57\n", + "ams:0.9.6\n" ] } ], @@ -89,7 +89,7 @@ "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 0.1199 seconds.\n", + "Input file parsed in 0.1016 seconds.\n", "System set up in 0.0016 seconds.\n", "-> Systen size:\n", "Base: 100 MVA; Frequency: 60 Hz\n", @@ -129,9 +129,9 @@ "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 0.0025 seconds.\n", + "Input file parsed in 0.0020 seconds.\n", "Zero line rates detacted in rate_c, adjusted to 999.\n", - "System set up in 0.0061 seconds.\n", + "System set up in 0.0024 seconds.\n", "-> Systen size:\n", "Base: 100 MVA; Frequency: 60 Hz\n", "14 Buses; 20 Lines; 5 Static Generators\n", @@ -166,9 +166,9 @@ "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 0.0056 seconds.\n", + "Input file parsed in 0.0045 seconds.\n", "Zero line rates detacted in rate_a, rate_b, rate_c, adjusted to 999.\n", - "System set up in 0.0022 seconds.\n", + "System set up in 0.0021 seconds.\n", "-> Systen size:\n", "Base: 100.0 MVA; Frequency: 60 Hz\n", "14 Buses; 20 Lines; 5 Static Generators\n", @@ -220,9 +220,9 @@ "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 0.0099 seconds.\n", + "Input file parsed in 0.0114 seconds.\n", "Zero line rates detacted in rate_c, adjusted to 999.\n", - "System set up in 0.0032 seconds.\n", + "System set up in 0.0027 seconds.\n", "-> Systen size:\n", "Base: 100.0 MVA; Frequency: 60.0 Hz\n", "14 Buses; 20 Lines; 5 Static Generators\n", diff --git a/examples/ex5.ipynb b/examples/ex5.ipynb index f764aa6e..e5595b82 100644 --- a/examples/ex5.ipynb +++ b/examples/ex5.ipynb @@ -40,9 +40,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last run time: 2024-03-25 22:19:14\n", + "Last run time: 2024-04-21 17:30:07\n", "andes:1.9.1\n", - "ams:0.9.5\n" + "ams:0.9.6\n" ] } ], @@ -80,7 +80,7 @@ "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 0.1175 seconds.\n", + "Input file parsed in 0.1103 seconds.\n", "System set up in 0.0016 seconds.\n" ] } @@ -100,7 +100,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0147 seconds.\n" + " initialized in 0.0161 seconds.\n" ] }, { @@ -127,7 +127,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0172 seconds, converged in 12 iterations with ECOS.\n" + " solved as optimal in 0.0265 seconds, converged in 12 iterations with ECOS.\n" ] }, { @@ -166,17 +166,31 @@ "execution_count": 7, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating code for 3 models on 8 processes.\n" + ] + }, { "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", "Following PFlow models in addfile will be overwritten: , , , , , , \n", - "Addfile parsed in 0.0510 seconds.\n", - "System converted to ANDES in 0.1511 seconds.\n", - "/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/ams/interop/andes.py:907: 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", + "Addfile parsed in 0.0533 seconds.\n", + "System converted to ANDES in 0.3059 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 0x28ac68a00 is linked to the ANDES system 0x28f68dcd0.\n" + "AMS system 0x17feb0a60 is linked to the ANDES system 0x294c7b220.\n", + " initialized in 0.0021 seconds.\n", + " 0: |F(x)| = 0.4665790376\n", + " 1: |F(x)| = 0.01697226536\n", + " 2: |F(x)| = 3.214367637e-05\n", + " 3: |F(x)| = 1.533550661e-10\n", + " solved in 0.0099 seconds, converged in 3 iterations with PYPOWER-Newton.\n", + "Power flow results are consistent.\n" ] } ], @@ -407,7 +421,7 @@ "output_type": "stream", "text": [ " initialized in 0.0033 seconds.\n", - " solved in 0.2602 seconds, converged in 12 iterations with PYPOWER-PIPS.\n", + " solved in 0.2284 seconds, converged in 12 iterations with PYPOWER-PIPS.\n", " converted to AC.\n" ] }, @@ -464,7 +478,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Send results to ANDES <0x28f68dcd0>...\n", + "Send results to ANDES <0x294c7b220>...\n", + "*Send to StaticGen.v0\n", "Send to Bus.v0\n", "Send to StaticGen.u\n", "Send to StaticGen.p0\n" @@ -663,7 +678,7 @@ { "data": { "text/plain": [ - "array([1.81221392, 0.47703089, 0.01000084, 0.02000084, 0.01000085])" + "array([1.80245706, 0.47703089, 0.01000084, 0.02000084, 0.01000085])" ] }, "execution_count": 19, diff --git a/examples/ex6.ipynb b/examples/ex6.ipynb index c92099d1..90a23075 100644 --- a/examples/ex6.ipynb +++ b/examples/ex6.ipynb @@ -37,8 +37,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last run time: 2024-03-25 22:19:22\n", - "ams:0.9.5\n" + "Last run time: 2024-04-21 17:30:15\n", + "ams:0.9.6\n" ] } ], @@ -75,9 +75,9 @@ "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 0.0930 seconds.\n", + "Input file parsed in 0.0926 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 0.0025 seconds.\n" ] } ], @@ -371,9 +371,9 @@ { "data": { "text/plain": [ - "OrderedDict([('TimeSlot', TimeSlot (0 devices) at 0x2920d3a30),\n", - " ('EDTSlot', EDTSlot (6 devices) at 0x2920de4f0),\n", - " ('UCTSlot', UCTSlot (6 devices) at 0x2920de910)])" + "OrderedDict([('TimeSlot', TimeSlot (0 devices) at 0x2905f8af0),\n", + " ('EDTSlot', EDTSlot (6 devices) at 0x2906045b0),\n", + " ('UCTSlot', UCTSlot (6 devices) at 0x2906049d0)])" ] }, "execution_count": 7, @@ -553,7 +553,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0213 seconds.\n" + " initialized in 0.0200 seconds.\n" ] }, { @@ -580,7 +580,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0232 seconds, converged in 11 iterations with ECOS.\n" + " solved as optimal in 0.0233 seconds, converged in 11 iterations with ECOS.\n" ] }, { @@ -677,7 +677,7 @@ { "data": { "text/plain": [ - "array([2.1])" + "2.099999999839607" ] }, "execution_count": 14, diff --git a/examples/ex7.ipynb b/examples/ex7.ipynb index 222b4787..ff69e470 100644 --- a/examples/ex7.ipynb +++ b/examples/ex7.ipynb @@ -42,8 +42,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last run time: 2024-03-25 22:19:40\n", - "ams:0.9.5\n" + "Last run time: 2024-04-21 17:30:21\n", + "ams:0.9.6\n" ] } ], @@ -79,9 +79,9 @@ "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 0.1487 seconds.\n", + "Input file parsed in 0.0997 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 0.0021 seconds.\n" ] } ], @@ -100,8 +100,8 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0136 seconds.\n", - " solved as optimal in 0.0123 seconds, converged in 10 iterations with ECOS.\n" + " initialized in 0.0089 seconds.\n", + " solved as optimal in 0.0098 seconds, converged in 10 iterations with ECOS.\n" ] }, { @@ -144,7 +144,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Report saved to \"pjm5bus_demo_out.txt\" in 0.0022 seconds.\n" + "Report saved to \"pjm5bus_demo_out.txt\" in 0.0050 seconds.\n" ] }, { @@ -178,12 +178,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "AMS 0.9.5\n", + "AMS 0.9.6\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: 03/25/2024 10:19:41 PM\n", + "Report time: 04/21/2024 05:30:21 PM\n", "\n", "\n", "========== System Statistics ==========\n", @@ -266,8 +266,8 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0183 seconds.\n", - " solved as optimal in 0.0231 seconds, converged in 11 iterations with ECOS.\n" + " initialized in 0.0187 seconds.\n", + " solved as optimal in 0.0216 seconds, converged in 11 iterations with ECOS.\n" ] }, { diff --git a/examples/ex8.ipynb b/examples/ex8.ipynb index 7bae342f..4d2c9eab 100644 --- a/examples/ex8.ipynb +++ b/examples/ex8.ipynb @@ -39,8 +39,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last run time: 2024-03-25 22:19:51\n", - "ams:0.9.5.post0.dev0+g90e045b\n" + "Last run time: 2024-04-21 17:30:31\n", + "ams:0.9.6.post3.dev0+gfd3786e\n" ] } ], @@ -76,9 +76,9 @@ "output_type": "stream", "text": [ "Parsing input file \"/Users/jinningwang/Documents/work/ams/ams/cases/5bus/pjm5bus_demo.xlsx\"...\n", - "Input file parsed in 0.1406 seconds.\n", + "Input file parsed in 0.1127 seconds.\n", "Zero line rates detacted in rate_b, rate_c, adjusted to 999.\n", - "System set up in 0.0023 seconds.\n" + "System set up in 0.0020 seconds.\n" ] } ], @@ -105,7 +105,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0101 seconds.\n" + " initialized in 0.0093 seconds.\n" ] }, { @@ -431,7 +431,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0077 seconds.\n" + " initialized in 0.0071 seconds.\n" ] }, { @@ -465,7 +465,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0133 seconds, converged in 10 iterations with ECOS.\n" + " solved as optimal in 0.0116 seconds, converged in 10 iterations with ECOS.\n" ] }, { @@ -567,9 +567,9 @@ "output_type": "stream", "text": [ "Parsing input file \"/Users/jinningwang/Documents/work/ams/ams/cases/5bus/pjm5bus_demo.xlsx\"...\n", - "Input file parsed in 0.0378 seconds.\n", + "Input file parsed in 0.0385 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 0.0023 seconds.\n" ] } ], @@ -588,8 +588,8 @@ "name": "stderr", "output_type": "stream", "text": [ - " initialized in 0.0067 seconds.\n", - " solved as optimal in 0.0098 seconds, converged in 10 iterations with ECOS.\n" + " initialized in 0.0071 seconds.\n", + " solved as optimal in 0.0091 seconds, converged in 10 iterations with ECOS.\n" ] }, { diff --git a/examples/verification/ams_dcopf_verification.ipynb b/examples/verification/ams_dcopf_verification.ipynb index 9d717f80..d8a3820d 100644 --- a/examples/verification/ams_dcopf_verification.ipynb +++ b/examples/verification/ams_dcopf_verification.ipynb @@ -43,8 +43,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last run time: 2024-03-25 22:37:30\n", - "ams: 0.9.5\n" + "Last run time: 2024-04-21 17:30:41\n", + "ams: 0.9.6\n" ] } ], @@ -103,12 +103,12 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0084 seconds, converged in 13 iterations with ECOS.\n", - " solved as optimal in 0.0093 seconds, converged in 17 iterations with ECOS.\n", - " solved as optimal in 0.0192 seconds, converged in 15 iterations with ECOS.\n", - " solved as optimal in 0.0315 seconds, converged in 17 iterations with ECOS.\n", - " solved as optimal in 0.0203 seconds, converged in 17 iterations with ECOS.\n", - " solved as optimal in 0.0363 seconds, converged in 17 iterations with ECOS.\n" + " solved as optimal in 0.0080 seconds, converged in 13 iterations with ECOS.\n", + " solved as optimal in 0.0097 seconds, converged in 17 iterations with ECOS.\n", + " solved as optimal in 0.0194 seconds, converged in 15 iterations with ECOS.\n", + " solved as optimal in 0.0200 seconds, converged in 17 iterations with ECOS.\n", + " solved as optimal in 0.0211 seconds, converged in 17 iterations with ECOS.\n", + " solved as optimal in 0.0321 seconds, converged in 17 iterations with ECOS.\n" ] } ], @@ -176,7 +176,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " solved as optimal in 0.0120 seconds, converged in 14 iterations with ECOS.\n" + " solved as optimal in 0.0181 seconds, converged in 14 iterations with ECOS.\n" ] }, { From ef1d8a03683829d406cd48f10535f8b3097b4185 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Sun, 21 Apr 2024 17:38:35 -0400 Subject: [PATCH 04/44] Add test on Routine.get() input type --- tests/test_routine.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/tests/test_routine.py b/tests/test_routine.py index 66b8e2df..b04e6ec1 100644 --- a/tests/test_routine.py +++ b/tests/test_routine.py @@ -1,6 +1,8 @@ import unittest import numpy as np +from andes.shared import pd + import ams @@ -52,6 +54,9 @@ def test_routine_get(self): np.testing.assert_equal(self.ss.DCOPF.get('pg', 'PV_30', 'v'), self.ss.StaticGen.get('p', 'PV_30', 'v')) + # test input type + self.assertIsInstance(self.ss.DCOPF.get('pg', pd.Series(['PV_30']), 'v'), np.ndarray) + # test return type self.assertIsInstance(self.ss.DCOPF.get('pg', 'PV_30', 'v'), float) self.assertIsInstance(self.ss.DCOPF.get('pg', ['PV_30'], 'v'), np.ndarray) From 9cf9a89d4eadbb5281d931b2c955bdc5848b9bf9 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Thu, 2 May 2024 23:06:55 -0400 Subject: [PATCH 05/44] Add roadmap --- docs/source/release-notes.rst | 41 ++++++++++++++++++++++++++++++++++- 1 file changed, 40 insertions(+), 1 deletion(-) diff --git a/docs/source/release-notes.rst b/docs/source/release-notes.rst index 2f3fd7b6..7e3b09f3 100644 --- a/docs/source/release-notes.rst +++ b/docs/source/release-notes.rst @@ -226,4 +226,43 @@ v0.5 (2023-02-17) v0.4 (2023-01) ------------------- -This release outlines the package. \ No newline at end of file +This release outlines the package. + +Roadmap +======= + +This section lists out some potential features that may be added in the future. +Note that the proposed features are not guaranteed to be implemented and subject to change. + +Electric Vehicle for Grid Service +------------------------------------------ + +A charging-time-constrained EV aggregation based on the state-space model + +References: + +[1] J. Wang et al., "Electric Vehicles Charging Time Constrained Deliverable Provision of Secondary +Frequency Regulation," in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2024.3356948. + +[2] M. Wang et al., "State Space Model of Aggregated Electric Vehicles for Frequency Regulation," in +IEEE Transactions on Smart Grid, vol. 11, no. 2, pp. 981-994, March 2020, doi: 10.1109/TSG.2019.2929052. + +Distribution OPF +-------------------------- + +- Distribution networks OPF and its LMP +- DG siting and sizing considering energy equity + +References: + +[1] H. Yuan, F. Li, Y. Wei and J. Zhu, "Novel Linearized Power Flow and Linearized OPF Models for +Active Distribution Networks With Application in Distribution LMP," in IEEE Transactions on Smart Grid, +vol. 9, no. 1, pp. 438-448, Jan. 2018, doi: 10.1109/TSG.2016.2594814. + +[2] C. Li, F. Li, S. Jiang, X. Wang and J. Wang, "Siting and Sizing of DG Units Considering Energy +Equity: Model, Solution, and Guidelines," in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2024.3350914. + +Planning +-------------------------- + +- Transmission expansion planning From 82f3d414a4e1ebd141a8f13693ecf9745d3ba009 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Fri, 3 May 2024 11:28:33 -0400 Subject: [PATCH 06/44] Update docstring --- ams/models/renewable/regc.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/ams/models/renewable/regc.py b/ams/models/renewable/regc.py index 9bf414cc..7241624b 100644 --- a/ams/models/renewable/regc.py +++ b/ams/models/renewable/regc.py @@ -1,5 +1,5 @@ """ -RenGen dispatch model. +RenGen scheduling model. """ from andes.core.param import NumParam, IdxParam, ExtParam @@ -9,7 +9,7 @@ class REGCData(ModelData): """ - Data container for RenGen dispatch model. + Data container for RenGen scheduling model. """ def __init__(self): @@ -37,7 +37,7 @@ def __init__(self): class REGCA1(REGCData, Model): """ - Renewable generator dispatch model. + Renewable generator scheduling model. Reference: @@ -104,7 +104,7 @@ def __init__(self, system=None, config=None) -> None: class REGCV2(REGCV1): """ - Voltage-controlled VSC. + Voltage-controlled VSC, identical to REGCV1. Reference: From 22639ed61f9e209d4df454c76ee6d0cae0db55d2 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Fri, 3 May 2024 11:29:40 -0400 Subject: [PATCH 07/44] [WIP] Add EV aggregator model --- ams/extension/__init__.py | 3 +++ ams/extension/eva.py | 11 +++++++++++ ams/models/distributed/evs.py | 26 ++++++++++++++++++++++++++ 3 files changed, 40 insertions(+) create mode 100644 ams/extension/__init__.py create mode 100644 ams/extension/eva.py create mode 100644 ams/models/distributed/evs.py diff --git a/ams/extension/__init__.py b/ams/extension/__init__.py new file mode 100644 index 00000000..b5c0e6f0 --- /dev/null +++ b/ams/extension/__init__.py @@ -0,0 +1,3 @@ +""" +Extension module. +""" \ No newline at end of file diff --git a/ams/extension/eva.py b/ams/extension/eva.py new file mode 100644 index 00000000..3aa6ca3c --- /dev/null +++ b/ams/extension/eva.py @@ -0,0 +1,11 @@ +""" +EV Aggregator. +""" + +class mcs(): + """ + Class for EV aggregation Monte Carlo simulation. + """ + + def __init__(self): + pass diff --git a/ams/models/distributed/evs.py b/ams/models/distributed/evs.py new file mode 100644 index 00000000..2b4554bc --- /dev/null +++ b/ams/models/distributed/evs.py @@ -0,0 +1,26 @@ +""" +EV model. +""" + +from andes.core.param import NumParam + +from ams.core.model import Model + +class EV(Model): + """ + EV aggregation model at transmission level. + + It is expected to be used in conjunction with the `EV1` or `EV2` model + in ANDES. + + Reference: + + [1] J. Wang et al., "Electric Vehicles Charging Time Constrained Deliverable Provision of Secondary + Frequency Regulation," in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2024.3356948. + """ + + def __init__(self, system, config): + Model.__init__(self, system, config) + self.group = 'DG' + + self.N = NumParam(default=1, info='Number of EVs') From e5ca6ec7ad78459d6af2762b5ecc067efae60493 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Fri, 3 May 2024 11:43:53 -0400 Subject: [PATCH 08/44] Change dispatch to scheduling in description --- LICENSE | 4 ++-- README.md | 6 +++--- ams/core/model.py | 2 +- ams/core/service.py | 2 +- ams/interop/andes.py | 2 +- ams/models/__init__.py | 2 +- ams/models/distributed/esd1.py | 2 +- ams/models/distributed/evs.py | 3 +-- ams/models/distributed/pvd1.py | 2 +- ams/models/group.py | 2 +- ams/models/timeslot.py | 2 +- ams/routines/__init__.py | 2 +- ams/routines/ed.py | 14 +++++++------- ams/routines/routine.py | 2 +- ams/routines/rted.py | 2 +- ams/routines/uc.py | 4 ++-- ams/system.py | 2 +- docs/source/conf.py | 2 +- docs/source/getting_started/formats/pypower.rst | 2 +- docs/source/getting_started/index.rst | 2 +- docs/source/getting_started/overview.rst | 2 +- docs/source/index.rst | 8 ++++---- docs/source/modeling/example.rst | 2 +- docs/source/modeling/routine.rst | 10 +++++----- setup.py | 2 +- 25 files changed, 42 insertions(+), 43 deletions(-) diff --git a/LICENSE b/LICENSE index f7c4a82e..6f4c393f 100644 --- a/LICENSE +++ b/LICENSE @@ -1,4 +1,4 @@ -AMS: Python Software for Dispatch Modeling and Co-Simulation with Dynanic +AMS: Python Software for Scheduling Modeling and Co-Simulation with Dynanic Copyright (c) 2023-2024 Jinning Wang @@ -649,7 +649,7 @@ to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. - AMS, a python software for dispatch modeling and co-simulation with dynanic + AMS, a python software for scheduling modeling and co-simulation with dynanic Copyright (C) 2023 Jinning Wang This program is free software: you can redistribute it and/or modify diff --git a/README.md b/README.md index 5040b831..40445326 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # LTB AMS -Python Software for Power System Dispatch Modeling and Co-Simulation with Dynanic, serving as the market simulator for the [CURENT Largescale Testbed][LTB Repository]. +Python Software for Power System Scheduling Modeling and Co-Simulation with Dynanic, serving as the market simulator for the [CURENT Largescale Testbed][LTB Repository]. [![License: GPL-3.0](https://img.shields.io/badge/License-GPL--3.0-blue.svg)](https://github.com/CURENT/ams/blob/master/LICENSE) ![platforms](https://anaconda.org/conda-forge/ltbams/badges/platforms.svg) @@ -31,7 +31,7 @@ Python Software for Power System Dispatch Modeling and Co-Simulation with Dynani With the built-in interface with dynamic simulation engine, ANDES, AMS enables Dynamics Interfaced Stability Constrained Production Cost and Market Operation Modeling. -AMS produces credible dispatch results and competitive performance. +AMS produces credible scheduling results and competitive performance. The following results show the comparison of DCOPF between AMS and other tools. | Cost [\$] | AMS | MATPOWER | pandapower | @@ -79,7 +79,7 @@ pip install git+https://github.com/CURENT/ams.git ``` # Sponsors and Contributors -AMS is the dispatch simulation engine for the CURENT Largescale Testbed (LTB). +AMS is the scheduling simulation engine for the CURENT Largescale Testbed (LTB). More information about CURENT LTB can be found at the [LTB Repository][LTB Repository]. This work was supported in part by the Engineering Research Center Program of the National Science Foundation and the Department of Energy diff --git a/ams/core/model.py b/ams/core/model.py index ae84161c..34fbabb3 100644 --- a/ams/core/model.py +++ b/ams/core/model.py @@ -20,7 +20,7 @@ class Model: """ - Base class for power system dispatch models. + Base class for power system scheduling models. This class is revised from ``andes.core.model.Model``. """ diff --git a/ams/core/service.py b/ams/core/service.py index 9d521959..84d1ed26 100644 --- a/ams/core/service.py +++ b/ams/core/service.py @@ -497,7 +497,7 @@ def v(self): n_gen = self.u.n n_ts = self.u.horizon.n tout = np.zeros((n_gen, n_ts)) - t = self.rtn.config.t # dispatch interval + t = self.rtn.config.t # scheduling interval # minimum online/offline duration td = np.ceil(self.u2.v/t).astype(int) diff --git a/ams/interop/andes.py b/ams/interop/andes.py index bcf15c50..2d0f99cd 100644 --- a/ams/interop/andes.py +++ b/ams/interop/andes.py @@ -516,7 +516,7 @@ def _sync_check(self, amsys, adsys): def send(self, adsys=None, routine=None): """ - Send results of the recent sovled AMS dispatch (``sp.recent``) to the + Send results of the recent sovled AMS routine (``sp.recent``) to the target ANDES system. Note that converged AC conversion DOES NOT guarantee successful dynamic diff --git a/ams/models/__init__.py b/ams/models/__init__.py index 31727bc7..b8dc3c0c 100644 --- a/ams/models/__init__.py +++ b/ams/models/__init__.py @@ -1,5 +1,5 @@ """ -The package for models used in dispatch modeling. +The package for models used in scheduling modeling. The file_classes includes the list of file classes and their corresponding classes. """ diff --git a/ams/models/distributed/esd1.py b/ams/models/distributed/esd1.py index 7ad133d2..9b94b2fc 100644 --- a/ams/models/distributed/esd1.py +++ b/ams/models/distributed/esd1.py @@ -48,7 +48,7 @@ def __init__(self): class ESD1(ESD1Data, Model): """ Distributed energy storage model, revised from ANDES ``ESD1`` model for - dispatch. + scheduling. Following parameters are omitted from the original dynamic model: ``fn``, ``busf``, ``xc``, ``pqflag``, ``igreg``, ``v0``, ``v1``, diff --git a/ams/models/distributed/evs.py b/ams/models/distributed/evs.py index 2b4554bc..bb4ab5c7 100644 --- a/ams/models/distributed/evs.py +++ b/ams/models/distributed/evs.py @@ -22,5 +22,4 @@ class EV(Model): def __init__(self, system, config): Model.__init__(self, system, config) self.group = 'DG' - - self.N = NumParam(default=1, info='Number of EVs') + diff --git a/ams/models/distributed/pvd1.py b/ams/models/distributed/pvd1.py index 2178dd18..527b8801 100644 --- a/ams/models/distributed/pvd1.py +++ b/ams/models/distributed/pvd1.py @@ -44,7 +44,7 @@ def __init__(self): class PVD1(PVD1Data, Model): """ Distributed PV model, revised from ANDES ``PVD1`` model for - dispatch. + scheduling. Following parameters are omitted from the original dynamic model: ``fn``, ``busf``, ``xc``, ``pqflag``, ``igreg``, ``v0``, ``v1``, diff --git a/ams/models/group.py b/ams/models/group.py index 2d565c3a..15fb217e 100644 --- a/ams/models/group.py +++ b/ams/models/group.py @@ -126,7 +126,7 @@ class VSG(GroupBase): """ Renewable generator with virtual synchronous generator (VSG) control group. - Note that this is a group separate from ``RenGen`` for VSG dispatch study. + Note that this is a group separate from ``RenGen`` for VSG scheduling study. """ def __init__(self): diff --git a/ams/models/timeslot.py b/ams/models/timeslot.py index d8fb81c3..a0f3b451 100644 --- a/ams/models/timeslot.py +++ b/ams/models/timeslot.py @@ -1,5 +1,5 @@ """ -Model for rolling horizon used in dispatch. +Model for rolling horizon used in scheduling. """ from andes.core import ModelData, NumParam diff --git a/ams/routines/__init__.py b/ams/routines/__init__.py index a6a02904..c3dd4b06 100644 --- a/ams/routines/__init__.py +++ b/ams/routines/__init__.py @@ -1,5 +1,5 @@ """ -Dispatch routines. +Scheduling routines. """ from collections import OrderedDict diff --git a/ams/routines/ed.py b/ams/routines/ed.py index 56144e80..02f17c1a 100644 --- a/ams/routines/ed.py +++ b/ams/routines/ed.py @@ -44,7 +44,7 @@ def __init__(self) -> None: name='dsr', tex_name=r'd_{s,r,z}', info='zonal spinning reserve requirement',) - # NOTE: define e_str in dispatch model + # NOTE: define e_str in the scheduling model self.prsb = Constraint(info='spinning reserve balance', name='prsb', is_eq=True,) self.rsr = Constraint(info='spinning reserve requirement', @@ -53,7 +53,7 @@ def __init__(self) -> None: class MPBase: """ - Base class for multi-period dispatch. + Base class for multi-period scheduling. """ def __init__(self) -> None: @@ -220,13 +220,13 @@ def __init__(self, system, config): def dc2ac(self, **kwargs): """ AC conversion ``dc2ac`` is not implemented yet for - multi-period dispatch. + multi-period scheduling. """ return NotImplementedError def unpack(self, **kwargs): """ - Multi-period dispatch will not unpack results from + Multi-period scheduling will not unpack results from solver into devices. # TODO: unpack first period results, and allow input @@ -247,7 +247,7 @@ def __init__(self, system, config): ED.__init__(self, system, config) DGBase.__init__(self) - self.config.t = 1 # dispatch interval in hour + self.config.t = 1 # scheduling interval in hour self.info = 'Economic dispatch with distributed generation' self.type = 'DCED' @@ -258,7 +258,7 @@ def __init__(self, system, config): class ESD1MPBase(ESD1Base): """ - Extended base class for energy storage in multi-period dispatch. + Extended base class for energy storage in multi-period scheduling. """ def __init__(self): @@ -301,7 +301,7 @@ def __init__(self, system, config): ED.__init__(self, system, config) ESD1MPBase.__init__(self) - self.config.t = 1 # dispatch interval in hour + self.config.t = 1 # scheduling interval in hour self.info = 'Economic dispatch with energy storage' self.type = 'DCED' diff --git a/ams/routines/routine.py b/ams/routines/routine.py index e8a27919..d09895ef 100644 --- a/ams/routines/routine.py +++ b/ams/routines/routine.py @@ -356,7 +356,7 @@ def run(self, force_init=False, no_code=True, **kwargs): def export_csv(self, path=None): """ - Export dispatch results to a csv file. + Export scheduling results to a csv file. For multi-period routines, the column "Time" is the time index of ``timeslot.v``, which usually comes from ``EDTSlot`` or ``UCTSlot``. The rest columns are the variables registered in ``vars``. diff --git a/ams/routines/rted.py b/ams/routines/rted.py index 23b56839..0b156388 100644 --- a/ams/routines/rted.py +++ b/ams/routines/rted.py @@ -92,7 +92,7 @@ def __init__(self): self.prd = Var(info='RegDn reserve', unit='p.u.', name='prd', tex_name=r'p_{r,d}', model='StaticGen', nonneg=True,) - # NOTE: define e_str in dispatch routine + # NOTE: define e_str in scheduling routine self.rbu = Constraint(name='rbu', is_eq=True, info='RegUp reserve balance',) self.rbd = Constraint(name='rbd', is_eq=True, diff --git a/ams/routines/uc.py b/ams/routines/uc.py index f21e521e..ba5735d4 100644 --- a/ams/routines/uc.py +++ b/ams/routines/uc.py @@ -315,13 +315,13 @@ def init(self, force=False, no_code=True, **kwargs): def dc2ac(self, **kwargs): """ AC conversion ``dc2ac`` is not implemented yet for - multi-period dispatch. + multi-period scheduling. """ return NotImplementedError def unpack(self, **kwargs): """ - Multi-period dispatch will not unpack results from + Multi-period scheduling will not unpack results from solver into devices. # TODO: unpack first period results, and allow input diff --git a/ams/system.py b/ams/system.py index 3b679408..365868d5 100644 --- a/ams/system.py +++ b/ams/system.py @@ -45,7 +45,7 @@ def disable_methods(methods): class System(andes_System): """ A subclass of ``andes.system.System``, this class encapsulates data, models, - and routines for dispatch modeling and analysis in power systems. + and routines for scheduling modeling and analysis in power systems. Some methods inherited from the parent class are intentionally disabled. Parameters diff --git a/docs/source/conf.py b/docs/source/conf.py index 0175b388..43a15ada 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -155,7 +155,7 @@ # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'ams', 'AMS Manual', - author, 'ams', 'Python Software for Dispatch Modeling and Co-Simulation with Dynanic', + author, 'ams', 'Python Software for Scheduling Modeling and Co-Simulation with Dynanic', 'Miscellaneous'), ] diff --git a/docs/source/getting_started/formats/pypower.rst b/docs/source/getting_started/formats/pypower.rst index 78ccda05..98d1e277 100644 --- a/docs/source/getting_started/formats/pypower.rst +++ b/docs/source/getting_started/formats/pypower.rst @@ -4,7 +4,7 @@ PYPOWER -------- AMS includes `PYPOWER cases `_ -in version 2 for dispatch modeling and analysis. PYPOWER cases follow the same format as MATPOWER. +in version 2 for scheduling modeling and analysis. PYPOWER cases follow the same format as MATPOWER. The PYPOWER case is defined as a Python dictionary that includes ``bus``, ``gen``, ``branch``, ``areas``, and ``gencost``. diff --git a/docs/source/getting_started/index.rst b/docs/source/getting_started/index.rst index 5e5d8e11..25a8f4bf 100644 --- a/docs/source/getting_started/index.rst +++ b/docs/source/getting_started/index.rst @@ -7,7 +7,7 @@

- Python Library for Flexible Dispatch Modeling and Dispatch-Dynamic Co-Simulation

+ Python Library for Flexible Scheduling Modeling and Co-Simulation with Dynamics

.. _getting-started: diff --git a/docs/source/getting_started/overview.rst b/docs/source/getting_started/overview.rst index 84f7d398..2e3e05df 100644 --- a/docs/source/getting_started/overview.rst +++ b/docs/source/getting_started/overview.rst @@ -4,7 +4,7 @@ Package Overview ================ -AMS is an open-source packages for flexible dispatch modeling and co-simulation with +AMS is an open-source packages for flexible scheduling modeling and co-simulation with the in-house dynanic simulation engine `ANDES `_. AMS is currently under active development. To get involved, diff --git a/docs/source/index.rst b/docs/source/index.rst index 8d9b7102..6c0c5987 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -15,10 +15,10 @@ AMS documentation .. _`ANDES Repository`: https://github.com/CURENT/andes .. _`LTB Repository`: https://github.com/CURENT/ -LTB AMS is an open-source packages for dispatch modeling, serving as the market +LTB AMS is an open-source packages for scheduling modeling, serving as the market simulator for the CURENT Large scale Testbed (LTB). -AMS enables **flexible** dispatch modeling and **interoprability** with the in-house +AMS enables **flexible** scheduling modeling and **interoprability** with the in-house dynamic simulator ANDES. .. panels:: @@ -44,7 +44,7 @@ dynamic simulator ANDES. Examples ^^^^^^^^ - The examples of using AMS for power system dispatch study. + The examples of using AMS for power system scheduling study. +++ @@ -58,7 +58,7 @@ dynamic simulator ANDES. Model development guide ^^^^^^^^^^^^^^^^^^^^^^^ - New dispatch modeling in AMS. + New scheduling modeling in AMS. +++ diff --git a/docs/source/modeling/example.rst b/docs/source/modeling/example.rst index d5de81c7..a774da7c 100644 --- a/docs/source/modeling/example.rst +++ b/docs/source/modeling/example.rst @@ -1,7 +1,7 @@ Examples ======== -One example is provided to demonstrate descriptive dispatch modeling. +One example is provided to demonstrate descriptive scheduling modeling. DCOPF ---------- diff --git a/docs/source/modeling/routine.rst b/docs/source/modeling/routine.rst index 0f11bd6a..7fcb736e 100644 --- a/docs/source/modeling/routine.rst +++ b/docs/source/modeling/routine.rst @@ -1,7 +1,7 @@ Routine =========== -Routine refers to dispatch-level model, and it includes two sectinos, namely, +Routine refers to scheduling-level model, and it includes two sectinos, namely, Data Section and Model Section. Data Section @@ -50,7 +50,7 @@ Model Section Descriptive Formulation ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Dispatch routine is the descriptive model of the optimization problem. +Scheduling routine is the descriptive model of the optimization problem. Further, to facilitate the routine definition, AMS developed a class :py:mod:`ams.core.param.RParam` to pass the model data to multiple routine modeling. @@ -93,7 +93,7 @@ In AMS, the built-in interface with ANDES is implemented in :py:mod:`ams.interop File Format Converter ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Power flow data is the bridge between dispatch study and dynamic study, +Power flow data is the bridge between scheduling study and dynamics study, where it defines grid topology and power flow. An AMS case can be converted to an ANDES case, with the option to supply additional dynamic data. @@ -105,8 +105,8 @@ data. Data Exchange in Simulation ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -To achieve dispatch-dynamic cosimulation, it requires bi-directional data exchange between -dispatch and dynamic study. +To achieve scheduling-dynamics cosimulation, it requires bi-directional data exchange between +scheduling and dynamics study. From the perspective of AMS, two functions, ``send`` and ``receive``, are developed. The maping relationship for a specific routine is defined in the routine class as ``map1`` and ``map2``. diff --git a/setup.py b/setup.py index d2dfdad4..71bf24c3 100644 --- a/setup.py +++ b/setup.py @@ -75,7 +75,7 @@ def get_extra_requires(filename, add_all=True): name='ltbams', version=versioneer.get_version(), cmdclass=versioneer.get_cmdclass(), - description="Python software for dispatch modeling and co-simulation with dynanic.", + description="Python software for scheduling modeling and co-simulation with dynanics.", long_description=readme, long_description_content_type='text/markdown', author="Jinning Wang", From 40166b61d1b925eacfb6cf8614ff8c84872ba592 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Fri, 3 May 2024 11:46:33 -0400 Subject: [PATCH 09/44] Typo --- ams/models/static/gen.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ams/models/static/gen.py b/ams/models/static/gen.py index 1f740c2a..dabe42fc 100644 --- a/ams/models/static/gen.py +++ b/ams/models/static/gen.py @@ -118,7 +118,7 @@ def __init__(self, system=None, config=None): info='Retrieved zone idx', vtype=str, default=None, ) - self.ud = Algeb(info='connection status decision', + self.ud = Algeb(info='commitment decision', unit='bool', tex_name=r'u_d', name='ud', From 4d79a24f76bc3a27ca7ba42a40ca4263dc7d2a47 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Fri, 3 May 2024 11:53:12 -0400 Subject: [PATCH 10/44] Typo --- ams/models/renewable/regc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ams/models/renewable/regc.py b/ams/models/renewable/regc.py index 7241624b..e9e7941a 100644 --- a/ams/models/renewable/regc.py +++ b/ams/models/renewable/regc.py @@ -104,7 +104,7 @@ def __init__(self, system=None, config=None) -> None: class REGCV2(REGCV1): """ - Voltage-controlled VSC, identical to REGCV1. + Voltage-controlled VSC, identical to :ref:`REGCV1`. Reference: From 43631cd0d02ec193a2c5b93bdbe74044aca06a21 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Fri, 3 May 2024 11:58:59 -0400 Subject: [PATCH 11/44] Draft EV scheduling model --- ams/models/__init__.py | 2 +- ams/models/distributed/__init__.py | 1 + ams/models/distributed/ev.py | 54 ++++++++++++++++++++++++++++++ ams/models/distributed/evs.py | 25 -------------- 4 files changed, 56 insertions(+), 26 deletions(-) create mode 100644 ams/models/distributed/ev.py delete mode 100644 ams/models/distributed/evs.py diff --git a/ams/models/__init__.py b/ams/models/__init__.py index b8dc3c0c..4d938067 100644 --- a/ams/models/__init__.py +++ b/ams/models/__init__.py @@ -11,7 +11,7 @@ ('static', ['PQ', 'Slack', 'PV']), ('shunt', ['Shunt']), ('line', ['Line']), - ('distributed', ['PVD1', 'ESD1']), + ('distributed', ['PVD1', 'ESD1', 'EV1', 'EV2']), ('renewable', ['REGCA1', 'REGCV1', 'REGCV2']), ('area', ['Area']), ('region', ['Region']), diff --git a/ams/models/distributed/__init__.py b/ams/models/distributed/__init__.py index e07807ce..1b4c8735 100644 --- a/ams/models/distributed/__init__.py +++ b/ams/models/distributed/__init__.py @@ -1,2 +1,3 @@ from ams.models.distributed.pvd1 import PVD1 # NOQA from ams.models.distributed.esd1 import ESD1 # NOQA +from ams.models.distributed.ev import EV1, EV2 # NOQA diff --git a/ams/models/distributed/ev.py b/ams/models/distributed/ev.py new file mode 100644 index 00000000..7a603402 --- /dev/null +++ b/ams/models/distributed/ev.py @@ -0,0 +1,54 @@ +""" +EV model. +""" + +from andes.core.param import NumParam, IdxParam + +from ams.core.model import Model + + +class EV1(Model): + """ + EV aggregation model at transmission level. + + For co-simulation with ADNES, it is expected to be used in + conjunction with the dynamic models `EV1` or `EV2`. + + Reference: + + [1] J. Wang et al., "Electric Vehicles Charging Time Constrained Deliverable Provision of Secondary + Frequency Regulation," in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2024.3356948. + """ + + def __init__(self, system, config): + Model.__init__(self, system, config) + self.group = 'DG' + + self.bus = IdxParam(model='Bus', + info="interface bus idx", + mandatory=True, + ) + self.gen = IdxParam(info="static generator index", + mandatory=True, + ) + self.Sn = NumParam(default=100.0, tex_name='S_n', + info='device MVA rating', + unit='MVA', + ) + self.gammap = NumParam(default=1.0, + info="P ratio of linked static gen", + tex_name=r'\gamma_P' + ) + self.gammaq = NumParam(default=1.0, + info="Q ratio of linked static gen", + tex_name=r'\gamma_Q' + ) + + +class EV2(EV1): + """ + EV aggregation model at transmission level, identical to :ref:`EV1`. + """ + + def __init__(self, system=None, config=None) -> None: + EV1.__init__(self, system, config) diff --git a/ams/models/distributed/evs.py b/ams/models/distributed/evs.py deleted file mode 100644 index bb4ab5c7..00000000 --- a/ams/models/distributed/evs.py +++ /dev/null @@ -1,25 +0,0 @@ -""" -EV model. -""" - -from andes.core.param import NumParam - -from ams.core.model import Model - -class EV(Model): - """ - EV aggregation model at transmission level. - - It is expected to be used in conjunction with the `EV1` or `EV2` model - in ANDES. - - Reference: - - [1] J. Wang et al., "Electric Vehicles Charging Time Constrained Deliverable Provision of Secondary - Frequency Regulation," in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2024.3356948. - """ - - def __init__(self, system, config): - Model.__init__(self, system, config) - self.group = 'DG' - From 91422b933373a58c029ed22e1cc5a11b12b10a9b Mon Sep 17 00:00:00 2001 From: jinningwang Date: Fri, 3 May 2024 11:59:16 -0400 Subject: [PATCH 12/44] Format --- ams/extension/__init__.py | 2 +- ams/extension/eva.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/ams/extension/__init__.py b/ams/extension/__init__.py index b5c0e6f0..bf0f783c 100644 --- a/ams/extension/__init__.py +++ b/ams/extension/__init__.py @@ -1,3 +1,3 @@ """ Extension module. -""" \ No newline at end of file +""" diff --git a/ams/extension/eva.py b/ams/extension/eva.py index 3aa6ca3c..453f1c72 100644 --- a/ams/extension/eva.py +++ b/ams/extension/eva.py @@ -2,6 +2,7 @@ EV Aggregator. """ + class mcs(): """ Class for EV aggregation Monte Carlo simulation. From 60654562ef8c92cae4c16b56d7b5bf33cb29bbbe Mon Sep 17 00:00:00 2001 From: jinningwang Date: Fri, 3 May 2024 16:50:11 -0400 Subject: [PATCH 13/44] Minor fix --- ams/models/distributed/ev.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/ams/models/distributed/ev.py b/ams/models/distributed/ev.py index 7a603402..d227281d 100644 --- a/ams/models/distributed/ev.py +++ b/ams/models/distributed/ev.py @@ -3,13 +3,14 @@ """ from andes.core.param import NumParam, IdxParam +from andes.core.model import ModelData from ams.core.model import Model -class EV1(Model): +class EV1(ModelData, Model): """ - EV aggregation model at transmission level. + EV aggregation model for scheduling at transmission level. For co-simulation with ADNES, it is expected to be used in conjunction with the dynamic models `EV1` or `EV2`. @@ -21,6 +22,7 @@ class EV1(Model): """ def __init__(self, system, config): + ModelData.__init__(self) Model.__init__(self, system, config) self.group = 'DG' From 7c17c59884e03cd5006761455c80a8453f0cf83e Mon Sep 17 00:00:00 2001 From: jinningwang Date: Sat, 4 May 2024 12:41:35 -0400 Subject: [PATCH 14/44] Add case file of EVA --- ams/cases/5bus/pjm5bus_uced_ev.xlsx | Bin 0 -> 29722 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ams/cases/5bus/pjm5bus_uced_ev.xlsx diff --git a/ams/cases/5bus/pjm5bus_uced_ev.xlsx b/ams/cases/5bus/pjm5bus_uced_ev.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..57355608e8468ae529fb8a6bf976825b75be16d7 GIT binary patch literal 29722 zcmeFZbx>SywwAV?q}AR-{+MC7>=U?3oBcpxAYAV?4$5qmoq zQ#%&}RZj;~XFYlkTN}bcFc8XoAP~U*|L^sG@f{dT(^KeWL=8QcdqI$~hdYjpw7}GT 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zXQJW!@r6jQk0mb2AoN8Wyi>RUrhNqM@+%wRh#!?R~IhrQG_-$rHGeyQSr8%=Fd64l>`Y zxo-{p0{ma;^r%w=uUG)yHoQHnf%M1zSwxmz(E@5doKC13)1`rUC(3~9Z7G0-+m9~C z(IN`8^X91wdh`#mW<#@^y{}Gi&E`j}Pt?B~&Ng^>{VJ=*KVA2&^&su2#XM$>Y9Q2S z!8?uq;R?~LLhk1@=7^(>o9qc|kk^kmo*2rW_4Vgop7<|({ARlD;+|+oWWy?%y`w5f zx4b6C3VLRmw|k6k{VrDH5=|C2nuq5e=pKkB65O~*CAS6VAP4Y&PhlB04rfV$X2^Tc zq80Ve8KP%pYw-V51T;7PeshnOljykX)Q7nh=yglZOV$ghfr{e|f@b}2MjJ|IH2#QOz-^I@X%%-}cFx|m~yw0bqgrA{P zIu9XftR)${t>KW9qk^PG+eGw_O@IWiI06vSjg6Rs;cpJP82fH!hG|~pNAFLcCraDo zo43K)*b{!n53`sP-uoP}eEi~fjf?A+$kW_?$F3(o3YTK>v`g4sLh}6qnq8mg&R_l{ z4f>*YLs@-#^{hrRfvS0&wKQH+| zkB{t51`hbwEsy>QynDPW)cmml5_sUhZUggYAQWh+0;=r%hfQI?IpAA#JdjF3b~b;z z%KWpf40tfON&iFeHmJVzckrK#v3&liAWz?~ExFi!N3WA1OLz%k(M<`0-erbjXNsXuTE zcnj+T0rTd~ Date: Sat, 4 May 2024 12:44:39 -0400 Subject: [PATCH 15/44] Add parameter N in EV model --- ams/models/distributed/ev.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/ams/models/distributed/ev.py b/ams/models/distributed/ev.py index d227281d..1d594eed 100644 --- a/ams/models/distributed/ev.py +++ b/ams/models/distributed/ev.py @@ -10,7 +10,7 @@ class EV1(ModelData, Model): """ - EV aggregation model for scheduling at transmission level. + Aggregated EV model for scheduling at transmission level. For co-simulation with ADNES, it is expected to be used in conjunction with the dynamic models `EV1` or `EV2`. @@ -45,6 +45,10 @@ def __init__(self, system, config): info="Q ratio of linked static gen", tex_name=r'\gamma_Q' ) + self.N = NumParam(default=10000, + info="number of related EVs", + tex_name='N' + ) class EV2(EV1): From b1c4ffb52712c3ca90f86ff69cb10d98b8deb6e6 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Sat, 4 May 2024 12:45:06 -0400 Subject: [PATCH 16/44] [WIP] EVA, normal distribution parameter initialization --- ams/__init__.py | 3 +- ams/extension/__init__.py | 2 + ams/extension/eva.py | 200 +++++++++++++++++++++++++++++++++++++- 3 files changed, 200 insertions(+), 5 deletions(-) diff --git a/ams/__init__.py b/ams/__init__.py index 4963ace1..1b86b2ba 100644 --- a/ams/__init__.py +++ b/ams/__init__.py @@ -9,6 +9,7 @@ from ams import opt # NOQA from ams import pypower # NOQA from ams import report # NOQA +from ams import extension # NOQA from ams.main import config_logger, load, run # NOQA from ams.utils.paths import get_case # NOQA @@ -16,4 +17,4 @@ __author__ = 'Jining Wang' -__all__ = ['io', 'utils', 'models', 'system'] +__all__ = ['io', 'utils', 'models', 'system', 'extension'] diff --git a/ams/extension/__init__.py b/ams/extension/__init__.py index bf0f783c..8d37ca39 100644 --- a/ams/extension/__init__.py +++ b/ams/extension/__init__.py @@ -1,3 +1,5 @@ """ Extension module. """ + +from ams.extension import eva # NOQA diff --git a/ams/extension/eva.py b/ams/extension/eva.py index 453f1c72..750e599e 100644 --- a/ams/extension/eva.py +++ b/ams/extension/eva.py @@ -2,11 +2,203 @@ EV Aggregator. """ +from collections import OrderedDict -class mcs(): +import scipy.stats as stats + +from andes.core import Config +from andes.core.param import NumParam +from andes.core.model import ModelData +from andes.shared import pd + +from ams.core.model import Model + + +class EVA(ModelData, Model): """ - Class for EV aggregation Monte Carlo simulation. + State space modeling based EV aggregation model. + + In the EVA, each single EV is recorded as a device with its own parameters. + The parameters are generated from given statistical distributions. + + Reference: + [1] J. Wang et al., "Electric Vehicles Charging Time Constrained Deliverable Provision of Secondary + Frequency Regulation," in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2024.3356948. + [2] M. Wang, Y. Mu, Q. Shi, H. Jia and F. Li, "Electric Vehicle Aggregator Modeling and Control for + Frequency Regulation Considering Progressive State Recovery," in IEEE Transactions on Smart Grid, + vol. 11, no. 5, pp. 4176-4189, Sept. 2020, doi: 10.1109/TSG.2020.2981843. """ - def __init__(self): - pass + def __init__(self, N=10000, Ns=20, Tagc=4, SOCf=0.2, r=0.5, + seed=None,): + """ + Initialize the EV aggregation model. + + Parameters + ---------- + N: int, optional + Number of related EVs, default is 10000. + Ns : int, optional + Number of SOC intervals, default is 20. + Tagc : int, optional + AGC time intervals in seconds, default is 4. + SOCf : float, optional + Force charge SOC level between 0 and 1, default is 0.2. + r : float, optional + Ratio of time range 1 to time range 2 between 0 and 1, default is 0.5. + """ + # inherit attributes and methods from ANDES `ModelData` and AMS `Model` + ModelData.__init__(self) + Model.__init__(self, system=None, config=None) + + # manually set config as EVA is not processed by the system + self.config = Config(self.__class__.__name__) + self.config.add(OrderedDict((('ns', Ns), + ('tagc', Tagc), + ('socf', SOCf), + ('r', r), + ('socl', 0), + ('socu', 1), + ('seed', seed), + ))) + self.config.add_extra("_help", + ns="SOC intervals", + tagc="AGC time intervals in seconds", + socf="Force charge SOC level", + r="ratio of time range 1 to time range 2", + socl="lowest SOC limit", + socu="highest SOC limit", + seed='seed (or None) for random number generator', + ) + self.config.add_extra("_tex", + ns='N_s', + tagc='T_{agc}', + socf='SOC_f', + r='r', + socl='SOC_{l}', + socu='SOC_{u}', + seed='seed', + ) + self.config.add_extra("_alt", + ns="int", + tagc="float", + socf="float", + r="float", + socl="float", + socu="float", + seed='int or None', + ) + + # manually set attributes as EVA is not processed by the system + self.n = int(N) + self.idx.v = ['SEV_' + str(i+1) for i in range(self.n)] + self.uid = {self.idx.v[i]: i for i in range(self.n)} + + def setup(self): + """ + Setup the EV aggregation model. + + Populate itself with generated EV devices based on the given parameters. + """ + # --- parameters --- + self.namax = NumParam(default=0, + info='maximum number of action') + self.ts = NumParam(default=0, + info='arrive time, in 24 hours') + self.tf = NumParam(default=0, + info='departure time, in 24 hours') + self.tt = NumParam(default=0, + info='Tolerance of increased charging time') + self.soci = NumParam(default=0, + info='initial SOC') + self.socd = NumParam(default=0, + info='demand SOC') + self.Pc = NumParam(default=0, + info='rated charging power, in kW') + self.Pd = NumParam(default=0, + info='rated discharging power, in kW') + self.nc = NumParam(default=0, + info='charging efficiency') + self.nd = NumParam(default=0, + info='discharging efficiency') + self.Q = NumParam(default=0, + info='rated capacity') + + # --- initialization --- + # NOTE: following definition comes from ref[2] + # normal distribution parameters + ndist = {'soci': {'mu': 0.3, 'var': 0.05, 'lb': 0.2, 'ub': 0.4}, + 'socd': {'mu': 0.8, 'var': 0.03, 'lb': 0.7, 'ub': 0.9}, + 'ts1': {'mu': -6.5, 'var': 3.4, 'lb': 0.0, 'ub': 5.5}, + 'ts2': {'mu': 17.5, 'var': 3.4, 'lb': 5.5, 'ub': 24.0}, + 'tf1': {'mu': 8.9, 'var': 3.4, 'lb': 0.0, 'ub': 20.9}, + 'tf2': {'mu': 32.9, 'var': 3.4, 'lb': 20.9, 'ub': 24.0}, + 'tt': {'mu': 0.5, 'var': 0.02, 'lb': 0, 'ub': 1}} + # uniform distribution parameters + udist = {'Pc': {'lb': 5.0, 'ub': 7.0}, + 'Pd': {'lb': 5.0, 'ub': 7.0}, + 'nc': {'lb': 0.88, 'ub': 0.95}, + 'nd': {'lb': 0.88, 'ub': 0.95}, + 'Q': {'lb': 20.0, 'ub': 30.0}} + + # --- set soci, socd --- + self.soci.v = build_truncnorm(ndist['soci']['mu'], ndist['soci']['var'], + ndist['soci']['lb'], ndist['soci']['ub'], + self.n, self.config.seed) + self.socd.v = build_truncnorm(ndist['socd']['mu'], ndist['socd']['var'], + ndist['socd']['lb'], ndist['socd']['ub'], + self.n, self.config.seed) + + # --- set ts, tf --- + tdf = pd.DataFrame({ + col: build_truncnorm(ndist[col]['mu'], ndist[col]['var'], + ndist[col]['lb'], ndist[col]['ub'], + self.n, self.config.seed) + for col in ['ts1', 'ts2', 'tf1', 'tf2'] + }) + + nev_t1 = int(self.n * self.config.r) # number of EVs in time range 1 + tp1 = tdf[['ts1', 'tf1']].sample(n=nev_t1, random_state=self.config.seed) + tp2 = tdf[['ts2', 'tf2']].sample(n=self.n-nev_t1, random_state=self.config.seed) + tp = pd.concat([tp1, tp2], axis=0).reset_index(drop=True).fillna(0) + tp['ts'] = tp['ts1'] + tp['ts2'] + tp['tf'] = tp['tf1'] + tp['tf2'] + # Swap ts and tf if ts > tf + check = tp['ts'] > tp['tf'] + tp.loc[check, ['ts', 'tf']] = tp.loc[check, ['tf', 'ts']].values + + self.ts.v = tp['ts'].values + self.tf.v = tp['tf'].values + + # --- variables --- + # self.soc0 = Algeb(info='previous SOC') + # self.u0 = Algeb(info='previous online status') + # self.na0 = Algeb(info='previous action number') + # self.soc = Algeb(info='SOC') + # self.u = Algeb(info='online status') + # self.na = Algeb(info='action number') + +def build_truncnorm(mu, var, lb, ub, n, seed): + """ + Helper function to generate truncated normal distribution + using scipy.stats. + + Parameters + ---------- + mu : float + Mean of the normal distribution. + var : float + Variance of the normal distribution. + lb : float + Lower bound of the truncated distribution. + ub : float + Upper bound of the truncated distribution. + n : int + Number of samples to generate. + seed : int + Random seed to use. + """ + a = (lb - mu) / var + b = (ub - mu) / var + distribution = stats.truncnorm(a, b, loc=mu, scale=var) + return distribution.rvs(n, random_state=seed) From 815d13fe2e45a226f49745faf48128a7794025c1 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Sat, 4 May 2024 12:45:30 -0400 Subject: [PATCH 17/44] [WIP] EVA, normal distribution parameter initialization --- ams/extension/eva.py | 1 + 1 file changed, 1 insertion(+) diff --git a/ams/extension/eva.py b/ams/extension/eva.py index 750e599e..10d16a4f 100644 --- a/ams/extension/eva.py +++ b/ams/extension/eva.py @@ -178,6 +178,7 @@ def setup(self): # self.u = Algeb(info='online status') # self.na = Algeb(info='action number') + def build_truncnorm(mu, var, lb, ub, n, seed): """ Helper function to generate truncated normal distribution From 49f0e981ead86e7fba03034b1f784fb4c6dfe81b Mon Sep 17 00:00:00 2001 From: jinningwang Date: Sat, 4 May 2024 14:41:47 -0400 Subject: [PATCH 18/44] [WIP] EVA setup, populate parameters and variables --- ams/extension/eva.py | 142 ++++++++++++++++++++++++++++++++----------- 1 file changed, 108 insertions(+), 34 deletions(-) diff --git a/ams/extension/eva.py b/ams/extension/eva.py index 10d16a4f..495ad9f5 100644 --- a/ams/extension/eva.py +++ b/ams/extension/eva.py @@ -2,6 +2,7 @@ EV Aggregator. """ +import logging from collections import OrderedDict import scipy.stats as stats @@ -9,10 +10,13 @@ from andes.core import Config from andes.core.param import NumParam from andes.core.model import ModelData -from andes.shared import pd +from andes.shared import np, pd +from andes.utils.misc import elapsed from ams.core.model import Model +logger = logging.getLogger(__name__) + class EVA(ModelData, Model): """ @@ -30,7 +34,7 @@ class EVA(ModelData, Model): """ def __init__(self, N=10000, Ns=20, Tagc=4, SOCf=0.2, r=0.5, - seed=None,): + t=18, seed=None,): """ Initialize the EV aggregation model. @@ -46,69 +50,88 @@ def __init__(self, N=10000, Ns=20, Tagc=4, SOCf=0.2, r=0.5, Force charge SOC level between 0 and 1, default is 0.2. r : float, optional Ratio of time range 1 to time range 2 between 0 and 1, default is 0.5. + seed : int or None, optional + Seed for random number generator, default is None. + t : int, optional + Current time in 24 hours, default is 18. """ # inherit attributes and methods from ANDES `ModelData` and AMS `Model` ModelData.__init__(self) Model.__init__(self, system=None, config=None) + # internal flags + self.is_setup = False # if EVA has been setup + + self.t = np.array(t, dtype=float) # time in 24 hours + # manually set config as EVA is not processed by the system self.config = Config(self.__class__.__name__) - self.config.add(OrderedDict((('ns', Ns), + self.config.add(OrderedDict((('n', int(N)), + ('ns', Ns), ('tagc', Tagc), ('socf', SOCf), ('r', r), ('socl', 0), ('socu', 1), + ('tf', self.t), + ('prumax', 0), + ('prdmax', 0), ('seed', seed), ))) self.config.add_extra("_help", + n="Number of related EVs", ns="SOC intervals", tagc="AGC time intervals in seconds", socf="Force charge SOC level", r="ratio of time range 1 to time range 2", socl="lowest SOC limit", socu="highest SOC limit", + tf="EVA running end time in 24 hours", + prumax="maximum power of regulation up, in MW", + prdmax="maximum power of regulation down, in MW", seed='seed (or None) for random number generator', ) self.config.add_extra("_tex", + n='N_{ev}', ns='N_s', tagc='T_{agc}', socf='SOC_f', r='r', socl='SOC_{l}', socu='SOC_{u}', + tf='T_f', + prumax='P_{ru,max}', + prdmax='P_{rd,max}', seed='seed', ) self.config.add_extra("_alt", + n='int', ns="int", tagc="float", socf="float", r="float", socl="float", socu="float", + tf="float", + prumax="float", + prdmax="float", seed='int or None', ) - # manually set attributes as EVA is not processed by the system - self.n = int(N) - self.idx.v = ['SEV_' + str(i+1) for i in range(self.n)] - self.uid = {self.idx.v[i]: i for i in range(self.n)} - - def setup(self): - """ - Setup the EV aggregation model. + # NOTE: the parameters and variables are declared here and populated in `setup()` + # param `idx`, `name`, and `u` are already included in `ModelData` + # variables here are actually declared as parameters for memory saving + # because ams.core.var.Var has more overhead - Populate itself with generated EV devices based on the given parameters. - """ # --- parameters --- self.namax = NumParam(default=0, info='maximum number of action') - self.ts = NumParam(default=0, + self.ts = NumParam(default=0, vrange=(0, 24), info='arrive time, in 24 hours') - self.tf = NumParam(default=0, + self.tf = NumParam(default=0, vrange=(0, 24), info='departure time, in 24 hours') self.tt = NumParam(default=0, - info='Tolerance of increased charging time') + info='Tolerance of increased charging time, in hours') self.soci = NumParam(default=0, info='initial SOC') self.socd = NumParam(default=0, @@ -118,14 +141,46 @@ def setup(self): self.Pd = NumParam(default=0, info='rated discharging power, in kW') self.nc = NumParam(default=0, - info='charging efficiency') + info='charging efficiency', + vrange=(0, 1)) self.nd = NumParam(default=0, - info='discharging efficiency') + info='discharging efficiency', + vrange=(0, 1)) self.Q = NumParam(default=0, - info='rated capacity') + info='rated capacity, in kWh') + + # --- variables --- + self.soc0 = NumParam(default=0, + info='previous SOC') + self.u0 = NumParam(default=0, + info='previous online status') + self.na0 = NumParam(default=0, + info='previous action number') + self.soc = NumParam(default=0, + info='SOC') + self.na = NumParam(default=0, + info='action number') + + def setup(self): + """ + Setup the EV aggregation model. + + Populate itself with generated EV devices based on the given parameters. + """ + if self.is_setup: + logger.warning('EVA has been setup, setup twice is not allowed.') + return False + + t0, _ = elapsed() + + # manually set attributes as EVA is not processed by the system + self.idx.v = ['SEV_' + str(i+1) for i in range(self.config.n)] + self.u.v = np.array(self.u.v, dtype=int) + self.uid = {self.idx.v[i]: i for i in range(self.config.n)} - # --- initialization --- - # NOTE: following definition comes from ref[2] + # --- populate parameters' value --- + # NOTE: following definition comes from ref[2], except `tt` + # tt is assumend by experience in ref[1] # normal distribution parameters ndist = {'soci': {'mu': 0.3, 'var': 0.05, 'lb': 0.2, 'ub': 0.4}, 'socd': {'mu': 0.8, 'var': 0.03, 'lb': 0.7, 'ub': 0.9}, @@ -144,22 +199,25 @@ def setup(self): # --- set soci, socd --- self.soci.v = build_truncnorm(ndist['soci']['mu'], ndist['soci']['var'], ndist['soci']['lb'], ndist['soci']['ub'], - self.n, self.config.seed) + self.config.n, self.config.seed) self.socd.v = build_truncnorm(ndist['socd']['mu'], ndist['socd']['var'], ndist['socd']['lb'], ndist['socd']['ub'], - self.n, self.config.seed) - + self.config.n, self.config.seed) + # --- set tt --- + self.tt.v = build_truncnorm(ndist['tt']['mu'], ndist['tt']['var'], + ndist['tt']['lb'], ndist['tt']['ub'], + self.config.n, self.config.seed) # --- set ts, tf --- tdf = pd.DataFrame({ col: build_truncnorm(ndist[col]['mu'], ndist[col]['var'], ndist[col]['lb'], ndist[col]['ub'], - self.n, self.config.seed) + self.config.n, self.config.seed) for col in ['ts1', 'ts2', 'tf1', 'tf2'] }) - nev_t1 = int(self.n * self.config.r) # number of EVs in time range 1 + nev_t1 = int(self.config.n * self.config.r) # number of EVs in time range 1 tp1 = tdf[['ts1', 'tf1']].sample(n=nev_t1, random_state=self.config.seed) - tp2 = tdf[['ts2', 'tf2']].sample(n=self.n-nev_t1, random_state=self.config.seed) + tp2 = tdf[['ts2', 'tf2']].sample(n=self.config.n-nev_t1, random_state=self.config.seed) tp = pd.concat([tp1, tp2], axis=0).reset_index(drop=True).fillna(0) tp['ts'] = tp['ts1'] + tp['ts2'] tp['tf'] = tp['tf1'] + tp['tf2'] @@ -170,13 +228,24 @@ def setup(self): self.ts.v = tp['ts'].values self.tf.v = tp['tf'].values - # --- variables --- - # self.soc0 = Algeb(info='previous SOC') - # self.u0 = Algeb(info='previous online status') - # self.na0 = Algeb(info='previous action number') - # self.soc = Algeb(info='SOC') - # self.u = Algeb(info='online status') - # self.na = Algeb(info='action number') + # --- set Pc, Pd, nc, nd, Q --- + # NOTE: here it assumes (1) Pc == Pd, (2) nc == nd given by ref[2] + if self.config.seed is not None: + np.random.seed(self.config.seed) + self.Pc.v = np.random.uniform(udist['Pc']['lb'], udist['Pc']['ub'], self.config.n) + self.Pd.v = self.Pc.v + self.nc.v = np.random.uniform(udist['nc']['lb'], udist['nc']['ub'], self.config.n) + self.nd.v = self.nc.v + self.Q.v = np.random.uniform(udist['Q']['lb'], udist['Q']['ub'], self.config.n) + + self.is_setup = True + + _, s = elapsed(t0) + msg = f'EVA setup in {s}. It is {self.t} H now, ' + msg += f'with {self.config.n} EVs in total and {self.u.v.sum()} EVs online.' + logger.info(msg) + + return self.is_setup def build_truncnorm(mu, var, lb, ub, n, seed): @@ -198,6 +267,11 @@ def build_truncnorm(mu, var, lb, ub, n, seed): Number of samples to generate. seed : int Random seed to use. + + Returns + ------- + samples : ndarray + Generated samples. """ a = (lb - mu) / var b = (ub - mu) / var From 062dfa02d500449a4d1e750de7855fdb9e960b53 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Sat, 4 May 2024 15:08:45 -0400 Subject: [PATCH 19/44] [WIP] EVA setup, populate soc_intv and state --- ams/extension/eva.py | 30 ++++++++++++++++++++++++++---- 1 file changed, 26 insertions(+), 4 deletions(-) diff --git a/ams/extension/eva.py b/ams/extension/eva.py index 495ad9f5..060e23a6 100644 --- a/ams/extension/eva.py +++ b/ams/extension/eva.py @@ -3,6 +3,7 @@ """ import logging +import itertools from collections import OrderedDict import scipy.stats as stats @@ -118,6 +119,17 @@ def __init__(self, N=10000, Ns=20, Tagc=4, SOCf=0.2, r=0.5, seed='int or None', ) + unit = self.config.socu / self.config.ns + self.soc_intv = OrderedDict({ + i: (np.around(i * unit, 2), np.around((i + 1) * unit, 2)) + for i in range(self.config.ns) + }) + + # states of EV, intersection of charging status and SOC intervals + # C: charging, I: idle, D: discharging + states = list(itertools.product(['C', 'I', 'D'], self.soc_intv.keys())) + self.state = OrderedDict(((''.join(str(i) for i in s), 0.0) for s in states)) + # NOTE: the parameters and variables are declared here and populated in `setup()` # param `idx`, `name`, and `u` are already included in `ModelData` # variables here are actually declared as parameters for memory saving @@ -196,18 +208,17 @@ def setup(self): 'nd': {'lb': 0.88, 'ub': 0.95}, 'Q': {'lb': 20.0, 'ub': 30.0}} - # --- set soci, socd --- + # set `soci`, `socd`, `tt` self.soci.v = build_truncnorm(ndist['soci']['mu'], ndist['soci']['var'], ndist['soci']['lb'], ndist['soci']['ub'], self.config.n, self.config.seed) self.socd.v = build_truncnorm(ndist['socd']['mu'], ndist['socd']['var'], ndist['socd']['lb'], ndist['socd']['ub'], self.config.n, self.config.seed) - # --- set tt --- self.tt.v = build_truncnorm(ndist['tt']['mu'], ndist['tt']['var'], ndist['tt']['lb'], ndist['tt']['ub'], self.config.n, self.config.seed) - # --- set ts, tf --- + # set `ts`, `tf` tdf = pd.DataFrame({ col: build_truncnorm(ndist[col]['mu'], ndist[col]['var'], ndist[col]['lb'], ndist[col]['ub'], @@ -228,7 +239,7 @@ def setup(self): self.ts.v = tp['ts'].values self.tf.v = tp['tf'].values - # --- set Pc, Pd, nc, nd, Q --- + # set `Pc`, `Pd`, `nc`, `nd`, `Q` # NOTE: here it assumes (1) Pc == Pd, (2) nc == nd given by ref[2] if self.config.seed is not None: np.random.seed(self.config.seed) @@ -238,6 +249,9 @@ def setup(self): self.nd.v = self.nc.v self.Q.v = np.random.uniform(udist['Q']['lb'], udist['Q']['ub'], self.config.n) + # --- adjust variables given current time --- + self.g_u() # update online status + self.is_setup = True _, s = elapsed(t0) @@ -247,6 +261,14 @@ def setup(self): return self.is_setup + def g_u(self): + """ + Update online status of EVs based on current time. + """ + self.u.v = ((self.ts.v <= self.t) & (self.t <= self.tf.v)).astype(int) + + return True + def build_truncnorm(mu, var, lb, ub, n, seed): """ From f236b20867c552d6ddd78d19b92987db52697d19 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Sun, 5 May 2024 08:23:38 -0400 Subject: [PATCH 20/44] [WIP] EVA setup, adjust SOC after populating --- ams/extension/eva.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/ams/extension/eva.py b/ams/extension/eva.py index 060e23a6..b6e79a59 100644 --- a/ams/extension/eva.py +++ b/ams/extension/eva.py @@ -251,6 +251,23 @@ def setup(self): # --- adjust variables given current time --- self.g_u() # update online status + # adjust SOC considering random behavior + # NOTE: here we ignore the AGC participation before the current time `self.t` + + # stayed time for the EVs arrived before t, reset negative time to 0 + tc = np.maximum(self.t - self.ts.v, 0) + self.soc.v = self.soci.v + tc * self.Pc.v * self.nc.v / self.Q.v # charge them + + tr = (self.socd.v - self.soci.v) * self.Q.v / self.Pc.v / self.nc.v # time needed to charge to socd + + # ratio of stay/required time, stay less than required time reset to 1 + kt = np.maximum(tc / tr, 1) + socp = self.socd.v + np.log(kt) * (1 - self.socd.v) # log scale higher than socd + mask = kt > 1 + self.soc.v[mask] = socp[mask] # Update soc + + # clip soc to min/max + self.soc.v = np.clip(self.soc.v, self.config.socl, self.config.socu) self.is_setup = True From 446a58614e7e4fd12736b7cc8190580b54d43cca Mon Sep 17 00:00:00 2001 From: jinningwang Date: Sun, 5 May 2024 16:39:35 -0400 Subject: [PATCH 21/44] [WIP] EVA setup, add state space modeling variables --- ams/extension/Aest.csv | 61 ++++++++++++++++++++++++++++++++++++++ ams/extension/eva.py | 67 ++++++++++++++++++++++++++++++++++++++---- 2 files changed, 123 insertions(+), 5 deletions(-) create mode 100644 ams/extension/Aest.csv diff --git a/ams/extension/Aest.csv b/ams/extension/Aest.csv new file mode 100644 index 00000000..631e76c4 --- /dev/null +++ b/ams/extension/Aest.csv @@ -0,0 +1,61 @@ 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name='EVA'): """ Initialize the EV aggregation model. Parameters ---------- - N: int, optional + N : int, optional Number of related EVs, default is 10000. Ns : int, optional Number of SOC intervals, default is 20. @@ -55,11 +56,18 @@ def __init__(self, N=10000, Ns=20, Tagc=4, SOCf=0.2, r=0.5, Seed for random number generator, default is None. t : int, optional Current time in 24 hours, default is 18. + A_csv : str, optional + Path to the CSV file containing the state space matrix A, default is None. + name : str, optional + Name of the EVA, default is 'EVA'. """ # inherit attributes and methods from ANDES `ModelData` and AMS `Model` ModelData.__init__(self) Model.__init__(self, system=None, config=None) + # NOTE: Overwrite DataParam `name` to be a string attribute + self.name = name + # internal flags self.is_setup = False # if EVA has been setup @@ -130,6 +138,43 @@ def __init__(self, N=10000, Ns=20, Tagc=4, SOCf=0.2, r=0.5, states = list(itertools.product(['C', 'I', 'D'], self.soc_intv.keys())) self.state = OrderedDict(((''.join(str(i) for i in s), 0.0) for s in states)) + # --- state space modeling (SSM) variables --- + self.Pave = 0 # average charging power, in MW + + # NOTE: 3*ns comes from the intersection of charging status and SOC intervals + ns = self.config.ns + # NOTE: x, A will be updated in `setup()` + self.x = np.zeros(3*ns) + + # A matrix + default_A_csv = ams_root() + '/extension/Aest.csv' + if A_csv: + try: + self.A = pd.read_csv(A_csv).values + logger.debug(f'Loaded A matrix from {A_csv}.') + except FileNotFoundError: + self.A = pd.read_csv(default_A_csv).values + logger.debug(f'File {A_csv} not found, using default A matrix.') + else: + self.A = pd.read_csv(default_A_csv).values + logger.debug('No A matrix provided, using default A matrix.') + + mate = np.eye(ns) + mat0 = np.zeros((ns, ns)) + self.B = np.vstack((-mate, mate, mat0)) + self.C = np.vstack((mat0, -mate, mate)) + + # NOTE: D, Da, Db, Dc, Dd will be scaled by Pave later in `setup()` + vec1 = np.ones((1, ns)) + vec0 = np.zeros((1, ns)) + self.D = np.hstack((-vec1, vec0, vec0)) + self.Da = np.hstack((vec0, vec0, vec1)) + self.Db = np.hstack((vec1, vec1, vec1)) + self.Db[0, ns] = 0 # low charged EVs don't DC + self.Dc = np.hstack((-vec1, vec0, vec0)) + self.Dd = np.hstack((-vec1, -vec1, -vec1)) + self.Dd[0, 2*ns-1] = 0 # over chargeds EV don't C + # NOTE: the parameters and variables are declared here and populated in `setup()` # param `idx`, `name`, and `u` are already included in `ModelData` # variables here are actually declared as parameters for memory saving @@ -180,7 +225,7 @@ def setup(self): Populate itself with generated EV devices based on the given parameters. """ if self.is_setup: - logger.warning('EVA has been setup, setup twice is not allowed.') + logger.warning(f'{self.name} aggregator has been setup, setup twice is not allowed.') return False t0, _ = elapsed() @@ -269,11 +314,23 @@ def setup(self): # clip soc to min/max self.soc.v = np.clip(self.soc.v, self.config.socl, self.config.socu) + # SSM variables + kde = stats.gaussian_kde(self.Pc.v) + step = 0.01 + Pl_values = np.arange(self.Pc.v.min(), self.Pc.v.max(), step) + self.Pave = 1e-3 * np.sum([Pl * kde.integrate_box(Pl, Pl + step) for Pl in Pl_values]) # kw to MW + + self.D *= self.Pave + self.Da *= self.Pave + self.Db *= self.Pave + self.Dc *= self.Pave + self.Dd *= self.Pave + self.is_setup = True _, s = elapsed(t0) - msg = f'EVA setup in {s}. It is {self.t} H now, ' - msg += f'with {self.config.n} EVs in total and {self.u.v.sum()} EVs online.' + msg = f'{self.name} aggregator setup in {s}, and the current time is {self.t} H.\n' + msg += f'It has {self.config.n} EVs in total and {self.u.v.sum()} EVs online.' logger.info(msg) return self.is_setup From 048e7d76c43398c3461651d8c418948fa49a7ac2 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Sun, 5 May 2024 16:45:37 -0400 Subject: [PATCH 22/44] [WIP] EVA setup, refactor distribution parameters --- ams/extension/eva.py | 48 ++++++++++++++++++++++++++------------------ 1 file changed, 29 insertions(+), 19 deletions(-) diff --git a/ams/extension/eva.py b/ams/extension/eva.py index 0def5ad7..5cf8b723 100644 --- a/ams/extension/eva.py +++ b/ams/extension/eva.py @@ -20,6 +20,23 @@ logger = logging.getLogger(__name__) +# NOTE: following definition comes from ref[2], except `tt` that is assumed by ref[1] +# normal distribution parameters +ndist = {'soci': {'mu': 0.3, 'var': 0.05, 'lb': 0.2, 'ub': 0.4}, + 'socd': {'mu': 0.8, 'var': 0.03, 'lb': 0.7, 'ub': 0.9}, + 'ts1': {'mu': -6.5, 'var': 3.4, 'lb': 0.0, 'ub': 5.5}, + 'ts2': {'mu': 17.5, 'var': 3.4, 'lb': 5.5, 'ub': 24.0}, + 'tf1': {'mu': 8.9, 'var': 3.4, 'lb': 0.0, 'ub': 20.9}, + 'tf2': {'mu': 32.9, 'var': 3.4, 'lb': 20.9, 'ub': 24.0}, + 'tt': {'mu': 0.5, 'var': 0.02, 'lb': 0, 'ub': 1}} +# uniform distribution parameters +udist = {'Pc': {'lb': 5.0, 'ub': 7.0}, + 'Pd': {'lb': 5.0, 'ub': 7.0}, + 'nc': {'lb': 0.88, 'ub': 0.95}, + 'nd': {'lb': 0.88, 'ub': 0.95}, + 'Q': {'lb': 20.0, 'ub': 30.0}} + + class EVA(ModelData, Model): """ State space modeling based EV aggregation model. @@ -218,11 +235,21 @@ def __init__(self, N=10000, Ns=20, Tagc=4, SOCf=0.2, r=0.5, self.na = NumParam(default=0, info='action number') - def setup(self): + def setup(self, ndist=ndist, udist=udist): """ Setup the EV aggregation model. - Populate itself with generated EV devices based on the given parameters. + Parameters + ---------- + ndist : dict, optional + Normal distribution parameters, default by built-in `ndist`. + udist : dict, optional + Uniform distribution parameters, default by built-in `udist`. + + Returns + ------- + is_setup : bool + If the setup is successful. """ if self.is_setup: logger.warning(f'{self.name} aggregator has been setup, setup twice is not allowed.') @@ -236,23 +263,6 @@ def setup(self): self.uid = {self.idx.v[i]: i for i in range(self.config.n)} # --- populate parameters' value --- - # NOTE: following definition comes from ref[2], except `tt` - # tt is assumend by experience in ref[1] - # normal distribution parameters - ndist = {'soci': {'mu': 0.3, 'var': 0.05, 'lb': 0.2, 'ub': 0.4}, - 'socd': {'mu': 0.8, 'var': 0.03, 'lb': 0.7, 'ub': 0.9}, - 'ts1': {'mu': -6.5, 'var': 3.4, 'lb': 0.0, 'ub': 5.5}, - 'ts2': {'mu': 17.5, 'var': 3.4, 'lb': 5.5, 'ub': 24.0}, - 'tf1': {'mu': 8.9, 'var': 3.4, 'lb': 0.0, 'ub': 20.9}, - 'tf2': {'mu': 32.9, 'var': 3.4, 'lb': 20.9, 'ub': 24.0}, - 'tt': {'mu': 0.5, 'var': 0.02, 'lb': 0, 'ub': 1}} - # uniform distribution parameters - udist = {'Pc': {'lb': 5.0, 'ub': 7.0}, - 'Pd': {'lb': 5.0, 'ub': 7.0}, - 'nc': {'lb': 0.88, 'ub': 0.95}, - 'nd': {'lb': 0.88, 'ub': 0.95}, - 'Q': {'lb': 20.0, 'ub': 30.0}} - # set `soci`, `socd`, `tt` self.soci.v = build_truncnorm(ndist['soci']['mu'], ndist['soci']['var'], ndist['soci']['lb'], ndist['soci']['ub'], From b757ffb825f6daee77b80928f63bf4eb808e60f5 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Sun, 5 May 2024 20:38:14 -0400 Subject: [PATCH 23/44] [WIP] EVA setup, refactor as EVD and EVA --- ams/extension/eva.py | 154 +++++++++++++++++++++++-------------------- 1 file changed, 82 insertions(+), 72 deletions(-) diff --git a/ams/extension/eva.py b/ams/extension/eva.py index 5cf8b723..d495e438 100644 --- a/ams/extension/eva.py +++ b/ams/extension/eva.py @@ -1,5 +1,14 @@ """ -EV Aggregator. +EV Aggregator module. + +EVD is the generated datasets, and EVA is the aggregator model. + +Reference: +[1] J. Wang et al., "Electric Vehicles Charging Time Constrained Deliverable Provision of Secondary +Frequency Regulation," in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2024.3356948. +[2] M. Wang, Y. Mu, Q. Shi, H. Jia and F. Li, "Electric Vehicle Aggregator Modeling and Control for +Frequency Regulation Considering Progressive State Recovery," in IEEE Transactions on Smart Grid, +vol. 11, no. 5, pp. 4176-4189, Sept. 2020, doi: 10.1109/TSG.2020.2981843. """ import logging @@ -37,23 +46,14 @@ 'Q': {'lb': 20.0, 'ub': 30.0}} -class EVA(ModelData, Model): +class EVD(ModelData, Model): """ - State space modeling based EV aggregation model. - - In the EVA, each single EV is recorded as a device with its own parameters. + In the EVD, each single EV is recorded as a device with its own parameters. The parameters are generated from given statistical distributions. - - Reference: - [1] J. Wang et al., "Electric Vehicles Charging Time Constrained Deliverable Provision of Secondary - Frequency Regulation," in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2024.3356948. - [2] M. Wang, Y. Mu, Q. Shi, H. Jia and F. Li, "Electric Vehicle Aggregator Modeling and Control for - Frequency Regulation Considering Progressive State Recovery," in IEEE Transactions on Smart Grid, - vol. 11, no. 5, pp. 4176-4189, Sept. 2020, doi: 10.1109/TSG.2020.2981843. """ def __init__(self, N=10000, Ns=20, Tagc=4, SOCf=0.2, r=0.5, - t=18, seed=None, A_csv=None, name='EVA'): + t=18, seed=None, name='EVA', A_csv=None): """ Initialize the EV aggregation model. @@ -73,22 +73,23 @@ def __init__(self, N=10000, Ns=20, Tagc=4, SOCf=0.2, r=0.5, Seed for random number generator, default is None. t : int, optional Current time in 24 hours, default is 18. - A_csv : str, optional - Path to the CSV file containing the state space matrix A, default is None. name : str, optional Name of the EVA, default is 'EVA'. + A_csv : str, optional + Path to the CSV file containing the state space matrix A, default is None. """ # inherit attributes and methods from ANDES `ModelData` and AMS `Model` ModelData.__init__(self) Model.__init__(self, system=None, config=None) - # NOTE: Overwrite DataParam `name` to be a string attribute - self.name = name + self.evdname = name # internal flags self.is_setup = False # if EVA has been setup self.t = np.array(t, dtype=float) # time in 24 hours + self.eva = None # EV Aggregator + self.A_csv = A_csv # path to the A matrix # manually set config as EVA is not processed by the system self.config = Config(self.__class__.__name__) @@ -150,48 +151,6 @@ def __init__(self, N=10000, Ns=20, Tagc=4, SOCf=0.2, r=0.5, for i in range(self.config.ns) }) - # states of EV, intersection of charging status and SOC intervals - # C: charging, I: idle, D: discharging - states = list(itertools.product(['C', 'I', 'D'], self.soc_intv.keys())) - self.state = OrderedDict(((''.join(str(i) for i in s), 0.0) for s in states)) - - # --- state space modeling (SSM) variables --- - self.Pave = 0 # average charging power, in MW - - # NOTE: 3*ns comes from the intersection of charging status and SOC intervals - ns = self.config.ns - # NOTE: x, A will be updated in `setup()` - self.x = np.zeros(3*ns) - - # A matrix - default_A_csv = ams_root() + '/extension/Aest.csv' - if A_csv: - try: - self.A = pd.read_csv(A_csv).values - logger.debug(f'Loaded A matrix from {A_csv}.') - except FileNotFoundError: - self.A = pd.read_csv(default_A_csv).values - logger.debug(f'File {A_csv} not found, using default A matrix.') - else: - self.A = pd.read_csv(default_A_csv).values - logger.debug('No A matrix provided, using default A matrix.') - - mate = np.eye(ns) - mat0 = np.zeros((ns, ns)) - self.B = np.vstack((-mate, mate, mat0)) - self.C = np.vstack((mat0, -mate, mate)) - - # NOTE: D, Da, Db, Dc, Dd will be scaled by Pave later in `setup()` - vec1 = np.ones((1, ns)) - vec0 = np.zeros((1, ns)) - self.D = np.hstack((-vec1, vec0, vec0)) - self.Da = np.hstack((vec0, vec0, vec1)) - self.Db = np.hstack((vec1, vec1, vec1)) - self.Db[0, ns] = 0 # low charged EVs don't DC - self.Dc = np.hstack((-vec1, vec0, vec0)) - self.Dd = np.hstack((-vec1, -vec1, -vec1)) - self.Dd[0, 2*ns-1] = 0 # over chargeds EV don't C - # NOTE: the parameters and variables are declared here and populated in `setup()` # param `idx`, `name`, and `u` are already included in `ModelData` # variables here are actually declared as parameters for memory saving @@ -252,12 +211,13 @@ def setup(self, ndist=ndist, udist=udist): If the setup is successful. """ if self.is_setup: - logger.warning(f'{self.name} aggregator has been setup, setup twice is not allowed.') + logger.warning(f'{self.evdname} aggregator has been setup, setup twice is not allowed.') return False t0, _ = elapsed() # manually set attributes as EVA is not processed by the system + self.n = self.config.n self.idx.v = ['SEV_' + str(i+1) for i in range(self.config.n)] self.u.v = np.array(self.u.v, dtype=int) self.uid = {self.idx.v[i]: i for i in range(self.config.n)} @@ -324,22 +284,12 @@ def setup(self, ndist=ndist, udist=udist): # clip soc to min/max self.soc.v = np.clip(self.soc.v, self.config.socl, self.config.socu) - # SSM variables - kde = stats.gaussian_kde(self.Pc.v) - step = 0.01 - Pl_values = np.arange(self.Pc.v.min(), self.Pc.v.max(), step) - self.Pave = 1e-3 * np.sum([Pl * kde.integrate_box(Pl, Pl + step) for Pl in Pl_values]) # kw to MW - - self.D *= self.Pave - self.Da *= self.Pave - self.Db *= self.Pave - self.Dc *= self.Pave - self.Dd *= self.Pave + self.evd = EVA(evd=self, A_csv=self.A_csv) self.is_setup = True _, s = elapsed(t0) - msg = f'{self.name} aggregator setup in {s}, and the current time is {self.t} H.\n' + msg = f'{self.evdname} aggregator setup in {s}, and the current time is {self.t} H.\n' msg += f'It has {self.config.n} EVs in total and {self.u.v.sum()} EVs online.' logger.info(msg) @@ -354,6 +304,66 @@ def g_u(self): return True +class EVA: + """ + State space modeling based EV aggregation model. + """ + + def __init__(self, evd, A_csv=None): + """ + Parameters + ---------- + EVD : ams.extension.eva.EVD + EV Aggregator model. + """ + self.parent = evd + + # states of EV, intersection of charging status and SOC intervals + # C: charging, I: idle, D: discharging + states = list(itertools.product(['C', 'I', 'D'], self.parent.soc_intv.keys())) + self.state = OrderedDict(((''.join(str(i) for i in s), 0.0) for s in states)) + + # NOTE: 3*ns comes from the intersection of charging status and SOC intervals + ns = self.parent.config.ns + # NOTE: x, A will be updated in `setup()` + self.x = np.zeros(3*ns) + + # A matrix + default_A_csv = ams_root() + '/extension/Aest.csv' + if A_csv: + try: + self.A = pd.read_csv(A_csv).values + logger.debug(f'Loaded A matrix from {A_csv}.') + except FileNotFoundError: + self.A = pd.read_csv(default_A_csv).values + logger.debug(f'File {A_csv} not found, using default A matrix.') + else: + self.A = pd.read_csv(default_A_csv).values + logger.debug('No A matrix provided, using default A matrix.') + + mate = np.eye(ns) + mat0 = np.zeros((ns, ns)) + self.B = np.vstack((-mate, mate, mat0)) + self.C = np.vstack((mat0, -mate, mate)) + + # SSM variables + kde = stats.gaussian_kde(self.parent.Pc.v) + step = 0.01 + Pl_values = np.arange(self.parent.Pc.v.min(), self.parent.Pc.v.max(), step) + self.Pave = 1e-3 * np.sum([Pl * kde.integrate_box(Pl, Pl + step) for Pl in Pl_values]) # kw to MW + + # NOTE: D, Da, Db, Dc, Dd will be scaled by Pave later in `setup()` + vec1 = np.ones((1, ns)) + vec0 = np.zeros((1, ns)) + self.D = self.Pave * np.hstack((-vec1, vec0, vec0)) + self.Da = self.Pave * np.hstack((vec0, vec0, vec1)) + self.Db = self.Pave * np.hstack((vec1, vec1, vec1)) + self.Db[0, ns] = 0 # low charged EVs don't DC + self.Dc = self.Pave * np.hstack((-vec1, vec0, vec0)) + self.Dd = self.Pave * np.hstack((-vec1, -vec1, -vec1)) + self.Dd[0, 2*ns-1] = 0 # overcharged EVs don't C + + def build_truncnorm(mu, var, lb, ub, n, seed): """ Helper function to generate truncated normal distribution From 6d950a3cdfc48f0090081c6972994de9eff4df49 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Fri, 17 May 2024 10:28:23 -0400 Subject: [PATCH 24/44] [WIP] EVA setup, docstring --- ams/extension/eva.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/ams/extension/eva.py b/ams/extension/eva.py index d495e438..5a6ac20c 100644 --- a/ams/extension/eva.py +++ b/ams/extension/eva.py @@ -219,6 +219,7 @@ def setup(self, ndist=ndist, udist=udist): # manually set attributes as EVA is not processed by the system self.n = self.config.n self.idx.v = ['SEV_' + str(i+1) for i in range(self.config.n)] + self.name.v = ['SEV ' + str(i+1) for i in range(self.config.n)] self.u.v = np.array(self.u.v, dtype=int) self.uid = {self.idx.v[i]: i for i in range(self.config.n)} @@ -284,6 +285,9 @@ def setup(self, ndist=ndist, udist=udist): # clip soc to min/max self.soc.v = np.clip(self.soc.v, self.config.socl, self.config.socu) + self.soc0.v = self.soc.v.copy() + self.u0.v = self.u.v.copy() + self.evd = EVA(evd=self, A_csv=self.A_csv) self.is_setup = True @@ -315,6 +319,8 @@ def __init__(self, evd, A_csv=None): ---------- EVD : ams.extension.eva.EVD EV Aggregator model. + A_csv : str, optional + Path to the CSV file containing the state space matrix A, default is None. """ self.parent = evd From 1780523028c48a21c7daea4c5aba68698cacfb38 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Fri, 17 May 2024 10:39:57 -0400 Subject: [PATCH 25/44] Update release notes --- docs/source/release-notes.rst | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/docs/source/release-notes.rst b/docs/source/release-notes.rst index 7e3b09f3..8cc67802 100644 --- a/docs/source/release-notes.rst +++ b/docs/source/release-notes.rst @@ -9,6 +9,17 @@ The APIs before v3.0.0 are in beta and may change without prior notice. Pre-v1.0.0 ========== +v0.9.7 (2024-xx-xx) +------------------- + +This patch release add the Roadmap section in the release notes, to list out some potential features. +It also drafts the EV Aggregation model based on the state space modelg, but the finish date remains unknown. + +References: + +[1] J. Wang et al., "Electric Vehicles Charging Time Constrained Deliverable Provision of Secondary +Frequency Regulation," in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2024.3356948. + v0.9.6 (2024-04-21) ------------------- From bf34d1fb3c0b80dd1ab95caca84c86ce419d2552 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Fri, 17 May 2024 11:59:20 -0400 Subject: [PATCH 26/44] Fix bug that PFlow didn't update flag --- ams/routines/pflow.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ams/routines/pflow.py b/ams/routines/pflow.py index 88e7e7f0..70fae0d9 100644 --- a/ams/routines/pflow.py +++ b/ams/routines/pflow.py @@ -76,7 +76,8 @@ def solve(self, method="newton"): ppopt = ppoption(PF_ALG=alg, ENFORCE_Q_LIMS=self.config.qlim) res, success, sstats = runpf(casedata=ppc, ppopt=ppopt) - return res, success, sstats + self.converged = bool(success) + return res, self.converged, sstats def run(self, force_init=False, no_code=True, method="newton", **kwargs): """ From c8e2c44171a37d2cf9522362f463a869bfef0234 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Fri, 17 May 2024 13:24:36 -0400 Subject: [PATCH 27/44] Format, unify PYPOWER routines return variables --- ams/pypower/routines/pflow.py | 8 +++----- ams/routines/acopf.py | 3 +-- ams/routines/dcpf.py | 10 +++++----- ams/routines/pflow.py | 5 ++--- 4 files changed, 11 insertions(+), 15 deletions(-) diff --git a/ams/pypower/routines/pflow.py b/ams/pypower/routines/pflow.py index 87698bbf..d18db2cb 100644 --- a/ams/pypower/routines/pflow.py +++ b/ams/pypower/routines/pflow.py @@ -52,10 +52,8 @@ def runpf(casedata, ppopt): ------- results : dict or None Solved power flow results. None if the power flow did not converge. - success : bool - True if the algorithm successfully found a solution, False otherwise. - et : float - Elapsed time in seconds for running the power flow. + sstats : dict + Solver statistics. Notes ----- @@ -303,7 +301,7 @@ def runpf(casedata, ppopt): IDX.branch.PT, IDX.branch.QT])] = 0 - return results, success, sstats + return results, sstats def dcpf(B, Pbus, Va0, ref, pv, pq): diff --git a/ams/routines/acopf.py b/ams/routines/acopf.py index f72fabfe..3f46f6d7 100644 --- a/ams/routines/acopf.py +++ b/ams/routines/acopf.py @@ -91,8 +91,7 @@ def solve(self, method=None, **kwargs): ppc = system2ppc(self.system) ppopt = ppoption() res, sstats = runopf(casedata=ppc, ppopt=ppopt, **kwargs) - self.converged = res['success'] - return res, self.converged, sstats + return res, sstats def run(self, force_init=False, no_code=True, method=None, **kwargs): diff --git a/ams/routines/dcpf.py b/ams/routines/dcpf.py index 08d610c5..b3ee1ef6 100644 --- a/ams/routines/dcpf.py +++ b/ams/routines/dcpf.py @@ -124,9 +124,8 @@ def solve(self, method=None): ppc = system2ppc(self.system) ppopt = ppoption(PF_DC=True) - res, success, sstats = runpf(casedata=ppc, ppopt=ppopt) - self.converged = bool(success) - return res, self.converged, sstats + res, sstats = runpf(casedata=ppc, ppopt=ppopt) + return res, sstats def run(self, force_init=False, no_code=True, method=None, **kwargs): @@ -155,8 +154,9 @@ def run(self, force_init=False, no_code=True, if not self.initialized: self.init(force=force_init, no_code=no_code) t0, _ = elapsed() - res, success, sstats = self.solve(method=method) - self.exit_code = 0 if success else 1 + res, sstats = self.solve(method=method) + self.converged = res['success'] + self.exit_code = 0 if res['success'] else 1 _, s = elapsed(t0) self.exec_time = float(s.split(' ')[0]) n_iter = int(sstats['num_iters']) diff --git a/ams/routines/pflow.py b/ams/routines/pflow.py index 70fae0d9..057b8c92 100644 --- a/ams/routines/pflow.py +++ b/ams/routines/pflow.py @@ -75,9 +75,8 @@ def solve(self, method="newton"): raise ValueError(msg) ppopt = ppoption(PF_ALG=alg, ENFORCE_Q_LIMS=self.config.qlim) - res, success, sstats = runpf(casedata=ppc, ppopt=ppopt) - self.converged = bool(success) - return res, self.converged, sstats + res, sstats = runpf(casedata=ppc, ppopt=ppopt) + return res, sstats def run(self, force_init=False, no_code=True, method="newton", **kwargs): """ From b9ce40e3011e5cd84aa388e042ad04ae466ef702 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Fri, 17 May 2024 14:30:16 -0400 Subject: [PATCH 28/44] Fix mpc reading when gencost is not complete --- ams/io/matpower.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/ams/io/matpower.py b/ams/io/matpower.py index 7dc392dd..f48a3851 100644 --- a/ams/io/matpower.py +++ b/ams/io/matpower.py @@ -202,7 +202,12 @@ def mpc2system(mpc: dict, system) -> bool: gcost_idx = 0 gen_idx = np.arange(mpc['gen'].shape[0]) + 1 - for data, gen in zip(mpc['gencost'], gen_idx): + mpc_cost = np.zeros((mpc['gen'].shape[0], 7)) + if mpc['gencost'].shape[1] < 7: + mpc_cost[:, :mpc['gencost'].shape[1]] = mpc['gencost'] + else: + mpc_cost = mpc['gencost'] + for data, gen in zip(mpc_cost, gen_idx): # NOTE: only type 2 costs are supported for now # type startup shutdown n c2 c1 c0 # 0 1 2 3 4 5 6 From 4b2a1bd979dfd382574528c3939e21d34ef01bfa Mon Sep 17 00:00:00 2001 From: jinningwang Date: Fri, 17 May 2024 21:43:06 -0400 Subject: [PATCH 29/44] In DCPF, check results before unpack --- ams/routines/dcpf.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/ams/routines/dcpf.py b/ams/routines/dcpf.py index b3ee1ef6..ff0e89de 100644 --- a/ams/routines/dcpf.py +++ b/ams/routines/dcpf.py @@ -165,7 +165,11 @@ def run(self, force_init=False, no_code=True, msg = f"<{self.class_name}> solved in {s}, converged in " msg += n_iter_str + f"with {sstats['solver_name']}." logger.info(msg) - self.unpack(res) + try: + self.unpack(res) + except Exception: + logger.warning(f"Failed to unpack results from {self.class_name}.") + return False return True else: msg = f"{self.class_name} failed in " From c7efd4a5f943463036e3a2791380c03a76239b47 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Tue, 21 May 2024 13:37:45 -0400 Subject: [PATCH 30/44] Skip add gencost when mpc has no gencost --- ams/io/matpower.py | 56 ++++++++++++++++++++++++---------------------- 1 file changed, 29 insertions(+), 27 deletions(-) diff --git a/ams/io/matpower.py b/ams/io/matpower.py index f48a3851..d79a27f4 100644 --- a/ams/io/matpower.py +++ b/ams/io/matpower.py @@ -200,33 +200,35 @@ def mpc2system(mpc: dict, system) -> bool: if ('bus_name' in mpc) and (len(mpc['bus_name']) == len(system.Bus.name.v)): system.Bus.name.v[:] = mpc['bus_name'] - gcost_idx = 0 - gen_idx = np.arange(mpc['gen'].shape[0]) + 1 - mpc_cost = np.zeros((mpc['gen'].shape[0], 7)) - if mpc['gencost'].shape[1] < 7: - mpc_cost[:, :mpc['gencost'].shape[1]] = mpc['gencost'] - else: - mpc_cost = mpc['gencost'] - for data, gen in zip(mpc_cost, gen_idx): - # NOTE: only type 2 costs are supported for now - # type startup shutdown n c2 c1 c0 - # 0 1 2 3 4 5 6 - if data[0] != 2: - raise ValueError('Only MODEL 2 costs are supported') - gcost_idx += 1 - gctype = int(data[0]) - startup = data[1] - shutdown = data[2] - c2 = data[4] * base_mva ** 2 - c1 = data[5] * base_mva - c0 = data[6] - system.add('GCost', gen=int(gen), - u=1, type=gctype, - idx=gcost_idx, - name=f'GCost {gcost_idx}', - csu=startup, csd=shutdown, - c2=c2, c1=c1, c0=c0 - ) + # --- gencost --- + if 'gencost' in mpc: + gcost_idx = 0 + gen_idx = np.arange(mpc['gen'].shape[0]) + 1 + mpc_cost = np.zeros((mpc['gen'].shape[0], 7)) + if mpc['gencost'].shape[1] < 7: + mpc_cost[:, :mpc['gencost'].shape[1]] = mpc['gencost'] + else: + mpc_cost = mpc['gencost'] + for data, gen in zip(mpc_cost, gen_idx): + # NOTE: only type 2 costs are supported for now + # type startup shutdown n c2 c1 c0 + # 0 1 2 3 4 5 6 + if data[0] != 2: + raise ValueError('Only MODEL 2 costs are supported') + gcost_idx += 1 + gctype = int(data[0]) + startup = data[1] + shutdown = data[2] + c2 = data[4] * base_mva ** 2 + c1 = data[5] * base_mva + c0 = data[6] + system.add('GCost', gen=int(gen), + u=1, type=gctype, + idx=gcost_idx, + name=f'GCost {gcost_idx}', + csu=startup, csd=shutdown, + c2=c2, c1=c1, c0=c0 + ) # --- region --- zone_id = np.unique(system.Bus.zone.v).astype(int) From 0cba9ca07157ada8d5a6cfc74b45e97f10ec6be3 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Wed, 22 May 2024 12:38:47 -0400 Subject: [PATCH 31/44] Fix OTDF calculation --- ams/core/matprocessor.py | 29 ++++------------------------- 1 file changed, 4 insertions(+), 25 deletions(-) diff --git a/ams/core/matprocessor.py b/ams/core/matprocessor.py index 5a23ba8f..36ca2a6d 100644 --- a/ams/core/matprocessor.py +++ b/ams/core/matprocessor.py @@ -471,13 +471,11 @@ def build_lodf(self): self.LODF._v = LODF return self.LODF._v - def build_otdf(self, line=None): + def build_otdf(self): """ - Build the DC OTDF matrix. + Build the DC OTDF matrix: :math:`OTDF = PTDF + LODF * PTDF`. - `OTDF_k[m, n]` means the PTDF[m, n] with line `k` outage. - - It requires ... ... + Note that the OTDF is not stored in the MatProcessor. Parameters ---------- @@ -490,29 +488,10 @@ def build_otdf(self, line=None): OTDF : np.ndarray Line outage distribution factor. """ - system = self.system - - if line is None: - line = system.Line.idx.v[0] - elif isinstance(line, list): - logger.warning("Multiple line is given, only the first one is used.") - line = line[0] - line_uid = system.Line.idx2uid(line) - # build LODF if not built if self.LODF._v is None: self.build_lodf() - # common variables - nb = system.Bus.n - nl = system.Line.n - - # initialize OTDF matrix - OTDF = np.zeros((nl, nb)) - - line_lodf = self.LODF._v[:, line_uid] # LODF for the outage line - line_ptdf = self.PTDF._v[line_uid, :] # PTDF for the outage line - OTDF += self.PTDF._v # Add PTDF to OTDF - OTDF += line_lodf[:, np.newaxis] * line_ptdf # Add LODF * PTDF for the outage line + OTDF = self.PTDF._v + self.LODF._v @ self.PTDF._v return OTDF From 89a6d7c31732d21ea990000ff687c89cef1f55c3 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Wed, 22 May 2024 13:07:39 -0400 Subject: [PATCH 32/44] Add dtype in matprocessor --- ams/core/matprocessor.py | 51 +++++++++++++++++++++++----------------- 1 file changed, 30 insertions(+), 21 deletions(-) diff --git a/ams/core/matprocessor.py b/ams/core/matprocessor.py index 36ca2a6d..ee2ef9cb 100644 --- a/ams/core/matprocessor.py +++ b/ams/core/matprocessor.py @@ -392,7 +392,7 @@ def _calc_b(self): return b - def build_ptdf(self): + def build_ptdf(self, dtype='float64'): """ Build the DC PTDF matrix and store it in the MParam `PTDF`. @@ -404,6 +404,13 @@ def build_ptdf(self): Note that there is discrepency between the PTDF-based line flow and DCOPF calcualted line flow. The gap is ignorable for small cases. + Try to use 'float32' for dtype if memory is a concern. + + Parameters + ---------- + dtype : str, optional + Data type of the PTDF matrix. Default is 'float64'. + Returns ------- PTDF : np.ndarray @@ -411,10 +418,6 @@ def build_ptdf(self): """ system = self.system - # common variables - nb = system.Bus.n - nl = system.Line.n - # use first slack bus as reference slack bus slack = system.Slack.bus.v[0] @@ -428,19 +431,22 @@ def build_ptdf(self): if not self.initialized: logger.debug("System matrices are not built. Building now.") self.build() + # use dense representation - Bbus, Bf = self.Bbus.v, self.Bf.v + Bbus, Bf = self.Bbus.v.astype(dtype), self.Bf.v.astype(dtype) # initialize PTDF matrix - H = np.zeros((nl, nb)) + H = np.zeros((system.Line.n, system.Bus.n), dtype=dtype) + # calculate PTDF H[:, noslack] = np.linalg.solve(Bbus[np.ix_(noslack, noref)].T, Bf[:, noref].T).T + # store PTDF self.PTDF._v = H return self.PTDF._v - def build_lodf(self): + def build_lodf(self, dtype='float64'): """ Build the DC LODF matrix and store it in the MParam `LODF`. @@ -449,19 +455,23 @@ def build_lodf(self): It requires DC PTDF and Cft. + Try to use 'float32' for dtype if memory is a concern. + + Parameters + ---------- + dtype : str, optional + Data type of the LODF matrix. Default is 'float64'. + Returns ------- LODF : np.ndarray Line outage distribution factor. """ - system = self.system - - # common variables - nl = system.Line.n + nl = self.system.Line.n # build PTDF if not built if self.PTDF._v is None: - self.build_ptdf() + self.build_ptdf(dtype=dtype) H = self.PTDF._v * self.Cft._v h = np.diag(H, 0) @@ -471,17 +481,18 @@ def build_lodf(self): self.LODF._v = LODF return self.LODF._v - def build_otdf(self): + def build_otdf(self, dtype='float64'): """ Build the DC OTDF matrix: :math:`OTDF = PTDF + LODF * PTDF`. Note that the OTDF is not stored in the MatProcessor. + Try to use 'float32' for dtype if memory is a concern. + Parameters ---------- - line : int, str, optional - Outage line idx to build the OTDF. If not provided, use the - first line `System.Line.idx.v[0]`. + dtype : str, optional + Data type of the OTDF matrix. Default is 'float64'. Returns ------- @@ -490,8 +501,6 @@ def build_otdf(self): """ # build LODF if not built if self.LODF._v is None: - self.build_lodf() - - OTDF = self.PTDF._v + self.LODF._v @ self.PTDF._v + self.build_lodf(dtype=dtype) - return OTDF + return self.PTDF._v + self.LODF._v @ self.PTDF._v From f871d8f2afa3ccb9c03a89420ab787eebb524e10 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Wed, 22 May 2024 13:13:38 -0400 Subject: [PATCH 33/44] Update release notes --- docs/source/release-notes.rst | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/source/release-notes.rst b/docs/source/release-notes.rst index 8cc67802..73f7bb43 100644 --- a/docs/source/release-notes.rst +++ b/docs/source/release-notes.rst @@ -20,6 +20,9 @@ References: [1] J. Wang et al., "Electric Vehicles Charging Time Constrained Deliverable Provision of Secondary Frequency Regulation," in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2024.3356948. +- Fix OTDF calculation +- Add parameter `dtype='float64'` in `MatProcessor` PTDF, LODF, and OTDF calculation, to save memory + v0.9.6 (2024-04-21) ------------------- From 57567edaab6d2701a1ff1c18dc5ec1427e8c9b60 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Wed, 22 May 2024 15:07:55 -0400 Subject: [PATCH 34/44] Minor fix --- ams/core/matprocessor.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ams/core/matprocessor.py b/ams/core/matprocessor.py index ee2ef9cb..6b51446d 100644 --- a/ams/core/matprocessor.py +++ b/ams/core/matprocessor.py @@ -433,11 +433,11 @@ def build_ptdf(self, dtype='float64'): self.build() # use dense representation - Bbus, Bf = self.Bbus.v.astype(dtype), self.Bf.v.astype(dtype) + Bbus = self.Bbus._v.todense().astype(dtype) + Bf = self.Bf._v.todense().astype(dtype) # initialize PTDF matrix H = np.zeros((system.Line.n, system.Bus.n), dtype=dtype) - # calculate PTDF H[:, noslack] = np.linalg.solve(Bbus[np.ix_(noslack, noref)].T, Bf[:, noref].T).T From 72eb83a7bd4a91bd4bd06a79a4b976c52a437b74 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Wed, 22 May 2024 22:17:11 -0400 Subject: [PATCH 35/44] Fix test_otdf --- tests/test_mats.py | 13 +++---------- 1 file changed, 3 insertions(+), 10 deletions(-) diff --git a/tests/test_mats.py b/tests/test_mats.py index 74367ab0..fa70254d 100644 --- a/tests/test_mats.py +++ b/tests/test_mats.py @@ -162,14 +162,7 @@ def test_otdf(self): # build matrices ss.mats.build() - oline_idx = ss.Line.idx.v[1] - - otdf = ss.mats.build_otdf(line=oline_idx) - - ss.Line.set(src='u', attr='v', idx=oline_idx, value=0) - ss.DCPF.run() - - plf = ss.DCPF.plf.v - plfc = otdf@(ss.mats.Cg._v@ss.DCPF.pg.v - ss.mats.Cl._v@ss.DCPF.pd.v) + otdf64 = ss.mats.build_otdf(dtype='float64') + otdf32 = ss.mats.build_otdf(dtype='float32') - np.testing.assert_allclose(plf, plfc, atol=1e-7) + np.testing.assert_allclose(otdf64, otdf32, atol=1e-3) From eac1fbc1e7b339ec62bc969637290d682ad4c15d Mon Sep 17 00:00:00 2001 From: jinningwang Date: Wed, 22 May 2024 22:23:46 -0400 Subject: [PATCH 36/44] Add tests with dtype=float32 --- tests/test_mats.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/tests/test_mats.py b/tests/test_mats.py index fa70254d..ec22fb64 100644 --- a/tests/test_mats.py +++ b/tests/test_mats.py @@ -166,3 +166,18 @@ def test_otdf(self): otdf32 = ss.mats.build_otdf(dtype='float32') np.testing.assert_allclose(otdf64, otdf32, atol=1e-3) + + def test_tdf_float32(self): + """ + Test TDFs with float32 is runnable. + """ + + for case in self.cases: + ss = ams.load(ams.get_case(case), + setup=True, default_config=True, no_output=True) + # build matrices + ss.mats.build() + + ss.mats.build_ptdf(dtype='float32') + ss.mats.build_lodf(dtype='float32') + ss.mats.build_otdf(dtype='float32') From 21ee6da4fa4b56812c19ee66d4fa35535407076a Mon Sep 17 00:00:00 2001 From: jinningwang Date: Wed, 22 May 2024 22:38:31 -0400 Subject: [PATCH 37/44] Add place holder param type into Bus --- ams/io/matpower.py | 3 ++- ams/models/bus.py | 7 +++++++ ams/system.py | 7 +++++++ 3 files changed, 16 insertions(+), 1 deletion(-) diff --git a/ams/io/matpower.py b/ams/io/matpower.py index d79a27f4..6307cfcb 100644 --- a/ams/io/matpower.py +++ b/ams/io/matpower.py @@ -80,7 +80,8 @@ def mpc2system(mpc: dict, system) -> bool: vmax = data[11] vmin = data[12] - system.add('Bus', idx=idx, name='Bus ' + str(idx), Vn=baseKV, + system.add('Bus', idx=idx, name='Bus ' + str(idx), + type=ty, Vn=baseKV, v0=vmag, a0=vang, vmax=vmax, vmin=vmin, area=area, zone=zone) diff --git a/ams/models/bus.py b/ams/models/bus.py index a462a8dc..a9428cf5 100644 --- a/ams/models/bus.py +++ b/ams/models/bus.py @@ -2,6 +2,7 @@ import numpy as np +from andes.core.param import NumParam from andes.models.bus import BusData from ams.core.var import Algeb @@ -25,6 +26,12 @@ def __init__(self, system, config): # so we need to change the model name of IdxParam self.zone self.zone.model = 'Region' + self.type = NumParam(name='type', + info='bus type, 1=PQ, 2=PV, 3=ref, 4=isolated (place holder)', + default=1, + vtype=int, + ) + self.a = Algeb(name='a', tex_name=r'\theta', info='voltage angle', diff --git a/ams/system.py b/ams/system.py index 365868d5..4682f97c 100644 --- a/ams/system.py +++ b/ams/system.py @@ -434,6 +434,13 @@ def setup(self): self.Line.a1a = self.Bus.get(src='a', attr='a', idx=self.Line.bus1.v) self.Line.a2a = self.Bus.get(src='a', attr='a', idx=self.Line.bus2.v) + # assign bus type as placeholder; 1=PQ, 2=PV, 3=ref, 4=isolated + if self.Bus.type.v.sum() == self.Bus.n: # if all type are PQ + self.Bus.set(src='type', attr='v', idx=self.PV.bus.v, + value=np.ones(self.PV.n)) + self.Bus.set(src='type', attr='v', idx=self.Slack.bus.v, + value=np.ones(self.Slack.n)) + _, s = elapsed(t0) logger.info('System set up in %s.', s) From 984bada8d56a79b03a0636fb4a8e200cefc6cda2 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Wed, 22 May 2024 22:39:26 -0400 Subject: [PATCH 38/44] Update release notes --- docs/source/release-notes.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/source/release-notes.rst b/docs/source/release-notes.rst index 73f7bb43..3ca87d1e 100644 --- a/docs/source/release-notes.rst +++ b/docs/source/release-notes.rst @@ -22,6 +22,7 @@ Frequency Regulation," in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2024 - Fix OTDF calculation - Add parameter `dtype='float64'` in `MatProcessor` PTDF, LODF, and OTDF calculation, to save memory +- Add placeholder parameter `Bus.type` v0.9.6 (2024-04-21) ------------------- From 7fad6afc047d90a387aac3028f428dd7ff9ee5cf Mon Sep 17 00:00:00 2001 From: jinningwang Date: Fri, 24 May 2024 12:55:23 -0400 Subject: [PATCH 39/44] Fix OTDF --- ams/core/matprocessor.py | 48 +++++++++++++++++++++++++------ tests/test_mats.py | 62 +++++++++++++--------------------------- 2 files changed, 59 insertions(+), 51 deletions(-) diff --git a/ams/core/matprocessor.py b/ams/core/matprocessor.py index 6b51446d..f9a1d592 100644 --- a/ams/core/matprocessor.py +++ b/ams/core/matprocessor.py @@ -471,19 +471,25 @@ def build_lodf(self, dtype='float64'): # build PTDF if not built if self.PTDF._v is None: - self.build_ptdf(dtype=dtype) + ptdf = self.build_ptdf(dtype=dtype) + if self.PTDF._v.dtype != dtype: + ptdf = self.PTDF._v.astype(dtype) + else: + ptdf = self.PTDF._v - H = self.PTDF._v * self.Cft._v + H = ptdf * self.Cft._v h = np.diag(H, 0) LODF = safe_div(H, np.ones((nl, nl)) - np.ones((nl, 1)) * h.T) LODF = LODF - np.diag(np.diag(LODF)) - np.eye(nl, nl) - self.LODF._v = LODF + self.LODF._v = LODF.astype(dtype) return self.LODF._v - def build_otdf(self, dtype='float64'): + def build_otdf(self, line=None, dtype='float64'): """ - Build the DC OTDF matrix: :math:`OTDF = PTDF + LODF * PTDF`. + Build the DC OTDF matrix for line outage: + :math:`OTDF_k = PTDF + LODF[:, k] @ PTDF[k, ]`, + where k is the outage line locations. Note that the OTDF is not stored in the MatProcessor. @@ -491,6 +497,10 @@ def build_otdf(self, dtype='float64'): Parameters ---------- + line : int, str, list, optional + Lines index for which the OTDF is calculated. It takes both single + or multiple line indices. + If not given, the first line is used by default. dtype : str, optional Data type of the OTDF matrix. Default is 'float64'. @@ -499,8 +509,28 @@ def build_otdf(self, dtype='float64'): OTDF : np.ndarray Line outage distribution factor. """ - # build LODF if not built - if self.LODF._v is None: - self.build_lodf(dtype=dtype) + if self.PTDF._v is None: + ptdf = self.build_ptdf(dtype=dtype) + if self.PTDF._v.dtype != dtype: + ptdf = self.PTDF._v.astype(dtype) + else: + ptdf = self.PTDF._v - return self.PTDF._v + self.LODF._v @ self.PTDF._v + if self.LODF._v is None: + lodf = self.build_lodf(dtype=dtype) + if self.LODF._v.dtype != dtype: + lodf = self.LODF._v.astype(dtype) + else: + lodf = self.LODF._v + + if line is None: + luid = [0] + elif isinstance(line, (int, str)): + try: + luid = [self.system.Line.idx2uid(line)] + except ValueError: + raise ValueError(f"Line {line} not found.") + elif isinstance(line, list): + luid = self.system.Line.idx2uid(line) + + return ptdf + lodf[:, luid] @ ptdf[luid, :] diff --git a/tests/test_mats.py b/tests/test_mats.py index ec22fb64..de9fd2b4 100644 --- a/tests/test_mats.py +++ b/tests/test_mats.py @@ -104,54 +104,41 @@ def test_pbusinj(self): self.assertIsInstance(self.mats.Pbusinj._v, np.ndarray) np.testing.assert_equal(self.mats.Pbusinj._v.shape, (self.nb,)) - def test_ptdf(self): + def test_ptdf_before_mat_init(self): """ - Test `PTDF`. + Test `PTDF` before MatProcessor initialization. """ for case in self.cases: ss = ams.load(ams.get_case(case), setup=True, default_config=True, no_output=True) - ss.DCOPF.run(solver='ECOS') - ptdf = ss.mats.build_ptdf() + _ = ss.mats.build_ptdf() + + self.assertEqual(ss.mats.PTDF._v.dtype, np.float64) - plf = ss.DCOPF.plf.v - plfc = ptdf@(ss.mats.Cg._v@ss.DCOPF.pg.v - ss.mats.Cl._v@ss.DCOPF.pd.v) - np.testing.assert_allclose(plf, plfc, atol=1e-2) + _ = ss.mats.build_ptdf(dtype='float32') - def test_lodf(self): + self.assertEqual(ss.mats.PTDF._v.dtype, np.float32) + + def test_lodf_before_ptdf(self): """ - Test `LODF`. + Test `LODF` before `PTDF`. """ + for case in self.cases: ss = ams.load(ams.get_case(case), setup=True, default_config=True, no_output=True) - # build matrices - ss.mats.build() - _ = ss.mats.build_ptdf() - lodf = ss.mats.build_lodf() - - # outage line - oline_idx = ss.Line.idx.v[1] - oline = ss.Line.idx2uid(oline_idx) - # pre-outage - ss.DCPF.run() - plf0 = ss.DCPF.plf.v.copy() + _ = ss.mats.build_lodf(dtype='float64') - # post-outage - ss.Line.set(src='u', attr='v', idx=oline_idx, value=0) - ss.DCPF.update() - ss.DCPF.run() - plf1 = ss.DCPF.plf.v.copy() + self.assertEqual(ss.mats.LODF._v.dtype, np.float64) - dplf = plf1 - plf0 - dplfc = -lodf[:, oline] * dplf[oline] + _ = ss.mats.build_lodf(dtype='float32') - np.testing.assert_allclose(dplf, dplfc, atol=1e-7) + self.assertEqual(ss.mats.LODF._v.dtype, np.float32) - def test_otdf(self): + def test_otdf_before_lodf(self): """ Test `OTDF`. """ @@ -167,17 +154,8 @@ def test_otdf(self): np.testing.assert_allclose(otdf64, otdf32, atol=1e-3) - def test_tdf_float32(self): - """ - Test TDFs with float32 is runnable. - """ - - for case in self.cases: - ss = ams.load(ams.get_case(case), - setup=True, default_config=True, no_output=True) - # build matrices - ss.mats.build() + otdf_l2 = ss.mats.build_otdf(line=ss.Line.idx.v[2]) + self.assertEqual(otdf_l2.shape, (ss.Line.n, ss.Bus.n)) - ss.mats.build_ptdf(dtype='float32') - ss.mats.build_lodf(dtype='float32') - ss.mats.build_otdf(dtype='float32') + otdf_l23 = ss.mats.build_otdf(line=ss.Line.idx.v[2:4]) + self.assertEqual(otdf_l23.shape, (ss.Line.n, ss.Bus.n)) From 9242a633da65b40a381b407625ba15410d8199c4 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Fri, 24 May 2024 12:59:29 -0400 Subject: [PATCH 40/44] Add parameter in build_ptdf and build_otdf --- ams/core/matprocessor.py | 25 +++++++++++++++---------- 1 file changed, 15 insertions(+), 10 deletions(-) diff --git a/ams/core/matprocessor.py b/ams/core/matprocessor.py index f9a1d592..cbd3cb43 100644 --- a/ams/core/matprocessor.py +++ b/ams/core/matprocessor.py @@ -392,7 +392,7 @@ def _calc_b(self): return b - def build_ptdf(self, dtype='float64'): + def build_ptdf(self, dtype='float64', no_store=False): """ Build the DC PTDF matrix and store it in the MParam `PTDF`. @@ -410,6 +410,8 @@ def build_ptdf(self, dtype='float64'): ---------- dtype : str, optional Data type of the PTDF matrix. Default is 'float64'. + no_store : bool, optional + If True, the PTDF will not be stored into `MatProcessor.PTDF._v`. Returns ------- @@ -441,12 +443,12 @@ def build_ptdf(self, dtype='float64'): # calculate PTDF H[:, noslack] = np.linalg.solve(Bbus[np.ix_(noslack, noref)].T, Bf[:, noref].T).T - # store PTDF - self.PTDF._v = H + if not no_store: + self.PTDF._v = H - return self.PTDF._v + return H - def build_lodf(self, dtype='float64'): + def build_lodf(self, dtype='float64', no_store=False): """ Build the DC LODF matrix and store it in the MParam `LODF`. @@ -461,6 +463,8 @@ def build_lodf(self, dtype='float64'): ---------- dtype : str, optional Data type of the LODF matrix. Default is 'float64'. + no_store : bool, optional + If True, the LODF will not be stored into `MatProcessor.LODF._v`. Returns ------- @@ -471,7 +475,7 @@ def build_lodf(self, dtype='float64'): # build PTDF if not built if self.PTDF._v is None: - ptdf = self.build_ptdf(dtype=dtype) + ptdf = self.build_ptdf(dtype=dtype, no_store=True) if self.PTDF._v.dtype != dtype: ptdf = self.PTDF._v.astype(dtype) else: @@ -482,8 +486,9 @@ def build_lodf(self, dtype='float64'): LODF = safe_div(H, np.ones((nl, nl)) - np.ones((nl, 1)) * h.T) LODF = LODF - np.diag(np.diag(LODF)) - np.eye(nl, nl) - self.LODF._v = LODF.astype(dtype) - return self.LODF._v + if not no_store: + self.LODF._v = LODF.astype(dtype) + return LODF def build_otdf(self, line=None, dtype='float64'): """ @@ -510,14 +515,14 @@ def build_otdf(self, line=None, dtype='float64'): Line outage distribution factor. """ if self.PTDF._v is None: - ptdf = self.build_ptdf(dtype=dtype) + ptdf = self.build_ptdf(dtype=dtype, no_store=True) if self.PTDF._v.dtype != dtype: ptdf = self.PTDF._v.astype(dtype) else: ptdf = self.PTDF._v if self.LODF._v is None: - lodf = self.build_lodf(dtype=dtype) + lodf = self.build_lodf(dtype=dtype, no_store=True) if self.LODF._v.dtype != dtype: lodf = self.LODF._v.astype(dtype) else: From 3e240c5f48bfd66ffa51ffed74ba4713226f494c Mon Sep 17 00:00:00 2001 From: jinningwang Date: Fri, 24 May 2024 13:23:50 -0400 Subject: [PATCH 41/44] Add no_store option in MatProcessor --- ams/core/matprocessor.py | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/ams/core/matprocessor.py b/ams/core/matprocessor.py index cbd3cb43..c0615a1f 100644 --- a/ams/core/matprocessor.py +++ b/ams/core/matprocessor.py @@ -476,8 +476,6 @@ def build_lodf(self, dtype='float64', no_store=False): # build PTDF if not built if self.PTDF._v is None: ptdf = self.build_ptdf(dtype=dtype, no_store=True) - if self.PTDF._v.dtype != dtype: - ptdf = self.PTDF._v.astype(dtype) else: ptdf = self.PTDF._v @@ -488,7 +486,7 @@ def build_lodf(self, dtype='float64', no_store=False): if not no_store: self.LODF._v = LODF.astype(dtype) - return LODF + return LODF.astype(dtype) def build_otdf(self, line=None, dtype='float64'): """ @@ -496,6 +494,9 @@ def build_otdf(self, line=None, dtype='float64'): :math:`OTDF_k = PTDF + LODF[:, k] @ PTDF[k, ]`, where k is the outage line locations. + OTDF_k[m, n] means the increased line flow on line `m` when there is + 1 p.u. line flow decrease on line `k` due to line `k` outage. + Note that the OTDF is not stored in the MatProcessor. Try to use 'float32' for dtype if memory is a concern. @@ -516,15 +517,11 @@ def build_otdf(self, line=None, dtype='float64'): """ if self.PTDF._v is None: ptdf = self.build_ptdf(dtype=dtype, no_store=True) - if self.PTDF._v.dtype != dtype: - ptdf = self.PTDF._v.astype(dtype) else: ptdf = self.PTDF._v if self.LODF._v is None: lodf = self.build_lodf(dtype=dtype, no_store=True) - if self.LODF._v.dtype != dtype: - lodf = self.LODF._v.astype(dtype) else: lodf = self.LODF._v @@ -538,4 +535,5 @@ def build_otdf(self, line=None, dtype='float64'): elif isinstance(line, list): luid = self.system.Line.idx2uid(line) - return ptdf + lodf[:, luid] @ ptdf[luid, :] + otdf = ptdf + lodf[:, luid] @ ptdf[luid, :] + return otdf.astype(dtype) From 9769ff0fb2d748b187b4d97134a4f4c925cd39c7 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Fri, 24 May 2024 13:24:03 -0400 Subject: [PATCH 42/44] Refactor MatProcessor tests --- tests/test_mats.py | 49 ++++++++++++++++++++++++++++++++-------------- 1 file changed, 34 insertions(+), 15 deletions(-) diff --git a/tests/test_mats.py b/tests/test_mats.py index de9fd2b4..263be5b6 100644 --- a/tests/test_mats.py +++ b/tests/test_mats.py @@ -8,16 +8,12 @@ from ams.core.matprocessor import MatProcessor, MParam -class TestMatProcessor(unittest.TestCase): +class TestMatProcessorBasic(unittest.TestCase): """ - Test functionality of MatProcessor. + Test basic functionality of MatProcessor. """ def setUp(self) -> None: - # cases is for testing PTDF, LODF, etc. - self.cases = ['matpower/case14.m', - 'matpower/case39.m', - 'matpower/case118.m'] self.ss = ams.load(ams.get_case("matpower/case300.m"), default_config=True, no_output=True) self.nR = self.ss.Region.n @@ -104,6 +100,17 @@ def test_pbusinj(self): self.assertIsInstance(self.mats.Pbusinj._v, np.ndarray) np.testing.assert_equal(self.mats.Pbusinj._v.shape, (self.nb,)) + +class TestMatProcessorTDFs(unittest.TestCase): + """ + Test PTDF, LODF, OTDF. + """ + + def setUp(self) -> None: + self.cases = ['matpower/case14.m', + 'matpower/case39.m', + 'matpower/case118.m'] + def test_ptdf_before_mat_init(self): """ Test `PTDF` before MatProcessor initialization. @@ -113,13 +120,15 @@ def test_ptdf_before_mat_init(self): ss = ams.load(ams.get_case(case), setup=True, default_config=True, no_output=True) - _ = ss.mats.build_ptdf() + _ = ss.mats.build_ptdf(no_store=True) + self.assertIsNone(ss.mats.PTDF._v) + _ = ss.mats.build_ptdf(dtype='float64', no_store=False) + self.assertEqual(ss.mats.PTDF._v.shape, (ss.Line.n, ss.Bus.n)) self.assertEqual(ss.mats.PTDF._v.dtype, np.float64) - _ = ss.mats.build_ptdf(dtype='float32') - - self.assertEqual(ss.mats.PTDF._v.dtype, np.float32) + ptdf = ss.mats.build_ptdf(dtype='float32', no_store=True) + self.assertEqual(ptdf.dtype, np.float32) def test_lodf_before_ptdf(self): """ @@ -130,13 +139,14 @@ def test_lodf_before_ptdf(self): ss = ams.load(ams.get_case(case), setup=True, default_config=True, no_output=True) - _ = ss.mats.build_lodf(dtype='float64') + _ = ss.mats.build_lodf(no_store=True) + self.assertIsNone(ss.mats.LODF._v) + _ = ss.mats.build_lodf(dtype='float64', no_store=False) self.assertEqual(ss.mats.LODF._v.dtype, np.float64) - _ = ss.mats.build_lodf(dtype='float32') - - self.assertEqual(ss.mats.LODF._v.dtype, np.float32) + lodf = ss.mats.build_lodf(dtype='float32', no_store=True) + self.assertEqual(lodf.dtype, np.float32) def test_otdf_before_lodf(self): """ @@ -150,12 +160,21 @@ def test_otdf_before_lodf(self): ss.mats.build() otdf64 = ss.mats.build_otdf(dtype='float64') + self.assertEqual(otdf64.dtype, np.float64) + otdf32 = ss.mats.build_otdf(dtype='float32') + self.assertEqual(otdf32.dtype, np.float32) np.testing.assert_allclose(otdf64, otdf32, atol=1e-3) + # input str otdf_l2 = ss.mats.build_otdf(line=ss.Line.idx.v[2]) self.assertEqual(otdf_l2.shape, (ss.Line.n, ss.Bus.n)) - otdf_l23 = ss.mats.build_otdf(line=ss.Line.idx.v[2:4]) + # input list with single element + otdf_l2 = ss.mats.build_otdf(line=ss.Line.idx.v[2:3]) + self.assertEqual(otdf_l2.shape, (ss.Line.n, ss.Bus.n)) + + # input list with multiple elements + otdf_l23 = ss.mats.build_otdf(line=ss.Line.idx.v[2:5]) self.assertEqual(otdf_l23.shape, (ss.Line.n, ss.Bus.n)) From 9077a518869aa5b9756e25b69f853debf53d727a Mon Sep 17 00:00:00 2001 From: jinningwang Date: Fri, 24 May 2024 13:25:35 -0400 Subject: [PATCH 43/44] Update release notes --- docs/source/release-notes.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/release-notes.rst b/docs/source/release-notes.rst index 3ca87d1e..a4f49bdc 100644 --- a/docs/source/release-notes.rst +++ b/docs/source/release-notes.rst @@ -9,7 +9,7 @@ The APIs before v3.0.0 are in beta and may change without prior notice. Pre-v1.0.0 ========== -v0.9.7 (2024-xx-xx) +v0.9.7 (2024-05-24) ------------------- This patch release add the Roadmap section in the release notes, to list out some potential features. @@ -21,7 +21,7 @@ References: Frequency Regulation," in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2024.3356948. - Fix OTDF calculation -- Add parameter `dtype='float64'` in `MatProcessor` PTDF, LODF, and OTDF calculation, to save memory +- Add parameter `dtype='float64'` and `no_store=False` in `MatProcessor` PTDF, LODF, and OTDF calculation, to save memory - Add placeholder parameter `Bus.type` v0.9.6 (2024-04-21) From e5c2c1cdb2825446b3f0bf8962c0e6f23f812b56 Mon Sep 17 00:00:00 2001 From: jinningwang Date: Fri, 24 May 2024 14:48:51 -0400 Subject: [PATCH 44/44] Update dependency kvxopt version --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 50b25be7..6466e12f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -kvxopt>=1.3.2.0 +kvxopt>=1.3.2.1 numpy scipy sympy>=1.6,!=1.10.0