{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# pandapower Interface"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This example (1) shows how to convert an ANDES system (`ssa`) to a pandapower network (`ssp`), (2) benchmarks the powerflow results, (3) shows how to alter `ssa` active power setpoints according to `ssp` results.\n",
"\n",
"This interface is mainly developed by [Jinning Wang](https://www.linkedin.com/in/jinning-wang-846490199/)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import andes\n",
"\n",
"from andes.interop.pandapower import to_pandapower, \\\n",
" make_link_table, runopp_map, add_gencost\n",
"\n",
"andes.config_logger(20)\n",
"\n",
"import pandapower as pp\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from math import pi"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load case"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we use the same ANDES system `ss0` to do the pandapower conversion, which leaves the `ssa` untouched.\n",
"\n",
"This will be useful if you need to modify the `ssa` parametes or setpoints."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Working directory: \"/Users/jinningwang/Documents/work/andes/docs/source/examples\"\n",
"> Loaded generated Python code in \"/Users/jinningwang/.andes/pycode\".\n",
"Parsing input file \"/Users/jinningwang/Documents/work/andes/andes/cases/ieee14/ieee14_ieeet1.xlsx\"...\n",
"Input file parsed in 0.2310 seconds.\n",
"System internal structure set up in 0.0481 seconds.\n",
"Working directory: \"/Users/jinningwang/Documents/work/andes/docs/source/examples\"\n",
"> Reloaded generated Python code of module \"pycode\".\n",
"Parsing input file \"/Users/jinningwang/Documents/work/andes/andes/cases/ieee14/ieee14_ieeet1.xlsx\"...\n",
"Input file parsed in 0.0895 seconds.\n",
"System internal structure set up in 0.0383 seconds.\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ssa = andes.load(andes.get_case('ieee14/ieee14_ieeet1.xlsx'),\n",
" setup=False,\n",
" no_output=True,\n",
" default_config=True)\n",
"ssa.Toggle.u.v = [0, 0]\n",
"ssa.setup()\n",
"\n",
"ss0 = andes.load(andes.get_case('ieee14/ieee14_ieeet1.xlsx'),\n",
" setup=False,\n",
" no_output=True,\n",
" default_config=True)\n",
"ss0.Toggle.u.v = [0, 0]\n",
"ss0.setup()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Convert to pandapower net"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Convert ADNES system `ssa` to pandapower net `ssp`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"-> System connectivity check results:\n",
" No islanded bus detected.\n",
" System is interconnected.\n",
" Each island has a slack bus correctly defined and enabled.\n",
"\n",
"-> Power flow calculation\n",
" Sparse solver: KLU\n",
" Solution method: NR method\n",
"Power flow initialized in 0.0031 seconds.\n",
"0: |F(x)| = 0.5605182134\n",
"1: |F(x)| = 0.006202200332\n",
"2: |F(x)| = 5.819382825e-06\n",
"3: |F(x)| = 6.967745825e-12\n",
"Converged in 4 iterations in 0.0071 seconds.\n",
"Power flow results are consistent. Conversion is successful.\n"
]
}
],
"source": [
"ssp = to_pandapower(ss0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Add generator cost data."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gen_cost = np.array([\n",
" [2, 0, 0, 3, 0.0430293, 20, 0], \n",
" [2, 0, 0, 3, 0.25, 20, 0], \n",
" [2, 0, 0, 3, 0.01, 40, 0], \n",
" [2, 0, 0, 3, 0.01, 40, 0], \n",
" [2, 0, 0, 3, 0.01, 40, 0]\n",
"])\n",
"\n",
"add_gencost(ssp, gen_cost)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Inspect the pandapower net `ssp`."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"This pandapower network includes the following parameter tables:\n",
" - bus (14 elements)\n",
" - load (11 elements)\n",
" - gen (5 elements)\n",
" - shunt (2 elements)\n",
" - line (16 elements)\n",
" - trafo (4 elements)\n",
" - poly_cost (5 elements)\n",
" and the following results tables:\n",
" - res_bus (14 elements)\n",
" - res_line (16 elements)\n",
" - res_trafo (4 elements)\n",
" - res_load (11 elements)\n",
" - res_shunt (2 elements)\n",
" - res_gen (5 elements)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ssp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Comapre Power Flow Results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run power flow of `ssa`."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"-> System connectivity check results:\n",
" No islanded bus detected.\n",
" System is interconnected.\n",
" Each island has a slack bus correctly defined and enabled.\n",
"\n",
"-> Power flow calculation\n",
" Sparse solver: KLU\n",
" Solution method: NR method\n",
"Power flow initialized in 0.0037 seconds.\n",
"0: |F(x)| = 0.5605182134\n",
"1: |F(x)| = 0.006202200332\n",
"2: |F(x)| = 5.819382825e-06\n",
"3: |F(x)| = 6.967745825e-12\n",
"Converged in 4 iterations in 0.0071 seconds.\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ssa.PFlow.run()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# ssa\n",
"ssa_res_gen = pd.DataFrame(columns=['name', 'p_mw', 'q_mvar', 'va_degree', 'vm_pu'])\n",
"ssa_res_gen['name'] = ssa.PV.as_df()['name']\n",
"ssa_res_gen['p_mw'] = ssa.PV.p.v * ssa.config.mva\n",
"ssa_res_gen['q_mvar'] = ssa.PV.q.v * ssa.config.mva\n",
"ssa_res_gen['va_degree'] = ssa.PV.a.v * 180 / pi\n",
"ssa_res_gen['vm_pu'] = ssa.PV.v.v\n",
"ssa_res_slack = pd.DataFrame([[ssa.Slack.name.v[0], ssa.Slack.p.v[0] * ssa.config.mva,\n",
" ssa.Slack.q.v[0] * ssa.config.mva, ssa.Slack.a.v[0] * 180 / pi,\n",
" ssa.Slack.v.v[0]]],\n",
" columns=ssa_res_gen.columns,\n",
" )\n",
"ssa_res_gen = pd.concat([ssa_res_gen, ssa_res_slack]).reset_index(drop=True)\n",
"\n",
"# ssp\n",
"pp.runpp(ssp)\n",
"ssp_res_gen = pd.concat([ssp.gen['name'], ssp.res_gen], axis=1)\n",
"\n",
"res_gen_concat = pd.concat([ssa_res_gen, ssp_res_gen], axis=1)\n",
"\n",
"# ssa\n",
"ssa_pf_bus = ssa.Bus.as_df()[[\"name\"]].copy()\n",
"ssa_pf_bus['v_andes'] = ssa.Bus.v.v\n",
"ssa_pf_bus['a_andes'] = ssa.Bus.a.v * 180 / pi\n",
"\n",
"# ssp\n",
"ssp_pf_bus = ssa.Bus.as_df()[[\"name\"]].copy()\n",
"ssp_pf_bus['v_pp'] = ssp.res_bus['vm_pu']\n",
"ssp_pf_bus['a_pp'] = ssp.res_bus['va_degree']\n",
"\n",
"pf_bus_concat = pd.concat([ssa_pf_bus, ssp_pf_bus], axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the table below, the left half are ANDES results, and the right half are from pandapower"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" name | \n",
" p_mw | \n",
" q_mvar | \n",
" va_degree | \n",
" vm_pu | \n",
" name | \n",
" p_mw | \n",
" q_mvar | \n",
" va_degree | \n",
" vm_pu | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 2 | \n",
" 40.0000 | \n",
" 30.4361 | \n",
" -1.7641 | \n",
" 1.03 | \n",
" 2 | \n",
" 40.0000 | \n",
" 30.4361 | \n",
" -1.7641 | \n",
" 1.03 | \n",
"
\n",
" \n",
" 1 | \n",
" 3 | \n",
" 40.0000 | \n",
" 12.5971 | \n",
" -3.5371 | \n",
" 1.01 | \n",
" 3 | \n",
" 40.0000 | \n",
" 12.5971 | \n",
" -3.5371 | \n",
" 1.01 | \n",
"
\n",
" \n",
" 2 | \n",
" 4 | \n",
" 30.0000 | \n",
" 20.9866 | \n",
" -6.4527 | \n",
" 1.03 | \n",
" 4 | \n",
" 30.0000 | \n",
" 20.9866 | \n",
" -6.4527 | \n",
" 1.03 | \n",
"
\n",
" \n",
" 3 | \n",
" 5 | \n",
" 35.0000 | \n",
" 7.3964 | \n",
" -1.5400 | \n",
" 1.03 | \n",
" 5 | \n",
" 35.0000 | \n",
" 7.3964 | \n",
" -1.5400 | \n",
" 1.03 | \n",
"
\n",
" \n",
" 4 | \n",
" 1 | \n",
" 81.4272 | \n",
" -21.6171 | \n",
" 0.0000 | \n",
" 1.03 | \n",
" 1 | \n",
" 81.4272 | \n",
" -21.6171 | \n",
" 0.0000 | \n",
" 1.03 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" name p_mw q_mvar va_degree vm_pu name p_mw q_mvar va_degree \\\n",
"0 2 40.0000 30.4361 -1.7641 1.03 2 40.0000 30.4361 -1.7641 \n",
"1 3 40.0000 12.5971 -3.5371 1.01 3 40.0000 12.5971 -3.5371 \n",
"2 4 30.0000 20.9866 -6.4527 1.03 4 30.0000 20.9866 -6.4527 \n",
"3 5 35.0000 7.3964 -1.5400 1.03 5 35.0000 7.3964 -1.5400 \n",
"4 1 81.4272 -21.6171 0.0000 1.03 1 81.4272 -21.6171 0.0000 \n",
"\n",
" vm_pu \n",
"0 1.03 \n",
"1 1.01 \n",
"2 1.03 \n",
"3 1.03 \n",
"4 1.03 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res_gen_concat.round(4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bus voltage and angle"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Likewise, the left half are ANDES results, and the right half are from pandapower"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" name | \n",
" v_andes | \n",
" a_andes | \n",
" name | \n",
" v_pp | \n",
" a_pp | \n",
"
\n",
" \n",
" uid | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" BUS1 | \n",
" 1.0300 | \n",
" 0.0000 | \n",
" BUS1 | \n",
" 1.0300 | \n",
" 0.0000 | \n",
"
\n",
" \n",
" 1 | \n",
" BUS2 | \n",
" 1.0300 | \n",
" -1.7641 | \n",
" BUS2 | \n",
" 1.0300 | \n",
" -1.7641 | \n",
"
\n",
" \n",
" 2 | \n",
" BUS3 | \n",
" 1.0100 | \n",
" -3.5371 | \n",
" BUS3 | \n",
" 1.0100 | \n",
" -3.5371 | \n",
"
\n",
" \n",
" 3 | \n",
" BUS4 | \n",
" 1.0114 | \n",
" -4.4098 | \n",
" BUS4 | \n",
" 1.0114 | \n",
" -4.4098 | \n",
"
\n",
" \n",
" 4 | \n",
" BUS5 | \n",
" 1.0173 | \n",
" -3.8430 | \n",
" BUS5 | \n",
" 1.0173 | \n",
" -3.8430 | \n",
"
\n",
" \n",
" 5 | \n",
" BUS6 | \n",
" 1.0300 | \n",
" -6.4527 | \n",
" BUS6 | \n",
" 1.0300 | \n",
" -6.4527 | \n",
"
\n",
" \n",
" 6 | \n",
" BUS7 | \n",
" 1.0225 | \n",
" -4.8852 | \n",
" BUS7 | \n",
" 1.0225 | \n",
" -4.8852 | \n",
"
\n",
" \n",
" 7 | \n",
" BUS8 | \n",
" 1.0300 | \n",
" -1.5400 | \n",
" BUS8 | \n",
" 1.0300 | \n",
" -1.5400 | \n",
"
\n",
" \n",
" 8 | \n",
" BUS9 | \n",
" 1.0218 | \n",
" -7.2459 | \n",
" BUS9 | \n",
" 1.0218 | \n",
" -7.2459 | \n",
"
\n",
" \n",
" 9 | \n",
" BUS10 | \n",
" 1.0155 | \n",
" -7.4155 | \n",
" BUS10 | \n",
" 1.0155 | \n",
" -7.4155 | \n",
"
\n",
" \n",
" 10 | \n",
" BUS11 | \n",
" 1.0191 | \n",
" -7.0797 | \n",
" BUS11 | \n",
" 1.0191 | \n",
" -7.0797 | \n",
"
\n",
" \n",
" 11 | \n",
" BUS12 | \n",
" 1.0174 | \n",
" -7.4730 | \n",
" BUS12 | \n",
" 1.0174 | \n",
" -7.4730 | \n",
"
\n",
" \n",
" 12 | \n",
" BUS13 | \n",
" 1.0145 | \n",
" -7.7208 | \n",
" BUS13 | \n",
" 1.0145 | \n",
" -7.7208 | \n",
"
\n",
" \n",
" 13 | \n",
" BUS14 | \n",
" 1.0163 | \n",
" -9.4811 | \n",
" BUS14 | \n",
" 1.0163 | \n",
" -9.4811 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" name v_andes a_andes name v_pp a_pp\n",
"uid \n",
"0 BUS1 1.0300 0.0000 BUS1 1.0300 0.0000\n",
"1 BUS2 1.0300 -1.7641 BUS2 1.0300 -1.7641\n",
"2 BUS3 1.0100 -3.5371 BUS3 1.0100 -3.5371\n",
"3 BUS4 1.0114 -4.4098 BUS4 1.0114 -4.4098\n",
"4 BUS5 1.0173 -3.8430 BUS5 1.0173 -3.8430\n",
"5 BUS6 1.0300 -6.4527 BUS6 1.0300 -6.4527\n",
"6 BUS7 1.0225 -4.8852 BUS7 1.0225 -4.8852\n",
"7 BUS8 1.0300 -1.5400 BUS8 1.0300 -1.5400\n",
"8 BUS9 1.0218 -7.2459 BUS9 1.0218 -7.2459\n",
"9 BUS10 1.0155 -7.4155 BUS10 1.0155 -7.4155\n",
"10 BUS11 1.0191 -7.0797 BUS11 1.0191 -7.0797\n",
"11 BUS12 1.0174 -7.4730 BUS12 1.0174 -7.4730\n",
"12 BUS13 1.0145 -7.7208 BUS13 1.0145 -7.7208\n",
"13 BUS14 1.0163 -9.4811 BUS14 1.0163 -9.4811"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pf_bus_concat.round(4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generator dispatch based on OPF from pandapower"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Prepare the link table."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Asign the StaticGen with OPF, in this case, all the SynGen are GENROU\n",
"link_table = make_link_table(ssa)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" stg_name | \n",
" stg_idx | \n",
" bus_idx | \n",
" syn_idx | \n",
" exc_idx | \n",
" gov_idx | \n",
" bus_name | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 2 | \n",
" 2 | \n",
" 2 | \n",
" GENROU_2 | \n",
" EXST1_1 | \n",
" TGOV1_2 | \n",
" BUS2 | \n",
"
\n",
" \n",
" 1 | \n",
" 3 | \n",
" 3 | \n",
" 3 | \n",
" GENROU_3 | \n",
" ESST3A_3 | \n",
" TGOV1_3 | \n",
" BUS3 | \n",
"
\n",
" \n",
" 2 | \n",
" 4 | \n",
" 4 | \n",
" 6 | \n",
" GENROU_4 | \n",
" ESST3A_4 | \n",
" TGOV1_4 | \n",
" BUS6 | \n",
"
\n",
" \n",
" 3 | \n",
" 5 | \n",
" 5 | \n",
" 8 | \n",
" GENROU_5 | \n",
" 1 | \n",
" TGOV1_5 | \n",
" BUS8 | \n",
"
\n",
" \n",
" 4 | \n",
" 1 | \n",
" 1 | \n",
" 1 | \n",
" GENROU_1 | \n",
" ESST3A_2 | \n",
" TGOV1_1 | \n",
" BUS1 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" stg_name stg_idx bus_idx syn_idx exc_idx gov_idx bus_name\n",
"0 2 2 2 GENROU_2 EXST1_1 TGOV1_2 BUS2\n",
"1 3 3 3 GENROU_3 ESST3A_3 TGOV1_3 BUS3\n",
"2 4 4 6 GENROU_4 ESST3A_4 TGOV1_4 BUS6\n",
"3 5 5 8 GENROU_5 1 TGOV1_5 BUS8\n",
"4 1 1 1 GENROU_1 ESST3A_2 TGOV1_1 BUS1"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"link_table"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the TDS in ADNES to 2s."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"-> Time Domain Simulation Summary:\n",
"Sparse Solver: KLU\n",
"Simulation time: 0.0-2 s.\n",
"Fixed step size: h=33.33 ms. Shrink if not converged.\n",
"Initialization for dynamics completed in 0.0435 seconds.\n",
"Initialization was successful.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "23a6233b6079469db993d06825b9e085",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/100 [00:00, ?%/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Simulation completed in 0.0734 seconds.\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(, )"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ssa.TDS.config.tf = 2\n",
"ssa.TDS.run()\n",
"\n",
"ssa.TDS.plt.plot(ssa.GENROU.Pe)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the OPF results from pandapower. The `ssp_res` has been converted to p.u.."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" name | \n",
" p | \n",
" q | \n",
" vm_pu | \n",
" bus_name | \n",
" bus_idx | \n",
" controllable | \n",
" syn_idx | \n",
" gov_idx | \n",
" exc_idx | \n",
" stg_idx | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 2 | \n",
" 0.500000 | \n",
" 0.129214 | \n",
" 1.096908 | \n",
" BUS2 | \n",
" 2 | \n",
" True | \n",
" GENROU_2 | \n",
" TGOV1_2 | \n",
" EXST1_1 | \n",
" 2 | \n",
"
\n",
" \n",
" 1 | \n",
" 3 | \n",
" 0.412728 | \n",
" 0.149999 | \n",
" 1.082554 | \n",
" BUS3 | \n",
" 3 | \n",
" True | \n",
" GENROU_3 | \n",
" TGOV1_3 | \n",
" ESST3A_3 | \n",
" 3 | \n",
"
\n",
" \n",
" 2 | \n",
" 4 | \n",
" 0.343962 | \n",
" 0.099999 | \n",
" 1.086078 | \n",
" BUS6 | \n",
" 6 | \n",
" True | \n",
" GENROU_4 | \n",
" TGOV1_4 | \n",
" ESST3A_4 | \n",
" 4 | \n",
"
\n",
" \n",
" 3 | \n",
" 5 | \n",
" 0.496196 | \n",
" 0.085231 | \n",
" 1.099999 | \n",
" BUS8 | \n",
" 8 | \n",
" True | \n",
" GENROU_5 | \n",
" TGOV1_5 | \n",
" 1 | \n",
" 5 | \n",
"
\n",
" \n",
" 4 | \n",
" 1 | \n",
" 0.500000 | \n",
" -0.079092 | \n",
" 1.100000 | \n",
" BUS1 | \n",
" 1 | \n",
" True | \n",
" GENROU_1 | \n",
" TGOV1_1 | \n",
" ESST3A_2 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" name p q vm_pu bus_name bus_idx controllable syn_idx \\\n",
"0 2 0.500000 0.129214 1.096908 BUS2 2 True GENROU_2 \n",
"1 3 0.412728 0.149999 1.082554 BUS3 3 True GENROU_3 \n",
"2 4 0.343962 0.099999 1.086078 BUS6 6 True GENROU_4 \n",
"3 5 0.496196 0.085231 1.099999 BUS8 8 True GENROU_5 \n",
"4 1 0.500000 -0.079092 1.100000 BUS1 1 True GENROU_1 \n",
"\n",
" gov_idx exc_idx stg_idx \n",
"0 TGOV1_2 EXST1_1 2 \n",
"1 TGOV1_3 ESST3A_3 3 \n",
"2 TGOV1_4 ESST3A_4 4 \n",
"3 TGOV1_5 1 5 \n",
"4 TGOV1_1 ESST3A_2 1 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ssp_res = runopp_map(ssp, link_table)\n",
"ssp_res"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now dispatch the resutls into `ssa`, where the active power setpoitns are updated to `TurbinGov.pref0`."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.49999999, 0.41272757, 0.34396221, 0.49619557, 0.50000009])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ssa_gov_idx = list(ssp_res['gov_idx'][~ssp_res['gov_idx'].isna()])\n",
"ssa.TurbineGov.set(src='pref0', idx=ssa_gov_idx, attr='v', value=ssp_res['p'][~ssp_res['gov_idx'].isna()])\n",
"ssa.TurbineGov.get(src='pref0', idx=ssa_gov_idx, attr='v')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now run the TDS to 50s."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b35d11c0cb8f466f8c888a83869ef149",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/100 [00:00, ?%/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Simulation completed in 2.6138 seconds.\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ssa.TDS.config.tf = 50\n",
"ssa.TDS.run()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see the outputs of `GENROU` are rearranged by the OPF results."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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UFnjkGgSoKErBTetut3vOvnuHwwGfz1dw4JPcGqHlkavsdrsdbW1tGUHLxo0bixIsaIGKFigkzwgppbIKFgwV6stlVwQRkUobr5DvRux2u9Hd3Y3+/v55zzpwuVzo6urK+JauKEpiNcR0uY4DyFhpMVs3hyzL2LJlS9Z8fD5fxnGLxQJZlhODFXt7exPrN3R1dWXcpLXXni0Y0dZgSK+f7u7uebcQuN3uRFeLx+OBx+NJrFRZapIQi/fGKUmSGUAgEAjAbDYvOL97XziEz9/ixjPf3Qi52rjwAhIRnaQURUFvby/6+vrg8Xhyjh9QFAVXXHEFhoaGErMJChmAmLzcs9aa0NbWhoGBgURrhraEc/qSzMDscs8ejwdCiMQsjf7+frS1tSWWdE7W2tqaGOio5a2NG9Ca97VrtC6Y9vZ2yLKcKJO23HP6rI65bvzaTA5teWstiJiLoig488wzs45vONb79NjYGOrr6wGgXggxdkyZxJVVsDDw4hF87qZBPPXtK7C8rnLhBSQiOsX19/ejr68vsdDSYpjGR/NTzGCB3RBERJSTLMtobm6GzWYr6iA+OrmU2WwIdQZEhAMciYjmxWazweVyJQb8zTW+gU5NZRUsaLMhuOQzEdH8FXNaJZ2cyqobokKbOhlhNwQREdF8lVWwoHVDsGWBiIho/soqWNC6ISIxBgtERETzVVbBArshiIiICldWwQK7IYiIiApXVsECuyGIiIgKV1bBgoHdEERERAUrq2Chgt0QREREBSurRZkMXJSJiCiDx+OBw+FAb28vWlpa0NzcDK/Xm1ja2eFwHPOqjckbSQHqCpBtbW2w2+2JxZ60zZ60HRazbSRlsVjgcrky0qbvPmm329Hb24umpqbEJlPZNqnyer1QFCVjN0uNtpFUc3MzgNkdMdM3ktI21+rq6kqUPXmjKq08NpsNnZ2dGVtbnzSEEIv2AcAMQAQCAVEMkWhMnG6/S/Q9tbco+RERnSqGhoaEektI5XQ6hSzLx5Sf1WoVQ0NDKceHh4dFS0uLsFqtGddYrVbR1taWcdzv9wubzZZyzOFwCFmWRUdHR0b6bMdsNltG3sPDw0KW5YwyDg0NCZvNJvx+f8pxp9OZUY65yp6rPNn4/X7R09Mjenp6REdHh2hpackoQyECgYAAIACYxQLvx2XVDaHXSdBJQIgtC0REKQYGBrJ+67VarVAUBW63u6D8rrjiCjgcjow807egTtbQ0JD1uCzL2LhxY8Yxp9OJ7u7ujM2t0reVzsVisaCpqQmbN2/OKLvdbs/Y+lrbbjpb+XOVvZDy2O122Gy2xNbbDQ0NaG1tnde1x1tZBQuA2hXBbggiolQulwtNTU0Zx7UgoZDmc207a5vNlvW8zWbLe3PVKIqSCAS0oCU9H5vNtqAbqizLKflq3RK5yq51KxyP3Te1Lg3N+vXrMTg4WPTnORZlFywY9TpEuEU1EVGKwcHBjG/vgHpz7OjoKCivgYGBrIFHer5z8Xg8iRu5zWbL+KYPAE6nE263G729vQWVEVCDkYGBgZSNsvr7+/OOz9DOJd/Ui8XlcqXU9Y4dO3IGLSdaWQ1wBABDhY7dEERUFKNToxidHs2bZlXNKiypXAL/jB+HJg/hgsYLAAC7ArswHZnOe+3Z8tkw6A3YN74PMRHD6ebTEY1F8Yr/lUSaZVXLsKx62YJeh3ZTTr4xeTwe2O12tLe3FxwseDyeOZvec90EBwcH0d3dDa/Xi/7+fjidzrz5yLIMh8MBu92OTZs2ZQ0oksul3eQ9Hg+Gh4exffv2lFYTj8eTs5tEY7FYsGPHjrxpFqq/vx+Kosz5+k+UsgsWKnQSuyGIqCicrzrxy2d/mTfN99/yfXzknI/ggb0P4PuPfx/Pf/p5AMC/P/bveG70ubzXulpcWFmzEjcM3YDJ8CR6NvZgOjKNa+66JpHmC2/8Ar54yRcX9DoGBgYgyzK2bduWOGaxWEpyo2pqaio4OOno6EBPTw82b96ct8wWiwUtLS0A1O6Vvr6+rF0YXq+3sEIXkaIo2LZtGxRFQWtra97g50Qqu2CBYxaIqFhaz2nFu9e+O2+aVTWrAACXr7sc5zeenzj+w7f9cM6WhcZKdbrhVzd8FTGhfm5VVVSh70N9iTTLqhbWqgCozd/awLpisFqtGVMaATUocbvd6Onpgc/nQ2dnZ97AoL29PWU8gdvtzjl2wul0YsOGDfMeiGm1WtHZ2YnW1lb4/f7EcYvFMud4hPm0PiRLH2uRjza1FAB6e3uxZMkS7Nq1q+RBQ8HBgiRJHQCU+K+yEKJ7jvROAH0APEnXAQCEEMUfITIHYwXHLBBRcSyrnn8XwJLKJVhSuSTx+5n1Z877edbWrU38W6/TJ7oyiiW9336hbDZb1j59bUCi1sUwVwtC+tiBwcHBnMGC1WpFW1sbWltb5zUeQstfG0SpPZfNZsPAwEDOa7RgJL0bZT5BRj7aug6dnZ2JwMBmsyXGVWgtIqVS0ADHeKAAIUSvEKIXgFuSpJ45LrMCcAIYAjCc9CjeO7MAFTqJYxaIiOIURckYr7BQWuCRaxCgoigFf1N2u91zLgzlcDjg8/kKDnySWyO0PHKV3W63o62tLSNo2bhx44KCBY/Hg+7u7sTiT8Bsi0SpWxWAwmdDdAJIDDkVQgwAmKvdqkcIISU/ALQLIUoyeZTdEEREs7TxCvluxG63G93d3ejv75/3rAOXy4Wurq6Mb+mKoqTcEJPlOg4gY6XFbN0csixjy5YtWfPx+XwZxy0WC2RZTgxW7O3tTazf0NXVlXHz1157tmBEW4MhvX66u7vn1b1jtVrR0dGR8hr7+vpgtVoXxYyIeXdDSJJkgdrtoGQ5Z4sHDtn0p6cFULKJowZ2QxARQVEU9Pb2oq9PHf/Q3d2dtVtAURRs3rwZQ0NDGesA5GOxWDA0NAS73Q6Xy4XGxsbEN2Sn05kSRGhLOLvdbiiKgu7u2d5tbblnratAm6WhzRbQlnTWtLS0JF5Tct7aVE7t5i3LciIwcDgcKVMmbTYbtm/fjq6uroxZHS6XK+drdrlc6O7uht1uTyxv3dLSMu+Wgc7OzpTXrigKtm/fPq9rjzdJiPndOOM3eVe8ZSD5uB/AZiHEnO+geMBhi3dhZDtvAmBKOlQHYH8gEIDZbJ5XOefS+qu/YW1DNa7fdElR8iMiOpX19/ejr68vsdDSse4RQSfe2NgY6uvrAaBeCDG2kLyKsSiTD8DcS3Gp7LkChbhOAIGkx/4Fli1DhU6HMFsWiIjmRZZlNDc3w2azLXgQH528ihEszCtQkCRpPmuFdgGoT3qsWUC5sjJU6BCOcMwCEdF8JM9e6O/vZ7BQpgqZOpnrHSLnOZesHeosiJyEEEEAQe13SZLypD42Rr2ESIzBAhHRfBVzWiWdnObdshBfE0GJjztIP5d7UuosG9LWWSiFCp0OIXZDEBERzVuh3RBdUG/6AABJklqQNJVSkiSLthZDFhbMrwXiuGI3BBERUWEKChbiqzXKkiS1xAOFZiFE8pqXNqjdDdl4oA6GLCkDuyGIiIgKUvByz2nLO/ennetFUktD2rn8W5CdIEY9uyGIiIgKUYzZECeVCr3EbggiIqIClF2wwOWeiYiIClN2wYJRr0Mkxm4IIiKi+Sp4zMLJrkIvIcRuCCKiBI/HA4fDgd7eXrS0tKC5uRlerzexAJPD4TjmZZ617aK1vRJkWUZbWxvsdnti/QZt/4be3l40NDSgvX12nLy2N4TFYoHL5cpIm76hlN1uR29vL5qamhL7RvT09KC7uxsWiyWRt9frhaIoGRtUabQto5ubmwHMbnKVvimUtl9GV1dXouza3hPJ5bHZbOjs7My5xfaiJ4RYtA8AZgAiEAiIYrnB9Ypo/qGraPkREZ0KhoaGhHpLSOV0OoUsy8eUn9VqFUNDQynHh4eHRUtLi7BarRnXWK1W0dbWlnHc7/cLm82WcszhcAhZlkVHR0dG+mzHbDZbRt7Dw8NCluWMMg4NDQmbzSb8fn/KcafTmVGOucqeqzzzkeu55isQCAgAAoBZLPB+XHbdEAZ2QxARZRgYGMj6rddqtUJRFLjd7oLyu+KKK+BwODLyTP52n66hIfvuAbIsY+PGjRnHnE4nuru7M5agTt8pMheLxYKmpiZs3rw5o+x2uz1jt0htG+ps5c9V9kLKk6y/vz9je+9SKrtgwajnokxEROlcLldiG+dkWpBQSPO5tkOlzWbLet5ms+W9uWoURUkEAlrQkp6PzWZDa2vrvMuWTpbllHy1bolcZde6FY7nHhmKoiS6PRaLsgsWKvQSQpwNQUSUYnBwMOPbO6DeHDs6ci3Mm93AwEDWwCM937l4PJ7Ejdxms2V80wcAp9MJt9uN3t58GxpnpygKBgYGUva+6O/vzzs+QzvX39+fM81Cbdu2DZs2bTpu+R+LshvgyKmTRFQs4ZERRP0KKs89BwAQfP116GpqYFi1CrFgEMHXX4fx9DOgr61B5OhRRI4eReV556lpPbugMxlhWL0aIhzGzKuvwrhuHfR1dYj4fIgcPozKCy4AAIR27wYqKmBcswYiGsXMyy/DuGYN9PX1RXkd2k05+du0x+OB3W5He3t7wcGCx+OZs+k91zf3wcFBdHd3J3a6dDqdefORZRkOhwN2ux2bNm3KGlAkl0u7yXs8HgwPD2P79u0prSYejydnN4nGYrFgx44dedMcq4GBgZx1U0pl17Jg1OsQE0CU4xaIaIGUvm3YlzQ6/sDXvg7vr38DAIgcPozdH2nBzM6dAIDAn/6EPZ/+TCLtoc5OjP7yl2pavx+7P9KCqaEhAMDYX/6C3dd8dDbttddi9PobAACx6Wns/kgLJh9/vGivY2BgALIsY9u2bejt7U00szudzoIDhYVqampCR0cHHA4HWlpa5nVNR0cHGhoaMsYepLNYLGhpaUFLSwtsNhsGBwezNvd7vd5jKnsxKIpyzDNPjqeya1mo0KvbXoejMeh1+hKXhohOZvI1m1D33vcmfl99/XXQ1dQAACpWrsQZt/XDePoZAID6q65Czdvelki7qqsLOpNRTbtkiZp23ToAgPkDH0D1pZfOpv3e94AK9eNaV1Wlpl2zpmivw+VywWazZUwLPFZWqzVjSiOgBiVutxs9PT3w+Xzo7OzMG4y0t7enjCdwu905x044nU5s2LBh3gMxrVYrOjs70draCr/fnzhusVjmHI8wn9aHZOljLXLp7e0t2v9BsZVdy4JBr75kdkUQ0UIZli9PdEEAgOmss2BYtQoAoDOZUHXhhdDXxoOHpUsTXRAAYLKcCcPq1QAAyWBQ09bVqWkbGhJdEABgPOOMRHAg6fVq2iJ1QQDqTTzbeIVjZbPZso7kt9ls6OjoQEtLCxoaGuZstbBYLCnBweDgYM60VqsVbW1tBQ12tFgsKYMotTLmCzi0c+ldBfMJMvJxu91zjvMopbINFiLcTIqICIqiZIxXWChtwGCuQYCKouQdW5CN2+2es3ne4XDA5/OlDFicb97peeQqu91uR1tbW0YLx8aNGxcULPh8PgwMDKC7uxvd3d2JAaDd3d3HdTDlfJVdsGCsmO2GICIqd9p4hXw3YrfbnbhpzXfWgcvlQldXV0YLQ75pgfmmC6avtJitm0OWZWzZsiVrPj6fL+O4xWKBLMuJwYq9vb2J9Ru6uroybv7aa88WjGhrMKTXT3d397y6FrRWF+2hdXNoLTGlVn5jFnRqfMTpk0RUzhRFQW9vL/r6+gCoN7Vs3QKKomDz5s0YGhpKmU0wF4vFgqGhIdjtdrhcLjQ2NiZaE5xOZ0oQoS3h7Ha7oSgKuru7E+e05Z49Hk+iqd9ut6O/vx+KoiSWdNa0tLQkXlNy3loTv3bzlmU5ERg4HI6UKZM2mw3bt29HV1dXxqwOl8uV8zW7XK5Eq4C2vHVLS0vBrSj9/f2J12C327Fx48aSz5CQhFi8zfGSJJkBBAKBAMxmc1HyfHzYi49teQIPfuPdOHNpTVHyJCI6VWk3Lm2hpcU4Up+yGxsbQ706tqVeCDG2kLzKthsiwpYFIqI5ybKM5uZm2Gy2BQ/io5MXuyGIiCgnm80Gl8uV6H6Ya3wDnZrKLliYnTq5eLtfiIgWk0JnF9Cph90QRERElFfZBQtaywK7IYiIiOan7IKFCnZDEBERFaTsggWDtjdEhC0LRERE81F2wYJRW+45NhssPL8/gPfd8Ah2HgyUqlhERESLVtkFCxWJMQuz3RC3PrUXrxwZxy1P7ClVsYiIiBatsgsWsnVDvHBAbVF4eq9SiiIREREtauW3zoIusxvigDKNhhojdh2dRCwmoNNJpSoeEdEJ5/F44HA40Nvbi5aWFjQ3N8Pr9SZWa3Q4HMe8EJO2e6K2V4Isy2hra4Pdbk+s36Dt39Db24uGhobEJkrA7N4QFosFLpcrI236hlJ2ux29vb1oampK7BvR09OD7u5uWCyWRN5erxeKomRsUKVRFAVdXV1obm4GMLvJVfqmUNp+GV1dXYmya3tPJJfHZrOhs7MzY7fKk4YQYtE+AJgBiEAgIIppfefd4qbHdwshhJgORcTp9rvEF28ZEqfb7xKHlOmiPhcR0clgaGhIqLeEVE6nU8iyfEz5Wa1WMTQ0lHJ8eHhYtLS0CKvVmnGN1WoVbW1tGcf9fr+w2WwpxxwOh5BlWXR0dGSkz3bMZrNl5D08PCxkWc4o49DQkLDZbMLv96ccdzqdGeWYq+y5ypONy+USLS0toqenR7hcLtHR0SGcTue8rs0mEAgIAAKAWSzwflx23RAAUKGXEt0QB5VpAMCb16tR727vZMnKRURUKgMDA1m/9VqtViiKArfbXVB+V1xxBRwOR0aeyd/u0zU0NGQ9LssyNm7cmHHM6XSiu7s7Y7+K9J0ic7FYLGhqasLmzZszym632zN2i9S2oc5W/lxlL6Q8iqJgYGAA7e3taG9vx/r16xfF9tRAGY5ZANSFmcJRLViYAQC86cwGSBKw1ztVyqIREZWEy+VKbOOcTAsSCmk+13aozLWtss1my3tz1SiKkggEtKAlPR+bzYbW1tZ5ly2dLMsp+WrdErnKrnUrHK8NtXbt2gUhBIaHhzO6PEqpLIMFo16HSEydDXEooLYsnN5YjRV1ldjnZ7BAROVncHAw49s7oN4cOzo6CsprYGAga+CRnu9cPB5P4kZus9kyvukDgNPphNvtRm9vb0FlBGa/ySfvfdHf3593fIZ2TttYq1yU3QBHQO2GCMW7IbyTIZgrK2Cq0OM0uTLR0kBElE84FIVyeHF8uZBXVsNg1B/z9dpNOfnbtMfjgd1uR3t7e8HBgsfjmbPpPdc398HBQXR3d8Pr9aK/vx9OpzNvPrIsw+FwwG63Y9OmTVkDiuRyaTd5j8eD4eFhbN++PaXVxOPx5Owm0VgsFuzYsSNvmmO1bds2NDQ0wOfzYXh4eNFs4lWWwUJyN4R/KgS52ggAWCVXJcYwEBHloxyewrb/Oj43jEJt+rdmLFtXd8zXDwwMQJZlbNu2LXHMYrHMeaM+HpqamgoOTjo6OtDT04PNmzfnLbPFYkmMAXC73ejr68vaheH1egsrdJFoQYvWetHb24vW1taS/D+kK8tgIbkbIjAVxpJqAwBgtVyFnQe4iiMRzU1eWY1N/9Zc6mIAUMuyEC6XCzabrWh95FarNWNKI6AGJW63Gz09PfD5fOjs7MwbGLS3t6eMJ3C73TnHTjidTmzYsGHeAzGtVis6OzvR2toKv9+fOG6xWOYcjzCf1odk6WMtcknv/ti0aVOiDvK1mJwIZRksGPS6RDdESstCfSUOBmYghIAkca0FIsrNYNQv6Nv8YpLeb79QNpsta5++NiBR62KYqwUh/eY5ODiYM1iwWq1oa2tDa2vrvMZDaPlrgyi157LZbBgYGMh5jRaMpHejzCfImEt/f3/K7ActQPB4PCVfn6E8BzhW6BBMBAuzLQunyVUIRWLwToZKWTwiohNGUZSM8QoLpQUeuQYBHss3ZbfbPefCUA6HAz6fr+DAJ7k1QssjV9ntdjva2toybt4bN25cULCgKApaW1tT8tBaJI51QaxiKstgocqgx0w4CgBQkloWTquvAgCOWyCisqGNV8h3Q3K73eju7kZ/f/+8Zx24XC50dXVlfEtXFCWxGmK6XMcBZKy0mK2bQ5ZlbNmyJWs+Pp8v47jFYoEsy4nBir29vYn1G7q6ujJu/tprzxaMaGswpNdPd3f3vLp3ZFlGR0dHymvUVtQsdRcEUKbdEFVGPaZCEQCAMhWGnGhZqASgrr3whjUlKx4R0XGnKAp6e3vR19cHQL2pZesWUBQFmzdvxtDQUMpsgrlYLBYMDQ3BbrfD5XKhsbExcdNzOp0pQYS2hLPb7YaiKOju7k6c05Z71roKtFka/f39UBQlsaSzpqWlJfGakvPWpnJqN29ZlhOBgcPhSJkyabPZsH37dnR1dWXM6nC5XDlfs8vlQnd3N+x2e2J560Ju9p2dnSmv3ev1LorBjQAgCSHmTlUikiSZAQQCgQDMZnPR8v3CLUOYCEZw02cvw7n/fi++feX5+PRbz4AQAud9517Y338ePvv2M4v2fEREJ6v+/n709fUlFlpaDE3iND9jY2Oor68HgHohxNhC8irrboipUBShaCzRsiBJEk7j9EkiogRZltHc3AybzVaUQXx0cirjbogo/FPqQMYl8TELgDoj4lCACzMREQFqk7zL5Up0P8w1voFOTWUZLFQb9ZgORaFMhQGkBgunyVV4fWSiVEUjIlp0FssqglQ6BQcLkiR1AFDiv8pCiO48yZOvcwDQhq/6hBAlW1i7yqDHdHg2WNC6IQA1WPjra6OlKhoREdGiU1CwEA8UIITojf9ukySpRwiRcykrSZJkANsBXCGEUCRJsgIYAlCyVY+qjBUp3RApwUJ9JUbGgwhFYjBWlOWQDiIiohSF3g07ASQmkQohBgDMNYHUAaBPCKHEr3EDyNza7ATSuiH8UyEY9TrUmmZjptPkKggBHBnjuAUiIiKggGBBkiQL1G4HJcu5fEt/tQHolyTJoqWLBxnZnsMkSZJZewA4LmupVhn1CEVjGB0PYkmNIWVp59m1FjgjgoiICCisZSHX8FcFgJztRDzAAABrPI1HkqSePMFFJ4BA0mN/AeWbtyqDupXrAf90yuBGAFgtV0OSgD2+xbH1LBERUakVo1PeB6AhxzktWFCEEG4hhAeAHUCuJam6ANQnPY7LOorV8X3f9yvTaKhJDRaqjHqc0ViDlw4taP0KIiKiU0YxgoVcgUKyQe0f8W4MOVvrghAiKIQY0x4AxotQvgxV8WDhoDKNJWnBAgBccJoZLx5ksEBERAQUFizkWrZLznMu13EFubs1jrtEN4QyjcZswcIqM3YeHEM4GjvRRSMiIlp05h0sxLsQlKRxCMnnsg5YjF/jQWZgICOpteFEM1epUyWFQMaYBQB497nLMBGM4PFh74kuGhER0aJTaDdEF4BE94EkSS1ImkoZn/GQvm2ZHcA1adcMxKdQlsTSWlPSv7O3LJy5tAY/f/B1+CdDJ7JoREREi05BwUJ8tUZZkqSW+E2/OW1BJhuA9rRr+gEMS5LUEQ8kmoUQJV1nwVw5u67CmiXVGeclScIPr74IT+9VYP2hC5/bugPjM+ETWUQiIqJFoyy3qAaAM751NwDg/q++E+esyL6cw8j4DLa/NIL/vPslvP+ilfhx6xuLWgYiIqLjhVtUF8E5K2oBAOuX1eZMs7yuEh+7bB3sHzgP/UP7MTzKDaaIiKj8lG2w8PvNb8b2r78Let3cW1RsalqDpbVG/Pax3ce/YERERItM2QYLjbWmvK0KyUwVenz8Taejf2g/xy4QEVHZKdtgoVDXNK/FTCSKu547VOqiEBERnVAMFubpNLkK7zpnGfp27Ct1UYiIiE4oBgsF+GjzWjyzT8Erh4/LKtRERESLEoOFAlx+3go01hjZukBERGWFwUIBjBU6fGTDGvzx6f2YCUdLXRwiIqITgsFCgT7+pnUYn4nglif2lLooREREJwSDhQKd3liDFusa/PKhYQSmOI2SiIhOfQwWjsFXN56DUCSGH9z9YqmLQkREdNwxWDgGK+sr8e8fOh/9Q/vx4MsjpS4OERHRccVg4RhtalqLd5+7DN/sfw4+bmNNRESnMAYLx0iSJHR/5A2IxGLo/ONzWMy7dxIRES0Eg4UFWG6uRNeHL8Z9O4/gNveBUheHiIjouGCwsEAfuHgV/sG6Gt+/cyf2+aZKXRwiIqKiY7BQBN//+wtRX2XA17Y9g2iM3RFERHRqYbBQBOZKA67f9EYM7vGj55HhUheHiIioqBgsFMmbLI34/LvW4/r7X8Xz+wOlLg4REVHRMFgooq/azsEFp5nRfvMgRsZmSl0cIiKiomCwUETGCh16P9mEqBD43E2DmA5xsykiIjr5MVgospX1lfj1p5vx2pEJfP6WIQQjDBiIiOjkxmDhOLhodT22fKoJj3u8+PKtTyMcjZW6SERERMeMwcJx8vazl+JXn7DiwVdG8LVtz3JKJRERnbQYLBxHl5+3Aj/96KW45/lD+GY/AwYiIjo5VZS6AKe6D1y8CtdHY/jatmcRDMdwwzWXwFjBGI2IiE4evGudAFddshq/+LgVrhePoO3mQUyFIqUuEhER0bwxWDhB3nfhSvz6M014apcPH/nl49xHgoiIThoMFk6gd5y9DLd94a2YCIZx1c8fwyOvjpa6SERERHNisHCCnb/KjDu/9HZceJoZn/rNU/iPP7+ImTDXYiAiosWLwUIJLKkxYus/XYZ/v/J83PLEHlz988fw4sGxUheLiIgoKwYLJaLTSfjcOyy440tvQ0wI/N3/PIque17i4EciIlp0JCEW79x/SZLMAAKBQABms7nUxTluQpEYtvzVg59ufw1La034zocuwPsuXAFJkkpdNCIiOkmNjY2hvr4eAOqFEAtqvmawsIjs8U7iu3/aiYdfHcUla2V0vP9cvHX90lIXi4iITkIMFk5xj752FN33vYzn9gfwFksj2t5lwbvPWcaWBiIimjcGC2VACIH7dh7GLx4axnP7AzhnRS0+93YL/u6Np6HKqC918YiIaJFjsFBGhBB4apcPW/66CwMvHUGtqQJ/98ZVaNmwFtZ1MlsbiIgoKwYLZWqfbwrOof24bWg/DijTOHNpDd534Uq8/6KVeMPqeuh0DByIiEjFYKHMxWICfxv24s5nD2DgpRH4JkNYaa7Ee85bjred1Yi3WBrRWGsqdTGJiKiEGCxQQiQaw47dfty38zD++toohkcnAagrRb7F0ohL1sm4ZI2MtQ1V7LIgIiojDBYop8OBGfxt+Cgee92Lp3Z7sc83DQBoqDHijWvqccFpZpyzog5nLa/F+mW1qDRwsCQR0amopMGCJEkdAJT4r7IQonuO9DYA7QBcADwANgLYIYTon8dzMVhYIO9EEM/tD+CZfQqe3a/g5UPjODw2AwDQScDpjTVYv6wGa5ZUY82SKqxtmP1prjSUuPRERHSsShYsxAMFaAFCPBBoFUK057mmBcAWADLUYMEhhOid5/MxWDgOAtNhvD4ygdeOjOPVIxPwHJ3Afv809vunMBOOJdLVmiqwvM6EpXUmLKszYVmtCcvN6s/GWiPqqwyorzLAXGWAudLAVgoiokWklMGCH8CZQggl6ZgQQuTsDI8HCwPJ1xTwfAwWTiAhBI5OhLDPP4V9vikcGZvByFgQoxNBjI7HHxNBKFPhrNebKnSoq6xAtbECNaYK1Bj16k+THjXxY9XxY1UGPYwVOhgrdDBV6GDU6xK/a/82VegT5w16HXQ6oEKng14nQa+TUKGToJPiPzkThIgoRTGDhYr5JpQkyQK120HJcs4mhBhYSEGo9CRJwrKaCiwzVsB62jKgwghM+YDJUSBaAUQMQNSIkLEOvuqzMDYWgG74fsxMz2BmZgYzoSDC4TCebLgKU+EY1o/cj9rJwwgHIohEIohGo3hQWPFseC3OCr+Ky/EkJBGDHjHoILBHLMdN0ffBgAj+o+L/oEcMeikGCWpAe234UwigFp/V/wVv1A1DgkicuzP6VjyIZlyiG8Zm/Z8hSYAEQC8BI9JS/NTwz5AkCdeGr4MJQeiA+LUSfm76HA7pV+Lq0N14c3QokacE4DHDW3Bf5QdwenQPNk//L5JDkimpBj+W/w0SgK+MXYclMV/8KtXvzf+EXYaz8c6pAbx9+oGUun7BdCnurG3FkuhRfEG5HuljT/+r8b8AAJ8O/AKrI/tTzt1Zuwk7TZdiw/Tf8N6pP6ec8xjORp/5szDFZvB13/eReCFxNzZ8B9O6GrSObcVZoZdSrh2ouRKD1e/AecHncPXYrSnnDlesxm+XfBkA0DH6beiQuq36/y75GrwVK/DBcScunhlMOfd49XvwSM37sS40jI8FUhsVx3UyftHYCQD4kvc/URtL/Tz7fX0b9hrX492Tf8Gbph5KOfdcZTP+UteCZZFD+Kz/xpRzUakCP176nwCAf/L/BMsjB1PO32H+OF4xvQGXTT2CyyfvTjn3mvEC3Fb/aVTHxvEV7w+R7vrGaxHSVeIa5X9xZvi1lHP3114Nd9VbcNHMID407kw5d8CwDjfLXwKEwLeOfisj3181dEDRN+Lvx36PC4LPpJx7tNqGR2s2whJ6BZsCv0k559c3oqehAwDwr0evRZWYSjl/k/xFHDScDtvEnWiafizl3FDVW+CqvRorw/vxGeVnKeeCUiVuWHotBAQ2+67D0uhIynmn+dN43XQB3jr1AN41eV/KuZdMb8Ad5o/DHFXwJV9XxmvtXvqfiEoV+LjSg3VhT8q5e2pb8GxVMy6ZfhIfmPhjyrm9Bgt+J7dDL8LoOPrvGfn+rOHfMKGvxz+M3YJzg8+nnHuo5v14vPo9OCe4Ex8Zuynl3Kh+Jf634asAgK8f/S6MIphy/jdLvoIjFavxvvHbYZ15IuXcU1XvwPbaD2F1eDc+pfwy5dyUrhY/afwOAOALvm7IUW/K+T/U/zN2Gc/BOyZdePtU6i30BZMVfzZfg4bIKNr9P854rV3LHACAT/l/jtWRvSnn7qz7KHZWXooLfQ9kXHes5h0sALDkOK5A7WLIZ5MkST4ADQDWCyHs2RJJkmQCkDznr66A8lFoCpg4Akz7gGkFqF0OrLwYGDsIPPkr9VhwDAhNApCAj29Tr9v6d8DhF4DwFBBRxzPgY38Azv0AMPgb4IEfpDyN8YKrsHLTTVgJL/DQv6SWQdLjPR/9GlBhAm7+LnB0ByDpAJ0e0Ovw6fe9G3jDe4EXxoHtz0FIegjoICQdQivrsfmK9yAUmsFpf7weMSEhJukRE+rtu/Etb8CMaQksLzyGJaNRCEANFwTQsHYl3rnyQjR4g7hwWKeeE+r5ZSYTWixrEBPA2leMqIiJRJghIOFt65bCb1qO9SMy6gK1QPw4AKw112NDwxI0zozBcEhGcjucQVeNs5fXIiaAirAZIhoFBBJpGmqrMFNZiRqpBrFYfUo1maprsKq+EuZIFWLT9fHnnLXCXAkA0AfNiIRSr62rqcbyahNqdNWIRFLP6U11WFZngiEmEJ6UM94ijTUmzOhN0AVrEU77s62qqkFDjQnVumqEZ9KuNdShocYEIQTCRjMkEUs5XVdlQtRggC5Ui1A0tUwGUzXqqw2o1psQmko9F9HXQa5Wx8ZExusRiqVuhFtdVQnZZEBFpBqhSGqZdKZayNUG1IQrEUp7rVFUJPKNTpoR0qXeQKsqqyBXGWCMVSMYTnut8Xwro0YEjWnnANRXGxHWGYDpOgSl1PPGyirI1QaYUI1gMPVczGBWyyRE1nzrqoxAhQFSsBZBkXq+orIGcrUBVbpKBKdTz0X09YnXGjLK0AljyvmaqkrIRgN0kVoEo6nX6k1qvjUhE4JTqefCkimpDusRjKS2KFZXVWFJpRGGaA2C4SUp5yRTHZZUG1ETNSE4kXoOAORqE2KSHmLajKCUet5UVY0l1Ua1DkOp52JGM5ZUG6EXEoLGzHzrq00w6I1AsA5BkXreUFmDJdVGVOmqEZxJy7dCzRdQ61CIUMr5uqpKhAxG6MO1CMZSr9VX1mJJtRG1oUoEp1LPhXXViXwjE2YEo6l/N9VVVVhiitdhJPVaXTzfukglgpOZr1XLV0yZEdSlnq+sqsaSKiPGRwu5xec3726I+PgEV3qXgyRJw8gzDiHeIgEhhCf+exuAjUKI1ixpvw/ge+nH2Q0Rd/h54OirQGA/MH5EDQze+Q1g+fnAwLXAo9enpr/0E8BVPweOvg78rgWokgGTGTDVAZX1wNW/UNM9tUUNIgw1gLEaMFQDp78NMK8ClH3q81UYAb0RMRgR0dchYlqKyEwY0ekJREUFYtAjFpMQi8YQjQrEogKxSEz9GRWIRmf/HYvGIGJqt0fiZ/K/Y0K90aecR/x48r/Vc4gJ9UYbfyur/07+JelGnPh99kTGn0D8QPI1IjOD7PkmZbaIJxqduk7iSj95S06L1dT0BK755tuBE9kNkUdDvpNakJBkG4AeSZKydWl0AUi+49UB2I9yEYsBgb3A6Cvqw/s6MHkU+Fi8Sdj5GfWYyQzUrgDqVqqtCQBw/oeAZeeprQnVjWpgUN2onlt6FmJffhozE2FMj4cwMxFGcDqC4N8OITQdQXDqCgSnI/F/qz/DoQOIhPYiEo4hEooiEppEJDSGWHRhH2lSfHyBpFO7PSSdpHYZSEnHJMSPZ0kX/wkpno80m2/iORL/lFJ/Twpz09ecUPPMcV1KHtJsijzPI2VcSCdEiet7IU/PdwoVWzHHchUSLKTf9DVynnOQJKkleZqkEEKJf1BbALiT0wohggCCSdcWULyTUHAC2Ps4EA0D530Q8O8CfmZVzxlrgcb1QMN6IBoB9BXAx/qA6gb1kSQSjmKi4nyMG87E+KEZjHmnMeGbwNSYF1NjYUyNhzAzHsr6pavCqIOpqgLGagNMVXoYqwyoNhtRYdKjwqiHwahDhVGPCoMeFUZd/KGPP3TQV+ig1+ug00uJh75C+332uJZG4kBEIqITYmxsDPh6cfI6ltkQG5JbC/LNhpAkSQbghzpOwZN2bMlcMyROydkQ/t3As38APA8D+58CYhFg/eXAJ28HYlFg+EFg+XmAeXXG15RwMArfoUn4Dk7Ae3ASvgMT8B2cxGQgqX9NAmrqTahrMKGm3oQqsxHVZiOq6tSf1WYjKmsMMNVUwFhVAb0+tY+YiIhODSWZDRHXBcAGoBdITItMjFWIj09o0dZhiLcidKd1RbQB6D+WqZQnnWgE8DwE7HoIWHER8MaPAoEDwBO/AM54B/D+/wYs7wYaz1LT6/TA2TYAQHAqjNG94xjZM46RPWMY3TeBsaPTiA/gh3lpFRpPq8F5b12F+mXVqGusRF1DJWqXmKCvYABARETFc6wrOGo3/+bkmQ3xwYt2IcT6pGMy1ABB05hrNkSW5zo5WxYiQeDJHuDx/1EHIZrXAJdtBt7+/9RxCQCgS72hi5jAyN5x7HnBiz0veDGyZwwQgMGkx7J1dVi2rg6Nq2vRuLoGS1bWwGDiAkhERJQb94ZY7F6+G+j7JGD9JND0WWDlG7KOfApOR7DvRR/2PH8Ue3Z6MT0ehrGqAmvPb8C6Cxuw0lKPJSuq2c9PREQFY7CwWETDasvB5ChwZCfgHQZs31Onb/l3AQ2ZS1NMBoLwPD2K4adHcOi1AGIxgYbTanDGxY04/aKlWGkxQ8dxBEREtEClHLNAmn07gN9+EIgmDS484x1AcFxdxyAtUJiZDGPHXbvwwsMHAABrzl+Cd1xzNtZd1AhzY9WJLDkREVFBGCwUSpvGuKYJuPI6oO40oGYpUL9G/ZmePBrDzkcO4Kk/70IsJnDZ35+JC9+xGpU13NGRiIhODgwWChGNAL+9Erjww8CbPw9YP5U7aTSGXc8cxVN/9sB/ZArnv3UV3vT3FtTUm3JeQ0REtBgxWMjmsZ8Aex4H3vsDYOnZs8f/9lN1bYT3/iDrZYHRKQy7R7H/FT8Ova4gEoph7QUN2PjZC7FsHbe5ICKik1N5Bwvjh9Ulk5PtfQJwfVddUvm3HwLaH1bT+DzAww7gLV8C1l6WckksGsPf/jiMZx/YhwqjHqedJaP5yjOx7sIGLF3DIIGIiE5u5RssPN8PbL8W+NfnUqc11q8F3v1vahfDlvcAd3xR3YHxT/8C1CwH3t2Zko0QAttvegmv7RjBWz68Hhe/ew0MRq6BQEREp46yDRZEdSPu9HwWVb94HO/90ltnT9SvBt4dXzPqo79Td2AUUaBuFfCebwPGmpR8dj5yAK8+eQTv/dyFOLtpxQl8BURERCdGeQYLf70Ooy+8hv2hTcDzM3j7WAjVZqO6TsLO24HL2oBKM7B6w+w1Lb/OyMZ3aBKP9r+Oi961moECERGdsspz9Z8jL2J0rD7x6+jecfUfr90PPNwN6I0pyYUQSF+8KhqO4f5f74S5sRJv/chZx73IREREpVKeLQvjh+CPWmGunkAwqMfo3jGcflEjsPtRYE0zYKhMJPUfnsRffvU8psZCuPS963DJxnXQ6SQ81v8a/Icn0fqtJo5RICKiU1p5BgtjBzEVa0DdqhWo0+kwum9C3eBp96PAmz6fSCaEwMBvX0I0KnBW0wo8+ScPXn3qCGpkE/a96MO7/vFcznYgIqJTXvkFC0IA44cxFalF9coamGoMOPiaAozsBGYU4Iy3J5Ie9oxhZPcY/u7Lb8S6CxtxwdtWYcfduxGcCuO9/3whzm7mOAUiIjr1lWewcM0tmLrZhMY6I8z7+/HSkTchpm+E7rJ2tRsi7tUnD6O2wYS15zcAAJafbsaVX3xDqUpORERUEuU3wFGnA862YWpCoLreCDn2OqJRCRPSauCD3YnxCkII7NnpxZlvWMYtoomIqKyVX7Cg7EX0gR8jOBVBtdkIef2Z6uG/3gbMBBLJAqPTGPfOYO35S0pVUiIiokWh/IKFo69h6sFfAACqzSbUvflq6BCG8re7gUgwkeywRw0cTjtbLkUpiYiIFo3yCxZmApiJqWssVNUZoDvtYtQ3GqCc/2Wgdnki2ZFdY5BXVMNUza2kiYiovJXfAMeZAIKiFgBgrFJfvrxmKQITsZRkI7vHsOJM8wkvHhER0WJTli0LoYpGAICxMh4sLK+GMjKVSBIJR3F0/wRWnMFggYiIqPxaFhrXI3xaDBgBjFXqyovyimqMeWcQDcegN+hwdN8EYlHBlgUiIiKUY8vC+X+H0HmboNNL0FeoL19eUQ0IdQYEoI5X0Bt0aFxTW8qSEhERLQrlFywEDiAUCMBYVQFJUtdPkFdUA0CiK+KwJ4Dlp9dBry+/6iEiIkpXfnfDu/4fQjvvh7FydvOnqjoDjJV6KEdmg4WVZ9bnyoGIiKislF+wMBNACDWJmRAAIEkS5BXqIMdx3wwm/EGsXM9ggYiICCjXYCFWnZgJoalfXg3lyBQODSsAgJUWBgtERERAuQYLoiqlGwIAlq6pxejecby2YwTL1tWh2mwsUQGJiIgWl/ILFkJTCEVMMKS1LJzxhqWIhGLY/dxRnLVheY6LiYiIyk/5BQvf2oOQcXnKmAUAaFhVg6YPnoHTL27ERe9cXaLCERERLT7ltyiTJCE0E4WpSp9x6k1/bylBgYiIiBa38mpZ8O8Bet6J8ORMRjcEERERZVdewcLUUeDQswgFRcZsCCIiIsquvIKFmQCiogLR6Oy+EERERJRf2QULIVEFAGxZICIimqfyCham/QgKdXMop+cPJS4MERHRyaG8ggXLuxF497UAgPsP3oud3p0ZSSbDk9g3tu9El4yIiGjRKq9gocEC4xnvBwDojBIeP/h4ymkhBL60/Ut4YN8DpSgdERHRolReHfcv34Phl0YBrMX5K8/Bk4eexOcu/lzi9LAyjKEjQ/jk+Z9EOBrGvol9sNSfemsvCCEQEzHoJB0kSUI4FkYkFoEQAgLqOQGRSCcgUGuohVFvxERoAtORaSyrXgYAODRxCJFYJO/zralbA0mScGTyCCp0FWisakQ4GsbhycN5rzNVmLC8ejliIoYD4wfQWNWIakM1xkJjCMwE8l5bX1kPs9GMqfAUvNNenFZ7GvQ6PUamRhCMBPNeu6JmBYx6I7zTXoRjYaysWQkAc7Y46XQ6rK5VF/Q6OHEQNYYa1JvqMR2ZxtGpo3mvrTZUp9TLsuplqKyohH/Gj/HQeN5rG6saUWOoSdTLWvNaAOr/TTgWznvt6trViXqRIGFZ9TJEYhEcnDiY9zqj3piol71je7GkcgnqjHWYCE3AN+PLe63ZaIZcKWM6Mo3RqVGsql0Fg86Ao9NHMRWeynvt8urliXqZjkzjtNrTAAD7xvdBCJHzOglSSr2YKkxoqGxAMBrEkckjeZ+zqqIqpV6WVi1FtaEagWAAgWD+92F6vaytWwtJknB48jBC0VDea5PrJRqLYkXNCsREDPvH9+e9rkJXkVIvZqMZ9aZ6TIWncHQ6//uw1libUi8ra1Ym/hYmw5N5r02ul/HQONbUrQEAHJg4gGgsmvfa5Hqp0FVgadVShKNhHJo8lPc6k96UUi/JfwvKjJL32npTfUq9JP8tzERm8l6bXC/JnxF7x/bmvU4n6VLqpaaiJvG3MDI1kvfaGkNNol4OTh7EiuoVqKyohG/Gl/MzYnws/2dHIcorWHj6Zvz2kBGX4tO4eNVF+PVrvYjGotDr1JkRD+1/CFUVVXj7mrfDscOBRw88irs/fHfiPADc+tKt+PXzv8Z0dBot57Tgaxu+lvE0f3r9T9jp3YnpyHTipqw9JEgIRoPoaO5AvakeP3/m55gITcB+mR1T4Sl89O6Ppt60027ga+vW4jfv+w0A4Mo/XolPXvBJfPS8j+LBvQ/i249+GzHEMq7Xjn3svI/Bfpkdzx19Dp+45xO47e9vwzlLzsF/PvGfuO212/JW3S+u+AXeseYduPnFm9H/aj+2b9oOAPjUvZ+a86b/7KeehQQJ33j4Gzij/gz84G0/wL7xfbjqT1flve7Nq96MLe/dgpnIDD54+wfxo3f+CO8/8/340+t/QveO7rzXfn3D1/GZiz6Dxw4+hq899DU89rHHYDaa8d2/fRePHXgs77Vavfzs6Z/hVf+ruPXKWwEAV95+JQRy35CWVS3DA5vUVqlP3/tpXLX+KvzLpf+Cp0eeRrurPe9zXn3W1Sn1ctMHbsKlyy/Fr5//Nba+uDXvtcn18rOnf4anPv4UAOCL27+I15XX816bXC9V+irc8J4b4Jvx4crbr8x73cVLL07Uy4du/xC+85bvoPWcVrj2uPDdv30377Xtb2hPqZf7P3I/VtWuwn8/9d+4b/d9ea9NrpdHDjyCO6++EwBwzZ+vwXg49wdjVUVVSr28edWbYb/Mjld8r+Dj93w873Pa1tlS6kX7W7j15Vvxi2d+kffa777luyn1ov0tfPPhb+KZ0WfyXptcL4FgIPG3MNf/zZn1Z6bUS9sb2lL+FvL5xPmfSKmX5L+F+X5G3PryrXC+4kz8LXzm3s/M+zPimw9/85g/I668/cqSf0Z86PYPzfsz4jP3fqYonxG/ef43OT8jotP5g7RCSPmi8VKTJMkMIBAIBGA2mxee4W/ej8cn3wr3zrfD+u/VaNu+GXdcdQfWy+sBAB+/5+NYXrUcN7znBuwb34dgJIizlpyVuPy3L/wW1w1dh6vPuhqralZhZc1K/MPZ/4Cd3p249m/XYusHtqKqogpfeeAr2De+D9WGauglPWIilvKorKjEj9/1YyyvXo4/vPwHTEem8U8X/RNC0RBudN8IHXSJb/1agKH9e4lpCf7x/H8EAGzduRWXLL8Eb1z2RuwO7MZD+x6CJEmQIKVcr4P677OXnI1Ll18K34wPD+97GJevuxz1pno8N/oc9o3vS1yjXa+DDpAAHXS4eNnFWFq1FHvH9uLw5GFctuoyAMAzI88gFA1BkqSc1d60ogmSJOFF74uorKiEpd6CqfBU1jEjycxGM85tOBfRWBTuETcs9RY0VjXi8ORh7BvP/y1/bd1arKxZCd+MD8PKMC5dfikqdBV4xfcKxkJjea+9sPFCVBuqsSuwC1ORKVzYeCEAYMfhHXmvM+gMuGT5JQDUellatRRr6tYgEAzgVf+rea9trGpMqZfzGs5DnbEO+8b24fBU/g/a5Ho5MHEAG1ZsAAC8cPQFTEem816bXC86SYezl5yNUDSEZ0efzXtdtaE6pV5ON5+O5dXLMTo1it1ju/Neu6pmVUq9vGHZG2DSm/C6/3X4g/681ybXixJUcPGyiwEA7iNuREXuD0adpEupF7PRjHXmdZgITeAl30t5n1M2ySn1crZ8NuRKGQcmDszZApNeL8l/C3N9U0+ul6iIpvwt5FOpr0ypl9NqT0v5W8hnRfWKlHrR/hZ2B3ZjdHo077XnLDkH9aZ6HJg4gNGp0cTfwrOjz87ZiqLVy0vel1BZUYkz6888KT4jdgd2YyoyhQsaLwBQ2GfEs6PPYmnVUqyuXT2vz4ilVUtT6mU+nxET4xO4/JzLAaBeCJH/Rc2hvIKF/7kMz+s+jUd3XoBP3NCEt/3+bfjB236Aq866Ckenj+LybZfjh2//If5+/d8nLonEIoiJGO7ZdQ++89h38LmLP4d/tf5rSrbPjz6PPw3/CZsv3owVNSsghMh78yQiIjrexsbGUF9fDxQhWCirbogHowE8FfSh0aRHnbEOZ9SfgeePPo+rzroKD+97GJIk4R2r35FIH46Fcc1d1yAcDWPP2B60nNOCr1z6lYx8L152cSKSB8BAgYiITikFBwuSJHUAUOK/ykKI/B1Dmde7hBAbC33eYnh82Toc3RPDSpM6BuEbTd9AY1UjAOChfQ/hkmWXYEnlkkR6g86Ab2z4Bm566Sa0ntOKT1zwCQYCRERUdgoKFuKBAoQQvfHfbZIk9Qgh8o/MmL2+BYCt4FIWyStLTsO5R9bAEFSDhXeueScAYCo8hccPPY4vX/rljGveuvqteOvqt57QchIRES0mha6z0AmgV/tFCDEAoG0+F0qSJANoKPD5iiYy5cWrvpexRNcIQ7xlISZi2PLcFjy8/2G0nNOCy9ddXqriERERLVrzblmQJMkCtdtByXLOFg8c8tkEYBuAnjzPYQJgSjpUBwC33PcvqKwyQADqo7oRYulZEOEpiIPP4t2163CeaSl2zozi4ck9+OLfqdNI+rbbcWjyIAQEDigeTIgpNOobYahUgwWdpMPD+x/GpnM34VuXfWu+VUFERFRWCumGyLU6kQJAznehJEk2AHMFE4DacvG99IO/Hd0BQ5UOEgBJAFKFCdLok5BEDNKUD+uGH8Z502HsrjLg9roqfDF+3eP7H8LLkXH1OgAt8rmomTADptlxB70be1FtqJ5H0YiIiMpTMWZD+DB394IshPDEuyLy6QJwfdLvdQD2D3ziyXlNnbwy/tDc+OknM9LccYMb1ebZxgsGCkRERPkVI1jIGyhIktSmDYicixAiCCCxFu/xmHkQnonCsEw/d0IiIiICUNgAR0+O43Kuc5IkWQEMFlim4yocjCYGOBIREdHc5t2yEO9GUCRJsgghPGnnco1HaABgjY9ZAID1QGIKpkcI0X8shV4IBgtERESFKbQbogvqOgnaOgstSJpKGZ8x0aIt1BQPIgaSzlsBtBW6kFMxMVggIiIqTEHrLMRv8rIkSS3xQKE5bUEmG4CsCzTF03fG/+1Iam04oRgsEBERFabgAY5prQL9aed6kdTSkHauPz39iRaNxBCLisQ6C0RERDS3QldwPKmFg+oWtmxZICIimj8GC0RERJRXeQULM1qwUFY7cxMRES1IeQULbFkgIiIqWJkFCxEADBaIiIgKUWbBAlsWiIiICsVggYiIiPIqq2AhOBWBTi+hwlhWL5uIiGhByuquGZwKw1RjOC67WRIREZ2qyipYmJmMoLKa0yaJiIgKUVbBQnAyjMoaQ6mLQUREdFIpq2BhZioCE4MFIiKigpRVsBCcDLMbgoiIqEBlFSzMTIbZskBERFSgsgoWglMRVNawZYGIiKgQZRMsiJhQWxaq2bJARERUiLIJFiaUIGJRgbrGylIXhYiI6KRSNsFCYGQKAFC/rKrEJSEiIjq5nBQd+Hf+9GlUV9YuKI+x0WlU1RlQv7y6SKUiIiIqDydFsFDXUIWaqoW1CJiXVuHcy1ZCp+NSz0RERAURQizaBwAzABEIBAQtTCwaFdGZGRGLxYQQQkT8fhE+ejRxfubVV0XY51PPKYqYevZZEQuF1HPDHjG9c2ci7cRjj4ngvn1CCCHCR4+KwH33iejkpBBCiKnnXxDjjzySSBu4/34x/coralqvVyh33CEi8f/PqedfEIH7759Ne+99Yuq55xPl823blijj1PMvCOXOO2fT/uUvYnJwUE07Nia8t9wiQocOqWlfeEH4+vqS0t4rJv72NyGEENHJSeG9+ZZE+adffkV4f/e7RNqxgQEx/shf1ToLBtW0u3ap9fD668J78y0iFomoaR98UIw98ICaNhYT3ptvETOvvy6EECK4e7fw3nSziAaDQgghxh/5a8pr9f7ud2L65ZfVtPv2q2knJtT6ffxxEbjnnkRaX1+fmHr+BSGEEKHDh4X3ppsTdTj51FNCufPPibT+/tvE1DPPqPU9Oiq8N90swl6vmnbILfx/vD2RVvnTn2br0O8X3ptuFqEjR9Q6fO454Xc6Z9PedZeYePwJNe34uJr2wAG1DnfuFL7f/362vu+9T4z/9VG1vqenhXfrTSK4d6+a9pVXhHfrTbP17XKJ8YceUuswHBberTeJmWGPWt/Dw8K79SYRC4dn63tgYLYOt96UeG8F9+wR3q03iej0tFrff31UBO69b7YOb71VTL/4olqH+/cL79abRGR8tr6Vu+5Kre/nnovX9xE1rd+v1uGOHUK5447Z+r7tj2JyyK3W99Gjwrv1psR7dtLtFv7+/pT6nnjySbUOFUV4t24VocOHE/Xt+/0fUutbe89OTAjv1q0iuG+/Wocvvii8N9+SWt/xv7nozIyadvdutQ5ffVV4t25N/N2PDQzMvmcjEeHdujXxnp0Z9qhptffsQw+lvmdvunn2Pbt3r/Bu3SqiU1Nq2kcfTX3P3nqrmHoh/p49eFB4t24VkbGxeH0/kfKe9W3blnjPho4cEd6tWxOfRZODgynvWf8fbxeTQ0Nqfft8atqREbUOn35a+LZtm63DO+8UE0/E63tsTK3vgwfVtM+/kPqeveceMf5o/D07ORmvb+0z4mXhvenm2bT33SfGH35YrcNgUK1DT/w9+9prah1qnxHbHxBj27eraWMxNe2rr6p1uGuXWoeJz4hHUt6z3ptvEdMvvaSm3bdfTat9Rjz2mAjcffdsHf7+D4nPztChQ2p9K4oIBAICgABgFgu8H5fNmIWThQiHEfF6E79PDQ5i6umnAQCx6WmMXHcdZl55BQAw+be/Yd/nv6AFVjj0ve/j0Pe/r6YNhfBK82UI3HU3AGDsnr/glTdeAjE9rab9/rU4+M2OxPN4rv4wxu+7T33OoSHs3nQNomNjAADvli04/IMfJtLu/8q/Yvx+FwAg+OqrOPCVf0XE5wMABP54G0auvyGR9sh//ADjAwMAgPDevTho/xYiR46oZfrLPRj98XWJtCPXXYfx+9UyhI+M4PB3vovw/v0AgImHH8LIddcn0np7t2DsXjVtVFEw8t8OhPbsVcu/YwdGf/qzRFr/736Hsb/cq9bL5CRGfvQjhIaHAQDTzzyD0aR8/du2IfDnO+N1GMbIj36EmZfV+p7ZuRMjP/oREIupr/X2OxD44+3x/ziBkR/9CNPPP6+mfeUVjPz4xxDBoPpa77oLSt+2xPOM3nAjpt1uAEBo1y6M/PjHiE5Mqmnvuw/+392aSHv0f36OqaeeUutl3z41rd8PABh/4EH4fvvb2bS9PZh47DE17eEjGPnxjxEZHQUATD76KLz/+7+zdfib/8PEQw8DACI+H0auuw6RgwfVtE88gaO//NVsvdzyu8T/Y2xsDCPXXYfQnj1qfQ+5U+pb6fsDxu+L1/f0NEauvx7B119X6+W55zByw+z7Q/nj7Qjc+We1CiMRNe3LL6lpX3oJI9dfDxFVt5Yfu/PPULT6BjBy/fWYidd38PXXMXL99YjF399j9/4FSl/fbH3/7H8wpdX33r0YueEGxMbV9/f4wPaU+vb29M7W98EDGLnhBkR8an1PPPwIvFu3zqb99a8xGa/vyOiomjb+/p58/HF4//fXibS+m27G5COPAACifj9Gbrgx8f6e2jGIoz09s/Xy+z9g3KXWd3RiEiM33IjQnt0A1Pfs0Z8l1Xd/f+L9LWZmMHLDjQi+9pqadudOjNxwYyJt4I4/YezPan0jGsXIDTdi5kW1voOvvoKRG26ECIfVOrznHgRu++NsHf7kJ5h+7jm1Dj0ejNxwI2KT6nt2/L774U9+f//iF5geGorX9z6M3HAjogG1vicefBD+W26ZrcPeLZh84km1Dg8fVtMePaqm/etfU97fvv/7P0z89a9J9X0jwocPq/X9xJPwbpl9f/tuvgUTDz6ovtRAQK3DffvUehkaxNFf/DKR1v+HvsRnWmxqSk3r8ahpn30Woz/96Wwd/vF2jN19j5o2FFbr+5VXAcTfszfcOPsZceedCNzxJ/VCIeL1/aKa9rXX1PrWPiPu+QuU2/pn6/BnP8P0M8+odbh7t1ovWn0PDMD/+z8k0h791a8wNTgIAAjv36+mVRQUk6TdaBYjSZLMAAKBQABms7nUxSmasfvuh67ShNp3vQsRvx/7P/8FLP/G11Hd3AzfrbfiyH/+F8574XlIkoS9//w56GpqsOanP0Fsehqeq67Gym//G2rf9S5MPv44fDffgjU/uRGSwQDltj8COh3kD18NEYvB93+/Re073wHT2WcjtP8Apt1DML///ZCMRsy89BJEJIqqiy8CoP5BGFavRsXSpYiOjyN88CBMFgskgwGR0VGIaBSGlSsBAOEjR6CrqYW+tgYiHEZschK6ujpIej1ioRAQjUIX7zaKzcxA0ushGQwQsRhEJAKpogKSTqfeBGIxSAZ1OquIRgFJUs8Jwd1BiYgWYGxsDPX19QBQL4QYW0heZRksxEIh7P2nz6K6uQnL/9//K1q+GhEKYfyBB1B50UUwrlmDwN1348h/deHsRx6GpNdj3xe+CH19PU777y6IUAiHvvd9LPn4x1F10YUI7d+P4GuvofZd74Kk0yE6Pg7JZILOaCx6OYmI6NTFYKEIJp94AsFXX0XDpz51TNdHx8Yw/fzzqHnLWyDpdIlmn1XXfh8iHMbLl1qx8nvfxZLWVsy88iomHnkYDZ/8JHSVlfzWTEREx10xg4WTYjbE8VDz5jej5s1vLuia2OQkopOTMCxfjim3G/s//wWc9eADMKxaBePaNYg1LAEASAYDzvnbY9DHA5zKc89B5bnnJPJhoEBERCeTshzguPsfPw6/04nQ/gMYc7lSzsWCQcy8pA74ifj9OPD1byC0Vx04t+cz/4SR/3YAAGqam7H+vntRsXw5AEBuaUHDpz+dyEd/Co2xICKi8lZ2LQsiFkP1Zc0wrl2L8Xv/Au+W/0Xde94DqUKtCt9vt8L3f/+Hsx7YjsjICIK7PInBeiu+ZU8M8tPV1MBYU1Oy10FERHSilO2YBQDq4EG9Hrrq2VUdY9PT6liEyy4r+vMRERGdKMUcs1B23RARnw/B11+HiMWgr6uDrro6sU6BCIWgq6pioEBERJSk7IKFsb/8BZ4P/8Ps7/fdj10f+QhCe/fitfdcnljYgoiIiFRlN2bB/MEPouriiyHp1DjJuG4tai57EySjEfVXXwXTeeeXuIRERESLS1mPWSAiIjpVcczCAvhuugnj8fXCiYiIaG5lFyxM/PXRxEYeRERENLeyG7OwbktvqYtARER0Uim7lgUiIiIqTFkFCzMvvYTXbRsR3LWr1EUhIiI6aZRVsKCrq4P5A+9HRUNDqYtCRER00uDUSSIiolMQp04eo+DwMEL79pW6GERERCeVgmdDSJLUAUCJ/yoLIbrnSC8D2BT/dT0AC4DNQggl1zXHy8iPfgwIgbU9vzrRT01ERHTSKihYiAcKEEL0xn+3SZLUI4Roz3OZA4BDCOGJX9MDwAlg47EV+ditvPZaiJnpE/20REREJ7VCuyE6ASQWKhBCDABom+MaC4CWpN+HATQV+LxFYVixHMbTTy/FUxMREZ205h0sSJJkgdrtoGQ5Z8t1nRBiY1pXRTOAgUIKGR2bHZcR8fsRnZhQ845EED50CLFgMJEutP9AIm34wAGER0YAADMvv4xD116LiN9fyFMTERGVvUJaFiw5jisA5PlkIElSSzzt5hznTZIkmbUHgDoA2P3RjyXS7P3UpzD6k58CAML79+P191yO6WefBQD4+/qw+yMfSaTd/9Wv4ejPfgYA0NXWYfKvj0JfUzOfohIREVFcMZZ79gHIu3BB0iBHGYAzz+DGTgDfSz+44tv/Nvvvf/8O9EtkAEDFihVYu2ULKs85BwBQ/8EPovrSSxNpV/3HtdBVVgIADMuX4cw/3gbJaJzXiyIiIiLVvNdZiHc1uIQQUtpxPwC7NuhxHvm0QR30eGZ60CBJkgmAKelQHYD9XGeBiIioMKVaZ8GT47ic65wkSbIkSY54y4JmIH5NxjgHIURQCDGmPQCMF1A+IiIiOg7mHSzEpz4q8YGO6edyDVi0AOhAajeFHP+pzPe5iYiIqHQKnTrZhaQWgfiAxd6k3y3aWgwAIIRwA+jW1liIuwaAO0+AQURERItIQQMchRDdkiR1xIMEAGhOW5DJBqAdQPJUya7kAAJqy8IVx1JYIiIiOvG4kRQREdEpiBtJERER0QnDYIGIiIjyYrBAREREeTFYICIiorwYLBAREVFexdgb4rgbG1vQIE4iIqKyU8x752IPFhoAYO3ataUuBxER0cmqAcCCIofFHiz44j/XgPtEnCh1APaDdX4isc5PPNb5icc6P/G0OvfNlXAuiz1Y0IwvdEEJmh9JSmwqyjo/QVjnJx7r/MRjnZ94SXW+YBzgSERERHkxWCAiIqK8FnuwEARwbfwnnRis8xOPdX7isc5PPNb5iVe0Ol/UG0kRERFR6S32lgUiIiIqMQYLRERElBeDBSIiIsqLwQIRERHltWgXZZIkqQOAEv9VFkJ0l7A4pxxJkmQAmwC0CiE2ZjnP+j8O4vUKAOsBQAjRnuW8Ev+V9b5ASe9zQK1zC4DNQgglKQ3r/DiSJMmV/hnDOi8uSZJsANoBuAB4AGwEsEMI0Z+UZkF1vihbFrQPVCFErxCiF4BbkqSeEhfrlCFJkhXqB6iM+P4baedZ/8eBJEkOIUR3/NEeP+ZKOs96Lz4HgIF4ndqhLnvr1E6yzo8vSZJaANjSjrHOi0+GWs898cdwlkBhQXW+KKdOSpLkB3BmWvQvhBDFW7uStD/kTiHEhrTjrP8ii3/DdUJtyVHix6wAhgCsF0J4WO/FFw/GXNq3qPiHZqcQYkn8d9b5cZLUqtOTXJ+s8+KLf5YPJNdp2vkF1/mia1mQJMkCtYlEyXLOlnkFFRPr/7hqgtoMrvHEf8qs9+NDCLExrbm1GcAAwPf6CbAJwLbkA6zzE69Ydb4YxyxYchxXoDa10PHF+j8O4n+oS9IOa3+oHqiBRDYKWO9FEf/2JQNojR/ie/04id+EBrKcYp0fP5skSfJB7VpeH+92A4pU54sxWMhFqwQqDdZ/8XUCaBdCKHl2h2O9L1BSc7gMwJmrqTYJ63zh5HjXmjzP9KzzhXEDgBDCAwCSJLVJkuQUQrTmuaagOj+ZggW+kUqL9V9EkiQ5APTFBxvlw3pfoHhw0AskPkT9AM7McwnrfAEkSWqbx/s6Het8AbQgIck2AD1zBGsF1fmiG7OA2X7cdHKec1Q8rP/jLN4cPpzWl856LzJJkmRJkhxpH5gDmB05zjovsvig3cE8SVjnx0H8MyUhqfXMgiLV+aILFuIRkhIflJF+LlsfGBUR6//40gYUad+84jc0C+v9uLAA6EDqNyg5/lNhnR8XDQBskiR1xGeeOAB1FookSS2s8+LTZlol12lSgOwpVp0vumAhrgtJc3PjUVOhzVo0t1zNUKz/4yD+rcsKdY6zJf7H2wa17xBgvReVEMINoDutifYaAO6kD0nWeREJIQaS1hLphjrnH/HftXn/rPMiircipL/P2wD0J7UwLLjOF+U6C0BiPrT24puTRnbSAsVvUi1QPzitALqRfbUv1n+RxCP9Xcgy+jhtDjrrvYji9d6WdGg9AHuWFRxZ50UWvyFdA/WzphvqehfatFXWeRFleZ83ptfpQut80QYLREREtDgs1m4IIiIiWiQYLBAREVFeDBaIiIgoLwYLRERElBeDBSIiIsqLwQIRERHlxWCBiIiI8mKwQERERHkxWCCikpEkqSf+kPOkaYunacmVhoiOr5Npi2oimkN8Ke8hqLsraku7tgBQAPQBaER8jXghxIYSFDEhHiAocy07m7TplgNAf760RHR8MFggOrXIALqSt7+OfyMfSDvmSvq3A4BFCNF6IgtKRCcPBgtEp5YGZO4mp2RJ50z6twtZNrgiItIwWCA6tcjJOyrm4ZEkSRZCKIXsaU9E5YnBAtEpJHmb8TnSaVsFWwFo3RDr48ds8WMAsBmABWqLxQYhRLskSW0AfFC3H+4SQriT807aCtcCwDPfMsWvbYtfK8evV7QxC0RUOgwWiMqYEMItSZIdSd0SQoiB+LEeAA3azV6SpGFJkhzagERJkgBgC4DEQElJkpwA+pKucUmS5EkPKLLRZjskBTIWxAdjElFpceokEWXjg9rakNxF4UlL44b67R9A4ubektaS4ATQXsDztmrTKIUQHgCDhRSaiI4PtiwQUS7pwYECYDhPehsAJd6NoVmPpIAiHyFEvyRJ7QD8kiS5obZQdM91HREdfwwWiKhYZKhjFJJbIwoaPCmE2BgfR2ED0C5JEhgwEJUeuyGIqFhSuiU0+VZnTEvXBqjjKOIBwgaogyiJqMQYLBCd+mQUZx2FvHnEWxQGsyzLvGm++WsBQ5L0rhAiKgF2QxCdouJTGBuhftu3aVMakwcgxpv8OwFYJEnqEEJ05zjWAaApfo0H6gDITqg3eAfUKZRKvBvBIUlSQzwNCpj6qMTz14INC9Spm0RUYpIQotRlIKIyFO+e6Jxrb4ik9I75piWi4mI3BBEREeXFYIGIiIjyYrBARKXUIklST74ZE5IktcVXhiSiEuGYBSIiIsqLLQtERESUF4MFIiIiyovBAhEREeXFYIGIiIjyYrBAREREeTFYICIiorwYLBAREVFeDBaIiIgor/8PjkSvPxVYpSEAAAAASUVORK5CYII=\n",
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