{
"cells": [
{
"cell_type": "markdown",
"source": [
"Line Modeling simulation with [PowerSimulationsDynamics.jl](https://github.com/NREL-SIIP/PowerSimulationsDynamics.jl)"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"**Originally Contributed by**: Rodrigo Henriquez and José Daniel Lara"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Introduction"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"This tutorial will introduce an example of considering dynamic lines in `PowerSimulationsDynamics`.\n",
"Note that this tutorial is for `PowerSimulationsDynamics`."
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"This tutorial presents a simulation of a three-bus system, with an infinite bus (represented as a\n",
"voltage source behind an impedance) at bus 1, a one d- one q- machine on bus 2 and an inverter\n",
"of 19 states, as a virtual synchronous machine at bus 3. The perturbation will be the trip of\n",
"two of the three circuits (triplicating its resistance and impedance) of the line that connects\n",
"bus 1 and bus 3. This case also consider a dynamic line model for connection between buses\n",
"2 and 3. We will compare it against a system without dynamic lines."
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"In addition, note that `PowerSimulationsDynamics` will convert ConstantPower loads to RLC\n",
"loads for transient simulations."
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"It is recommended to check `Tutorial 1: OMIB` first, since that includes more details and\n",
"explanations on all definitions and functions."
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"# Step 1: Package Initialization"
],
"metadata": {}
},
{
"outputs": [],
"cell_type": "code",
"source": [
"using SIIPExamples\n",
"using PowerSimulationsDynamics\n",
"PSID = PowerSimulationsDynamics\n",
"using PowerSystems\n",
"using Sundials\n",
"using Plots"
],
"metadata": {},
"execution_count": 1
},
{
"cell_type": "markdown",
"source": [
"# Step 2: Data creation"
],
"metadata": {}
},
{
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"┌ Warning: Rate 250.0 MW for BUS 1-BUS 3-i_1 is larger than the max expected in the range of (min = 47.0, max = 52.0).\n",
"└ @ PowerSystems ~/.julia/packages/PowerSystems/61h6O/src/utils/IO/branchdata_checks.jl:148\n",
"┌ Warning: Rate 250.0 MW for BUS 1-BUS 2-i_2 is larger than the max expected in the range of (min = 47.0, max = 52.0).\n",
"└ @ PowerSystems ~/.julia/packages/PowerSystems/61h6O/src/utils/IO/branchdata_checks.jl:148\n",
"┌ Warning: Rate 250.0 MW for BUS 2-BUS 3-i_3 is larger than the max expected in the range of (min = 47.0, max = 52.0).\n",
"└ @ PowerSystems ~/.julia/packages/PowerSystems/61h6O/src/utils/IO/branchdata_checks.jl:148\n",
"┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n",
"└ @ InfrastructureSystems ~/.julia/packages/InfrastructureSystems/3LlGM/src/validation.jl:51\n",
"┌ Warning: struct DynamicInverter does not exist in validation configuration file, validation skipped\n",
"└ @ InfrastructureSystems ~/.julia/packages/InfrastructureSystems/3LlGM/src/validation.jl:51\n",
"┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n",
"└ @ InfrastructureSystems ~/.julia/packages/InfrastructureSystems/3LlGM/src/validation.jl:51\n",
"┌ Warning: struct DynamicInverter does not exist in validation configuration file, validation skipped\n",
"└ @ InfrastructureSystems ~/.julia/packages/InfrastructureSystems/3LlGM/src/validation.jl:51\n"
]
}
],
"cell_type": "code",
"source": [
"file_dir = joinpath(\n",
" dirname(dirname(pathof(SIIPExamples))),\n",
" \"script\",\n",
" \"4_PowerSimulationsDynamics_examples\",\n",
" \"Data\",\n",
")\n",
"threebus_sys = System(joinpath(file_dir, \"threebus_sys.json\"));"
],
"metadata": {},
"execution_count": 2
},
{
"cell_type": "markdown",
"source": [
"In addition, we will create a new copy of the system on which we will simulate the same\n",
"case, but will consider dynamic lines:"
],
"metadata": {}
},
{
"outputs": [],
"cell_type": "code",
"source": [
"threebus_sys_dyn = deepcopy(threebus_sys);"
],
"metadata": {},
"execution_count": 3
},
{
"cell_type": "markdown",
"source": [
"# Step 3: Create the fault and simulation on the Static Lines system"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"First, we construct the perturbation, by properly computing the new Ybus on the system:"
],
"metadata": {}
},
{
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ Info: Validating connectivity with Goderya algorithm\n",
"[ Info: The System has no islands\n"
]
}
],
"cell_type": "code",
"source": [
"#Make a copy of the original system\n",
"sys2 = deepcopy(threebus_sys)\n",
"#Triplicates the impedance of the line named \"BUS 1-BUS 3-i_1\"\n",
"fault_branches = get_components(ACBranch, sys2)\n",
"for br in fault_branches\n",
" if get_name(br) == \"BUS 1-BUS 3-i_1\"\n",
" br.r = 3 * br.r\n",
" br.x = 3 * br.x\n",
" b_new = (from = br.b.from / 3, to = br.b.to / 3)\n",
" br.b = b_new\n",
" end\n",
"end\n",
"#Obtain the new Ybus\n",
"Ybus_fault = Ybus(sys2).data\n",
"#Define Fault: Change of YBus\n",
"Ybus_change = NetworkSwitch(\n",
" 1.0, #change at t = 1.0\n",
" Ybus_fault, #New YBus\n",
");"
],
"metadata": {},
"execution_count": 4
},
{
"cell_type": "markdown",
"source": [
"Now, we construct the simulation:"
],
"metadata": {}
},
{
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ Info: Unit System changed to InfrastructureSystems.UnitSystemModule.UnitSystem.DEVICE_BASE = 1\n",
"[ Info: Validating connectivity with Goderya algorithm\n",
"[ Info: The System has no islands\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": "Simulation()\n"
},
"metadata": {},
"execution_count": 5
}
],
"cell_type": "code",
"source": [
"#Time span of our simulation\n",
"tspan = (0.0, 30.0)\n",
"\n",
"#Define Simulation\n",
"sim = PSID.Simulation(\n",
" PSID.ImplicitModel, #Type of model used\n",
" threebus_sys, #system\n",
" pwd(), #folder to output results\n",
" tspan, #time span\n",
" Ybus_change, #Type of perturbation\n",
")"
],
"metadata": {},
"execution_count": 5
},
{
"cell_type": "markdown",
"source": [
"We can obtain the initial conditions as:"
],
"metadata": {}
},
{
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Voltage Variables\n",
"====================\n",
"BUS 1\n",
"====================\n",
"Vm 1.02\n",
"θ -0.0\n",
"====================\n",
"BUS 2\n",
"====================\n",
"Vm 1.0142\n",
"θ -0.0247\n",
"====================\n",
"BUS 3\n",
"====================\n",
"Vm 1.0059\n",
"θ 0.05\n",
"====================\n",
"====================\n",
"Differential States\n",
"generator-103-1\n",
"====================\n",
"ω_oc 1.0\n",
"θ_oc 0.4573\n",
"q_oc -0.4453\n",
"ξd_ic 0.0013\n",
"ξq_ic 0.0004\n",
"γd_ic 0.0615\n",
"γq_ic -0.0138\n",
"ϕd_ic 0.8765\n",
"ϕq_ic -0.1978\n",
"vd_pll 0.8986\n",
"vq_pll -0.0\n",
"ε_pll 0.0\n",
"θ_pll 0.2354\n",
"ir_cnv 0.7462\n",
"ii_cnv 0.757\n",
"vr_filter 0.8738\n",
"vi_filter 0.2095\n",
"ir_filter 0.7617\n",
"ii_filter 0.6923\n",
"====================\n",
"Differential States\n",
"generator-102-1\n",
"====================\n",
"eq_p 0.6478\n",
"ed_p 0.6672\n",
"δ 0.9386\n",
"ω 1.0\n",
"Vf 1.0781\n",
"Vr1 0.0333\n",
"Vr2 -0.1941\n",
"Vm 1.0142\n",
"====================\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": "Dict{String, Any} with 6 entries:\n \"generator-102-1\" => Dict(:Vf=>1.07808, :Vr2=>-0.194055, :ed_p=>0.667241, :ω=…\n \"V_R\" => Dict(103=>1.00464, 102=>1.01389, 101=>1.02)\n \"Vm\" => Dict(103=>1.0059, 102=>1.0142, 101=>1.02)\n \"θ\" => Dict(103=>0.0500046, 102=>-0.0247452, 101=>-8.28357e-14)\n \"V_I\" => Dict(103=>0.0502787, 102=>-0.0250941, 101=>-8.44924e-14)\n \"generator-103-1\" => Dict(:vi_filter=>0.209533, :γd_ic=>0.0615061, :vq_pll=>-…"
},
"metadata": {},
"execution_count": 6
}
],
"cell_type": "code",
"source": [
"#Will print the initial states. It also give the symbols used to describe those states.\n",
"print_device_states(sim)\n",
"#Will export a dictionary with the initial condition values to explore\n",
"x0_init = PSID.get_initial_conditions(sim)"
],
"metadata": {},
"execution_count": 6
},
{
"cell_type": "markdown",
"source": [
"# Step 4: Run the simulation of the Static Lines System"
],
"metadata": {}
},
{
"outputs": [],
"cell_type": "code",
"source": [
"#Run the simulation\n",
"PSID.execute!(\n",
" sim, #simulation structure\n",
" IDA(), #Sundials DAE Solver\n",
" dtmax = 0.02, #Maximum step size\n",
")"
],
"metadata": {},
"execution_count": 7
},
{
"cell_type": "markdown",
"source": [
"# Step 5: Store the solution"
],
"metadata": {}
},
{
"outputs": [],
"cell_type": "code",
"source": [
"series2 = get_voltage_magnitude_series(sim, 102)\n",
"zoom = [\n",
" (series2[1][ix], series2[2][ix]) for\n",
" (ix, s) in enumerate(series2[1]) if (s > 0.90 && s < 1.6)\n",
"];"
],
"metadata": {},
"execution_count": 8
},
{
"cell_type": "markdown",
"source": [
"# Step 3.1: Create the fault and simulation on the Dynamic Lines system"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"An important aspect to consider is that DynamicLines must not be considered in the computation of the Ybus. First we construct the Dynamic Line, by finding the Line named \"BUS 2-BUS 3-i_3\", and then adding it to the system."
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"get component return the Branch on threebus_sys_dyn named \"BUS 2-BUS 3-i_3\""
],
"metadata": {}
},
{
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "\nBUS 2-BUS 3-i_3 (PowerSystems.DynamicBranch):\n branch: BUS 2-BUS 3-i_3 (PowerSystems.Line)\n n_states: 2\n states: [:Il_R, :Il_I]\n internal: InfrastructureSystems.InfrastructureSystemsInternal"
},
"metadata": {},
"execution_count": 9
}
],
"cell_type": "code",
"source": [
"dyn_branch = DynamicBranch(get_component(Branch, threebus_sys_dyn, \"BUS 2-BUS 3-i_3\"))"
],
"metadata": {},
"execution_count": 9
},
{
"cell_type": "markdown",
"source": [
"Adding a dynamic line will inmediately remove the static line from the system."
],
"metadata": {}
},
{
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"┌ Warning: struct DynamicBranch does not exist in validation configuration file, validation skipped\n",
"└ @ InfrastructureSystems ~/.julia/packages/InfrastructureSystems/3LlGM/src/validation.jl:51\n"
]
}
],
"cell_type": "code",
"source": [
"add_component!(threebus_sys_dyn, dyn_branch)"
],
"metadata": {},
"execution_count": 10
},
{
"cell_type": "markdown",
"source": [
"Similarly, we construct the Ybus fault by creating a copy of the original system, but\n",
"removing the Line \"BUS 2-BUS 3-i_3\" to avoid considering it in the Ybus:"
],
"metadata": {}
},
{
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ Info: Validating connectivity with Goderya algorithm\n",
"[ Info: The System has no islands\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": "PowerSimulationsDynamics.NetworkSwitch(1.0, \n 0.91954-10.9011im -0.689655+8.27586im -0.229885+2.75862im\n -0.689655+8.27586im 0.689655-8.17586im ⋅ \n -0.229885+2.75862im ⋅ 0.229885-2.72529im)"
},
"metadata": {},
"execution_count": 11
}
],
"cell_type": "code",
"source": [
"#Make a copy of the original system\n",
"sys3 = deepcopy(threebus_sys);\n",
"#Remove Line \"BUS 2-BUS 3-i_3\"\n",
"remove_component!(Line, sys3, \"BUS 2-BUS 3-i_3\")\n",
"#Triplicates the impedance of the line named \"BUS 1-BUS 2-i_1\"\n",
"fault_branches2 = get_components(Line, sys3)\n",
"for br in fault_branches2\n",
" if get_name(br) == \"BUS 1-BUS 3-i_1\"\n",
" br.r = 3 * br.r\n",
" br.x = 3 * br.x\n",
" b_new = (from = br.b.from / 3, to = br.b.to / 3)\n",
" br.b = b_new\n",
" end\n",
"end\n",
"#Obtain the new Ybus\n",
"Ybus_fault_dyn = Ybus(sys3).data\n",
"#Define Fault: Change of YBus\n",
"Ybus_change_dyn = PowerSimulationsDynamics.NetworkSwitch(\n",
" 1.0, #change at t = 1.0\n",
" Ybus_fault_dyn, #New YBus\n",
")"
],
"metadata": {},
"execution_count": 11
},
{
"cell_type": "markdown",
"source": [
"# Step 4.1: Run the simulation of the Dynamic Lines System"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Now, we construct the simulation:"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Time span of our simulation"
],
"metadata": {}
},
{
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "(0.0, 30.0)"
},
"metadata": {},
"execution_count": 12
}
],
"cell_type": "code",
"source": [
"tspan = (0.0, 30.0)"
],
"metadata": {},
"execution_count": 12
},
{
"cell_type": "markdown",
"source": [
"Define Simulation"
],
"metadata": {}
},
{
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ Info: Unit System changed to InfrastructureSystems.UnitSystemModule.UnitSystem.DEVICE_BASE = 1\n",
"[ Info: Validating connectivity with Goderya algorithm\n",
"[ Info: The System has no islands\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": "Simulation()\n"
},
"metadata": {},
"execution_count": 13
}
],
"cell_type": "code",
"source": [
"sim_dyn = PSID.Simulation(\n",
" PSID.ImplicitModel, #Type of model used\n",
" threebus_sys_dyn, #system\n",
" pwd(), #folder to output results\n",
" tspan, #time span\n",
" Ybus_change_dyn, #Type of perturbation\n",
")"
],
"metadata": {},
"execution_count": 13
},
{
"cell_type": "markdown",
"source": [
"Run the simulation"
],
"metadata": {}
},
{
"outputs": [],
"cell_type": "code",
"source": [
"PSID.execute!(\n",
" sim_dyn, #simulation structure\n",
" IDA(), #Sundials DAE Solver\n",
" dtmax = 0.02, #Maximum step size\n",
")"
],
"metadata": {},
"execution_count": 14
},
{
"cell_type": "markdown",
"source": [
"We can obtain the initial conditions as:"
],
"metadata": {}
},
{
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Voltage Variables\n",
"====================\n",
"BUS 1\n",
"====================\n",
"Vm 1.02\n",
"θ -0.0\n",
"====================\n",
"BUS 2\n",
"====================\n",
"Vm 1.0142\n",
"θ -0.0247\n",
"====================\n",
"BUS 3\n",
"====================\n",
"Vm 1.0059\n",
"θ 0.05\n",
"====================\n",
"====================\n",
"Differential States\n",
"generator-103-1\n",
"====================\n",
"ω_oc 1.0\n",
"θ_oc 0.4573\n",
"q_oc -0.4453\n",
"ξd_ic 0.0013\n",
"ξq_ic 0.0004\n",
"γd_ic 0.0615\n",
"γq_ic -0.0138\n",
"ϕd_ic 0.8765\n",
"ϕq_ic -0.1978\n",
"vd_pll 0.8986\n",
"vq_pll -0.0\n",
"ε_pll 0.0\n",
"θ_pll 0.2354\n",
"ir_cnv 0.7462\n",
"ii_cnv 0.757\n",
"vr_filter 0.8738\n",
"vi_filter 0.2095\n",
"ir_filter 0.7617\n",
"ii_filter 0.6923\n",
"====================\n",
"Differential States\n",
"generator-102-1\n",
"====================\n",
"eq_p 0.6478\n",
"ed_p 0.6672\n",
"δ 0.9386\n",
"ω 1.0\n",
"Vf 1.0781\n",
"Vr1 0.0333\n",
"Vr2 -0.1941\n",
"Vm 1.0142\n",
"====================\n",
"====================\n",
"Line Current States\n",
"====================\n",
"Line BUS 2-BUS 3-i_3\n",
"Il_R -0.08348\n",
"Il_I -0.01213\n",
"====================\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": "Dict{String, Any} with 7 entries:\n \"generator-102-1\" => Dict(:Vf=>1.07808, :Vr2=>-0.194055, :ed_p=>0.667241…\n \"V_R\" => Dict(103=>1.00464, 102=>1.01389, 101=>1.02)\n \"Vm\" => Dict(103=>1.0059, 102=>1.0142, 101=>1.02)\n \"θ\" => Dict(103=>0.0500046, 102=>-0.0247452, 101=>-8.28357…\n \"V_I\" => Dict(103=>0.0502787, 102=>-0.0250941, 101=>-8.44924…\n \"generator-103-1\" => Dict(:vi_filter=>0.209533, :γd_ic=>0.0615061, :vq_p…\n \"Line BUS 2-BUS 3-i_3\" => Dict(:Il_I=>-0.0121293, :Il_R=>-0.083478)"
},
"metadata": {},
"execution_count": 15
}
],
"cell_type": "code",
"source": [
"#Will print the initial states. It also give the symbols used to describe those states.\n",
"print_device_states(sim_dyn)\n",
"#Will export a dictionary with the initial condition values to explore\n",
"x0_init_dyn = PSID.get_initial_conditions(sim_dyn)"
],
"metadata": {},
"execution_count": 15
},
{
"cell_type": "markdown",
"source": [
"# Step 5.1: Store the solution"
],
"metadata": {}
},
{
"outputs": [],
"cell_type": "code",
"source": [
"series2_dyn = get_voltage_magnitude_series(sim_dyn, 102)\n",
"zoom_dyn = [\n",
" (series2_dyn[1][ix], series2_dyn[2][ix]) for\n",
" (ix, s) in enumerate(series2_dyn[1]) if (s > 0.90 && s < 1.6)\n",
"];"
],
"metadata": {},
"execution_count": 16
},
{
"cell_type": "markdown",
"source": [
"# Step 6.1: Compare the solutions:"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"We can observe the effect of Dynamic Lines"
],
"metadata": {}
},
{
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "Plot{Plots.GRBackend() n=2}",
"image/png": 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",
"text/html": [
"\n",
"\n"
],
"image/svg+xml": [
"\n",
"\n"
]
},
"metadata": {},
"execution_count": 17
}
],
"cell_type": "code",
"source": [
"Plots.plot(series2_dyn, label = \"V_gen_dyn\")\n",
"Plots.plot!(series2, label = \"V_gen_st\", xlabel = \"Time [s]\", ylabel = \"Voltage [pu]\")"
],
"metadata": {},
"execution_count": 17
},
{
"cell_type": "markdown",
"source": [
"that looks quite similar. The differences can be observed in the zoom plot:"
],
"metadata": {}
},
{
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "Plot{Plots.GRBackend() n=2}",
"image/png": 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",
"text/html": [
"\n",
"\n"
],
"image/svg+xml": [
"\n",
"\n"
]
},
"metadata": {},
"execution_count": 18
}
],
"cell_type": "code",
"source": [
"Plots.plot(zoom_dyn, label = \"V_gen_dyn\")\n",
"Plots.plot!(zoom, label = \"V_gen_st\", xlabel = \"Time [s]\", ylabel = \"Voltage [pu]\")"
],
"metadata": {},
"execution_count": 18
},
{
"cell_type": "markdown",
"source": [
"---\n",
"\n",
"*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*"
],
"metadata": {}
}
],
"nbformat_minor": 3,
"metadata": {
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.6.0"
},
"kernelspec": {
"name": "julia-1.6",
"display_name": "Julia 1.6.0",
"language": "julia"
}
},
"nbformat": 4
}