{ "cells": [ { "cell_type": "markdown", "source": [ "One Machine against Infinite Bus (OMIB) 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 you to the functionality of `PowerSimulationsDynamics`\n", "for running power system dynamic simulations." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "This tutorial presents a simulation of a two-bus system with an infinite bus\n", "(represented as a voltage source behind an impedance) at bus 1, and a classic\n", "machine on bus 2. The perturbation will be the trip of one of the two circuits\n", "(doubling its resistance and impedance) of the line that connects both buses." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Dependencies" ], "metadata": {} }, { "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ Info: Precompiling PowerSimulationsDynamics [398b2ede-47ed-4edc-b52e-69e4a48b4336]\n", "[ Info: Precompiling Sundials [c3572dad-4567-51f8-b174-8c6c989267f4]\n" ] }, { "output_type": "execute_result", "data": { "text/plain": "Plots.GRBackend()" }, "metadata": {}, "execution_count": 1 } ], "cell_type": "code", "source": [ "using SIIPExamples #hide\n", "using PowerSimulationsDynamics\n", "PSID = PowerSimulationsDynamics\n", "using PowerSystems\n", "using Sundials\n", "using Plots\n", "gr()" ], "metadata": {}, "execution_count": 1 }, { "cell_type": "markdown", "source": [ "`PowerSystems` (abbreviated with `PSY`) is used to properly define the data structure and establish an equilibrium\n", "point initial condition with a power flow routine, while `Sundials` is\n", "used to solve the problem defined in `PowerSimulationsDynamics`." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Load the system\n", "_The following command requires that you have executed the\n", "[dynamic systems data example](../../notebook/2_PowerSystems_examples/09_loading_dynamic_systems_data.jl.ipynb)\n", "previously to generate the json file._" ], "metadata": {} }, { "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ Info: Loaded time series from storage file existing=omib_sys_time_series_storage.h5 new=/var/folders/27/2jr8c7gn4j72fvrg4qt81zrw8w_711/T/jl_CJpTN1\n", "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", "└ @ InfrastructureSystems ~/.julia/packages/InfrastructureSystems/v75Hd/src/validation.jl:51\n", "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", "└ @ InfrastructureSystems ~/.julia/packages/InfrastructureSystems/v75Hd/src/validation.jl:51\n", "┌ Warning: There are no ElectricLoad Components in the System\n", "└ @ PowerSystems ~/.julia/packages/PowerSystems/4kGrw/src/utils/IO/system_checks.jl:56\n" ] }, { "output_type": "execute_result", "data": { "text/plain": "System\n======\nSystem Units Base: SYSTEM_BASE\nBase Power: 100.0\nBase Frequency: 60.0\n\nComponents\n==========\nNum components: 10\n\n\u001b[1m8×3 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m ConcreteType \u001b[0m\u001b[1m SuperTypes \u001b[0m\u001b[1m C\u001b[0m ⋯\n\u001b[1m \u001b[0m│\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m I\u001b[0m ⋯\n─────┼──────────────────────────────────────────────────────────────────────────\n 1 │ Arc Topology <: Component <: Infrast… ⋯\n 2 │ Area AggregationTopology <: Topology …\n 3 │ Bus Topology <: Component <: Infrast…\n 4 │ DynamicGenerator{BaseMachine,Sin… DynamicInjection <: Device <: Co…\n 5 │ Line ACBranch <: Branch <: Device <: … ⋯\n 6 │ LoadZone AggregationTopology <: Topology …\n 7 │ Source StaticInjection <: Device <: Com…\n 8 │ ThermalStandard ThermalGen <: Generator <: Stati…\n\u001b[36m 1 column omitted\u001b[0m\n\nTimeSeriesContainer\n===================\nComponents with time series data: 0\nTotal StaticTimeSeries: 0\nTotal Forecasts: 0\n", "text/html": [ "
Base Power: 100.0
\n", "Num components: 10
\n", "ConcreteType | SuperTypes | |
---|---|---|
String | String | |
1 | Arc | Topology <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any |
2 | Area | AggregationTopology <: Topology <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any |
3 | Bus | Topology <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any |
4 | DynamicGenerator{BaseMachine,SingleMass,AVRFixed,TGFixed,PSSFixed} | DynamicInjection <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any |
5 | Line | ACBranch <: Branch <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any |
6 | LoadZone | AggregationTopology <: Topology <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any |
7 | Source | StaticInjection <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any |
8 | ThermalStandard | ThermalGen <: Generator <: StaticInjection <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any |
Components with time series data: 0
\n", "Total StaticTimeSeries: 0
\n", "Total Forecasts: 0
\n", "Resolution: 0 seconds
\n" ] }, "metadata": {}, "execution_count": 2 } ], "cell_type": "code", "source": [ "file_dir = joinpath(\n", " dirname(dirname(pathof(SIIPExamples))),\n", " \"script\",\n", " \"4_PowerSimulationsDynamics_examples\",\n", " \"Data\",\n", ")\n", "omib_sys = System(joinpath(file_dir, \"omib_sys.json\"))" ], "metadata": {}, "execution_count": 2 }, { "cell_type": "markdown", "source": [ "## Build the simulation and initialize the problem" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "The next step is to create the simulation structure. This will create the indexing\n", "of our system that will be used to formulate the differential-algebraic system of\n", "equations. To do so, it is required to specify the perturbation that will occur in\n", "the system. `PowerSimulationsDynamics` supports three types of perturbations:" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "- Network Switch: Change in the Y-bus values.\n", "- Branch Trip: Disconnects a line from the system.\n", "- Change in Reference Parameter" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "Here, we will use a Branch Trip perturbation, that is modeled by modifying the\n", "specifying which line we want to trip. In this case we disconnect one of the lines\n", "that connects BUS 1 and BUS 2, named \"BUS 1-BUS 2-i_1\"." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "With this, we are ready to create our simulation structure:" ], "metadata": {} }, { "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ Info: Serialized time series data to /var/folders/27/2jr8c7gn4j72fvrg4qt81zrw8w_711/T/jl_rz4W6B/sys_time_series_storage.h5.\n", "[ Info: Serialized System to /var/folders/27/2jr8c7gn4j72fvrg4qt81zrw8w_711/T/jl_rz4W6B/sys.json\n", "[ Info: Loaded time series from storage file existing=sys_time_series_storage.h5 new=/var/folders/27/2jr8c7gn4j72fvrg4qt81zrw8w_711/T/jl_5pYqF2\n", "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", "└ @ InfrastructureSystems ~/.julia/packages/InfrastructureSystems/v75Hd/src/validation.jl:51\n", "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", "└ @ InfrastructureSystems ~/.julia/packages/InfrastructureSystems/v75Hd/src/validation.jl:51\n", "┌ Warning: There are no ElectricLoad Components in the System\n", "└ @ PowerSystems ~/.julia/packages/PowerSystems/4kGrw/src/utils/IO/system_checks.jl:56\n" ] }, { "output_type": "execute_result", "data": { "text/plain": "Simulation()\n" }, "metadata": {}, "execution_count": 3 } ], "cell_type": "code", "source": [ "time_span = (0.0, 30.0)\n", "perturbation_trip = BranchTrip(1.0, \"BUS 1-BUS 2-i_1\")\n", "sim = PSID.Simulation(pwd(), omib_sys, time_span, perturbation_trip)" ], "metadata": {}, "execution_count": 3 }, { "cell_type": "markdown", "source": [ "This will automatically initialize the system by running a power flow\n", "and update `V_ref`, `P_ref` and hence `eq_p` (the internal voltage) to match the\n", "solution of the power flow. It will also initialize the states in the equilibrium,\n", "which can be printed with:" ], "metadata": {} }, { "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Voltage Variables\n", "====================\n", "BUS 1\n", "====================\n", "Vm 1.05\n", "θ -0.0\n", "====================\n", "BUS 2\n", "====================\n", "Vm 1.04\n", "θ 0.0229\n", "====================\n", "====================\n", "Differential States\n", "generator-102-1\n", "====================\n", "δ 0.1685\n", "ω 1.0\n", "====================\n" ] } ], "cell_type": "code", "source": [ "print_device_states(sim)" ], "metadata": {}, "execution_count": 4 }, { "cell_type": "markdown", "source": [ "To examine the calculated initial conditions, we can export them into a dictionary:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "Dict{String,Any} with 5 entries:\n \"generator-102-1\" => Dict(:ω=>1.0,:δ=>0.168525)\n \"V_R\" => Dict(102=>1.03973,101=>1.05)\n \"Vm\" => Dict(102=>1.04,101=>1.05)\n \"θ\" => Dict(102=>0.0228958,101=>-1.27016e-19)\n \"V_I\" => Dict(102=>0.0238095,101=>-1.33367e-19)" }, "metadata": {}, "execution_count": 5 } ], "cell_type": "code", "source": [ "x0_init = PSID.get_initial_conditions(sim)" ], "metadata": {}, "execution_count": 5 }, { "cell_type": "markdown", "source": [ "## Run the Simulation" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "Finally, to run the simulation we simply use:" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "PSID.execute!(\n", " sim, #simulation structure\n", " IDA(), #Sundials DAE Solver\n", " dtmax = 0.02,\n", "); #Arguments: Maximum timestep allowed" ], "metadata": {}, "execution_count": 6 }, { "cell_type": "markdown", "source": [ "In some cases, the dynamic time step used for the simulation may fail. In such case, the\n", "keyword argument `dtmax` can be used to limit the maximum time step allowed for the simulation." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Exploring the solution" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "`PowerSimulationsDynamics` has two functions to obtain different\n", "states of the solution:\n", " - `get_state_series(sim, (\"generator-102-1\", :δ))`: can be used to obtain the solution as\n", "a tuple of time and the required state. In this case, we are obtaining the rotor angle `:δ`\n", "of the generator named `\"generator-102-1\"`." ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.GRBackend() n=1}", "image/png": 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", "text/html": [ "\n", "\n" ], "image/svg+xml": [ "\n", "\n" ] }, "metadata": {}, "execution_count": 7 } ], "cell_type": "code", "source": [ "angle = get_state_series(sim, (\"generator-102-1\", :δ));\n", "Plots.plot(angle, xlabel = \"time\", ylabel = \"rotor angle [rad]\", label = \"rotor angle\")" ], "metadata": {}, "execution_count": 7 }, { "cell_type": "markdown", "source": [ "- `get_voltagemag_series(sim, 102)`: can be used to obtain the voltage magnitude as a\n", "tuple of time and voltage. In this case, we are obtaining the voltage magnitude at bus 102\n", "(where the generator is located)." ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.GRBackend() n=1}", "image/png": 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", "text/html": [ "\n", "\n" ], "image/svg+xml": [ "\n", "\n" ] }, "metadata": {}, "execution_count": 8 } ], "cell_type": "code", "source": [ "volt = get_voltagemag_series(sim, 102);\n", "Plots.plot(volt, xlabel = \"time\", ylabel = \"Voltage [pu]\", label = \"V_2\")" ], "metadata": {}, "execution_count": 8 }, { "cell_type": "markdown", "source": [ "## Optional: Small Signal Analysis" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "`PowerSimulationsDynamics` uses automatic differentiation to compute the reduced Jacobian\n", "of the system for the differential states. This can be used to analyze the local stability\n", "of the linearized system. We need to re-initialize our simulation:" ], "metadata": {} }, { "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ Info: Serialized time series data to /var/folders/27/2jr8c7gn4j72fvrg4qt81zrw8w_711/T/jl_ItVYcx/sys_time_series_storage.h5.\n", "[ Info: Serialized System to /var/folders/27/2jr8c7gn4j72fvrg4qt81zrw8w_711/T/jl_ItVYcx/sys.json\n", "[ Info: Loaded time series from storage file existing=sys_time_series_storage.h5 new=/var/folders/27/2jr8c7gn4j72fvrg4qt81zrw8w_711/T/jl_uOiNgv\n", "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", "└ @ InfrastructureSystems ~/.julia/packages/InfrastructureSystems/v75Hd/src/validation.jl:51\n", "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", "└ @ InfrastructureSystems ~/.julia/packages/InfrastructureSystems/v75Hd/src/validation.jl:51\n", "┌ Warning: There are no ElectricLoad Components in the System\n", "└ @ PowerSystems ~/.julia/packages/PowerSystems/4kGrw/src/utils/IO/system_checks.jl:56\n" ] }, { "output_type": "execute_result", "data": { "text/plain": "The system is small signal stable\n" }, "metadata": {}, "execution_count": 9 } ], "cell_type": "code", "source": [ "sim2 = PSID.Simulation(pwd(), omib_sys, time_span, perturbation_trip)\n", "\n", "small_sig = small_signal_analysis(sim2)" ], "metadata": {}, "execution_count": 9 }, { "cell_type": "markdown", "source": [ "The `small_sig` result can report the reduced jacobian for ``\\delta`` and ``\\omega``," ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "2×2 Array{Float64,2}:\n 0.0 376.991\n -0.466763 -0.317662" }, "metadata": {}, "execution_count": 10 } ], "cell_type": "code", "source": [ "small_sig.reduced_jacobian" ], "metadata": {}, "execution_count": 10 }, { "cell_type": "markdown", "source": [ "and can also be used to report the eigenvalues of the reduced linearized system:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "2-element Array{Complex{Float64},1}:\n -0.15883100381194412 - 13.264252860693972im\n -0.15883100381194412 + 13.264252860693972im" }, "metadata": {}, "execution_count": 11 } ], "cell_type": "code", "source": [ "small_sig.eigenvalues" ], "metadata": {}, "execution_count": 11 }, { "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": "1.5.2" }, "kernelspec": { "name": "julia-1.5", "display_name": "Julia 1.5.2", "language": "julia" } }, "nbformat": 4 }