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Basic Examples

Simple examples for setting the data visualization and changing the color or size of components.

Initialize

using PowerPlots, PowerModels
data = parse_file("$(joinpath(dirname(pathof(PowerModels)), ".."))/test/data/matpower/case5.m")
Dict{String, Any} with 13 entries:
  "bus"            => Dict{String, Any}("4"=>Dict{String, Any}("zone"=>1, "bus_…
  "source_type"    => "matpower"
  "name"           => "case5"
  "dcline"         => Dict{String, Any}()
  "source_version" => "2"
  "gen"            => Dict{String, Any}("4"=>Dict{String, Any}("vg"=>1.06414, "…
  "branch"         => Dict{String, Any}("4"=>Dict{String, Any}("br_r"=>0.00108,…
  "storage"        => Dict{String, Any}()
  "switch"         => Dict{String, Any}()
  "baseMVA"        => 100.0
  "per_unit"       => true
  "shunt"          => Dict{String, Any}()
  "load"           => Dict{String, Any}("1"=>Dict{String, Any}("source_id"=>Any…

Default Plot

powerplot(data)
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Change Plot Size

powerplot(data; width=300, height=300)
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Modify Colors

The colors of the components can be set, using simple keywords. Any valid CSS color can be used. If a single color is used, the component will not change color based on system data. See Color Ranges for how to use multiple colors.

powerplot(data; bus_color="orange",
                gen_color=:purple,
                branch_color="#AFAFAF",
                load_color=:black,
                width=300, height=300)
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The aliases node_color and edge_color can overwrite all nodes and edges respectively.

powerplot(data; node_color="red", edge_color="purple", width=300, height=300)
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Modify Component Size

The size of components can be set similarly. A good size for node devices is typically around 100x larger than edge devices.

powerplot(data, bus_size=1000, gen_size=100, load_size=200, branch_size=2, connector_size=10)
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Aliases to override all node and edge sizes.

powerplot(data, node_size=1000, edge_size=10, width=300, height=300)
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Visualizing System Data

Component data values from the PowerModels dictionary can be plotted by specifying the dictionary key. The key can be either a string or a symbol. The data type can be :ordinal, :nominal, or :quantitative.

p = powerplot(data, bus_data="bus_type",
                    bus_data_type="nominal",
                    branch_data="index",
                    branch_data_type="ordinal",
                    gen_data="pmax",
                    gen_data_type="quantitative",
                    load_data="pd",
                    load_data_type="quantitative",
                    width=300, height=300
)
Example block output

Color Ranges

Color ranges are automatically interpolated from a range that is provided. If only a single color is given, the component will not change color based on the data.

p = powerplot(data,
                    gen_data="pmax",
                    gen_data_type="quantitative",
                    gen_color=["#232323","#AAFAFA"],
                    width=300, height=300
)
Example block output

Color Schemes

Color schemes from the package ColorSchemes.jl can also be used to specify a color range.

using ColorSchemes
powerplot(data;
            gen_data=:pmax,
            gen_color=colorscheme2array(ColorSchemes.colorschemes[:summer]),
            gen_data_type=:quantitative,
            width=300, height=300
)
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Specify components for plotting

Supported components for plotting are specified as:

supported_component_types
7-element Vector{String}:
 "bus"
 "gen"
 "branch"
 "dcline"
 "load"
 "switch"
 "transformer"

By default, all of these components found in the data dictionary will be plotted. However, it is possible to plot a subset of the components using the components keyword.

powerplot(data; components=["bus","branch"], width=300, height=300)
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Power Flow

If the variables pf (power from) and pt (power to) exist in the data, power flow directions can be visualized using the show_flow boolean toggle (true by default).

# Solve AC power flow and add values to data dictionary
using Ipopt, PowerModels, PowerPlots, PGLib
data = pglib("case5_pjm")
result = solve_ac_opf(data, Ipopt.Optimizer)
update_data!(data, result["solution"])

p = powerplot(data, show_flow=true)
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Multinetworks

powerplot detects if a network is a multinetwork and will create a slider to select which network to view.

data_mn = PowerModels.replicate(data, 5)

# create random data for each time period
for (nwid,nw) in data_mn["nw"]
    for (branchid,branch) in nw["branch"]
        branch["value"] = rand()
    end
end

powerplot(data_mn, branch_data=:value, branch_data_type=:quantitative)
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Distribution Grids

Open a three-phase distribution system case using PowerModelsDistribution.jl and run the command powerplot on the data.

This dataset includes switches and transformers, which are then visualized in the powerplot.

# example works, but fails to run in documentation
using PowerModelsDistribution
using PowerPlots
eng = PowerModelsDistribution.parse_file("$(joinpath(dirname(pathof(PowerModelsDistribution)), ".."))/test/data/opendss/trans_3w_center_tap.dss")
math = transform_data_model(eng)
powerplot(math)
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