diff --git a/dev/api/internal/index.html b/dev/api/internal/index.html index 190f4b3d..cfcd1312 100644 --- a/dev/api/internal/index.html +++ b/dev/api/internal/index.html @@ -2,10 +2,10 @@ Internal API Reference · PowerNetworkMatrices.jl
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Internal API

PowerNetworkMatrices.get_data — Method
get_data(
     mat::PowerNetworkMatrices.PowerNetworkMatrix
 ) -> PowerNetworkMatrices.RowCache
-

returns the raw array data of the PowerNetworkMatrix

source
PowerNetworkMatrices.get_lookup — Method
get_lookup(
     mat::PowerNetworkMatrices.PowerNetworkMatrix
 ) -> Tuple{Dict, Dict}
 
returns the lookup tuple of the `PowerNetworkMatrix`. The first entry corresponds
 to the first dimension and the second entry corresponds to the second dimension. For
 instance in Ybus the first dimension is buses and second dimension is buses too, and in
-PTDF the first dimension is branches and the second dimension is buses
source
+PTDF the first dimension is branches and the second dimension is busessource diff --git a/dev/api/public/index.html b/dev/api/public/index.html index 6a49f3e1..07ad35c3 100644 --- a/dev/api/public/index.html +++ b/dev/api/public/index.html @@ -1,3 +1,3 @@ -Public API Reference · PowerNetworkMatrices.jl
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Public API Reference

PowerNetworkMatrices.AdjacencyMatrix — Type

Nodal incidence matrix (Adjacency) is an N x N matrix describing a power system with N buses. It represents the directed connectivity of the buses in a power system.

The AdjacencyMatrix Struct is indexed using the Bus Numbers, no need for them to be sequential

source
PowerNetworkMatrices.AdjacencyMatrix — Method

Builds a AdjacencyMatrix from a collection of buses and branches. The return is an N x N AdjacencyMatrix Array indexed with the bus numbers.

Keyword arguments

  • check_connectivity::Bool: Checks connectivity of the network using Goderya's algorithm
source
PowerNetworkMatrices.AdjacencyMatrix — Method

Builds a AdjacencyMatrix from the system. The return is an N x N AdjacencyMatrix Array indexed with the bus numbers.

Keyword arguments

  • check_connectivity::Bool: Checks connectivity of the network using Goderya's algorithm
source
PowerNetworkMatrices.LODF — Type

Line Outage Distribution Factors (LODFs) are a sensitivity measure of how a change in a line’s flow affects the flows on other lines in the system.

source
PowerNetworkMatrices.LODF — Type

Builds the LODF matrix from a group of branches and nodes. The return is a LOLDF array indexed with the branch name.

Keyword arguments

  • dist_slack::Vector{Float64}: Vector of weights to be used as distributed slack bus. The distributed slack vector has to be the same length as the number of buses
source
PowerNetworkMatrices.LODF — Type

Builds the LODF matrix from a system. The return is a LOLDF array indexed with the branch name.

Keyword arguments

  • dist_slack::Vector{Float64}: Vector of weights to be used as distributed slack bus. The distributed slack vector has to be the same length as the number of buses
source
PowerNetworkMatrices.PTDF — Type

Power Transfer Distribution Factors (PTDF) indicate the incremental change in real power that occurs on transmission lines due to real power injections changes at the buses.

The PTDF struct is indexed using the Bus numbers and branch names

source
PowerNetworkMatrices.PTDF — Method

Builds the PTDF matrix from a group of branches and nodes. The return is a PTDF array indexed with the bus numbers.

Keyword arguments

  • dist_slack::Vector{Float64}: Vector of weights to be used as distributed slack bus. The distributed slack vector has to be the same length as the number of buses
  • linear_solver::String: Linear solver to be used. Options are "Dense", "KLU" and "MKLPardiso
  • tol::Float64: Tolerance to eliminate entries in the PTDF matrix (default eps())
source
PowerNetworkMatrices.PTDF — Method

Builds the PTDF matrix from a system. The return is a PTDF array indexed with the bus numbers.

Keyword arguments

  • dist_slack::Vector{Float64}: Vector of weights to be used as distributed slack bus. The distributed slack vector has to be the same length as the number of buses
  • linear_solver::String: Linear solver to be used. Options are "Dense", "KLU" and "MKLPardiso
  • tol::Float64: Tolerance to eliminate entries in the PTDF matrix (default eps())
source
PowerNetworkMatrices.VirtualPTDF — Type

Power Transfer Distribution Factors (PTDF) indicate the incremental change in real power that occurs on transmission lines due to real power injections changes at the buses.

The PTDF struct is indexed using the Bus numbers and branch names

source
PowerNetworkMatrices.VirtualPTDF — Method

Builds the PTDF matrix from a group of branches and nodes. The return is a PTDF array indexed with the bus numbers.

Keyword arguments

  • dist_slack::Vector{Float64}: Vector of weights to be used as distributed slack bus. The distributed slack vector has to be the same length as the number of buses
source
PowerNetworkMatrices.VirtualPTDF — Method

Builds the PTDF matrix from a system. The return is a PTDF array indexed with the bus numbers.

Keyword arguments

  • dist_slack::Vector{Float64}: Vector of weights to be used as distributed slack bus. The distributed slack vector has to be the same length as the number of buses
source
PowerNetworkMatrices.Ybus — Type

Nodal admittance matrix (Ybus) is an N x N matrix describing a power system with N buses. It represents the nodal admittance of the buses in a power system.

The Ybus Struct is indexed using the Bus Numbers, no need for them to be sequential

source
PowerNetworkMatrices.Ybus — Type

Builds a Ybus from a collection of buses and branches. The return is a Ybus Array indexed with the bus numbers and the branch names.

Keyword arguments

  • check_connectivity::Bool: Checks connectivity of the network using Goderya's algorithm
source
PowerNetworkMatrices.Ybus — Method

Builds a Ybus from the system. The return is a Ybus Array indexed with the bus numbers and the branch names.

Keyword arguments

  • check_connectivity::Bool: Checks connectivity of the network
source
PowerNetworkMatrices.validate_connectivity — Method
validate_connectivity(sys::PowerSystems.System) -> Bool
-

Checks the network connectivity of the system.

Keyword arguments

  • connectivity_method::Function = goderya_connectivity: Specifies the method used as Goderya's algorithm (goderya_connectivity) or depth first search/network traversal (dfs_connectivity)
  • Note that the default Goderya method is more efficient, but is resource intensive and may not scale well on large networks.
source
+Public API Reference · PowerNetworkMatrices.jl
Edit on GitHub

Public API Reference

PowerNetworkMatrices.AdjacencyMatrix — Type

Nodal incidence matrix (Adjacency) is an N x N matrix describing a power system with N buses. It represents the directed connectivity of the buses in a power system.

The AdjacencyMatrix Struct is indexed using the Bus Numbers, no need for them to be sequential

source
PowerNetworkMatrices.AdjacencyMatrix — Method

Builds a AdjacencyMatrix from a collection of buses and branches. The return is an N x N AdjacencyMatrix Array indexed with the bus numbers.

Keyword arguments

  • check_connectivity::Bool: Checks connectivity of the network using Goderya's algorithm
source
PowerNetworkMatrices.AdjacencyMatrix — Method

Builds a AdjacencyMatrix from the system. The return is an N x N AdjacencyMatrix Array indexed with the bus numbers.

Keyword arguments

  • check_connectivity::Bool: Checks connectivity of the network using Goderya's algorithm
source
PowerNetworkMatrices.LODF — Type

Line Outage Distribution Factors (LODFs) are a sensitivity measure of how a change in a line’s flow affects the flows on other lines in the system.

source
PowerNetworkMatrices.LODF — Type

Builds the LODF matrix from a group of branches and nodes. The return is a LOLDF array indexed with the branch name.

Keyword arguments

  • dist_slack::Vector{Float64}: Vector of weights to be used as distributed slack bus. The distributed slack vector has to be the same length as the number of buses
source
PowerNetworkMatrices.LODF — Type

Builds the LODF matrix from a system. The return is a LOLDF array indexed with the branch name.

Keyword arguments

  • dist_slack::Vector{Float64}: Vector of weights to be used as distributed slack bus. The distributed slack vector has to be the same length as the number of buses
source
PowerNetworkMatrices.PTDF — Type

Power Transfer Distribution Factors (PTDF) indicate the incremental change in real power that occurs on transmission lines due to real power injections changes at the buses.

The PTDF struct is indexed using the Bus numbers and branch names

source
PowerNetworkMatrices.PTDF — Method

Builds the PTDF matrix from a group of branches and nodes. The return is a PTDF array indexed with the bus numbers.

Keyword arguments

  • dist_slack::Vector{Float64}: Vector of weights to be used as distributed slack bus. The distributed slack vector has to be the same length as the number of buses
  • linear_solver::String: Linear solver to be used. Options are "Dense", "KLU" and "MKLPardiso
  • tol::Float64: Tolerance to eliminate entries in the PTDF matrix (default eps())
source
PowerNetworkMatrices.PTDF — Method

Builds the PTDF matrix from a system. The return is a PTDF array indexed with the bus numbers.

Keyword arguments

  • dist_slack::Vector{Float64}: Vector of weights to be used as distributed slack bus. The distributed slack vector has to be the same length as the number of buses
  • linear_solver::String: Linear solver to be used. Options are "Dense", "KLU" and "MKLPardiso
  • tol::Float64: Tolerance to eliminate entries in the PTDF matrix (default eps())
source
PowerNetworkMatrices.VirtualPTDF — Type

Power Transfer Distribution Factors (PTDF) indicate the incremental change in real power that occurs on transmission lines due to real power injections changes at the buses.

The PTDF struct is indexed using the Bus numbers and branch names

source
PowerNetworkMatrices.VirtualPTDF — Method

Builds the PTDF matrix from a group of branches and nodes. The return is a PTDF array indexed with the bus numbers.

Keyword arguments

  • dist_slack::Vector{Float64}: Vector of weights to be used as distributed slack bus. The distributed slack vector has to be the same length as the number of buses
source
PowerNetworkMatrices.VirtualPTDF — Method

Builds the PTDF matrix from a system. The return is a PTDF array indexed with the bus numbers.

Keyword arguments

  • dist_slack::Vector{Float64}: Vector of weights to be used as distributed slack bus. The distributed slack vector has to be the same length as the number of buses
source
PowerNetworkMatrices.Ybus — Type

Nodal admittance matrix (Ybus) is an N x N matrix describing a power system with N buses. It represents the nodal admittance of the buses in a power system.

The Ybus Struct is indexed using the Bus Numbers, no need for them to be sequential

source
PowerNetworkMatrices.Ybus — Type

Builds a Ybus from a collection of buses and branches. The return is a Ybus Array indexed with the bus numbers and the branch names.

Keyword arguments

  • check_connectivity::Bool: Checks connectivity of the network using Goderya's algorithm
source
PowerNetworkMatrices.Ybus — Method

Builds a Ybus from the system. The return is a Ybus Array indexed with the bus numbers and the branch names.

Keyword arguments

  • check_connectivity::Bool: Checks connectivity of the network
source
PowerNetworkMatrices.validate_connectivity — Method
validate_connectivity(sys::PowerSystems.System) -> Bool
+

Checks the network connectivity of the system.

Keyword arguments

  • connectivity_method::Function = goderya_connectivity: Specifies the method used as Goderya's algorithm (goderya_connectivity) or depth first search/network traversal (dfs_connectivity)
  • Note that the default Goderya method is more efficient, but is resource intensive and may not scale well on large networks.
source
diff --git a/dev/assets/documenter.js b/dev/assets/documenter.js index 6adfbbbf..32e27c51 100644 --- a/dev/assets/documenter.js +++ b/dev/assets/documenter.js @@ -4,10 +4,9 @@ requirejs.config({ 'highlight-julia': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.5.1/languages/julia.min', 'headroom': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/headroom.min', 'jqueryui': 'https://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.12.1/jquery-ui.min', - 'katex-auto-render': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.13.24/contrib/auto-render.min', 'jquery': 'https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.0/jquery.min', + 'mathjax': 'https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/MathJax.js?config=TeX-AMS_HTML', 'headroom-jquery': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/jQuery.headroom.min', - 'katex': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.13.24/katex.min', 'highlight': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.5.1/highlight.min', 'highlight-julia-repl': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.5.1/languages/julia-repl.min', }, @@ -17,10 +16,8 @@ requirejs.config({ "highlight" ] }, - "katex-auto-render": { - "deps": [ - "katex" - ] + "mathjax": { + "exports": "MathJax" }, "headroom-jquery": { "deps": [ @@ -36,32 +33,44 @@ requirejs.config({ } }); //////////////////////////////////////////////////////////////////////////////// -require(['jquery', 'katex', 'katex-auto-render'], function($, katex, renderMathInElement) { -$(document).ready(function() { - renderMathInElement( - document.body, - { - "delimiters": [ - { - "left": "$", - "right": "$", - "display": false - }, - { - "left": "$$", - "right": "$$", - "display": true - }, - { - "left": "\\[", - "right": "\\]", - "display": true +require(['mathjax'], function(MathJax) { +MathJax.Hub.Config({ + "jax": [ + "input/TeX", + "output/HTML-CSS", + "output/NativeMML" + ], + "TeX": { + "equationNumbers": { + "autoNumber": "AMS" } + }, + "tex2jax": { + "inlineMath": [ + [ + "$", + "$" + ], + [ + "\\(", + "\\)" + ] + ], + "processEscapes": true + }, + "config": [ + "MMLorHTML.js" + ], + "extensions": [ + "MathMenu.js", + "MathZoom.js", + "TeX/AMSmath.js", + "TeX/AMSsymbols.js", + "TeX/autobold.js", + "TeX/autoload-all.js" ] } - - ); -}) +); }) //////////////////////////////////////////////////////////////////////////////// diff --git a/dev/index.html b/dev/index.html index 30cd80a9..36a086b8 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,2 +1,2 @@ -Welcome Page · PowerNetworkMatrices.jl
Edit on GitHub

PowerNetworkMatrices.jl

Overview

PowerNetworkMatrices.jl documentation and code are organized according to the needs of different users depending on their skillset and requirements. In broad terms there are three categories:

  • Modeler: Users that want to run a particular analysis or experiment and use PowerNetworkMatrices.jl to develop data sets.

  • Model Developer: Users that want to develop custom components and structs in order to exploit PowerNetworkMatrices.jl features to produce custom data sets.

  • Code Base Developers: Users that want to add new core functionalities or fix bugs in the core capabilities of PowerNetworkMatrices.jl.

PowerNetworkMatrices.jl is an active project under development, and we welcome your feedback, suggestions, and bug reports.

Installation

The latest stable release of PowerNetworkMatrices can be installed using the Julia package manager with

] add PowerNetworkMatrices

For the current development version, "checkout" this package with

] add PowerNetworkMatrices#master

PowerNetworkMatrices has been developed as part of the Scalable Integrated Infrastructure Planning (SIIP) initiative at the U.S. Department of Energy's National Renewable Energy Laboratory (NREL).

+Welcome Page · PowerNetworkMatrices.jl
Edit on GitHub

PowerNetworkMatrices.jl

Overview

PowerNetworkMatrices.jl documentation and code are organized according to the needs of different users depending on their skillset and requirements. In broad terms there are three categories:

  • Modeler: Users that want to run a particular analysis or experiment and use PowerNetworkMatrices.jl to develop data sets.

  • Model Developer: Users that want to develop custom components and structs in order to exploit PowerNetworkMatrices.jl features to produce custom data sets.

  • Code Base Developers: Users that want to add new core functionalities or fix bugs in the core capabilities of PowerNetworkMatrices.jl.

PowerNetworkMatrices.jl is an active project under development, and we welcome your feedback, suggestions, and bug reports.

Installation

The latest stable release of PowerNetworkMatrices can be installed using the Julia package manager with

] add PowerNetworkMatrices

For the current development version, "checkout" this package with

] add PowerNetworkMatrices#master

PowerNetworkMatrices has been developed as part of the Scalable Integrated Infrastructure Planning (SIIP) initiative at the U.S. Department of Energy's National Renewable Energy Laboratory (NREL).

diff --git a/dev/quick_start_guide/index.html b/dev/quick_start_guide/index.html index a2e2b8cf..e090d5b5 100644 --- a/dev/quick_start_guide/index.html +++ b/dev/quick_start_guide/index.html @@ -1,2 +1,2 @@ -Quick Start Guide · PowerNetworkMatrices.jl
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+Quick Start Guide · PowerNetworkMatrices.jl
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diff --git a/dev/search/index.html b/dev/search/index.html index d103f00f..7209be77 100644 --- a/dev/search/index.html +++ b/dev/search/index.html @@ -1,2 +1,2 @@ -Search · PowerNetworkMatrices.jl

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    +Search · PowerNetworkMatrices.jl

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      diff --git a/dev/tutorials/intro_page/index.html b/dev/tutorials/intro_page/index.html index 4431150a..2b1b7805 100644 --- a/dev/tutorials/intro_page/index.html +++ b/dev/tutorials/intro_page/index.html @@ -1,2 +1,2 @@ -Tutorials · PowerNetworkMatrices.jl
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      +Tutorials · PowerNetworkMatrices.jl
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