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PowerFlow.R
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PowerFlow.R
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#' Calculate DC power flow
#'
#' Calculates the PowerFlow from a graph that contains the following attributes,
#' named edges, edgeweights, a balanced power generation and demand column. Writes a new edge attribute of power flow
#' (existing attribute of the same name will be overwritten).
#' The function outputs a a graph with the correct power flow values
#' @param g An igraph object representing the power grid
#' @param AZero A numeric matrix The transmission matrix of the original network
#' @param LineProperties A numeric matrix. a diagonal matrix of the Y characteristic of the network
#' @param EdgeName The variable that holds the edge names, a character string.
#' @param VertexName The variable that holds the names of the nodes, to identify the slack ref. a character string
#' @param Net_generation The name that the net generation data for each node is held in
#' @param power_flow A character string. The name of the edge attribute that will hold the power flow information
#' @keywords power flow
#'
#' @return A graph with a PowerFlow attribute assigned to the edges
#' @export
#' @examples
#' PowerFlow(g, AZero, LineProperties,
#'EdgeName = "Link",
#'VertexName = "name",
#'Net_generation = "BalencedPower",
#'power_flow = "PowerFlow")
#Azero is calculated externally
PowerFlow <- function(g, AZero, LineProperties,
EdgeName = "Link",
VertexName = "name",
Net_generation = "BalencedPower",
power_flow = "PowerFlow"){
#generate the slack reference bus for each component of the network
#This creates much smaller matrices which are quicker to invert and operate on.
slack_ref_df <- SlackRefFunc(g, VertexName, Generation = Net_generation)
if(nrow(slack_ref_df)!=0){
#Calculate power flow for each component of the network as seperate networks
gList <- 1:nrow(slack_ref_df) %>%
purrr::map(~{
#print(paste("PowerFlow for componant", .x))
SlackRef <- slack_ref_df %>% dplyr::slice(.x)
gsubset <- igraph::delete.vertices(g, igraph::components(g)$membership != .x)
if(SlackRef$Nodes > 1){
#gsubset <- PowerFlow2(gsubset, SlackRef$name, AZero = AZero, LineProperties = LineProperties, EdgeName, VertexName, Net_generation, power_flow)
InjectionVector <- igraph::get.vertex.attribute(gsubset, name = Net_generation)[igraph::get.vertex.attribute(gsubset, name = VertexName)!=SlackRef$name]
Power <- ImpPTDF(gsubset,
SlackRef = SlackRef$name,
AZero = AZero,
LineProperties = LineProperties,
injection_vector = InjectionVector,
EdgeName,
VertexName)$Power
#The Power object is a sparse matrix of dimension 1 will all values will in. this has to be converted to a vector
#as sometimes there an error is thrown "Error in eattrs[[name]][index] <- value :"
gsubset <- igraph::set_edge_attr(gsubset, name = power_flow, value = as.vector(Power))
}
gsubset
})
#create a list of all the edge and vertex attributes for each of the subgraphs
#Transpose the list and join all edges into a dataframe and all vertices into a dataframe
gList_2 <- 1:length(gList) %>% map(~{
igraph::as_data_frame(gList[[.x]], what = "both")
}) %>%
purrr::transpose(.) %>%
map(dplyr::bind_rows)
#create a graph from the list of length two created in the previous step
g <- graph_from_data_frame(gList_2$edges, directed = FALSE, vertices = gList_2$vertices)
#This function could be replaced with the method that just matches edges but it is so fast I don't care enough
#It would mean that union could be completley removed which woul be more secure and simpler
# g <- gList %>%
# Reduce(union2, .)
}
return(g)
}