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* added upscale_droop folder * Addressed comments --------- Co-authored-by: Phoebe <[email protected]>
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impose_sample_size_threshold_compliance <- function(Proportions, sample_threshold){ | ||
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# Add OEM's to 'Other' if no corresponding capacity in CER | ||
Proportions <- Proportions %>% ungroup(manufacturer) %>% mutate(sample_threshold) | ||
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Proportions <- mutate(Proportions,manufacturer = ifelse(is.na(Count), | ||
'Other', manufacturer)) | ||
# Add manufacturers with a small sample size to Other group. | ||
Proportions <- mutate(Proportions, Sample_size = ifelse(is.na(Sample_size), 0, Sample_size)) | ||
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Proportions <- mutate(Proportions, manufacturer = ifelse(is.na(capacity), | ||
'Other', manufacturer)) | ||
Proportions <- mutate(Proportions, | ||
manufacturer = ifelse(Sample_size < sample_threshold | | ||
manufacturer == "Unknown" | | ||
manufacturer == "Multiple" | | ||
manufacturer == "Mixed" | | ||
manufacturer == "" | | ||
is.na(manufacturer), | ||
"Other", manufacturer) | ||
) | ||
# Recalculate disconnection count and sample size. | ||
Proportions <- group_by(Proportions, Standard_Version, manufacturer, StdComplianceCombined) | ||
Proportions <- summarise(Proportions, | ||
Count = sum(Count, na.rm = TRUE), | ||
Sample_size = sum(Sample_size, na.rm = TRUE), | ||
capacity = sum(capacity, na.rm = TRUE)) | ||
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# overwrite "Other capacity" | ||
cer_install_data <- filter(cer_install_data, state == region) | ||
total_install_on_event_date <- sum(filter(cer_install_data, date == min(event_date, last(cer_install_data$date)))$capacity) | ||
total_install_2005_std <- sum(filter(cer_install_data, date == "2016-10-16")$capacity) | ||
total_install_2015_std <- sum(filter(cer_install_data, date == "2022-01-01")$capacity) - total_install_2005_std | ||
total_install_2020_std <- total_install_on_event_date - total_install_2015_std - total_install_2005_std | ||
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Standard_Capacity <- c(total_install_2015_std, total_install_2020_std) | ||
Standard_Version <- c("AS4777.2:2015", "AS4777.2:2020") | ||
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Standard_cap <- data.frame(Standard_Version, Standard_Capacity) | ||
Standard_cap$Standard_Version <- as.character(Standard_cap$Standard_Version) | ||
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# | ||
# Set 'other' cap to be difference of total install of standard and the OEMS with > 30 samples' | ||
Proportions <- mutate(Proportions, capacity = ifelse(manufacturer == "Other", 0, capacity)) | ||
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Capacity_OEM <- group_by(Proportions, Standard_Version, manufacturer, capacity) %>% summarise() | ||
Capacity_OEM <- group_by(Capacity_OEM, Standard_Version) %>% summarise(capacity_OEM = sum(capacity)) | ||
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Capacities <- left_join(Standard_cap, Capacity_OEM, by = "Standard_Version") | ||
Other_capacity <- mutate(Capacities, other_capacity = Standard_Capacity - capacity_OEM) | ||
# | ||
Proportions <- left_join(Proportions, Other_capacity[c("Standard_Version", "other_capacity")], by = "Standard_Version") | ||
Proportions <- mutate(Proportions, capacity = ifelse(manufacturer == "Other", other_capacity, capacity)) | ||
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Proportions <- mutate(Proportions, proportion = Count / Sample_size) | ||
Proportions <- data.frame(Proportions) | ||
return(Proportions) | ||
} | ||
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combine_erroneous_OEMs <- function(df) { | ||
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# manufacturers to 'other' | ||
df <- mutate(df, manufacturer = ifelse(is.na(proportion_capacity), | ||
'Other', manufacturer)) | ||
# Also rename any manufacturers that may have been missed as 'other' (probably redundant after the step above??) | ||
df <- mutate(df, | ||
manufacturer = ifelse(manufacturer == "Unknown" | | ||
manufacturer == "Multiple" | | ||
manufacturer == "Mixed" | | ||
manufacturer == "" | | ||
is.na(manufacturer), | ||
"Other", manufacturer)) | ||
return(df) | ||
} | ||
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########################################### PARAMATERS and External Functions ##################################### | ||
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# Make sure to highlight and run the two functions at the bottom of the script first before running! | ||
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source("load_tool_environment.R") | ||
source("upscale_droop/upscale_droop_functions.R") | ||
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event_date <- "2022-11-12" | ||
region <- "SA" | ||
site_norm <- FALSE # If true, no external capacity factor used | ||
external_capacity_factor <- 0.28 | ||
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underlying_data_file <- "C:/Users/mtrollip/Local/GitHub/DER_disturbance_analysis/data/2022-11-12/phoebe_results_longer_window/20221112_underlying_35min_window.csv" | ||
CER_install_data_file <- "inbuilt_data/cer_cumulative_capacity_and_number.csv" | ||
CER_install_manufacturer_data_file <- "inbuilt_data/cer_cumulative_capacity_and_number_by_manufacturer.csv" | ||
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# Where you want to store your outputted results | ||
output_directory <- "C:/Users/mtrollip/Local/GitHub/DER_disturbance_analysis/data/2022-11-12/phoebe_results_longer_window/droop_scale_response" | ||
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########################################### Read in Data ################################### | ||
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# Underlying data | ||
UD_raw <- read.csv(file = underlying_data_file, header = TRUE, stringsAsFactors = FALSE) | ||
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########################################### Process Data ################################### | ||
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# Here we: | ||
#1. filter out bad sites | ||
#2. filter out standards that arent expected to provide droop | ||
#3. overwrite the 2015 VDRT and Transition 2020-21 standards as 2015 Standard | ||
#4. Sub in 2020 droop rsponse column into 2015 droop response column for 2020 circuits (the 2015 droop response | ||
# column becomes the combined droop response column) | ||
#5. bucket Compliant and Non-compliant responding together as one group | ||
#6. Bucket together droop response and standard into a combined standard and compliance column | ||
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# Filter out sites that arent expected to perform droop response | ||
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standards_with_droop <- c('AS4777.2:2015', 'AS4777.2:2015 VDRT', 'AS4777.2:2020', 'Transition 2020-21') | ||
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UD <- filter(UD_raw, Standard_Version %in% standards_with_droop) | ||
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# # Filter out any sites with "bad data" | ||
BadCategories <- c("Not enough data", "Undefined", "UFLS Dropout") | ||
# Select distinct site_id/response_category combos | ||
BadSiteIds <- group_by(UD_raw, site_id, compliance_status) %>% summarise() | ||
# Filter to get a list of site_ids to remove | ||
# Note that R reads in the NAs as actual NA values which is annoying. You do you R. | ||
BadSiteIds <- filter(BadSiteIds,compliance_status %in% BadCategories | is.na(compliance_status)) | ||
# Remove bad site_ids. Note the ! negates the %in% operator to make in 'not in' | ||
UD <- filter(UD,!site_id %in% BadSiteIds$site_id) | ||
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# Group 2015 VDRT and Transition 2020-21 into 2015 Standard | ||
UD <- mutate(UD, Standard_Version = | ||
ifelse(Standard_Version %in% c("AS4777.2:2015 VDRT", "Transition 2020-21"), | ||
"AS4777.2:2015", Standard_Version)) | ||
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# write in 2020 standard into the droop compliance column if standard is 2020 | ||
UD <- mutate(UD, compliance_status = ifelse(Standard_Version == "AS4777.2:2020", compliance_status_2020, compliance_status)) | ||
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# Concatenate standards with droop response (for grouping) | ||
UD <- mutate(UD, compliance_status = ifelse(compliance_status %in% c("Non-compliant Responding", "Compliant"), "Responding", | ||
ifelse(compliance_status == "Non-compliant", "Not Responding", compliance_status))) | ||
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UD <- mutate(UD, StdComplianceCombined = paste(Standard_Version, compliance_status)) | ||
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######################## Get proportion (by count) of each droop response per standard and OEM ############################### | ||
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Proportions <- group_by(UD, Standard_Version, c_id, compliance_status, manufacturer) %>% summarise() | ||
Proportions <- mutate(Proportions, StdComplianceCombined = paste(Standard_Version, compliance_status)) | ||
TotalPerStandard_and_OEM <- group_by(Proportions, Standard_Version, manufacturer) %>% summarise(Sample_size=n()) | ||
Proportions <- group_by(Proportions, Standard_Version, StdComplianceCombined, manufacturer) %>% summarise(Count=n()) | ||
Proportions <- left_join(Proportions, TotalPerStandard_and_OEM, by = c("Standard_Version","manufacturer")) | ||
Proportions <- mutate(Proportions, Proportion = Count/Sample_size) | ||
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################################### Get CER installed capacity per group ########################################## | ||
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#1. gets capacity per Standard and OEM | ||
#2. Assigns portions of that capacity to each group based on Proportions in previous section | ||
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# Get fleet capacity data | ||
cer_install_data <- read.csv(CER_install_data_file, | ||
header = TRUE, stringsAsFactors = FALSE) | ||
manufacturer_install_data <- read.csv(CER_install_manufacturer_data_file, | ||
header = TRUE, stringsAsFactors = FALSE) | ||
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# Get manufacturer installed capacity for each OEM and all Standards with droop compliance at the time of the event | ||
manufacturer_install_data <- calc_installed_capacity_by_standard_and_manufacturer(manufacturer_install_data) | ||
manufacturer_install_data <- get_manufacturer_capacitys(manufacturer_install_data, event_date, region) | ||
manufacturer_install_data <- filter(manufacturer_install_data, Standard_Version %in% standards_with_droop) | ||
# combined VDRT and Transition with 2015 | ||
manufacturer_install_data <- mutate(manufacturer_install_data, Standard_Version = | ||
ifelse(Standard_Version %in% c("AS4777.2:2015 VDRT", "Transition 2020-21"), | ||
"AS4777.2:2015", Standard_Version)) | ||
manufacturer_install_data <- group_by(manufacturer_install_data, Standard_Version, s_state, manufacturer) | ||
manufacturer_install_data <- summarise(manufacturer_install_data, capacity = sum(capacity)) | ||
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Proportions <- merge(Proportions, manufacturer_install_data, by = c('Standard_Version', 'manufacturer'), | ||
all = TRUE) | ||
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# Combined any OEM with less than 30 samples into 'Other' group. Correctly adjust 'Other' install capacity | ||
Proportions <- impose_sample_size_threshold_compliance(Proportions, 30) | ||
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# Get capacity per droop response as a proportion of total isntalled capacity for that OEM and Standard. | ||
Proportions <- mutate(Proportions, proportion_capacity = proportion*capacity) | ||
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##################################### Upscale MW profile by OEM ################################### | ||
# Two options here | ||
# Option 1: Use a 'site performance factor'. The power profile of a site is divided by the sites max capacity. | ||
# The average site performance factor for given OEM, Standard and Droop response is used to represent the capacity factor per timestep for that class. | ||
# The average site performance factor is multiplied by the proportional installed capacity of that OEM, Standard and droop response (stored in Proportions) | ||
# The result is summed over all OEMs for that given Standard and droop response to provide the upscaled MW profile per droop response class and Standard | ||
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# Option 2: Use 'external capacity factor'. The power profile is normalised to its output immediately before the event | ||
# (i.e this gives an output of 1 just before event). | ||
# The normalised power trace is multiplied by the proportional installed capacity for that OEM, Standard and droop response (store in Proportions) | ||
# This would mean each class would at be at its maximum capacity output immediately before the event (as normalised value is 1 at pre event interval) | ||
# the traces are then scaled by the external capacity factor, 0.X, such that the outputs is at X% its maximum capacity during the pre event interval | ||
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################################################################################################################### | ||
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# Option 1 | ||
if(site_norm) { | ||
# Get the average site performance factor in underlyng data for each class per timestep | ||
site_performance_factor <- group_by(UD, site_id, ts) %>% | ||
summarise(site_performance_factor = first(site_performance_factor), | ||
manufacturer = first(manufacturer), Standard_Version = first(Standard_Version), | ||
StdComplianceCombined = first(StdComplianceCombined)) | ||
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# frst join UD with proportions to identify which OEMs have been assigned to "other" (come up as NA in proportion_cap) | ||
site_performance_factor <- left_join(site_performance_factor, Proportions[c("StdComplianceCombined", "proportion_capacity", "manufacturer")], | ||
by = c("StdComplianceCombined","manufacturer")) | ||
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# combine any OEM's into 'Other' that are < 30 samples or have unknown, multiple / mixed. This apears as with a proportion capacity of 'na' following the join | ||
site_performance_factor <- combine_erroneous_OEMs(site_performance_factor) | ||
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# Combine and get average site performance capacity factor for each class with the OEMs > 30 samples. | ||
site_performance_factor <- group_by(site_performance_factor, ts, | ||
manufacturer, Standard_Version, StdComplianceCombined) %>% | ||
summarise(average_site_performance_factor = mean(site_performance_factor)) | ||
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# add back the proportion capacities | ||
upscale_MW_profile_OEM <- left_join(site_performance_factor, Proportions[c("StdComplianceCombined", "proportion_capacity", "manufacturer")], | ||
by = c("StdComplianceCombined","manufacturer")) | ||
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# upscale per class | ||
upscale_MW_profile_OEM <- mutate(upscale_MW_profile_OEM, upscale_MW = average_site_performance_factor*proportion_capacity/1000) | ||
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# Combine the total from each OEM | ||
upscale_MW_profile <- group_by(upscale_MW_profile_OEM, ts, StdComplianceCombined) | ||
upscale_MW_profile <- summarise(upscale_MW_profile, upscale_MW = sum(upscale_MW)) | ||
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pivot_upscaled_MW_profile <- pivot_wider(select(upscale_MW_profile, c("ts","StdComplianceCombined", "upscale_MW")), names_from = StdComplianceCombined, | ||
values_from =upscale_MW) | ||
write.csv(pivot_upscaled_MW_profile, paste(output_directory,"droop_compliance_upscale_by_OEM_site_normalisation.csv",sep=""), row.names = FALSE) | ||
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} else{ | ||
c_id_norm_power <- UD[c('ts', 'c_id', 'c_id_norm_power', 'manufacturer', 'Standard_Version', 'StdComplianceCombined')] | ||
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# Add OEMs < 30 samples or that dont exist in CER into 'other'. | ||
c_id_norm_power <- left_join(c_id_norm_power, Proportions[c("StdComplianceCombined", "proportion_capacity", "manufacturer")], by = c("StdComplianceCombined","manufacturer")) | ||
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# For all OEMs in the c_id_norm_power df that are not present in the list of OEMs with n>30, set these | ||
# manufacturers to 'other' | ||
c_id_norm_power <- combine_erroneous_OEMs(c_id_norm_power) | ||
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#Recombine and get an average normalised power profile for each class | ||
average_c_id_norm_power <- group_by(c_id_norm_power, ts, manufacturer, StdComplianceCombined, Standard_Version) %>% | ||
summarise(average_c_id_norm_power = mean(c_id_norm_power)) | ||
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# add back the porportion capacities | ||
upscale_MW_profile_OEM <- left_join(average_c_id_norm_power, | ||
Proportions[c("StdComplianceCombined", "proportion_capacity", "manufacturer")], | ||
by = c("StdComplianceCombined","manufacturer")) | ||
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# upscale per class | ||
upscale_MW_profile_OEM <- mutate(upscale_MW_profile_OEM, upscale_MW = average_c_id_norm_power*proportion_capacity/1000) | ||
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# Combine the total per timestamp from each OEM for each Standard and response type | ||
upscale_MW_profile <- group_by(upscale_MW_profile_OEM, ts, StdComplianceCombined) | ||
upscale_MW_profile <- summarise(upscale_MW_profile, upscale_MW = sum(upscale_MW)) | ||
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# filter out Off at t0 class as not applicable when dividing by pre-event interval output (will be dividing by 0) | ||
upscale_MW_profile <- filter(upscale_MW_profile, !StdComplianceCombined %in% c("AS4777.2:2015 Off at t0", "AS4777.2:2020 Off at t0")) | ||
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# Get fleet capacity per Standard and droop response type | ||
Prop_per_class <- group_by(Proportions, StdComplianceCombined) %>% summarise(total_capacity = sum(proportion_capacity)/1000) | ||
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# Add fleet capacity per Standard and droop response type to profile and divide to re-normalise the traces | ||
upscale_MW_profile <- left_join(upscale_MW_profile, Prop_per_class, by = c("StdComplianceCombined")) | ||
upscale_MW_profile <- mutate(upscale_MW_profile, normalised_power = upscale_MW/total_capacity) | ||
upscale_MW_profile <- mutate(upscale_MW_profile, upscale_MW = upscale_MW * external_capacity_factor) | ||
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pivot_upscaled_MW_profile <- pivot_wider(select(upscale_MW_profile, c("ts","StdComplianceCombined", "upscale_MW")), names_from = StdComplianceCombined, | ||
values_from =upscale_MW) | ||
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# Get the average c_id norm power per class based on | ||
write.csv(pivot_upscaled_MW_profile, paste(output_directory,"droop_compliance_upscale_by_OEM_external_cap_factor_norm.csv", sep=""), row.names = FALSE) | ||
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} |