Commit 479fa179 authored by Turner's avatar Turner
Browse files

yr2022 and scratch dir

parent b86e7c48

R/dam_conversion.R

+15 −3
Original line number Diff line number Diff line
@@ -36,9 +36,21 @@ convert_EIA_to_COMPLX <- function(EHA, 
              by = join_by(year)) |>

    mutate(cap_MWh = nameplate_MW * n_hrs,

           CF = gen_MWh / cap_MWh) |>

    arrange(COMPLXID) ->

    gen_cap_CF_1970_2021

    arrange(COMPLXID) |> 

    filter(!is.na(COMPLXID)) ->

    gen_cap_CF_1970_2022

  

  return(gen_cap_CF_1970_2021)

  gen_cap_CF_1970_2022 |> 

    filter(year == 2021) |> 

    select(COMPLXID, nameplate_2021_MW = nameplate_MW) ->

    nameplate_2021



  gen_cap_CF_1970_2022 |> 

    left_join(nameplate_2021, by = join_by(COMPLXID)) |> 

    mutate(CF_static = gen_MWh / (nameplate_2021_MW * n_hrs)) ->

    gen_cap_CF_1970_2022_plus_static

  

  

  return(gen_cap_CF_1970_2022_plus_static)



}

R/plots.R

+188 −0
Original line number Diff line number Diff line
@@ -1390,4 +1390,192 @@ plot_shift_sparks <- function(trend_shift_analysis, 
}





plot_trend_comparisons <- function(EHA,

                                   CF_trends,

                                   CF_trends_static){



  #tar_read(CF_trends) -> CF_trends

  #tar_read(CF_trends_fixed_CF) -> CF_trends_static

  #tar_read(EHA) -> EHA

  

  # ensure HUC2 grabbed proper

  options(scipen = 999)

  

  CF_trends |> 

    filter(!is.na(sens_slope)) |> 

    pull(COMPLXID) |> unique() ->

    study_dam_IDs_610

  

  st_read(EHA, quiet = TRUE) |> as_tibble() |> 

    mutate(COMPLXID = str_replace(EHA_PtID, "\\_..*", "")) |>

    filter(COMPLXID %in% study_dam_IDs_610,

           PtName != "Hoover Dam (NV)",

           PtName != "St Stephen",

           !(COMPLXID == "hc0027" & Mode == "Run-of-river"),

           !(COMPLXID == "hc0225" & Mode == "Run-of-river")) |>

    summarise(CH_MW = sum(CH_MW),

              .by = c(COMPLXID, State, HUC, Mode)) |> 

    mutate(size_cat = case_when(

      CH_MW > 1000 ~ "> 1  ",

      CH_MW > 500 & CH_MW <= 1000 ~ "0.5 - 1  ",

      CH_MW > 100 & CH_MW <= 500 ~ "0.1 - 0.5  ",

      CH_MW >= 5 & CH_MW <= 100 ~ "0.05 - 0.1  "

    )) |>

    mutate(size_cat = factor(size_cat, levels = c(

      "0.05 - 0.1  ",

      "0.1 - 0.5  ",

      "0.5 - 1  ",

      "> 1  "

    ))) |> 

    mutate(region = case_when(

      substr(HUC, 1, 2) == "18" ~ "California",

      substr(HUC, 1, 2) == "17" ~ "Pacific NW",

      substr(HUC, 1, 2) %in% c("14", "15") ~ "Colorado River Basin",

      State %in% c("NY", "VT",

                   "NH", "ME", "MA",

                   "RI", "CT") ~ "Northeast",

      State %in% c("TN", "AL",

                   "GA", "SC", "MD",

                   "VA", "WV", "KY") ~ "Southeast",

      TRUE ~ "Other"

    ),

    region = factor(region, levels = c(

      "Pacific NW", "Other", "Northeast", "California",

      "Colorado River Basin", "Southeast"

    ))) -> dam_detail



  

  CF_trends |> 

    filter(analysis %in% c(

      "flow period add 2022",

      "pre renewables period"

    )) |> 

    filter(!is.na(sens_slope)) ->

    full_vs_pre



  



  full_vs_pre |> select(COMPLXID, analysis, sens_slope) |> 

    mutate(sens_slope = sens_slope * 1000) |> 

    pivot_wider(names_from = analysis, values_from = sens_slope) ->

    compare_w_pre_renewables

    

  compare_w_pre_renewables |> 

    left_join(dam_detail,

              join_by(COMPLXID))  |> 

    mutate(type = case_when(

      Mode %in% c("Peaking",

                  "Intermediate Peaking",

                  "Run-of-river/Peaking") ~ "Peaking or intermediate peaking   ",

      TRUE ~ "Run-of-river, reregulating, canal/conduit"

    )) |> 

    ggplot(aes(`flow period add 2022`,

               `pre renewables period`,

               size = size_cat,

               fill = type)) +

    geom_abline(slope = 1) +

    geom_hline(yintercept = 0, linetype = 2) +

    geom_vline(xintercept = 0, linetype = 2) +

    geom_point(pch = 21,

               alpha = 0.5) +

    scale_size_manual(values = c(

      "0.05 - 0.1  " = 1,

      "0.1 - 0.5  " = 2,

      "0.5 - 1  " = 4,

      "> 1  " = 7

    )) +

    scale_fill_manual(values = c(

      "Peaking or intermediate peaking   " = "dodgerblue",

      "Run-of-river, reregulating, canal/conduit" = "grey"

    )) +

    facet_wrap(~region) +

    labs(size = "Nameplate (GW)",

         fill = "Mode of operation",

         x = "CF trend (PPPD)\n1980 - 2022",

         y = "CF trend (PPPD)\n1980 - 2009") +

    coord_equal() +

    theme_bw() +

    theme(strip.background = element_blank(),

          legend.position = "top",

          legend.direction = "horizontal",

          legend.box = "vertical",

          axis.title.y = element_text(angle = 0, vjust = 0.5))

  

  



  bind_rows(

    CF_trends_static |> 

      filter(COMPLXID %in% study_dam_IDs_610) |> 

      filter(analysis == "flow period add 2022") |> 

      mutate(trend_PPPD = sens_slope * 1000,

             data = "static") |> 

      select(COMPLXID, trend_PPPD, data),

    CF_trends |> 

      filter(COMPLXID %in% study_dam_IDs_610) |> 

      filter(analysis == "flow period add 2022") |> 

      mutate(trend_PPPD = sens_slope * 1000,

             data = "actual") |> 

      select(COMPLXID, trend_PPPD, data)

  ) -> CF_vs_gen_trends





  CF_vs_gen_trends |> #filter(is.na(trend_PPPD))

    # summarise(

    #   med = median(trend_PPPD, na.rm = T),

    #   q95 = quantile(trend_PPPD, 0.95, na.rm = T),

    #   q05 = quantile(trend_PPPD, 0.05, na.rm = T),

    #   .by = data

    # ) |> 

    pivot_wider(names_from = data, values_from = trend_PPPD) |> 

    left_join(dam_detail,

              join_by(COMPLXID)) |> 

    ggplot(aes(actual,

               static,

               size = size_cat)) +

    geom_abline(slope = 1) +

    geom_hline(yintercept = 0, linetype = 2) +

    geom_vline(xintercept = 0, linetype = 2) +

    geom_point(pch = 21,

               alpha = 0.5, fill = "dodgerblue") +

    scale_size_manual(values = c(

      "0.05 - 0.1  " = 1,

      "0.1 - 0.5  " = 2,

      "0.5 - 1  " = 4,

      "> 1  " = 7

    )) +

    # scale_fill_manual(values = c(

    #   "Peaking or intermediate peaking   " = "dodgerblue",

    #   "Run-of-river, reregulating, canal/conduit" = "grey"

    # )) +

    facet_wrap(~region) +

    labs(size = "Nameplate (GW)",

         fill = "Mode of operation",

         x = "CF trend (PPPD)\n1980 - 2022",

         y = "CF* trend (PPPD)\n1980 - 2022\n") +

    coord_equal() +

    theme_bw() +

    theme(strip.background = element_blank(),

          legend.position = "top",

          legend.direction = "horizontal",

          legend.box = "vertical",

          axis.title.y = element_text(angle = 0, vjust = 0.5))

  

  

  CF_vs_gen_trends |> #filter(is.na(trend_PPPD))

    # summarise(

    #   med = median(trend_PPPD, na.rm = T),

    #   q95 = quantile(trend_PPPD, 0.95, na.rm = T),

    #   q05 = quantile(trend_PPPD, 0.05, na.rm = T),

    #   .by = data

    # ) |> 

    pivot_wider(names_from = data, values_from = trend_PPPD) |> 

    filter(actual < static) |> #342 with cap additions

    filter(actual < 0) |> #of which 87% (298) trending down CF

    filter(static < 0)

  

    

  

}

R/trends.R

+3 −2
Original line number Diff line number Diff line
@@ -11,7 +11,8 @@ get_CF_trends <- function(annual_CFs){ 
    ~start_yr, ~end_yr, ~analysis,

    #1970, 2021, "full period",

    1980, 2019, "flow period",

    1980, 2021, "flow period plus",

    1980, 2009, "pre renewables period",

    #1980, 2021, "flow period plus",

    1980, 2022, "flow period add 2022"

    #2001, 2021, "923 period"

  ) |>
@@ -240,7 +241,7 @@ trend_vs_shift <- function(flow_CF_models, CF_trends, flow_cats){ 
  #tar_load(CF_trends)



  CF_trends |>

    filter(analysis == "flow period plus") |>

    filter(analysis == "flow period add 2022") |>

    filter(sens_slope < 0,

           p_value < 0.05) |>

    pull(COMPLXID) -> dams_with_signif_neg_trend

_targets.R

+8 −0
Original line number Diff line number Diff line
@@ -71,6 +71,14 @@ list( 
    get_CF_trends(annual_CFs = dam_annual_gen_cap_CF_1970_2022),

    format = "parquet"

  ),

  tar_target(

    CF_trends_fixed_CF,

    get_CF_trends(

      dam_annual_gen_cap_CF_1970_2022 |> 

        mutate(CF = CF_static)

      ),

    format = "parquet"

  ),

  tar_target(

    HESC,

    "./data/HESC_v2/",

_targets/meta/meta

+21 −64

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