Merge pull request #2 from joph/master

2 examples from R

Author: lumbric

.gitignore

@@ -39,3 +39,7 @@ coverage.xml
 
 # Pycharm
 .idea
+
+# data for r examples
+session3_r/data/time_series_60min_singleindex.csv
+

session3_r/data/results_ubrmskript.csv

@@ -0,0 +1,16 @@
+Scenario,Total Cost,Emissions,Emission Cost,Effective Cost
+EUA160,36565865.34,73998.29,11839726.76,24726138.58
+s1          ,17449018.62,310000,1834182.25,15614836.37
+s2          ,17582040.87,290000,1718150.62,15863890.25
+s3          ,17758154.39,270000,1599613.6,16158540.79
+s4          ,17967819.71,250000,1482636.85,16485182.86
+s5          ,18226832.31,230000,1362914.56,16863917.75
+s6          ,18553157.49,210000,1246233.1,17306924.39
+s7          ,19024149.18,190000,1129364.67,17894784.51
+s8          ,19609195.86,170000,1009683.16,18599512.7
+s9          ,20269960.94,150000,891754.21,19378206.73
+s10         ,21065706.54,130000,774290.22,20291416.32
+s11         ,22048343.39,110000,658222.07,21390121.32
+s12,23399710.17,90000,540872.81,22858837.36
+s13,25866808.68,70000,423623.79,25443184.89
+s14,33875183.48,50000,303411.2,33571772.28

session3_r/figures/figure_multi_objective.pdf

@@ -0,0 +1,33 @@
+library(tidyverse)
+library(ggplot)
+# Plot Figure on Multi-Objective Analysis of electricity systems
+# for UBRM-Book/Chapter 6.1
+
+### Set Working Directory - works in R-Studio only
+scriptDir<-dirname(rstudioapi::getActiveDocumentContext()$path)
+setwd(paste0(scriptDir,"/../"))
+
+
+tab_emissions_costs<-read_csv("data/results_ubrmskript.csv")
+
+points_scenarios<-tab_emissions_costs %>% 
+  filter(Scenario %in% c("EUA160","s8")) %>% 
+  mutate(Szenario=Scenario)
+
+points_scenarios$Szenario<-c("160 Euro CO2-Preis","170 Millionen tCO2 Limit")
+
+erc_colors<-c("#C72321","#6E9B9E")
+
+tab_emissions_costs %>% ggplot(aes(x=Emissions/1000,y=`Effective Cost`/10^6)) +
+  geom_line() + 
+  geom_point() + 
+  theme_bw() +
+  xlab("Emissionen (Millionen tCO2/Jahr)") + 
+  ylab("Brennstoff- und Kapitalkosten des Stromsystems (Milliarden Euro/Jahr)") +
+  geom_point(data=points_scenarios,aes(x=Emissions/1000,y=`Effective Cost`/10^6,col=Szenario,shape=Szenario),size=5) +
+  scale_color_manual(values=c(erc_colors,erc_colors))
+
+
+ggsave("figures/figure_multi_objective.png")
+
+ggsave("figures/figure_multi_objective.pdf")

session3_r/figures/figure_multi_objective.png

@@ -0,0 +1,52 @@
+# Calculates the market value of renewables (Wind and PV) for the German market
+# using Open Power System Data
+
+library(ggplot2)
+library(tidyverse)
+library(wesanderson)
+
+### Set Working Directory - works in R-Studio only
+scriptDir<-dirname(rstudioapi::getActiveDocumentContext()$path)
+setwd(paste0(scriptDir,"/../"))
+
+# Download file from open power system data (opsd)
+
+opsd_filename<-"data/time_series_60min_singleindex.csv"
+
+if(!file.exists(opsd_filename)){
+  download.file("https://data.open-power-system-data.org/time_series/2018-06-30/time_series_60min_singleindex.csv",
+              destfile=opsd_filename)
+}
+
+opsd<-read.csv(opsd_filename,sep=",") %>% 
+  as_tibble()
+
+SECONDS_PER_HOUR <- 3600
+MWH_TO_GWH <- 1/1000
+TO_PERCENT <- 100
+
+opsd$utc_timestamp<-strptime(as.character(opsd$utc_timestamp),format="%Y-%m-%dT%H:%M:%SZ")
+opsd$cet_cest_timestamp<-opsd$utc_timestamp + SECONDS_PER_HOUR
+
+market_value<-opsd %>% 
+  select(-utc_timestamp) %>% 
+  mutate(year=year(cet_cest_timestamp)) %>% 
+  filter(year>2009&year<2018) %>% 
+  select(year,DE_price_day_ahead,DE_wind_generation_actual,DE_solar_generation_actual,DE_load_entsoe_power_statistics) %>% 
+  gather(Generation_Type,Generation,-year,-DE_price_day_ahead,-DE_load_entsoe_power_statistics) %>% 
+  mutate(Price=as.numeric(DE_price_day_ahead),
+         Generation=as.numeric(Generation)*MWH_TO_GWH,
+         Load=as.numeric(DE_load_entsoe_power_statistics)*MWH_TO_GWH) %>% 
+  mutate(Market_Value=Generation*Price) %>% 
+  group_by(year,Generation_Type) %>% 
+  summarize(Market_Value=mean(Market_Value,na.rm=TRUE)/(mean(Generation,na.rm=TRUE)*mean(Price,na.rm=TRUE)),
+            Load=sum(Load,na.rm=TRUE),
+            Generation_Share=TO_PERCENT*sum(Generation,na.rm=TRUE)/Load) %>% 
+  na.omit() 
+
+market_value  %>% ggplot(aes(x=Generation_Share,y=Market_Value)) +
+  xlab("Market Share (%)") + ylab("Normalized Market Value") + 
+  scale_color_brewer("",type="qual",palette=2) +
+  geom_line(aes(col=Generation_Type),size=1,linetype=1) +
+  geom_point(aes(col=Generation_Type),size=2) 
+