1 About

1.1 Citation

1.2 Report circulation

Public – this report is intended for publication

1.3 Code

All code used to create this report is available from:

https://github.com/CfSOtago/gridCarbon

1.4 License

This work is made available under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) License.

This means you are free to:

Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material for any purpose, even commercially.

Under the following terms:

Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Notices:

You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.

For the avoidance of doubt and explanation of terms please refer to the full license notice and legal code.

1.5 History

See report’s edit history

1.6 Support

This work was supported by:

The University of Otago;
SPATIALEC - a Marie Skłodowska-Curie Global Fellowship based at the University of Otago’s Centre for Sustainability (2017-2019) & the University of Southampton’s Sustainable Energy Research Group (2019-2020).
The European Union via SPATIALEC, a Marie Skłodowska-Curie Global Fellowship based at the University of Otago’s Centre for Sustainability (2017-2019) & the University of Southampton’s Sustainable Energy Research Group (2019-2020) (Anderson);
The Uniersity of Otago via a Centre for Sustainability Summer Scholarship (Lotte) and PhD Studentship (Dortans)

2 Introduction

Building on (???), we are interested in GHG emissions from the NZ electricity generation over time. We are especially interested in how this might change during the NZ covid-19 lockdown period (2020-03-24 to 2020-07-10).

We use two different GHG emissions indicators:

% of total generation in a halfhour which was ‘low emissions’ where this is defined as wind + solar + hydro
Calculated CO2e emissions per half hour (not yet implemented)

3 Data

3.1 Wholesale generation data

Essentially ‘grid’ generation from major power stations of various kinds. Data downloaded from https://www.emi.ea.govt.nz/Wholesale/Datasets/Generation/Generation_MD/ and pre-processed.

Table 3.1: Grid gen data (first 6 rows)
Site_Code	POC_Code	Nwk_Code	Gen_Code	Fuel_Code	Tech_Code	Trading_date	Time_Period	kWh	strTime	rTime	rDate	rDateTime	month	year	rDateTimeOrig	rDateTimeNZT	rMonth
ARA	ARA2201	MRPL	aratiatia	Hydro	Hydro	2017-01-01	TP1	11510	0:15:00	00:15:00	2017-01-01	2017-01-01 00:15:00	1	2017	2017-01-01T00:15:00Z	2017-01-01 00:15:00	Jan
ARA	ARA2201	MRPL	aratiatia	Hydro	Hydro	2017-01-02	TP1	11570	0:15:00	00:15:00	2017-01-02	2017-01-02 00:15:00	1	2017	2017-01-02T00:15:00Z	2017-01-02 00:15:00	Jan
ARA	ARA2201	MRPL	aratiatia	Hydro	Hydro	2017-01-03	TP1	11090	0:15:00	00:15:00	2017-01-03	2017-01-03 00:15:00	1	2017	2017-01-03T00:15:00Z	2017-01-03 00:15:00	Jan
ARA	ARA2201	MRPL	aratiatia	Hydro	Hydro	2017-01-04	TP1	13970	0:15:00	00:15:00	2017-01-04	2017-01-04 00:15:00	1	2017	2017-01-04T00:15:00Z	2017-01-04 00:15:00	Jan
ARA	ARA2201	MRPL	aratiatia	Hydro	Hydro	2017-01-05	TP1	19680	0:15:00	00:15:00	2017-01-05	2017-01-05 00:15:00	1	2017	2017-01-05T00:15:00Z	2017-01-05 00:15:00	Jan
ARA	ARA2201	MRPL	aratiatia	Hydro	Hydro	2017-01-06	TP1	27190	0:15:00	00:15:00	2017-01-06	2017-01-06 00:15:00	1	2017	2017-01-06T00:15:00Z	2017-01-06 00:15:00	Jan

##    Fuel_Code          sd
## 1:      Coal  229.305827
## 2:    Diesel    2.377192
## 3:       Gas  396.418119
## 4:       Geo  610.506277
## 5:     Hydro 1888.429777
## 6:      Wind  156.224244
## 7:      Wood   22.686248

3.2 Embedded generation data

Essentially ‘non-grid’ generation from solar photovoltaic and small scale wind which is ‘embedded’ - i.e. non-grid connected as it is connected ‘downstream’ of the grid exit points (GXP). Data downloaded from https://www.emi.ea.govt.nz/Wholesale/Datasets/Metered_data/Embedded_generation and pre-processed.

Table 3.2: Embedded gen data (first 6 rows)
POC	NWK_Code	Participant_Code	Loss_Code	Flow_Direction	Trading_date	Time_Period	strTime	rTime	rDate	rDateTime	month	year	rDateTimeOrig	rDateTimeNZT	rMonth
ASB0661	EASH	TRUS	H01	I	01/01/2017	TP1	0:15:00	00:15:00	2017-01-01	2017-01-01 00:15:00	1	2017	2017-01-01T00:15:00Z	2017-01-01 00:15:00	Jan
ASB0661	EASH	TRUS	H01	I	02/01/2017	TP1	0:15:00	00:15:00	2017-01-02	2017-01-02 00:15:00	1	2017	2017-01-02T00:15:00Z	2017-01-02 00:15:00	Jan
ASB0661	EASH	TRUS	H01	I	03/01/2017	TP1	0:15:00	00:15:00	2017-01-03	2017-01-03 00:15:00	1	2017	2017-01-03T00:15:00Z	2017-01-03 00:15:00	Jan
ASB0661	EASH	TRUS	H01	I	04/01/2017	TP1	0:15:00	00:15:00	2017-01-04	2017-01-04 00:15:00	1	2017	2017-01-04T00:15:00Z	2017-01-04 00:15:00	Jan
ASB0661	EASH	TRUS	H01	I	05/01/2017	TP1	0:15:00	00:15:00	2017-01-05	2017-01-05 00:15:00	1	2017	2017-01-05T00:15:00Z	2017-01-05 00:15:00	Jan
ASB0661	EASH	TRUS	H01	I	06/01/2017	TP1	0:15:00	00:15:00	2017-01-06	2017-01-06 00:15:00	1	2017	2017-01-06T00:15:00Z	2017-01-06 00:15:00	Jan

Table 3.3: Mean kWh by flow direction
year	sumkWh_I	sumkWh_X	nObs_I	nObs_X
2017	5058591128	96830431	406372	1225680
2018	5069984029	121534268	527004	1577056
2019	5208373429	105300995	437276	1394172
2020	1522565086	35504132	144624	492048

Not entirely sure what these codes mean yet. Limited guidance available? TBC

For now embedded generation data is not included in the following analysis.

4 Background trends and patterns

Demand profiles by season

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Table 4.1: Mean half-hourly GWh by year and peak period - 2020 incomplete
year	Early morning	Morning peak	Day time	Evening peak	Late evening
2017	1.87	2.60	2.53	2.64	2.32
2018	1.86	2.61	2.53	2.65	2.32
2019	1.88	2.62	2.54	2.66	2.34
2020	5.84	7.76	7.90	8.07	7.18

5 Analysis (think this section needs some structure/sub-headings!)

In this section we analyse the current developments in electricity generation during the Covid-19 level four alert. The level four alert came into effect on March 25 2020 at 11.59pm as part of the fight agianst the novel Covid-19 virus. The minimum lockdown period is four weeks, ending on April 28 This period has wide-spread implications across New Zealand’s economy and electriicty consumption, amongst other effects such as everyday life in general.

All non-essential busineeses are closed and staff is asked to work from home. This analysis aims to provide insights on developments in electriicty generation. Essential research questions company this research:

To what extent has electricity generation shown deviation of ‘normal’ generation patterns during the level four alert?
Has the composition of fuel sources supplying electriicty demand in New Zealand changed during the level four alert?
Has the level four alert impacted greenhouse-gas emissions associated with electriicty generation?

Table 5.1: Number of observations by fuel
	nObs
Coal	19200
Diesel	19200
Gas	172800
Geo	211200
Hydro	726432
Wind	172800

We extracted electricity generation data from /INSERT DATES/. Table 5.1 shows the number of observations for each fuel type for the aforementioned dates. It becomes clear that Hydro was the most used fuel type, followed by Geothermal, Wind, Gas, Coal, and Diesel.

Table 5.2: Example summary data
Fuel_Code	rDateTimeNZT	Time_Period	rMonth	year	meankWh	sumkWh	nObs	meanMW	sumMW
Coal	2018-01-01 00:15:00	TP1	Jan	2018	102674	102674	1	205.348	205.348
Coal	2018-01-01 00:45:00	TP2	Jan	2018	90091	90091	1	180.182	180.182
Coal	2018-01-01 01:15:00	TP3	Jan	2018	85373	85373	1	170.746	170.746
Coal	2018-01-01 01:45:00	TP4	Jan	2018	84806	84806	1	169.612	169.612
Coal	2018-01-01 02:15:00	TP5	Jan	2018	63093	63093	1	126.186	126.186
Coal	2018-01-01 02:45:00	TP6	Jan	2018	54723	54723	1	109.446	109.446

Table 5.2 shows exemplary data of our data extraction. In addition, Figure 5.1 pictures total generation (this is the sum for each fuel type for all generatinfg plants and all trading periods). Hydro had the highest power output in January and February. Geothermnal was used for base load and an increase in Gas output is viible for the second half of February. Wind intermittently generated while Coal was occasionally integrated. Wood and Diesel did not contribute much in January and February.

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 5.1: Total generation by fuel

Figure 5.2 shows total generation for the first four months of 2020. A red box indicates the alert level four period.

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 5.2: Four months of total generation for 2020

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

In order to provide information on how generation changed over time it is helpful to compare it with previous years. Twenty matching weekdays were extracted for January to March for 2020, 2019, and 2018. We would like to make sure that the weekdays really ‘match up’. Figure 5.3 shows twenty days of extracted data for the three years for March We see that the weekdays match up.

Figure 5.3: Twenty days of total generation for March by year

Figure 5.4 shows a close-up for one day in March for the three years. We see that on this particular Saturdays during the lockdown period the morning peak was shifted towards mid-day. Total generation at morning peak dropped by 11% compared to previous years. Interestingly, the evening peak reached almost the same total generation despite the lockdown but was shifted by one hour.

Figure 5.4: Total generation over time for the third Saturday in April by year

A less distorted view on total generation data is provided in Figure 5.5. Instead of 15-minute granularity data daily means represent ttoal generation. We can see how close the analysed years were togetehr before the lockdowm and as the lockdown starts a large rop in generation occurs.

Figure 5.5: Daily mean generation by year for March

A mean over 2018 and 2019 data was compared with 2020 in Figure 5.6. In addition, previous months were considered in this plot as well and show that the variation apparent during the lockdown period is significant between the two reference periods.

Figure 5.7 highlights the percentage difference between 2020 and the reference period of 2018-2019. On a daily mean granularity, generation dropped by over 15% during the lockdown period.

Figure 5.6: Daily mean total generation for 2020 and reference period by month

Figure 5.7: Percentage difference in daily mean between 2018-2019 and 2020

Seems to break

This also breaks?

6 Experiments with openair

openair is an R package designed for air quality analysis but it has some useful plots that we can make use of.

For example, Figure ?? uses the openair TheilSen() function (Carslaw and Ropkins 2012) to create a de-seasoned trend plot for mean monthly half-hourly (i.e. the mean of all half-hourly GW values for each month) generation in GW split by weekdays and weekend days. These plots show that there has been a steady decline in generation (and thus demand) over the period.

currently fails as openair needs latticeExtra which needs R (≥ 3.6.0)

7 Annexes

7.1 Wholesale generation data (‘grid’)

Table 7.1: Data summary

Name	allGridDT
Number of rows	5048944
Number of columns	19
_______________________
Column type frequency:
character	11
difftime	1
factor	2
numeric	3
POSIXct	2
________________________
Group variables	None

Variable type: character

skim_variable	complete_rate	min	max	n_unique
Site_Code	1	3	3	61
POC_Code	1	7	7	61
Nwk_Code	1	4	4	25
Gen_Code	1	3	15	63
Fuel_Code	1	3	6	7
Tech_Code	1	3	5	5
Trading_date	1	10	10	1216
Time_Period	1	3	4	48
strTime	1	7	8	48
rDate	1	10	10	1216
rDateTimeOrig	1	20	20	58356

Variable type: difftime

skim_variable	n_missing	complete_rate	min	max	median	n_unique
rTime	0	1	900 secs	85500 secs	43200 secs	48

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
rMonth	0	1	TRUE	12	Mar: 1354176, Jan: 418128, Apr: 405216, Feb: 380832
season	0	1	FALSE	4	Aut: 2074848, Sum: 1112736, Win: 936192, Spr: 925168

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
kWh	1	32527.65	48455.12	0	3943.64	16470	41741	402430	▇▁▁▁▁
month	1	5.55	3.39	1	3.00	4	8	12	▇▃▂▂▃
year	1	2018.53	1.12	2017	2018.00	2019	2020	2020	▇▇▁▇▇

Variable type: POSIXct

skim_variable	n_missing	complete_rate	min	max	median	n_unique
rDateTime	0	1	2017-01-01 00:15:00	2020-04-30 23:45:00	2019-01-18 03:15:00	58356
rDateTimeNZT	0	1	2016-12-31 11:15:00	2020-04-30 11:45:00	2019-01-17 14:15:00	58356

7.2 Embedded generation data (‘nongrid’)

Table 7.2: Data summary

Name	allEmbeddedDT
Number of rows	6204232
Number of columns	17
_______________________
Column type frequency:
character	10
difftime	1
factor	1
numeric	3
POSIXct	2
________________________
Group variables	None

Variable type: character

skim_variable	complete_rate	min	max	n_unique
POC	1	7	7	21
NWK_Code	1	4	4	17
Participant_Code	1	4	4	41
Loss_Code	1	3	7	23
Flow_Direction	1	1	1	2
Trading_date	1	10	10	1216
Time_Period	1	3	4	48
strTime	1	7	8	48
rDate	1	10	10	1216
rDateTimeOrig	1	20	20	58356

Variable type: difftime

skim_variable	n_missing	complete_rate	min	max	median	n_unique
rTime	0	1	900 secs	85500 secs	43200 secs	48

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
rMonth	0	1	TRUE	12	Apr: 615552, Mar: 603936, Jan: 596928, Feb: 552960

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
kWh	1	2775.31	7238.99	0	0.02	13.48	222.74	63472.69	▇▁▁▁▁
month	1	6.23	3.50	1	3.00	6.00	9.00	12.00	▇▅▅▅▆
year	1	2018.24	0.96	2017	2017.00	2018.00	2019.00	2020.00	▆▇▁▇▂

Variable type: POSIXct

skim_variable	n_missing	complete_rate	min	max	median	n_unique
rDateTime	0	1	2017-01-01 00:15:00	2020-04-30 23:45:00	2018-09-18 00:15:00	58356
rDateTimeNZT	0	1	2016-12-31 11:15:00	2020-04-30 11:45:00	2018-09-17 12:15:00	58356

7.3 Conversion factors

8 Runtime

Analysis completed in 163.71 seconds ( 2.73 minutes) using knitr in RStudio with R version 3.6.3 (2020-02-29) running on x86_64-apple-darwin15.6.0.

9 R environment

9.1 R packages used

base R (R Core Team 2016)
bookdown (Xie 2016a)
data.table (Dowle et al. 2015)
ggplot2 (Wickham 2009)
kableExtra (Zhu 2018)
knitr (Xie 2016b)
lubridate (Grolemund and Wickham 2011)
rmarkdown (Allaire et al. 2018)

9.2 Session info

## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.5
## 
## Matrix products: default
## BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_NZ.UTF-8/en_NZ.UTF-8/en_NZ.UTF-8/C/en_NZ.UTF-8/en_NZ.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] lubridate_1.7.4   kableExtra_1.1.0  ggplot2_3.3.0     skimr_2.1         drake_7.11.0      data.table_1.12.8
## [7] here_0.1          gridCarbon_0.1.0 
## 
## loaded via a namespace (and not attached):
##  [1] httr_1.4.1          tidyr_1.0.2         maps_3.3.0          jsonlite_1.6.1      viridisLite_0.3.0  
##  [6] splines_3.6.3       openair_2.7-0       R.utils_2.9.2       assertthat_0.2.1    highr_0.8          
## [11] latticeExtra_0.6-29 base64url_1.4       yaml_2.2.1          progress_1.2.2      pillar_1.4.3       
## [16] backports_1.1.5     lattice_0.20-40     glue_1.3.2          digest_0.6.25       RColorBrewer_1.1-2 
## [21] rvest_0.3.5         colorspace_1.4-1    Matrix_1.2-18       htmltools_0.4.0     R.oo_1.23.0        
## [26] pkgconfig_2.0.3     bookdown_0.18       purrr_0.3.3         scales_1.1.0        webshot_0.5.2      
## [31] jpeg_0.1-8.1        tibble_2.1.3        mgcv_1.8-31         txtq_0.2.0          farver_2.0.3       
## [36] withr_2.1.2         repr_1.1.0          hexbin_1.28.1       cli_2.0.2           magrittr_1.5       
## [41] crayon_1.3.4        evaluate_0.14       storr_1.2.1         R.methodsS3_1.8.0   fansi_0.4.1        
## [46] MASS_7.3-51.5       nlme_3.1-145        forcats_0.5.0       xml2_1.2.5          tools_3.6.3        
## [51] prettyunits_1.1.1   hms_0.5.3           lifecycle_0.2.0     stringr_1.4.0       munsell_0.5.0      
## [56] cluster_2.1.0       packrat_0.5.0       compiler_3.6.3      rlang_0.4.5         grid_3.6.3         
## [61] rstudioapi_0.11     igraph_1.2.5        base64enc_0.1-3     labeling_0.3        rmarkdown_2.1      
## [66] gtable_0.3.0        R6_2.4.1            zoo_1.8-7           knitr_1.28          dplyr_0.8.5        
## [71] utf8_1.1.4          filelock_1.0.2      rprojroot_1.3-2     readr_1.3.1         stringi_1.4.6      
## [76] parallel_3.6.3      Rcpp_1.0.4          vctrs_0.2.4         mapproj_1.2.7       png_0.1-7          
## [81] tidyselect_1.0.0    xfun_0.12

References

Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, and Winston Chang. 2018. Rmarkdown: Dynamic Documents for R. https://CRAN.R-project.org/package=rmarkdown.

Carslaw, David C., and Karl Ropkins. 2012. “Openair — an R Package for Air Quality Data Analysis.” Environmental Modelling & Software 27–28 (0): 52–61. https://doi.org/10.1016/j.envsoft.2011.09.008.

Dowle, M, A Srinivasan, T Short, S Lianoglou with contributions from R Saporta, and E Antonyan. 2015. Data.table: Extension of Data.frame. https://CRAN.R-project.org/package=data.table.

Grolemund, Garrett, and Hadley Wickham. 2011. “Dates and Times Made Easy with lubridate.” Journal of Statistical Software 40 (3): 1–25. http://www.jstatsoft.org/v40/i03/.

R Core Team. 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. http://ggplot2.org.

Xie, Yihui. 2016a. Bookdown: Authoring Books and Technical Documents with R Markdown. Boca Raton, Florida: Chapman; Hall/CRC. https://github.com/rstudio/bookdown.

———. 2016b. Knitr: A General-Purpose Package for Dynamic Report Generation in R. https://CRAN.R-project.org/package=knitr.

Zhu, Hao. 2018. KableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.

NZ Electricity Generation

covid 19 lockdown v1.0

Ben Anderson, Carsten Dortans and Marilette Lotte

Last run at: 2020-07-10 16:35:27