Supplementary materials for the paper "A Portfolio approach to wind and solar deployment in Australia" https://www.eprg.group.cam.ac.uk/wp-content/uploads/2020/08/2022-Text.pdf
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
Carmen Li a91a81f3e5 Fixed references. 1 month ago
VRE Added key file for the NEM sampling coordinates and other VRE parameters. 1 month ago
output_maximisation Added code files used in the base case and the computation for the marginal impact of the transmission constraints. 1 month ago
readme.md Fixed references. 1 month ago

readme.md

A Portfolio approach to wind and solar deployment in Australia

Overview

This repository contains the source codes used to generate and analyse the results of [1]. The directories are named using the same terminology as in the paper.

Wind and solar capacity factor time series are obtained from [2].

Demand data are taken from the “P5MIN_REGIONSOLUTION” dataset published on the AEMO Market Data site [3]. We take the “DEMAND_AND_NONSCHEDGEN” column in the data set as the gross demand and “TOTALDEMAND” as the operational demand. The raw data captured from AEMO are recorded state by state; to disaggregate the state demand into zone demand we use the population proportion method illustrated in [4].

The current transmission limits between zones are also estimated using the data from [4].

References

[1] C. K. Chong, C. Li, D. Reiner and F. Roques, A Portfolio approach to wind and solar deployment in Australia. EPRG working papers 2022 (2020), https://www.eprg.group.cam.ac.uk/eprg-working-paper-2022/

[2] S. Pfenninger and I. Staffell Renewables.ninja, https://www.renewables.ninja/

[3] Australian Energy Market Operator (AEMO). NEMWEB Market Data, http://www.nemweb.com.au/

[4] Xenophon, A. and Hill, D. Open grid model of australia’s national electricity market allowing backtesting against historic data. Scientific Data, volume 5, 180203 (2018) doi: https://doi.org/10.1038/sdata.2018.203