6th International Conference Energy & Meteorology: Abstract Submission

Comparing capacity expansion outcomes for the Australia’s National Electricity Market using three sources of weather-related input data (753)

Roger Dargaville 1 , Changlong Wang 2
  1. Monash University, Clayton , VICTORIA, Australia
  2. The University of Melbourne, Carlton, VICTORIA, Australia

This paper compares optimised generation and transmission expansion outcomes for the Australia’s National Electricity Market (NEM) using time-sequential traces capturing large scale solar and wind generation from three different data sources – the Australian Energy Market Operator (AEMO)’s 100% renewable study, the AEMO’s Integrated System Planning (ISP) study and the Renewables.ninja database developed by Imperial College London and ETH Zurich.

In the AEMO’s 100% renewables study, ROAM Consulting’s Wind Energy Simulation Tool (WEST) and the National Renewable Energy Laboratory (NREL)’s System Advisor Model (SAM) were used to convert wind speed (above 80m) and solar irradiation into power traces respectively. The weather data was retrieved from the Australian Bureau of Meteorology (BOM)’s Numerical Weather Prediction (NWP) system, ACCESS-A.

For the AEMO’s ISP study, wind resource quality assessment was based on mesoscale wind flow modelling at a height of 150 m above ground level, while Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) data from the BOM were used to assess solar resource quality.

Renewables.ninja database takes weather data from NASA MERRA reanalysis. Solar irradiance data is converted into power traces using the Global Solar Energy Estimator (GSEE) model. Wind speeds (above 80 - 150 m) are converted into power traces using the Virtual Wind Farm (VWF) model.

The representative traces of solar and wind power generation time-series for each candidate location were derived from the above data sources. We use the Melbourne/Monash University Renewable Energy Integration Laboratory (MUREIL) to conduct the NEM capacity expansion planning simulations. MUREIL aims to explore different least-cost configurations for the NEM generation mix that satisfies specified demand projections and emission abatement targets, subject to system inertia constraints, unit commitment, and economic dispatch with DC optimal power flows. The model explores a broad range of generation options including large-scale solar PV, concentrating solar power, wind, large-scale batteries, pumped hydro storage, demand response, and a range of fossil fuel options. The model uses the most updated cost projections for various generation technologies from CSIRO – the federal research agency and AEMO. The existing generation units and their decommissioning lifetime are also taken into account. The transmission model used is a DC flow approximation with 21 nodes over the eastern states of Australia representing the main load and generation centres.

Results show some differences in the optimal generation mix, the optimal locations for the generation sites, transmission augmentation, and the total system costs using the input weather-related data from the three sources. This provides information on the uncertainties in the planning simulations resulting from uncertainties in the weather data inputs.