Generation Mix Optimization for Australia's Low Carbon Energy System Transition

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Wednesday, 5 February 2014: 4:30 PM
Room C114 (The Georgia World Congress Center )
R. J. Dargaville, University of Melbourne, Melbourne, VIC, Australia; and M. Jeppesen, M. J. Brear, C. Manzie, T. Alpcan, R. Huva, and M. Gannon

As we consider the transition to a low emission energy system, there are a broad range of choices to be made between different technologies, location and timing of deployment. The technology choices are guided by cost, carbon intensity and ability of control output (dispatchability). The location choice is impacted by the quality of resource (i.e. wind speed, solar radiation) and access to infrastructure (transmission lines, load centers). The timing of deployment is determined by policy measures and the trajectory of cost of the different technologies including emerging technologies such as hot-rock geothermal or carbon capture and storage. Considering all these factors at the same time is a complex task.

To help guide the decision making process, we are developing a software tool to find least-cost pathways to low emission energy systems. The model simulates the different components of the energy system of Australia including generation, demand and transmission. The model is used in an optimization routine to find the lowest cost configurations of wind and solar technologies combined with conventional thermal generation (black and brown coal, natural gas) that meets demand on hourly timescales for a given carbon emission target.

The hourly output of potential wind and solar plant is determined by using the wind speed and solar radiation data from the archive of Australian Bureau of Meteorology's weather forecast model output. The simulated data allows assessment of potential renewable energy output at all points across the domain, not just the locations where in situ meteorological observations currently exist. A selection process is used to sub-sample the 12km resolution data set to keep the number of locations manageable.

For the dispatchable power sources, a dispatch model finds the optimal mix of available generation at each time step to meet demand once the non-dispatchable sources have been accommodated, using observed ramp-rates for hydro and thermal technologies. Transmission modeling costs the additional infrastructure required to link new plant to the existing grid, as well as identifying possible bottlenecks and costs required to upgrade transmission lines to facilitate the delivery of power from generating plant to demand centers.

Using discretised temporal and spatial grids, a close-to-optimal mix of conventional thermal power with wind and solar plant is developed. The presented approach also incorporates generation variability across the plants to propose a least cost pathway to a prescribed low emission target by the year 2050.