MUREIL designs our future energy systems

Under the current policy of the Mandatory Renewable Energy Target (MRET), 20 per cent of electricity in Australia will be generated by renewable sources by 2020.

Studies including the Stern Report and the International Energy Agency’s ETP2008 have shown also that developed countries like Australia need to reduce emissions from the electricity sector to essentially zero by 2050 to avoid the worst climate change scenarios.

Most recently, the Zero Carbon Australia Stationary Energy Plan has shown that it is technically possible and financially viable to reach zero emissions in the electricity sector within 10 years at a cost of as little as around 3 per cent of GDP. This plan is a research collaboration between the University of Melbourne and Beyond Zero Emissions and will be publicly launched by the Melbourne Energy Institute on Wednesday 14 July, 6-8pm at ‘The Spot’ 198 Berkeley Street.

So, it seems likely that at the very least we are going to see a big increase in the amount of renewable energy that we use, and possibly a revolution in the way we make and use electricity. But what will such an energy system look like and how do we design the best roadmap forward?

Researchers at the University of Melbourne’s Melbourne Energy Institute are building an energy system simulator (the Melbourne University Renewable Energy Infrastructure Lab, or MUREIL) to help design the best energy solution for Australia.

MUREIL is a weather model, energy technology model and economics model rolled into one. For a given technology and location, MUREIL simulates the wind, temperature and solar radiation and predicts how much power would be generated. It then works out the best mix of technologies and locations to reduce variability and increase reliability and security of the electricity grid at the least cost.

The main forms of renewable energy that are likely to produce green electricity in significant amounts in the next decades are hydropower (currently producing 6 per cent of Australia’s electricity) wind (currently 3 per cent) and solar (less than 0.5 per cent).

At the current levels of penetration there is little trouble for the variability in the production in wind and solar to be absorbed into an already variable grid due to changes in demand. Other forms of renewable such as tidal and wave power, and non-fossil fuel energy forms such as nuclear and geothermal could play a part in the future, but are currently more expensive or technically unproven.

As the volume of wind and solar on the grid reaches several per cent and beyond, the challenge of integrating the variable power becomes an issue.

However, the variability in wind and solar produced power is not random, but is governed by the daily cycle of the sun, the seasons, and, of course, the weather (“windiness”, “sunniness” and “cloudiness”). The weather also has a large impact on demand. Therefore the design of a renewable energy system is not a matter of simply deploying enough capacity, but should take into account the way that windiness and sunniness vary in time and space, both on daily timescales, but also over the course of a year with the changing of the seasons.

The complex relationship between sunniness and windiness means it’s difficult to make generalisations, but often when the weather is fine and sunny, the wind speeds are low. And, cloudy days also tend to be windy. So by carefully balancing the amount and placement of wind and solar energy farms, we can take advantage of the natural variability in the system.

MUREIL will be able to test various configurations of wind, and solar, along with existing hydro power and thermal power plants. The model can also test the effects of large-scale storage on the stability of supply through pumped hydro, large chemical battery banks, or electric vehicle fleets.

The average spot market price for electricity is $50 MWh (or 5 cents a kWh), but the price varies dramatically, sometimes hitting $10000 MWh at periods of extreme demand.

By modelling both the capital expenditure and the spot market under different scenarios we can determine the economic viability of various technologies under different scenarios and different carbon prices. The end result will be a better understanding of what the cheapest and most secure low carbon energy system will look like.

There will be a natural tendency to build wind farms in the windiest places. But if the winds farms are clustered together, for example in south-western Victoria, this would mean that on the occasion that south-western Victoria is under calm conditions, the power available from wind would be very low. And, when it is particularly windy, the wind farms may produce more power than can be used resulting in waste. But by placing some wind farms in sub-optimal places (i.e. East Gippsland) the variability will be reduced. Variable spot prices may mean that even though the amount of power produced in such areas is lower, they are just as economically viable of those in the windiest areas.

Electricity demand has a strong diurnal cycle, with a maximum in the afternoon in summer, and in winter there is a dual maximum in the early morning and again in the evening. Overnight, demand reaches a minimum, however this minimum is substantial as heavy industry runs 24-hour operations, such as aluminium smelting, as well as street lighting, off-peak hot water and various continuous power appliances chug away all the time (refrigerators, set-top boxes etc). On average, demand is higher in winter, but is much more variable in summer with extremely hot days seeing demand spike due to air-conditioner use.

Solar energy is quite good at following the demand curve, especially in summer, but of course produces no power at night (unless coupled to a storage system) when demand is still significant due to heavy industry needs. Solar photovoltaic is very expensive, and large-scale solar thermal power is yet to be built at a commercial scale in Australia. Wind power is far cheaper than solar power, but does not have a clear correlation with demand. But by distributing wind across large geographical regions (and Australia has no lack of space to spread out the wind farms) the variability in wind power can be reduced and supply can be more secure than from individual wind farms.

A mix of wind and solar appears to be the way to go, but what is the ideal ratio? These complexities mean a sophisticated model makes an important tool in the planning toolbox.

Data sources: Australian Energy Market Operator (AEMO) and Australian Bureau of Agricultural and Resource Economics (ABARE).

Dr Roger Dargaville is the Energy Analyst at the Melbourne Energy Institute

www.energy.unimelb.edu.au