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Developing Predictable Renewable Energy to Save Electricity Costs

Researchers at University of Adelaide have investigated how predictable wind or solar energy production is and the effect of it on the electricity market’s profit. Some of the advantages of this study include cheaper electricity for consumers and more dependable clean energy.

Developing Predictable Renewable Energy to Save Electricity Costs

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In an objective to avoid clean energy spillage, save millions of dollars in operating charges, and offer cheaper electricity, Ph.D. candidate Sahand Karimi-Arpanahi and Dr. Ali Pourmousavi Kani, Senior Lecturer from the University’s School of Electrical and Mechanical Engineering, have explored various ways to attain more predictable renewable energy.

One of the biggest challenges in the renewable energy sector is being able to reliably predict the amount of power generated. Owners of solar and wind farms sell their energy to the market ahead of time before it is generated; however, there are sizable penalties if they don’t produce what they promise, which can add up to millions of dollars annually.”

Peaks and troughs are the reality of this form of power generation, however using predictability of energy generation as part of the decision to locate a solar or wind farm means that we can minimize supply fluctuations and better plan for them.

Mr. Karimi-Arpanahi, Ph.D. Candidate, School of Electrical and Mechanical Engineering, University of Adelaide

The team’s research examined six already-present solar farms situated in New South Wales, Australia, and chose a maximum of nine alternate sites, comparing the sites depending on the current examination factors and when the predictability factor was also considered. This study was published in the data science journal Patterns.

The data revealed that the optimal location was altered when the predictability of energy production was considered and resulted in a considerable increase in the likely revenue produced by the site.

Dr. Pourmousavi Kani stated the outcomes of this paper would be important for the energy industry in planning new wind and solar farms and public policy design.

Researchers and practitioners in the energy sector have often overlooked this aspect, but hopefully our study will lead to change in the industry, better returns for investors, and lower prices for the customer,” he added.

The predictability of solar energy generation is the lowest in South Australia each year from August to October while it is highest in NSW during the same period. In the event of proper interconnection between the two states, the more predictable power from NSW could be used to manage the higher uncertainties in the SA power grid during that time.

Dr. Ali Pourmousavi Kani, Senior Lecturer, School of Electrical and Mechanical Engineering, University of Adelaide

The researchers’ examination of the fluctuations in energy output from solar farms might be implemented in other applications in the energy industry.

The average predictability of renewable generation in each state can also inform power system operators and market participants in determining the time frame for the annual maintenance of their assets, ensuring the availability of enough reserve requirements when renewable resources have lower predictability.

Dr. Ali Pourmousavi Kani, Senior Lecturer, School of Electrical and Mechanical Engineering, University of Adelaide

Journal Reference:

Karimi-Arpanahi, S., et al. (2023). Quantifying the predictability of renewable energy data for improving power systems decision-making. Patterns.


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