Projects:2019s1-140 Energy Storage Requirements for the SA Grid
Contents
Team Members
- Isaiah Turner
- Sean Fernandes
Supervisors
- Derek Abbott
- David Vowles
Advisors
- Angus Simpson
Abstract
South Australia is changing from the way energy is generated and used to satisfy the environment and economic development. However the problem with renewable energy is it's intermittent. The renewable energy generated is dependent on nature while coal energy isn't dependent and can be controlled to be supplied 24/7 without any issue. This raises issues on unreserved energy and renewable energy reliability to the SA grid.
The demand-supply model from 2018 is used to continue aiming at the original goal which is to reduce the maximum reserve generation capacity and increase the renewable energy intake. This model could be expanded upon.
Genetic Algorithms will be used to solve the energy usage by finding optimal solutions. This algorithm incorporates natural selection where it chooses the survival of the fittest. It is used to reduce the controllable generation in favor of more renewable usage. This was applied in the 2018 iteration of the project where they did several benchmark cases of various scenarios related to the supply and demand model using MATLAB. However the results had problems. This year will be used to improve their 2018 genetic algorithm code to satisfy the model and fitness result. MATLAB is used once again for this year.
Other objectives for this year project include measuring performance, forecasting and using AEMO real data that wasn't used 2018.
Introduction
Background
Australia's energy production is currently changing from using non-renewable energy sources to renewable energy sources. Non-renewable energy sources such as natural gas and coal fired powered generator are being retired to move towards generating energy from renewable energy sources such as the wind and solar. This is because support of the Australian government. Their supporting reasons are the reduction of environmental problems such as greenhouse gas emissions (GHG) and health problems such as breathing. Another reason is to meet the Australian Government scheme, Renewable Energy Target (RET) . This scheme’s purpose is to reduce GHG in the electricity sector by using sustainable and renewable sources for additional electricity generation. It aims to reduce the carbon dioxide emissions to achieve the Federal Government’s RET goal of annual renewable energy production of 33,000 gigawatt-hours (GWh) by the year 2020.
Motivation
ARenewable energy sources have many of benefits such as unlimited energy supply and not generating GHG but intermittency is a major downside. Intermittency for renewable energy sources refers to power sources that change in intensity leading to a point where they are not continuously available to provide energy for electricity. Common issues include wind not blowing and sun not shining in certain times of the day. This makes them unreliable for energy generation. So, intermittency leads to a problem called dispatchability where we are unable to decrease or increase the power supply to meet the demand. This problem doesn’t occur with non-renewable fossil-fuel sources, for it can easily adjust to changes in demand by removal or addition of coal or fossil-fuels. That is why non-renewable energy sources are often referred to as ‘controllable generation’.
An additional problem with renewable energy solutions is the lack of inertia present within the individual systems. In Australia, the frequency of the system has to stay stable at 50 Hz. A way to control the rate of change of that frequency within the grid is to have non-renewable controllable sources make use of large turbines with sizable masses. As these masses rotate, they inherently produce inertia. This inertia slows down the rate that the frequency of the system changes, providing an opportunity for additional power to be deployed. Renewable systems do not have such a synchronously connected rotating mass, and as a result do not have the same inertial response.
A technique to combat these limitations is to implement Energy Storage Systems (ESS) within the grid. These energy storage systems have the ability to dispatch power during periods of high-demand and retain power in periods of low-demand, ultimately allowing a balance to be reached within the grid. With appropriate control systems, ESS can also contribute to frequency control within the grid.
Background
Method
Results
Conclusion
From the results, it can be stated that the Genetic Algorithm produced, in conjunction with the supply-demand model, provide a solid foundation for future work. The results indicate that the GA can quickly and accurately produce a solution that minimises the controllable generation capacity while also satisfying the demand. In the isolated system that was used throughout this project, the GA provides near ideal solutions on a regular basis.
Future Work
There are many tasks that need to be done for future work. The following objectives for the future include:
- Convergence Monitoring Implementation – The GA Storage Control module doesn’t have the correct implementation of convergence monitoring. Implementation of this will help the GA program to stop once it converges at the optimum solution.
- Additional benchmarks cases – GA reability would be better if more benchmark needs to be created and tested.
- Real data – The use of real data within this GA will allow it to be used in a wide array of applications.
- Increase GA accuracy – Increasing the accuracy of the GA would allow it to be used to precisely determine the amount of power may be needed at a particular time.
References
[1] AEMO, “South Australian Electricity Report 2018”. 07 November 2018. [Online] AEMO, “http://www.aemo.com.au/-/media/Files/Electricity/NEM/Planning_and_Forecasting/SA_Advisory/2018/South-Australian-Electricity-Report-2018.pdf,” [Accessed 29 Mar. 2019]
[2] Clean energy and the electricity market | energy.gov.au", Energy.gov.au, 2019. [Online]. Available: https://www.energy.gov.au/government-priorities/energy-supply/renewable-energy-and-technology. [Accessed: 05- Apr- 2019].
[3] Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimisation & Machine Learning. Alabama: Addison-Wesley Publishing Company, Inc.
[4] A. Savic, G. Walters, R. Atkinson and M. Smith, "Genetic Algorithm Optimization of Large Water Distribution System Expansion", Measurement and Control, vol. 32, no. 4, pp. 104-109, 1999.
[5] F. Zheng, A. Zecchin and A. Simpson, "Self-Adaptive Differential Evolution Algorithm Applied to Water Distribution System Optimization", Journal of Computing in Civil Engineering, vol. 27, no. 2, pp. 148-158, 2013.
