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
Energy production is currently shifting from non-renewable energy sources such as coal fired power generators to renewable energy sources such as solar that are clean and environment friendly. The main driver for this change comes from the Australian Government scheme, Renewable Energy Target (RET) . The purpose of this scheme is to reduce the greenhouse gas emissions (GHG) in the electricity sector by using renewable and sustainable sources for additional electricity generation . By 2020, the Federal Government hopes to achieve the RET goal of producing 33,000 gigawatt-hours (GWh) (23.5 %) of energy from renewable energy sources.
Motivation
Although renewable energy sources have many benefits such as less GHG emissions and unlimited energy supply, it suffers a major downside which is intermittency. In fact, intermittency for renewable energy sources refers to power sources that changes in intensity leading to a point where they are not continuously available to provide energy for electricity . Indeed, they produce energy required to supply to the load only when the sun is shining or the wind is blowing. These natural sources cannot be controlled as they are completely dependent on nature. Therefore, intermittency leads to a problem called dispatchability where we cannot increase or decrease the power supply to meet the electricity demand . This issue makes them unreliable for energy generation. Fossil fuels, on the other hand, doesn’t have this problem, for it can easily respond to the changes in electricity demand by the removal or addition of coal or fossil fuels. That is why non-renewable energy sources are often referred to as ‘controllable generation’ while renewable energy is referred to as ‘uncontrollable 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.
Background
Method
Results
Conclusion
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.
