Difference between revisions of "Projects:2020s1-2274 Economic Optimisation of Water Distribution Pumping Systems with Integrated Renewable Power Generation"
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| − | + | At present, up to 80% of costs surrounding water distribution are attributed to energy expenses from pumping. This power is purchased from the National Electricity Market with volatile prices that can cause variations of up to half a million dollars in pumping expenses. Water distribution companies are now seeking to eliminate this issue by integrating renewable power generation into their systems with the aim of having zero net electricity costs. Doing so gives rise to questions such as: What should we be doing with this power? When should we be buying, storing or selling power? And what is the most economical way to pump while satisfying water demand? | |
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| + | The project aims to apply model predictive control as an approach to solving this problem for a case study of the Sellicks Hill pipeline in South Australia. | ||
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== Introduction == | == Introduction == | ||
| − | + | Optimal management of renewable assets (such as wind, solar and hydro) is essential to ensure water distribution is achieved in the most economic fashion with the added benefit of also reducing environmental impact. This project will follow SA Water’s behind the meter approach, modeling all generation, storage and pumping assets up to the interface with the meter; any power bought or sold will occur in accordance with how the National Electricity Market operates. | |
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| + | This project is divided into three key parts: | ||
| + | * Detailed simulation of renewable assets, batteries and the hydraulic behaviour of pumping systems | ||
| + | * Simplification of complex non-linear models to linearised representations | ||
| + | * Application of model predictive control and mathematical programming to determine optimal operating strategies | ||
| + | |||
| + | Forecasting techniques surrounding renewable power generation and water and electricity demands may also be considered. | ||
| + | |||
| + | This project will contribute to a major research program at the University of Adelaide using an interdisciplinary approach between the Civil Engineering and Electrical Engineering faculties to optimise energy planning management in Australia. | ||
=== Project team === | === Project team === | ||
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* Professor Lang White | * Professor Lang White | ||
* Professor Angus Simpson | * Professor Angus Simpson | ||
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== Background == | == Background == | ||
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== Method == | == Method == | ||
Latest revision as of 18:22, 25 March 2020
At present, up to 80% of costs surrounding water distribution are attributed to energy expenses from pumping. This power is purchased from the National Electricity Market with volatile prices that can cause variations of up to half a million dollars in pumping expenses. Water distribution companies are now seeking to eliminate this issue by integrating renewable power generation into their systems with the aim of having zero net electricity costs. Doing so gives rise to questions such as: What should we be doing with this power? When should we be buying, storing or selling power? And what is the most economical way to pump while satisfying water demand?
The project aims to apply model predictive control as an approach to solving this problem for a case study of the Sellicks Hill pipeline in South Australia.
Contents
Introduction
Optimal management of renewable assets (such as wind, solar and hydro) is essential to ensure water distribution is achieved in the most economic fashion with the added benefit of also reducing environmental impact. This project will follow SA Water’s behind the meter approach, modeling all generation, storage and pumping assets up to the interface with the meter; any power bought or sold will occur in accordance with how the National Electricity Market operates.
This project is divided into three key parts:
- Detailed simulation of renewable assets, batteries and the hydraulic behaviour of pumping systems
- Simplification of complex non-linear models to linearised representations
- Application of model predictive control and mathematical programming to determine optimal operating strategies
Forecasting techniques surrounding renewable power generation and water and electricity demands may also be considered.
This project will contribute to a major research program at the University of Adelaide using an interdisciplinary approach between the Civil Engineering and Electrical Engineering faculties to optimise energy planning management in Australia.
Project team
Project students
- Daniel Mignanelli
- Adam Kitto
- Zhiyuan Ren
Supervisors
- Professor Lang White
- Professor Angus Simpson
Background
Method
Results
Conclusion
References
[1] a, b, c, "Simple page", In Proceedings of the Conference of Simpleness, 2010.
[2] ...
