Rangitata Diversion Race Reliability Modeling – MSCI Hons 2010

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Overview

Background

Data Analysis

Model

Results

Acknowledgements

About the Authors

 

 

Model

To try and establish the effects that one or more reservoirs would have had on the current system we wrote a deterministic network flow model. This model took into account newly proposed enhancements to the scheme and looked at the effects that they would have had historically on the reliability of the scheme. The model determined a historical optimum release schedule from any reservoirs placed within the scheme.  Generation benefits are also modelled to determine any ill or beneficial effects and overall economic benefit.

 

The actual model is not displayed here, the diagram below shows a simple network flow model that is similar to what was applied to the RDR.                                                                                                                            

 

 

The model was written in Python 2.7 using the open source PuLP modelling language and solved using CPLEX 12.1. A full explanation of how to run and edit the model, along with detailed annotated code can be found in the accompanying user guide.

 

The model was only solved for a period of two years due to memory restrictions. When solving for a two year period we had to take into account the behaviour of the model at the end of the period. We firstly stepped the model through one year at a time so that each season was optimised twice, once as the first season and once as the second season. The levels of each reservoir were noted and also the dual price of the maximum storage constraints, this showed that in each of the ten seasons there was a “reset point” where storage was consistently full. Knowing this meant that when running the model for different storage configurations we could start with full storage and also constrain the model to end with full storage.

 

 

Owen Warburton and Chris Blackmore, University of Canterbury Management Department 2010