title: Exploration of a digital twin concept for income maximisation of the Waikaremoana Power Scheme

authors: Ly, Seng Theng

abstract: Digital twins are digital emulations of real-world objects or systems which mirrors the asset in terms of both behaviour and likeness. Genesis Energy Ltd, an electricity generation company in New Zealand, is interested in developing a digital twin for one of its hydropower assets, the Waikaremoana Power Scheme (WPS). The scheme is a multi-lake cascading system in the North Island with a generation capacity of 138 MW using seven turbines. The ultimate goal for digital twin development with Genesis is to create a tool capable of providing decision making support for traders managing the utilisation of the WPS in the NZ electricity market to maximise income and efficiency while minimising losses. This project is a tentative exploration into how an early digital twin concept could be built for the WPS with the end objective of maximising utilisation through optimising unit commitment and scheduling.
Plant data accuracy and reliability was examined as it is a foundational element to any digital twin. It was found that the WPS possessed accurate instruments for parameters like power output and water levels but relied on correlations for many flow readings around the scheme. Data sampling methods were also examined, and it was found that averaged data was better at short sampling intervals due to reduction of noise.
A flow model was built in Microsoft Excel using a first principles-based approach, assembled using mass and energy balances along with characteristic equations for the scheme. The accuracy of the model was tested against net flow values via lake sensor readings. It was found that on average, there was a difference of around 2 m3/s for all three lakes but increased in proportion to the model net flow rate. As part of the flow model, the efficiency characteristic functions were found using a regression refinement process, starting from linear regression refining eventually to a multivariable linear (polynomial) model. The model was validated using test data from each of the generation units with excellent or good fits found for all units. .
A profit-based optimisation formulation was developed based on literature and the flow model developed. The formulation was applied to a simplified case comprising of a single unit, lake, and time slice problem and testing the relationship between spot price and water value. The problem was solved using Excel Solver and a GRG nonlinear method. Depending on whether the spot price was greater or smaller than water value, the optimiser chose to generate at maximum efficiency before increasing to maximum output as spot price rose. The optimisation encountered difficulties when extending the problem to include multiple units. The Excel Solver was unable to find the global optimum without increasing compute times to unacceptable levels. To proceed further with this optimisation problem, it would need to be moved to a platform with more complex solvers.

text: Exploration of a digital twin concept for income maximisation of the Waikaremoana Power Scheme Digital twins are digital emulations of real-world objects or systems which mirrors the asset in terms of both behaviour and likeness. Genesis Energy Ltd, an electricity generation company in New Zealand, is interested in developing a digital twin for one of its hydropower assets, the Waikaremoana Power Scheme (WPS). The scheme is a multi-lake cascading system in the North Island with a generation capacity of 138 MW using seven turbines. The ultimate goal for digital twin development with Genesis is to create a tool capable of providing decision making support for traders managing the utilisation of the WPS in the NZ electricity market to maximise income and efficiency while minimising losses. This project is a tentative exploration into how an early digital twin concept could be built for the WPS with the end objective of maximising utilisation through optimising unit commitment and scheduling.
Plant data accuracy and reliability was examined as it is a foundational element to any digital twin. It was found that the WPS possessed accurate instruments for parameters like power output and water levels but relied on correlations for many flow readings around the scheme. Data sampling methods were also examined, and it was found that averaged data was better at short sampling intervals due to reduction of noise.
A flow model was built in Microsoft Excel using a first principles-based approach, assembled using mass and energy balances along with characteristic equations for the scheme. The accuracy of the model was tested against net flow values via lake sensor readings. It was found that on average, there was a difference of around 2 m3/s for all three lakes but increased in proportion to the model net flow rate. As part of the flow model, the efficiency characteristic functions were found using a regression refinement process, starting from linear regression refining eventually to a multivariable linear (polynomial) model. The model was validated using test data from each of the generation units with excellent or good fits found for all units. .
A profit-based optimisation formulation was developed based on literature and the flow model developed. The formulation was applied to a simplified case comprising of a single unit, lake, and time slice problem and testing the relationship between spot price and water value. The problem was solved using Excel Solver and a GRG nonlinear method. Depending on whether the spot price was greater or smaller than water value, the optimiser chose to generate at maximum efficiency before increasing to maximum output as spot price rose. The optimisation encountered difficulties when extending the problem to include multiple units. The Excel Solver was unable to find the global optimum without increasing compute times to unacceptable levels. To proceed further with this optimisation problem, it would need to be moved to a platform with more complex solvers.

year: 2022

title: How do ageing stereotypes affect performance on a previously unexplored false memory task?

authors: Ubha, Sukhandeep

abstract: False memories are memories that are thought to have happened but did not happen. Age-related neurological changes can cause an increase in false memories; however, researchers have started focusing on stereotype threat, to explain this increase. Stereotype threat occurs when negative beliefs about a particular group are introduced, causing them to perform poorly on tasks. Studies focused on the effects of age-based stereotype threat on false memories have been conflicted due to the methodological differences (such as the use of retrieval task and the focus of the task). Thus, Experiment 1 aimed to account for those methodological differences and investigated whether stereotype threat influenced performance on the memory conjunction task. In Experiment 1, 60 participants were given a memory conjunction task online and were randomly assigned to either a control or a stereotype threat group. Participants were then given a free recall and a recognition task. They were also asked to indicate their levels of education, employment and importance given to memory (moderating factors), as these factors can affect performance. The results indicated no difference in performance between the control and the stereotyped participants. This could be due to participants finding the memory conjunction task difficult, thus choosing to disengage from the task. Therefore, Experiment 2 aimed to investigate whether there were any changes in performance when an online DRM task with a different focus (prevention or promotion) was used. In Experiment 2, 120 participants were given a DRM task online and were randomly assigned to the stereotype threat-promotion, stereotype threat-prevention,
control-promotion and control-prevention groups. This experiment did not find any significant differences between the groups. It is possible that previous studies mistakenly induced subtle and blatant stereotype threats while the present experiment only used blatant threat. Recruiting participants from an online platform could also explain why present study found discrepant findings compared to the previous research. Even though present study was unable to replicate previous findings, it still added to the limited literature and focused on different methodological factors all at once. It is essential to keep investigating and focusing on different methodological factors to better understand a very complex topic. The threat could cause older adults to be diagnosed with dementia and receive unnecessary treatment when, in fact, they are healthy.

text: How do ageing stereotypes affect performance on a previously unexplored false memory task? False memories are memories that are thought to have happened but did not happen. Age-related neurological changes can cause an increase in false memories; however, researchers have started focusing on stereotype threat, to explain this increase. Stereotype threat occurs when negative beliefs about a particular group are introduced, causing them to perform poorly on tasks. Studies focused on the effects of age-based stereotype threat on false memories have been conflicted due to the methodological differences (such as the use of retrieval task and the focus of the task). Thus, Experiment 1 aimed to account for those methodological differences and investigated whether stereotype threat influenced performance on the memory conjunction task. In Experiment 1, 60 participants were given a memory conjunction task online and were randomly assigned to either a control or a stereotype threat group. Participants were then given a free recall and a recognition task. They were also asked to indicate their levels of education, employment and importance given to memory (moderating factors), as these factors can affect performance. The results indicated no difference in performance between the control and the stereotyped participants. This could be due to participants finding the memory conjunction task difficult, thus choosing to disengage from the task. Therefore, Experiment 2 aimed to investigate whether there were any changes in performance when an online DRM task with a different focus (prevention or promotion) was used. In Experiment 2, 120 participants were given a DRM task online and were randomly assigned to the stereotype threat-promotion, stereotype threat-prevention,
control-promotion and control-prevention groups. This experiment did not find any significant differences between the groups. It is possible that previous studies mistakenly induced subtle and blatant stereotype threats while the present experiment only used blatant threat. Recruiting participants from an online platform could also explain why present study found discrepant findings compared to the previous research. Even though present study was unable to replicate previous findings, it still added to the limited literature and focused on different methodological factors all at once. It is essential to keep investigating and focusing on different methodological factors to better understand a very complex topic. The threat could cause older adults to be diagnosed with dementia and receive unnecessary treatment when, in fact, they are healthy.

year: 2022

title: Covering tangata whenua in Aotearoa: a big data exploration of print media and Māori

authors: Phillips, Justin B.

abstract: A large body of academic research documents harmful media coverage of indigenous populations across the globe. New Zealand is no exception. Aotearoa’s indigenous people, Māori, share similar damaging experiences, leading one major NZ media company to publish an apology for their historically poor depiction of tangata whenua. This paper adds to that wealth of evidence using the automated methods of machine learning to examine coverage of Māori in NZ print media. Across roughly 800,000 sentences–spanning over two decades of coverage–this research investigates print media discourses involving Māori at a mass scale while demonstrating the applicability of such tools for further research. The results replicate a collection of existing findings at large-N scale, further documenting problematic discussions of violence, political representation, and culture, among several other concerns. The novel approach also hints at complex, obscure relationships embedded within problematic language in Aotearoa’s print media, identifying notions of division - both implied and otherwise - along with notable instances of resistance.

text: Covering tangata whenua in Aotearoa: a big data exploration of print media and Māori A large body of academic research documents harmful media coverage of indigenous populations across the globe. New Zealand is no exception. Aotearoa’s indigenous people, Māori, share similar damaging experiences, leading one major NZ media company to publish an apology for their historically poor depiction of tangata whenua. This paper adds to that wealth of evidence using the automated methods of machine learning to examine coverage of Māori in NZ print media. Across roughly 800,000 sentences–spanning over two decades of coverage–this research investigates print media discourses involving Māori at a mass scale while demonstrating the applicability of such tools for further research. The results replicate a collection of existing findings at large-N scale, further documenting problematic discussions of violence, political representation, and culture, among several other concerns. The novel approach also hints at complex, obscure relationships embedded within problematic language in Aotearoa’s print media, identifying notions of division - both implied and otherwise - along with notable instances of resistance.

year: 2022