On the optimal capacity expansion of electric power systems

The analysis of investment in the electric power has been the subject of intensive research for many years. The efficient generation and distribution of electrical energy is a difficult task involving the operation of a complex network of facilities, often located over very large geographical regions. Electric power utilities have made use of an enormous range of mathematical models. Some models address time spans which last for a fraction of a second, such as those that deal with lightning strikes on transmission lines while at the other end of the scale there are models which address time horizons consisting of ten or twenty years; these usually involve long range planning issues. This thesis addresses the optimal long term capacity expansion of an interconnected power system. The aim of this study has been to derive a new, long term planning model which recognises the regional differences which exist for energy demand and which are present in the construction and operation of power plant and transmission line equipment. Perhaps the most innovative feature of the new model is the direct inclusion of regional energy demand curves in the nonlinear form. This results in a nonlinear capacity expansion model. After review of the relevant literature, the thesis first develops a model for the optimal operation of a power grid. This model directly incorporates regional demand curves. The model is a nonlinear programming problem containing both integer and continuous variables. A solution algorithm is developed which is based upon a resource decomposition scheme that separates the integer variables from the continuous ones. The decompostion of the operating problem leads to an interactive scheme which employs a mixed integer programming problem, known as the master, to generate trial operating configurations. The optimum operating conditions of each trial configuration is found using a smooth nonlinear programming model. The dual vector recovered from this model is subsequently used by the master to generate the next trial configuration. The solution algorithm progresses until lower and upper bounds converge. A range of numerical experiments are conducted and these experiments are included in the discussion. Using the operating model as a basis, a regional capacity expansion model is then developed. It determines the type, location and capacity of additional power plants and transmission lines, which are required to meet predicted electicity demands. A generalised resource decompostion scheme, similar to that used to solve the operating problem, is employed. The solution algorithm is used to solve a range of test problems and the results of these numerical experiments are reported. Finally, the expansion problem is applied to the Queensland electricity grid in Australia.

On the optimal capacity expansion of electric power systems

The analysis of investment in the electric power has been the subject of intensive research for many years. The efficient generation and distribution of electrical energy is a difficult task involving the operation of a complex network of facilities, often located over very large geographical regions. Electric power utilities have made use of an enormous range of mathematical models. Some models address time spans which last for a fraction of a second, such as those that deal with lightning strikes on transmission lines while at the other end of the scale there are models which address time horizons consisting of ten or twenty years; these usually involve long range planning issues. This thesis addresses the optimal long term capacity expansion of an interconnected power system. The aim of this study has been to derive a new, long term planning model which recognises the regional differences which exist for energy demand and which are present in the construction and operation of power plant and transmission line equipment. Perhaps the most innovative feature of the new model is the direct inclusion of regional energy demand curves in the nonlinear form. This results in a nonlinear capacity expansion model. After review of the relevant literature, the thesis first develops a model for the optimal operation of a power grid. This model directly incorporates regional demand curves. The model is a nonlinear programming problem containing both integer and continuous variables. A solution algorithm is developed which is based upon a resource decomposition scheme that separates the integer variables from the continuous ones. The decompostion of the operating problem leads to an interactive scheme which employs a mixed integer programming problem, known as the master, to generate trial operating configurations. The optimum operating conditions of each trial configuration is found using a smooth nonlinear programming model. The dual vector recovered from this model is subsequently used by the master to generate the next trial configuration. The solution algorithm progresses until lower and upper bounds converge. A range of numerical experiments are conducted and these experiments are included in the discussion. Using the operating model as a basis, a regional capacity expansion model is then developed. It determines the type, location and capacity of additional power plants and transmission lines, which are required to meet predicted electicity demands. A generalised resource decompostion scheme, similar to that used to solve the operating problem, is employed. The solution algorithm is used to solve a range of test problems and the results of these numerical experiments are reported. Finally, the expansion problem is applied to the Queensland electricity grid in Australia

A leap into foodservice satisfaction in the private acute care setting

Monitoring foodservice satisfaction is a risk management strategy for malnutrition in the acute care sector, as low satisfaction may be associated with poor intake. This study aimed to investigate the relationship between age and foodservice satisfaction in the private acute care setting. Patient satisfaction was assessed using a validated tool, the Acute Care Hospital Foodservice Patient Satisfaction Questionnaire for data collected 2008–2010 (n = 779) at a private hospital, Brisbane. Age was grouped into three categories; <50 years, 51–70 years and >70 years. Fisher’s exact test assessed independence of categorical responses and age group; ANOVA or Kruskal–Wallis test was used for continuous variables. Dichotomised responses were analysed using logistic regression and odds ratios (95% confidence interval, p < 0.05). Overall foodservice satisfaction (5 point scale) was high (≥4 out of 5) and was independent of age group (p = 0.377). There was an increasing trend with age in mean satisfaction scores for individual dimensions of foodservice; food quality (p < 0.001), meal service quality (p < 0.001), staff service issues (p < 0.001) and physical environment (p < 0.001). A preference for being able to choose different sized meals (59.8% > 70 years vs 40.6% ≤50 years; p < 0.001) and response to ‘the foods are just the right temperature’ (55.3% >70 years vs 35.9% ≤50 years; p < 0.001) was dependent on age. For the food quality dimension, based on dichotomised responses (satisfied or not), the odds of satisfaction was higher for >70 years (OR = 5.0, 95% CI: 1.8–13.8; <50 years referent). These results suggest that dimensions of foodservice satisfaction are associated with age and can assist foodservices to meet varying generational expectations of clients.

Using a Bayesian network to model colonisation with Vancomycin Resistant Enterococcus (VRE)

Objective: Effective management of multi-resistant organisms is an important issue for hospitals both in Australia and overseas. This study investigates the utility of using Bayesian Network (BN) analysis to examine relationships between risk factors and colonization with Vancomycin Resistant Enterococcus (VRE). Design: Bayesian Network Analysis was performed using infection control data collected over a period of 36 months (2008-2010). Setting: Princess Alexandra Hospital (PAH), Brisbane. Outcome of interest: Number of new VRE Isolates Methods: A BN is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG). BN enables multiple interacting agents to be studied simultaneously. The initial BN model was constructed based on the infectious disease physician‟s expert knowledge and current literature. Continuous variables were dichotomised by using third quartile values of year 2008 data. BN was used to examine the probabilistic relationships between VRE isolates and risk factors; and to establish which factors were associated with an increased probability of a high number of VRE isolates. Software: Netica (version 4.16). Results: Preliminary analysis revealed that VRE transmission and VRE prevalence were the most influential factors in predicting a high number of VRE isolates. Interestingly, several factors (hand hygiene and cleaning) known through literature to be associated with VRE prevalence, did not appear to be as influential as expected in this BN model. Conclusions: This preliminary work has shown that Bayesian Network Analysis is a useful tool in examining clinical infection prevention issues, where there is often a web of factors that influence outcomes. This BN model can be restructured easily enabling various combinations of agents to be studied.

Distribution of conjunctival ultraviolet autoflourescence in a population-based study : the Norfolk Island Eye Study

OBJECTIVE: The objective of this study was to describe the distribution of conjunctival ultraviolet autofluorescence (UVAF) in an adult population. METHODS: We conducted a cross-sectional, population-based study in the genetic isolate of Norfolk Island, South Pacific Ocean. In all, 641 people, aged 15 to 89 years, were recruited. UVAF and standard (control) photographs were taken of the nasal and temporal interpalpebral regions bilaterally. Differences between the groups for non-normally distributed continuous variables were assessed using the Wilcoxon-Mann-Whitney ranksum test. Trends across categories were assessed using Cuzick's non-parametric test for trend or Kendall's rank correlation τ. RESULTS: Conjunctival UVAF is a non-parametric trait with a positively skewed distribution. Median amount of conjunctival UVAF per person (sum of four measurements; right nasal/temporal and left nasal/temporal) was 28.2 mm(2) (interquartile range 14.5-48.2). There was an inverse, linear relationship between UVAF and advancing age (P<0.001). Males had a higher sum of UVAF compared with females (34.4 mm(2) vs 23.2 mm(2), P<0.0001). There were no statistically significant differences in area of UVAF between right and left eyes or between nasal and temporal regions. CONCLUSION: We have provided the first quantifiable estimates of conjunctival UVAF in an adult population. Further data are required to provide information about the natural history of UVAF and to characterise other potential disease associations with UVAF. UVR protective strategies should be emphasised at an early age to prevent the long-term adverse effects on health associated with excess UVR.

Changes in health profiles of participants in an Australian police department’s Wellness program

Background: Little is known about the health effects of worksite wellness programs on police department staff. Objective: To examine 1-2 year changes in health profiles of participants in the Queensland Police Service’s wellness program. Methods: Participants underwent yearly physical assessments. Health profile data collected during assessments from 2008 to 2012 were included in the analysis. Data Analysis: Repeated-measures ANOVA was used for continuous outcome variables, related-samples Wilcoxon Signed Rank test for non-normally continuous variables, and McNemar’s test for binary variables. Results: Significant changes in physical measures included decreases in waist circumference and percent body fat, and increases in cardiorespiratory fitness and flexibility (p<0.01). Changes in serum cholesterol, haemoglobin, total cholesterol ratios, HDL, LDL and Triglyceride levels were also significant (p<0.01). Conclusion: Participants’ health profiles mostly improved between cycles although most changes were not clinically significant. As this evaluation used a single-group pre-test post-test design, it provides initial indications that wellness programs can benefit staff in police departments.

Weight-related risk perception among healthy and overweight pregnant women: A cross-sectional study

Objective The objective of this study was to evaluate weight-related risk perception in early pregnancy and to compare this perception between women commencing pregnancy healthy weight and overweight. Study design Pregnant women (n=664) aged 29±5 (mean±s.d.) years were recruited from a metropolitan teaching hospital in Australia. A self-administered questionnaire was completed at around 16 weeks of gestation. Height measured at baseline and self-reported pre-pregnancy weight were used to calculate body mass index. Cross-sectional analysis was conducted. Differences between groups were assessed using chi-squared tests for categorical variables and t-tests or Mann–Whitney U tests for continuous variables depending on distribution. Result Excess gestational weight gain (GWG) during pregnancy was more important in leading to health problems for women or their child compared with pre-pregnancy weight. Personal risk perception for complications was low for all women, although overweight women had slightly higher scores than healthy-weight women (2.4±1.0 vs 2.9±1.0; P<0.001). All women perceived their risk for complications to be below that of an average pregnant woman. Conclusion Women should be informed of the risk associated with their pre-pregnancy weight (in the case of maternal overweight) and excess GWG. If efforts to raise risk awareness are to result in preventative action, this information needs to be accompanied by advice and appropriate support on how to reduce risk.

Using Lifespan developmental theory and methods as a viable alternative to the study of generational differences at work

I agree with Costanza and Finkelstein (2015) that it is futile to further invest in the study of generational differences in the work context due to a lack of appropriate theory and methods. The key problem with the generations concept is that splitting continuous variables such as age or time into a few discrete units involves arbitrary cutoffs and atheoretical groupings of individuals (e.g., stating that all people born between the early 1960s and early 1980s belong to Generation X). As noted by methodologists, this procedure leads to a loss of information about individuals and reduced statistical power (MacCallum, Zhang, Preacher, & Rucker, 2002). Due to these conceptual and methodological limitations, I regard it as very difficult if not impossible to develop a “comprehensive theory of generations” (Costanza & Finkelstein, p. 20) and to rigorously examine generational differences at work in empirical studies.

Evaluation of financial incentives as a quality improvement strategy in the public hospital context : clinician attitudes, design variables, and economic costs

In 2008, a three-year pilot ‘pay for performance’ (P4P) program, known as ‘Clinical Practice Improvement Payment’ (CPIP) was introduced into Queensland Health (QHealth). QHealth is a large public health sector provider of acute, community, and public health services in Queensland, Australia. The organisation has recently embarked on a significant reform agenda including a review of existing funding arrangements (Duckett et al., 2008). Partly in response to this reform agenda, a casemix funding model has been implemented to reconnect health care funding with outcomes. CPIP was conceptualised as a performance-based scheme that rewarded quality with financial incentives. This is the first time such a scheme has been implemented into the public health sector in Australia with a focus on rewarding quality, and it is unique in that it has a large state-wide focus and includes 15 Districts. CPIP initially targeted five acute and community clinical areas including Mental Health, Discharge Medication, Emergency Department, Chronic Obstructive Pulmonary Disease, and Stroke. The CPIP scheme was designed around key concepts including the identification of clinical indicators that met the set criteria of: high disease burden, a well defined single diagnostic group or intervention, significant variations in clinical outcomes and/or practices, a good evidence, and clinician control and support (Ward, Daniels, Walker & Duckett, 2007). This evaluative research targeted Phase One of implementation of the CPIP scheme from January 2008 to March 2009. A formative evaluation utilising a mixed methodology and complementarity analysis was undertaken. The research involved three research questions and aimed to determine the knowledge, understanding, and attitudes of clinicians; identify improvements to the design, administration, and monitoring of CPIP; and determine the financial and economic costs of the scheme. Three key studies were undertaken to ascertain responses to the key research questions. Firstly, a survey of clinicians was undertaken to examine levels of knowledge and understanding and their attitudes to the scheme. Secondly, the study sought to apply Statistical Process Control (SPC) to the process indicators to assess if this enhanced the scheme and a third study examined a simple economic cost analysis. The CPIP Survey of clinicians elicited 192 clinician respondents. Over 70% of these respondents were supportive of the continuation of the CPIP scheme. This finding was also supported by the results of a quantitative altitude survey that identified positive attitudes in 6 of the 7 domains-including impact, awareness and understanding and clinical relevance, all being scored positive across the combined respondent group. SPC as a trending tool may play an important role in the early identification of indicator weakness for the CPIP scheme. This evaluative research study supports a previously identified need in the literature for a phased introduction of Pay for Performance (P4P) type programs. It further highlights the value of undertaking a formal risk assessment of clinician, management, and systemic levels of literacy and competency with measurement and monitoring of quality prior to a phased implementation. This phasing can then be guided by a P4P Design Variable Matrix which provides a selection of program design options such as indicator target and payment mechanisms. It became evident that a clear process is required to standardise how clinical indicators evolve over time and direct movement towards more rigorous ‘pay for performance’ targets and the development of an optimal funding model. Use of this matrix will enable the scheme to mature and build the literacy and competency of clinicians and the organisation as implementation progresses. Furthermore, the research identified that CPIP created a spotlight on clinical indicators and incentive payments of over five million from a potential ten million was secured across the five clinical areas in the first 15 months of the scheme. This indicates that quality was rewarded in the new QHealth funding model, and despite issues being identified with the payment mechanism, funding was distributed. The economic model used identified a relative low cost of reporting (under $8,000) as opposed to funds secured of over $300,000 for mental health as an example. Movement to a full cost effectiveness study of CPIP is supported. Overall the introduction of the CPIP scheme into QHealth has been a positive and effective strategy for engaging clinicians in quality and has been the catalyst for the identification and monitoring of valuable clinical process indicators. This research has highlighted that clinicians are supportive of the scheme in general; however, there are some significant risks that include the functioning of the CPIP payment mechanism. Given clinician support for the use of a pay–for-performance methodology in QHealth, the CPIP scheme has the potential to be a powerful addition to a multi-faceted suite of quality improvement initiatives within QHealth.

Cross-correlations between weather variables in Australia

The information on climate variations is essential for the research of many subjects, such as the performance of buildings and agricultural production. However, recorded meteorological data are often incomplete. There may be a limited number of locations recorded, while the number of recorded climatic variables and the time intervals can also be inadequate. Therefore, the hourly data of key weather parameters as required by many building simulation programmes are typically not readily available. To overcome this gap in measured information, several empirical methods and weather data generators have been developed. They generally employ statistical analysis techniques to model the variations of individual climatic variables, while the possible interactions between different weather parameters are largely ignored. Based on a statistical analysis of 10 years historical hourly climatic data over all capital cities in Australia, this paper reports on the finding of strong correlations between several specific weather variables. It is found that there are strong linear correlations between the hourly variations of global solar irradiation (GSI) and dry bulb temperature (DBT), and between the hourly variations of DBT and relative humidity (RH). With an increase in GSI, DBT would generally increase, while the RH tends to decrease. However, no such a clear correlation can be found between the DBT and atmospheric pressure (P), and between the DBT and wind speed. These findings will be useful for the research and practice in building performance simulation.