Impact of technology uptake on an Australian electricity distribution network

This paper presents simulation results for future electricity grids using an agent-based model developed with MODAM (MODular Agent-based Model). MODAM is introduced and its use demonstrated through four simulations based on a scenario that expects a rise of on-site renewable generators and electric vehicles (EV) usage. The simulations were run over many years, for two areas in Townsville, Australia, capturing variability in space of the technology uptake, and for two charging methods for EV, capturing people's behaviours and their impact on the time of the peak load. Impact analyses of these technologies were performed over the areas, down to the distribution transformer level, where greater variability of their contribution to the assets peak load was observed. The MODAM models can be used for different purposes such as impact of renewables on grid sizing, or on greenhouse gas emissions. The insights gained from using MODAM for technology assessment are discussed.

Optimal electricity distribution framework for public space: Assessing renewable energy proposals for Freshkills Park, New York City

Integrating renewable energy into public space is becoming more common as a climate change solution. However, this approach is often guided by the environmental pillar of sustainability, with less focus on the economic and social pillars. The purpose of this paper is to examine this issue in the speculative renewable energy propositions for Freshkills Park in New York City submitted for the 2012 Land Art Generator Initiative (LAGI) competition. This paper first proposes an optimal electricity distribution (OED) framework in and around public spaces based on relevant ecology and energy theory (Odum’s fourth and fifth law of thermodynamics). This framework addresses social engagement related to public interaction, and economic engagement related to the estimated quantity of electricity produced, in conjunction with environmental engagement related to the embodied energy required to construct the renewable energy infrastructure. Next, the study uses the OED framework to analyse the top twenty-five projects submitted for the LAGI 2012 competition. The findings reveal an electricity distribution imbalance and suggest a lack of in-depth understanding about sustainable electricity distribution within public space design. The paper concludes with suggestions for future research.

Application of a Bayesian Network complex system model to a successful community electricity demand reduction program

Utilities worldwide are focused on supplying peak electricity demand reliably and cost effectively, requiring a thorough understanding of all the factors influencing residential electricity use at peak times. An electricity demand reduction project based on comprehensive residential consumer engagement was established within an Australian community in 2008, and by 2011, peak demand had decreased to below pre-intervention levels. This paper applied field data discovered through qualitative in-depth interviews of 22 residential households at the community to a Bayesian Network complex system model to examine whether the system model could explain successful peak demand reduction in the case study location. The knowledge and understanding acquired through insights into the major influential factors and the potential impact of changes to these factors on peak demand would underpin demand reduction intervention strategies for a wider target group.

A smooth transition logit model of the effects of deregulation in the electricity market

This paper introduces the smooth transition logit (STL) model that is designed to detect and model situations in which there is structural change in the behaviour underlying the latent index from which the binary dependent variable is constructed. The maximum likelihood estimators of the parameters of the model are derived along with their asymptotic properties, together with a Lagrange multiplier test of the null hypothesis of linearity in the underlying latent index. The development of the STL model is motivated by the desire to assess the impact of deregulation in the Queensland electricity market and ascertain whether increased competition has resulted in significant changes in the behaviour of the spot price of electricity, specifically with respect to the occurrence of periodic abnormally high prices. The model allows the timing of any change to be endogenously determined and also market participants' behaviour to change gradually over time. The main results provide clear evidence in support of a structural change in the nature of price events, and the endogenously determined timing of the change is consistent with the process of deregulation in Queensland.

Public private partnership infrastructure delivery: Benefits and costs for society

This research provides additional knowledge on the benefits and costs to society, in particular of road transport procured through Public-Private Partnership (PPP) arrangements. Currently, the public sector comparator (PSC) and cost-benefit analysis (CBA) used to evaluate and measure the benefits and costs of PPP are limited in their capacity to predict and forecast long-term events. PPP is attractive to governments due to the non-upfront payment, perceived value for money, and risk allocation and transfer to the private investor. However, public sector remains the guarantor, and under-writer of the private investor's loan from financial institutions and other voluntary risks which are unlimited to future compensatory claims. The new knowledge from this research is the introduction of a framework capable of evaluating, and measuring the associated PPP benefits, as well as the costs, effects, and impacts to society which are protracted and sporadic by nature.

Improved residential electricity demand management through analysis of the customer perspective

Peak electricity demand requires substantial investment to update transmission, distribution and generation infrastructure. A successful community peak demand reduction project was examined to identify residential consumer motivational and contextual factors involved in their decision to adopt/not adopt interventions. Energy professionals actively worked to achieve community 'peer' membership and by becoming a trusted information source, facilitated voluntary home energy assessment requests from over 80% of the residential community. By combining and tailoring interventions to the specific needs and motivations of individual householders and the community, interventions promoting energy conservation and efficiency can be effective in achieving sustained reduction in peak demand.

Hospital infection control units: Staffing, costs, and priorities

Background: This article describes infection prevention and control professionals’ (ICPs’) staffing levels, patient outcomes, and costs associated with the provision of infection prevention and control services in Australian hospitals. A secondary objective was to determine the priorities for infection control units. Methods: A cross-sectional study design was used. Infection control units in Australian public and private hospitals completed a Web-based anonymous survey. Data collected included details about the respondent; hospital demographics; details and services of the infection control unit; and a description of infection prevention and control-related outputs, patient outcomes, and infection control priorities. Results: Forty-nine surveys were undertaken, accounting for 152 Australian hospitals. The mean number of ICPs was 0.66 per 100 overnight beds (95% confidence interval, 0.55-0.77). Privately funded hospitals have significantly fewer ICPs per 100 overnight beds compared with publicly funded hospitals (P < .01). Staffing costs for nursing staff in infection control units in this study totaled $16,364,392 (mean, $380,566). Infection control units managing smaller hospitals (<270 beds) identified the need for increased access to infectious diseases or microbiology support. Conclusion: This study provides valuable information to support future decisions by funders, hospital administrators, and ICPs on service delivery models for infection prevention and control. Further, it is the first to provide estimates of the resourcing and cost of staffing infection control in hospitals at a national level. Copyright

A theoretical framework for reducing tendering costs in the procurement of infrastructure projects

Purpose of this paper One way in which the tendering process can be further improved is by reviewing and clarifying the high costs that participants face during the course of the tendering phase. The study aims to provide project teams working in construction tender preparation a clear picture of what to expect when tendering for infrastructure projects. Design/methodology/approach Firstly, a review of current literature on tendering in infrastructure projects is conducted to identify the associated costs affecting traditional and PPP procurements as well as the potential measures contributing to tendering cost-reduction. A theoretical framework and its corresponding research hypotheses, which are based on the literature reviewed, are then proposed. An industry-wide questionnaire survey is currently under design to solicit industry practitioners’ views on tendering costs and the associated tendering cost-reduction measures. The data collected in the survey will subject to statistical analysis to test the proposed research hypotheses, which will be reported in a forthcoming paper. Findings and value The direct and indirect costs in public-private procurement have been identified and have been categorised into internal and external costs arising from working on tender submissions. A theoretical framework, mainly composed of five mechanisms of cost reduction, has been proposed and will be tested in a forthcoming industry-wide questionnaire survey. Originality/value of paper The findings are expected to lead to a transparent tendering process in infrastructure procurement, in which there is increased engagement from the private sector as well as an increase in competitive tendering.

Receivers' future liability for costs

In Juniper Property Holdings No.15 Pty Ltd v Caltabiano [2015] QSC 95, Jackson J considered what he described as a 'novel point' as to whether the court had jurisdiction to make a determination of the liability of receivers and managers appointed to the plaintiff to pay any costs orders that may be made in favour of the defendant.

Costs assessor selection: A question of timing, or cost?

In Lessbrook Pty Ltd (in liq) v Whap; Stephen; Bowie; Kepa & Kepa [2014] QCA 63 the Queensland Court of Appeal dealt with significant questions of general application relating to the appointment of assessors to conduct an assessment of costs under the Uniform Civil Procedure Rules 1999 (Qld) (UCPR).

Projecting excess emergency department visits and associated costs in Brisbane, Australia, under population growth and climate change scenarios

The direct and indirect health effects of increasingly warmer temperatures are likely to further burden the already overcrowded hospital emergency departments (EDs). Using current trends and estimates in conjunction with future population growth and climate change scenarios, we show that the increased number of hot days in the future can have a considerable impact on EDs, adding to their workload and costs. The excess number of visits in 2030 is projected to range between 98–336 and 42–127 for younger and older groups, respectively. The excess costs in 2012–13 prices are estimated to range between AU$51,000–184,000 (0–64) and AU$27,000–84,000 (65+). By 2060, these estimates will increase to 229–2300 and 145–1188 at a cost of between AU$120,000–1,200,000 and AU$96,000–786,000 for the respective age groups. Improvements in climate change mitigation and adaptation measures are likely to generate synergistic health co-benefits and reduce the impact on frontline health services.

Modelling interregional links in electricity price spikes

Abnormally high price spikes in spot electricity markets represent a significant risk to market participants. As such, a literature has developed that focuses on forecasting the probability of such spike events, moving beyond simply forecasting the level of price. Many univariate time series models have been proposed to dealwith spikes within an individual market region. This paper is the first to develop a multivariate self-exciting point process model for dealing with price spikes across connected regions in the Australian National Electricity Market. The importance of the physical infrastructure connecting the regions on the transmission of spikes is examined. It is found that spikes are transmitted between the regions, and the size of spikes is influenced by the available transmission capacity. It is also found that improved risk estimates are obtained when inter-regional linkages are taken into account.

Systems modelling of the socio-technical aspects of residential electricity use and network peak demand

Provision of network infrastructure to meet rising network peak demand is increasing the cost of electricity. Addressing this demand is a major imperative for Australian electricity agencies. The network peak demand model reported in this paper provides a quantified decision support tool and a means of understanding the key influences and impacts on network peak demand. An investigation of the system factors impacting residential consumers’ peak demand for electricity was undertaken in Queensland, Australia. Technical factors, such as the customers’ location, housing construction and appliances, were combined with social factors, such as household demographics, culture, trust and knowledge, and Change Management Options (CMOs) such as tariffs, price,managed supply, etc., in a conceptual ‘map’ of the system. A Bayesian network was used to quantify the model and provide insights into the major influential factors and their interactions. The model was also used to examine the reduction in network peak demand with different market-based and government interventions in various customer locations of interest and investigate the relative importance of instituting programs that build trust and knowledge through well designed customer-industry engagement activities. The Bayesian network was implemented via a spreadsheet with a tick box interface. The model combined available data from industry-specific and public sources with relevant expert opinion. The results revealed that the most effective intervention strategies involve combining particular CMOs with associated education and engagement activities. The model demonstrated the importance of designing interventions that take into account the interactions of the various elements of the socio-technical system. The options that provided the greatest impact on peak demand were Off-Peak Tariffs and Managed Supply and increases in the price of electricity. The impact in peak demand reduction differed for each of the locations and highlighted that household numbers, demographics as well as the different climates were significant factors. It presented possible network peak demand reductions which would delay any upgrade of networks, resulting in savings for Queensland utilities and ultimately for households. The use of this systems approach using Bayesian networks to assist the management of peak demand in different modelled locations in Queensland provided insights about the most important elements in the system and the intervention strategies that could be tailored to the targeted customer segments.

A CCG virtual system for big data application communication costs analysis

Network topology and routing are two important factors in determining the communication costs of big data applications at large scale. As for a given Cluster, Cloud, or Grid system, the network topology is fixed and static or dynamic routing protocols are preinstalled to direct the network traffic. Users cannot change them once the system is deployed. Hence, it is hard for application developers to identify the optimal network topology and routing algorithm for their applications with distinct communication patterns. In this study, we design a CCG virtual system (CCGVS), which first uses container-based virtualization to allow users to create a farm of lightweight virtual machines on a single host. Then, it uses software-defined networking (SDN) technique to control the network traffic among these virtual machines. Users can change the network topology and control the network traffic programmingly, thereby enabling application developers to evaluate their applications on the same system with different network topologies and routing algorithms. The preliminary experimental results through both synthetic big data programs and NPB benchmarks have shown that CCGVS can represent application performance variations caused by network topology and routing algorithm.

Communicating consequences with costs: A commentary on Corso et al's cost of injury

Estimating the economic burden of injuries is important for setting priorities, allocating scarce health resources and planning cost-effective prevention activities. As a metric of burden, costs account for multiple injury consequences—death, severity, disability, body region, nature of injury—in a single unit of measurement. In a 1989 landmark report to the US Congress, Rice et al1 estimated the lifetime costs of injuries in the USA in 1985. By 2000, the epidemiology and burden of injuries had changed enough that the US Congress mandated an update, resulting in a book on the incidence and economic burden of injury in the USA.2 To make these findings more accessible to the larger realm of scientists and practitioners and to provide a template for conducting the same economic burden analyses in other countries and settings, a summary3 was published in Injury Prevention. Corso et al reported that, between 1985 and 2000, injury rates declined roughly 15%. The estimated lifetime cost of these injuries declined 20%, totalling US$406 billion, including US$80 billion in medical costs and US$326 billion in lost productivity. While incidence reflects problem size, the relative burden of injury is better expressed using costs.