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Green building studies generally focus on singular performance aspects (e.g., energy, waste, water, indoor environment) with few tackling the relationships between each other, particularly the relationship between indoor environmental quality (IEQ) and building energy consumption. This study aims to explore the relationship between IEQ performance and energy consumption in National Australian Built Environment Rating System (NABERS) certified buildings. A verified climate normalization factor was localized to standardize energy use intensity in buildings from different climate zones of Australia. The normalized energy use intensity (NEUI) was calculated for all office buildings and correlated with their NABERS Energy and IE rating scores. Multivariate linear regression results reveal that one unit increase in NABERS Energy rating score and IE score can reduce NEUI by 21.98 kWh/m2 and 9.88 kWh/m2 per annum, respectively. Also, this study develops an Energy and Indoor Environment Index to benchmark the energy and IEQ performance of Australian office building. Buildings with excellent NABERS Energy and IE ratings (scores equal to/higher than 5) have been classified as high-performance NABERS buildings (HNBs) and the rest as low-performance NABERS buildings (LNBs). A comparison between 49 HNBs and 48 LNBs demonstrates that, on average, HNBs can deliver 12.6% better indoor environment quality with 35.9% less energy consumption than LNBs. In contrast, many LNBs either use excessive energy to provide a sufficient IEQ, or sacrifice IEQ to reduce energy costs and/or achieve a high NABERS Energy rating.

The effectiveness of deployment policies to promote the uptake of residential rooftop solar photovoltaic systems ultimately hinges on the behaviour of households who decide to accept or reject the technology. Over the past years, research has strived to understand, designate significant predictors, model the behaviour of heterogeneous households, and predict the diffusion rate by putting a wide range of approaches in place. Inspired by compiling a comprehensive database of home solar adoption studies, the present study systematically reviews the adopted theories, methods and approaches used within 199 original quantitative, qualitative, statistical, and non-statistical articles covering households’ attitudes, awareness, tendencies, knowledge, motives, willingness, intentions, and adoption decisions. The study provides a critical analysis of investigations on the adoption of solar photovoltaics, solar home systems, and solar photovoltaics coupled with battery energy storage systems. The outcome of the review revealed 108 future and 91 retroactive studies that exploit 10 key dependent variables by means of 52% primary (empirical data), 34% secondary (available sources), and 13% covering both modes of data collection. The sensitivity of these dependent variables was tested by 36 intervention variables that seek to capacitate effective managerial policies. The complexity of the individual decision was comprehended by 13 forms of behavioural theories, with the top-ranked two being the diffusion of innovation and the theory of planned behaviour. The literature showcased a total of 170 quantitative, 20 qualitative, and nine mixed-method studies, with statistical and non-statistical techniques being applied 139 and 86 times, respectively. Regression analysis was the most commonly used statistical analysis method, followed by spatial analysis for non-statistical models. At the heart of the predictive methods for analysing the diffusion rate of these solar technologies, nuances of 25 agent-based models and their social networks were examined in depth. The review further revealed 14 spectra of household categories as well as 12 typologies of household comparisons.

Water utility companies in urban areas face two major challenges: ensuring there is enough water for everyone during prolonged drought and maintaining adequate water pressure during the hours of peak demand. These issues can be overcome by applying data analytics and machine learning to the data gathered from digital water meters. For water conservation and demand management strategies to be effective, utility companies need to gain a better understanding of consumer behaviours, habits and routines. To accomplish this goal, we adapted a clustering approach to reveal residential water consumption patterns within metered data. In the experiment, we used two data sets (engineered features data set as well as the times of use and weighted probabilities of use data set) based on the data collected over 10 months from 306 households in Melbourne, Australia. For the engineered features data set, first, we identified the number of optimal clusters. We then performed extensive experiments to find the best clustering approach in terms of performance evaluation and clustering quality. We chose the hierarchical agglomerative clustering technique based on the nature of the data and the objective of the study. We observed that for the engineered features data set, k-means is the best performing clustering technique after considering performance metrics. For the other data set, we found that the number of clusters varies based on the type of water-consumption event, type of day (i.e., weekday or weekend), profiling interval and probability of use. In addition, we observed that insight into tap-water usage could be used to determine the population’s adaptation of hygiene practices in an unprecedented time, such as the COVID-19 pandemic. Finally, we recommend that future clustering studies also employ aligned socio-demographic data and other key features.

This work examines the effect of energy storage systems (ESSs) operation on the voltage stability and quality of the local power system. The variation of these two voltage dimensions is expressed in a collective manner by the novel voltage stability and quality index ( V S Q I ). For the calculation of the V S Q I , a complete voltage stability curve is required, and the coordinates associated with voltage stability and quality are identified. The distance between the abovementioned coordinates, that collectively indicates the effect of ESSs operation on the two voltage dimensions, is represented by the V S Q I . The process for V S Q I identification is repeated for each operating scenario, with the studied cases including grid operation prior to ESSs utilisation, grid support performed by a community level (CL) ESS, and grid operation with distributed (DIST) ESSs. The ESSs are powered by renewable energy sources (RESs), and a future scenario of 70% photovoltaic (PV) penetration was examined. Other ESSs applications presented in this work, include time-shifting of the energy produced by RESs, peak load shaving, and reducing the loading levels of transmission lines and of the substation transformer. The above stack of ESSs applications, highlights the multifunctional role of ESSs as support to the grid. A real low voltage (LV) distribution network located in south-east Queensland, Australia was employed for the study.

Distributed solar photovoltaic (PV) systems are a low-cost form of renewable energy technology that has had an exponential rate of uptake globally in the last decade. However, little attention has been paid to the potential environmental and human health related impacts associated with PV systems, if not managed properly at the end-of-life (EoL). Rare materials such as ruthenium, gallium, indium, and tellurium are essential components in PV panels, while battery energy storage systems (BESS) are composed of various chemistries (i.e. lithium-ion, lead acid, nickel cadmium, salt water, and flow batteries). An appropriate EoL management strategy for solar photovoltaic systems (i.e. PV modules, BESS) is necessary, not only to prevent and/or mitigate future environmental problems but also to reduce demand on rare earth materials. Drawn from a portfolio of 191 papers collected from Scopus and Web of Science databases between 2000 and 2018 (by 30 June 2018), a systematic quantitative literature review on solar energy systems EoL studies was conducted to examine the temporal trend of current research as well as methodological and geographical distributions of the published articles. Research has been concentrated within Europe, some parts of Asia, and North America, with experimental and modelling/simulation methods being mostly applied. The focus of this study was to compile and synthesise reported drivers, barriers, and enablers to EoL management of PV panels and BESS in the context of the circular economy. A conceptual framework is proposed to facilitate the transition of current PV system material flows and supply chain management practices to circular economy concepts. This paper also presents a future research agenda.

As rooftop solar photovoltaic (PV) adoption in Australia is exponentially growing in the past decade, there is a need to promote effective product stewardship for PV panels reaching their end-of-life (EoL). This paper presents the development of a System Dynamics (SD) model for managing EoL rooftop PV panels based on the circular economy concept. Four stages of the SD modelling process include problem scoping and variable identification, model conceptualisation, SD model development, and scenario analysis. Stakeholder engagement is central to this research as the system under study is underpinned by high uncertainties and limited data availability. Four socio-technical transition pathways examined in this study include market-driven growth, conservative development, shared responsibility, and disruptive change. The simulation results indicated an improvement of collection and recovery performance when a stringent product stewardship scheme is enabled and improvement of installers’ participation in the collection program. This study argued that a system of shared responsibility will be capable of balancing techno-economic motivations of stakeholders across the supply chain to participate in the recovery scheme, while being less disruptive to PV adoption. Under this scenario, a gradual change in regulatory requirements (e.g. recovery target and material recovery rate requirements) is introduced to allow a period of industry and market development.

Knowledge of the barriers and coping strategies for retrofitting government buildings for energy efficiency is essential for the success of these types of complex retrofitting programs. This study utilised thematic findings from two focused groups consist of government employees from two States within Australia to create a comprehensive list of barriers to retrofitting public building stock for energy efficiency and associated strategies to address them. Thematic analysis revealed that a lack of political will, financing protocols, department/agency capability, industry capability, quality assurance and misaligned incentives, are the key barriers to public building energy efficiency retrofitting projects. To address such barriers, research revealed that a government championed top-down approach is required. A key strategy identified was enabling government departments and agencies to take on debt to fund retrofit initiatives that would derive returns, in terms of reduced energy utility costs, over the short-medium term. Other important strategies included having a mandatory energy efficiency retrofitting policy, dedicated financing mechanism, flexible procurement model, facilitation team and list of pre-qualified professionals.

This research experiment is the first study to provide detailed insights into resource use by guests staying in self-contained B&B style accommodation. Self-contained accommodation is a neglected yet important sub-industry in the hospitality sector, as it represents the largest and fastest growing share of accommodation providers in many regions. The study provides valuable data from a smart metering system, collecting data every minute over four seasons and indicates sustainable consumption opportunities using renewable energy, guest behaviour change and eco-conservation. Our results reveal that guests, when exposed to sustainability advice and communication (i.e. Intervention Group), saved 27% electricity, 22% gas, 22% water and 10% firewood. Differences between Intervention and Control Groups were also observed in terms of daily peak consumption and seasonal patterns. There are further saving opportunities, but findings also highlight social practice complexities to navigate guests' individual thermal comfort knowledge and daily routines. Transitions can be achieved through advanced smart services for direct and indirect customer feedback and host training. This paper is the first to detail findings from smart meter monitoring using high resolution at tourist accommodation, providing new insights into social practices and analysis of water energy nexus.

A graph H is a square root of a graph G if G can be obtained from H by adding an edge between any two vertices in H that are of distance 2. The Square Root problem is that of deciding whether a given graph admits a square root. This problem is known to be NP-complete for chordal graphs and polynomial-time solvable for non-trivial minor-closed graph classes and a very limited number of other graph classes. We prove that Square Root is O(n)-time solvable for graphs of maximum degree 5 and O(n4)-time solvable for graphs of maximum degree at most 6.

© 2018 Elsevier Ltd Advanced metering technologies coupled with informatics creates an opportunity to form digital multi-utility service providers. These providers will be able to concurrently collect a customers’ medium-high resolution water, electricity and gas demand data and provide user-friendly platforms to feed this information back to customers and supply/distribution utility organisations. Providers that can install low-cost integrative systems will reap the benefits of derived operational synergies and access to mass markets not bounded by historical city, state or country limits. This paper provides a vision of the required transformative process and features of an integrated multi-utility service provider covering the system architecture, opportunities and benefits, impediments and strategies, and business opportunities. The heart of the paper is focused on demonstrating data modelling processes and informatics opportunities for contemporaneously collected demand data, through illustrative examples and four informative water-energy nexus case studies. Finally, the paper provides an overview of the transformative R&D priorities to realise the vision.

Population growth, coupled with declining water availability and changes in climatic conditions underline the need for sustainable and responsive water management instruments. Supply augmentation and demand management are the two main strategies used by water utilities. Water demand management has long been acknowledged as a least-cost strategy to maintain water security. This can be achieved in a variety of ways, including: i) educating consumers to limit their water use; ii) imposing restrictions/penalties; iii) using smart and/or efficient technologies; and iv) pricing mechanisms. Changing water consumption behaviours through pricing or restrictions is challenging as it introduces more social and political issues into the already complex water resources management process. This paper employs a participatory systems modelling approach for: (1) evaluating various forms of a proposed tiered scarcity adjusted water budget and pricing structure, and (2) comparing scenario outcomes against the traditional restriction policy regime. System dynamics modelling was applied since it can explicitly account for the feedbacks, interdependencies, and non-linear relations that inherently characterise the water tariff (price)-demand-revenue system. A combination of empirical water use data, billing data and customer feedback on future projected water bills facilitated the assessment of the suitability and likelihood of the adoption of scarcity-driven tariff options for a medium-sized city within Queensland, Australia. Results showed that the tiered scarcity adjusted water budget and pricing structure presented was preferable to restrictions since it could maintain water security more equitably with the lowest overall long-run marginal cost.

Current practice for the design of an urban water system usually relies on various models that are often founded on a number of assumptions on how bulk water consumption is attributed to customer connections and outdated demand information that does not reflect present consumption trends; meaning infrastructure is often unnecessarily overdesigned. The recent advent of high resolution smart water meters and advanced data analytics allow for a new era of using the continuous ‘big data’ generated by these meter fleets to create an intelligent system for urban water management to overcome this problem. The aim of this research is to provide infrastructure planners with a detailed understanding of how granular data generated by an intelligent water management system (Autoflow©) can be utilised to obtain significant efficiencies throughout different stages of an urban water cycle, from supply, distribution, customer engagement, and even wastewater treatment.

A multi-parametric sensitivity analysis of Solar Water Heater (SWH) systems was undertaken for the city of Brisbane in Australia using computational models calibrated by experimental data. The models were calculated using EnergyPlus 8.6. The following technical specification parameters were assessed in the modelling: (i) solar collector efficiency; (ii) solar collector area; (iii) tank volume; (iv) tank heat loss; (v) electric back-up heating power rate; (vi) electric back-up heating position (height) for vertical tanks; and (vii) electric back-up heating temperature range. The site-specific parameters included: (i) solar collector direction; (ii) solar collector tilt angle; (iii) solar collector shadowing; (iv) solar collector dust accumulation; (v) hot water pipe insulation; (vi) hot water pipe length; (vii) electricity tariff time-of-use; and (viii) cold water temperature. User behaviour patterns were comprised of the following parameters: (i) end-use water temperature; (ii) end-use water demand; and (iii) end-use time-of-use. For all parameters, two system types were assessed, namely: (i) thermosiphon systems with natural (passive) circulation in collectors and unstratified horizontal hot water storage tanks; and (ii) split systems with forced (pumped) circulation in collectors and stratified vertical hot water storage tanks. The performance of SWHs was analysed considering both energy performance indicators (i.e. total and peak-hour energy consumption, solar fraction and energy intensity) and level of service indicators (i.e. compliance with recommended hot water temperatures for Legionella spp. control and comfort levels). Notwithstanding the prevalence of thermosiphon systems among SWH technologies, results indicate that split systems usually outperformed thermosiphon systems both in terms of energy efficiency and level of service, and hence should be a preferred option for energy efficiency initiatives and policies.

Government departments and their agencies occupy a significant portion of building stock, whose annual water and energy costs are substantial. Although it has been demonstrated that retrofitting public buildings can lead to drastic reductions in water and energy consumption with rapid payback periods, in most countries the retrofitting rate is very low. In addition, the focus of retrofit projects is often solely on energy, while water conservation and the water-energy nexus are typically neglected. To improve the current lackluster retrofit rates, the key challenges and potential coping strategies must be identified and retrofit program implementation frameworks developed. This research study adopted a mixed methods approach including scenario modelling and stakeholder workshops and interviews, to achieve such objectives. The comprehensive review identified retrofit project financing and procurement impediments to be the greatest deterrent, although other factors such as education and awareness-raising, which are typically overlooked, were also recognized as being critical. Interviews with stakeholders revealed that governments' willingness to introduce a retrofit program and related supporting mechanisms is also a major barrier to public building retrofitting. Coping strategies for achieving accelerated implementation of retrofit projects were identified as being the introduction of project facilitation teams, revolving loan funding, performance contracting, to name a few. Once such barriers and strategies were acknowledged and refined through stakeholders’ consultation, scenario modelling was conducted in order to quantify the benefits of a proposed widespread retrofit program. Modelling results demonstrated a potential five-fold return of capital investment for a nationwide Australian hospital building energy and water retrofit program if the recommended financial and procurement mechanisms were put in place. Such results and final project outcomes were presented to key-stakeholders in order to incentivise decision-makers to implement some of the proposed strategies and finally boost the building retrofitting sector.

Long-term sustainable wind energy deployment faces an array of challenges due to various complex interconnected impediment factors. These inherent endogenous and exogenous uncertainties preclude obtaining an accurate future trend, which in turn complicates the design of the good policy. This study employs Fuzzy Cognitive Maps (FCMs) to semi-quantitatively explore the scenarios of wind energy deployment in Iran. The FCM-based framework was built using participatory workshops and a subsequent questionnaire survey to identify 26 influential factors shaping the dynamics of the system. The developed scenarios originated from the latest narratives of real-world geopolitical variations. The findings demonstrated that the sector is governed by six major groupings of factors predominated by economic and political concepts with strong interconnections between them. Five key concepts, including two economic, one legal, and two political, were ascertained that contribute to the stability of the system. Of the four scenarios, only one optimistic trajectory expects an acceleration in the deployment. Another scenario projects that there will not be any considerable change between the future scheme and the current state of development. However, the other scenarios envisage a substantially slower growth than the current trend.

A comprehensive spatial and temporal understanding of the manganese cycle in drinking water reservoirs has beneficial implications for the management of water treatment related issues. In this study, a three-dimensional manganese cycle model was developed and coupled with a hydrodynamic model, for the simulation of the distribution of manganese in a monomictic water reservoir. The model was successfully validated and used to model and predict the spatial-temporal manganese variation in the reservoir. Results showed that the variations of the thermal structure of the reservoir affect the seasonal change of manganese levels and in turn the peak concentrations of manganese in the epilimnion. It was also evident that the high level of dissolved oxygen in the epilimnion promotes the oxidation of soluble manganese, and the oxidised particulate Mn settles rapidly. The advection processes mainly caused by wind-driven currents play a vital role in rising or decreasing the level of soluble manganese near the dam wall. The developed numerical tool allows the user to gain a better understanding of spatial manganese distribution in different locations and depths of the reservoir under different wind/rain conditions, among others; such knowledge empowers the local water utility and enables more targeted, optimized water treatment strategies.

Managing water demand in many remote Indigenous communities is critical yet often poorly implemented due in part to a lack of understanding of the volume and nature of water use. A combination of quantitative and qualitative data has enabled a deeper understanding of water consumption patterns and drivers in three remote Australian communities as part of Stage 1 of the Remote and Isolated Communities Essential Services (RICES) project. Total daily per person use averaged from 270 L/p/d to over 1,500 L/p/d and outdoor water use activities comprised up to 86% of total residential water consumed. Structured interviews with participants identified five main drivers for outdoor water use of which some are traditionally the role of local government service provision (e.g. dust control) and all are closely linked to day to day functioning (e.g. cleaning food, cooling). Traditional demand management strategies such as pricing are not yet appropriate, nor is a reliance on improving local government service provision, due partly to the resource challenges in remote communities. Community-based engagement and education, supported by local government role modelling, has been identified as a more suitable approach and will be tested in later stages of the RICES project.

In Australia, the government spending on public buildings’ energy and water consumption is considerable; however the building energy and water retrofit market potential has been diminished by a number of barriers, especially financial. In contrast, in other advanced economies there are several reported financing strategies that have been shown to accelerate retrofit projects implementation. In this study, a coupled Bayesian Network – System Dynamics model was developed with the core aim to assess the likely influence of those novel financing options and procurement procedures on public building retrofit outcomes scenarios in the Australian context. A particular case-study focusing on Australian public hospitals was showcased as an example in this paper. Stakeholder engagement was utilised to estimate likely preferences and to conceptualise causal relationships of model parameters. The scenario modelling showed that a revolving loan fund supporting an energy performance contracting procurement procedure was preferred. Subsequently, the specific features of this preferred framework were optimised to yield the greatest number of viable retrofit projects over the long term. The results indicated that such a financing scheme would lead to substantial abatement of energy and water consumption, as well as carbon emissions. The strategic scenario analysis approach developed herein provides evidence-based support to policy-makers advocating novel financing and procurement models for addressing a government’s sustainability agenda in a financially responsible and net-positive manner.

Information and knowledge on maintenance vulnerabilities will be significantly useful to enhance the resilience of buildings against natural disasters. An ongoing project sponsored by the Sustainable Built Environment National Research Centre in Australia aims to: (a) investigate what could be the impacts of maintenance on building resilience in extreme events such as high winds, flash floods and bush fires; (b) review current approaches, practices and policies; and (c) explore opportunities to improve resilience and values over the lifecycle of buildings. In general, the performance of buildings decreases over time and without effective maintenance, their vulnerability to extreme events will increase. What kinds of maintenance will be effective in improving the resilience of public sector building stock (such as social housing) is the key question targeted in this research. This paper presents a set of preliminary summaries from this ongoing research. The outcomes in terms of implementation strategies will be useful to building owners, governments, and insurance institutions.

Australian housing affordability is influenced by both housing supply and demand factors. These factors include lengthy construction and planning process. The affordability crisis affects the housing rental sector, which accommodates more than 20 % of Australian household. This research developed a system dynamics model to simulate demography-linked affordable rental housing demand and supply in South East Queensland (SEQ). A Prefabricated Offsite Construction (OSC) housing supply strategy is compared with a traditional building approach (BAU) to investigate the effectiveness of OSC techniques to reduce informational asymmetries during development planning stages to deliver better affordable rental housing is linked to housing needs in SEQ. The model focuses on demographic groups housing demand of one, two- and three-bedroom apartment units and examines how reductions in the development process, through OSC methods, influence the efficiency of Government supported affordable rental housing supply schemes. Overall, the study finds that reduced planning and construction timeframes through OSC methods may improve demography-linked rental housing supply by approximately 6.6 % overall compared to BAU in SEQ. For 1,2- and 3-bedroom apartment demand, OSC strategies are expected to improve supply efficiency by 8.7 %, 8.4 % and 9.2 %, respectively. Optimal OSC strategies were assessed and found that flexibility in development sizes have an outsized positive effect. The study has implications for Government supported affordable rental housing strategies, including the build to rent sector.

This work introduces a new droop control for energy storage system (ESS) dispatch commands generation. The droop characteristic generates the ESS dispatch commands as a function of the power surplus at the substation transformer. The ESS with the new droop control aims to increase the level of Photovoltaic (PV) penetration in low voltage (LV) distribution networks, for the most economical solution in terms of ESS placement and sizing. The new droop control maintains two independent modes of operation; the first mode is fixed droop (FD) control, and the second mode is variable droop (VD) control. When FD control is employed, the droop characteristic remains invariable during hours of excessive PV generation, while the dispatch commands maintain the total of the grid parameters within the allowable range at all times. When VD control is performed, the slope of the droop characteristic was modified hourly, providing a sound grid operation at all times, while the accuracy of the dispatch commands was enhanced compared to the FD control mode. For the research, a real-life LV network case study that maintains a loop arrangement was utilised for modelling purposes. The operational grid constraints include transmission lines loading, terminal voltage variation, and substation transformer loading. Simulation results revealed that both of the proposed control modes maintain a sound grid operation for 70% PV penetration. The VD control performed better resulting in a lower cost ESS. The study demonstrated that an ESS located in the LV network performed a number of positive functions, including load management, peak load levelling and voltage support.

Distributed energy storage systems (ESSs) are becoming essential components for the operation of the increasingly complex electricity grid, where dispersed generation is causing power-flows occurring both top-down and bottom-up. Specifically, the combination of ESSs coupled with application-specific control methods can achieve the interdependent objectives of system stakeholders such as the system operator, electrical utilities, retailers, equipment vendors, the government and the electricity customers. The necessity for an accelerated rate of transition to a multi-directional grid arrangement has been exacerbated by the rapid proliferation of distributed renewable energy sources (RESs) that are now price comparable to traditional supply sources. While there are review articles covering ESS technologies and applications as well as the plethora of future advanced grid arrangements that may eventuate, there is none which comprehensively covers individual and aggregated ESS applications and the corresponding benefits, comparing different technology selection methods and providing application-specific controls. Both operational and monetary benefits are identified and critically reviewed from the perspective of the aforementioned stakeholders. Wholesale and retail energy market regimes are also considered for monetisation of the benefits. The control methods that are provided cover both balanced and unbalanced grid conditions. This comprehensive review paper will be of immense value to researchers and practitioners seeking to understand and unpack the aggregated benefits of ESS in the electricity grid.

A hybrid regressive and probabilistic model was developed that is able to forecast, six weeks ahead, the storage volume of Little Nerang dam. This is a small elevated Australian drinking water reservoir, gravity-fed to a nearby water treatment plant while a lower second main water supply source (Hinze dam) requires considerable pumping. The model applies a Monte Carlo approach combined with nonlinear threshold autoregressive models using the seasonal streamflow forecasts from the Bureau of Meteorology as input and it was validated over different historical conditions. Treatment operators can use the model for quantifying depletion rates and spill likelihood for the forthcoming six weeks, based on the seasonal climatic conditions and different intake scenarios. Greater utilization of the Little Nerang reservoir source means a reduced supply requirement from the Hinze dam source that needs considerable energy costs for pumping, leading to a lower cost water supply solution for the region.

Urban centres are increasingly requiring more water than existing groundwater and surface water sources can supply. Water authorities must consider energy intensive supply alternatives such as recycling and desalination, leading to a water-energy-climate conundrum. In this study, a systems perspective of the water-energy-climate nexus is applied to South-East Queensland (SEQ), Australia. Under a changing climate, SEQ is predicted to experience reduced reservoir inflows and increased evaporation rates, which will consequently lead to reduced water availability. To exacerbate this issue, anticipated high population growth in SEQ will increase water demand, putting even more stress on the traditional water supply sources. Clearly, there is a strong incentive to pursue solutions that increase water security without contributing to anthropogenic climate change. Using a system dynamics model, the water balance of the bulk water supply system is evaluated over a 100-year life cycle. The outputs of the model are used to investigate potential management and infrastructure options available to SEQ for adapting to increased water scarcity. The historical rainfall patterns of SEQ requires significant contingency to be built into surface water capacity in order to mitigate low rainfall years, and provide adequate water security. In contrast, reverse osmosis (RO) desalination plants do not require this excess capacity because they are rain-independent. However, RO has high energy consumption and associated greenhouse gas emissions when operating and their potential long periods of redundancy due to periods of sufficient surface water supplies remain unresolved issues. The model demonstrates that dual purpose pressure retarded osmosis desalination plants offer a potential solution, by providing water security at a lower cost than surface water reservoir augmentation, while offsetting energy use through renewable energy generation when RO plants would otherwise be sitting idle. Potentially this technology represents a future sustainable solution to overcome water security concerns.

© 2016 Elsevier Ltd Understanding energy–water system interactions is critical to the effective management of urban infrastructure. This paper explores the potential for hydropower as a co-benefit in a novel operating regime for Sydney׳s main water reservoir (Warragamba Dam). Hydropower could be generated as part of storage level management in the reservoir aimed at introducing flood retention ‘airspace’ (to mitigate downstream flood risk from extreme rainfall) whilst augmenting the use of installed desalination capacity to maintain secure supplies of water. A purpose-built systems dynamics model provides the mechanism for evaluating and comparing future operating scenarios over a 25 year period (i.e. until 2040). Importantly, the findings reveal the potential for desalination plants, integrated into a populous city׳s water supply network, to satisfy a much broader planning agenda. Specifically, the study provides evidence that Sydney's interdependent goals of deferring capital intensive flood storage works, maintaining water security, better utilising existing desalination and hydropower assets, and increasing renewable energy generation can be achieved through applying systems thinking to a complex citywide water planning problem. The work also makes a valuable contribution to the energy–water nexus literature at the under-explored city-scale.

Monitoring and understanding the dissolved organic matter (DOM) cycle in a drinking water reservoir is crucial to water authorities, since most water treatment practices aim to remove DOM to prevent the formation of potentially harmful disinfection by-products. A vertical profiling system (VPS) installed in reservoirs can continuously detect the fluorescent DOM (fDOM) and determine the fDOM transport process. Although the VPS can interprete fDOM concentrations, water treatment operators still collect and rely upon DOM datasets that are manually sampled throughout the year. A long-term historical database provides an opportunity to develop a three-dimensional fDOM prediction model. In the present study, we collected and analysed VPS and sampling data and developed and assessed an innovative coupled data-driven and process-based model. These models were able to forecast future fDOM in both temperate and extreme weather conditions. Modelling scenario analysis concluded that deeper layers of the reservoir as well as areas close to the riverine zone had higher fDOM concentrations than any other zones during storm events. Simulated fDOM can be a proxy for dissolved organic carbon concentration. The model also determined that inflow creeks were predominant fDOM sources during storm events and continuing winds transported the fDOM from bottom to surface water layers. This study has implications for reservoir and water treatment plant operators seeking to gain a better understanding of the DOM cycle in a reservoir and to more efficiently manage DOM removal.

This paper systematically reviewed system dynamics applications in water resource management with respect to spatial factors, research aims, modelling sub-systems and model calibration and validation methods. Decision-making context, consideration of climate change, scenarios or management measures were also examined. Results showed that the critical conceptual system dynamics model development phases are often neglected with only 40% of reviewed articles developing causal loop diagrams, and only three studies identifying system archetypes. Most reviewed publications applied scenario-based approaches (82%) to evaluate the effectiveness of management measures, whilst a minority of studies (1.8%) considered water management optimisation. Structure behaviour and behaviour-pattern tests (52%) were mostly applied to evaluate the validity of the model structure and accuracy of the behaviour's pattern, though model validation was not conducted in 12% of the studies. Finally, an integrated system dynamics modelling framework was proposed, exploring opportunities for integration of system dynamics with other modelling tools based on the strengths and limitations of system dynamics modelling; this framework can be used to enhance the simulation and optimisation for future water resource management and planning in the context of climate change and socio-economic development.

Optical sensors for fluorescence of chlorophyll a (f-Chl a) and phycocyanin (f-PC) are increasingly used as a proxy for biomass of algae and cyanobacteria, respectively. They provide measurements at high-frequency and modest cost. These sensors require site-specific calibration due to a range of interferences. Light intensity affects the fluorescence yield of cyanobacteria and algae through light harvesting regulation mechanisms, but is often neglected as a potential source of error for in-situ f-Chl a and f-PC measurements. We hypothesised that diel light variations would induce significant f-Chl a and f-PC suppression when compared to dark periods. We tested this hypothesis in a controlled experiment using three commercial fluorescence probes which continuously measured f-Chl a and f-PC from a culture of the cyanobacterium Dolichospermum variabilis as well as f-Chl a from a culture of the green alga Ankistrodesmus gracilis in a simulated natural light regime. Under light, all devices showed a significant (p

An environmental life cycle assessment (LCA) methodology was applied to compare the conventional SWRO desalination process to two contemporary low-pressure membrane desalination processes: an osmotic dilution desalination (ODD) and a mixing dilution desalination (MDD). The ODD process combines a forward osmosis (FO) membrane and a low-pressure RO membrane (LPRO) in a hybrid (FO-RO) configuration. The MDD process couples the ultrafiltration (UF) with LPRO in a hybrid UF-RO configuration. SimaPro™ was used to conduct the LCA study. CML environmental impact assessment method was used to evaluate 11 environmental impact categories. Results from LCIA reveal the strong dependency of environmental impacts on the clean in place (CIP) process in dilution desalination systems while the energy flow in the standalone RO shows almost similar levels of dependency of environmental impacts instead of CIP process. Overall, the ODD process with FO-RO hybrid membrane technology has the highest environmental impact. The normalised comparison LCIA indicates that standalone RO has the lowest total environmental impacts as the fossil fuel is considered as the main source of power generator. Compared to conventional SWRO, the MDD process coupled with hybrid UF-RO technology had a lowest contribution to global warming and ozone layer depletion but had higher impacts to marine and aquatic resource depletion. Scenarios substituting the conventional with renewable energy sources to power the water treatment options favoured the MDD process coupled with hybrid UF-RO technology and prove the hybrid UF-RO process as the most environmentally favourable comparing to the conventional SWRO and hybrid FO-RO.

The Australian affordable rental housing sector has historically faced barriers to green building (GB) adoption due to issues of split-incentives, despite financial support from the commonwealth. This paper explores how a recent shift towards bond-based funding mechanisms offer an opportunity to integrate green building practices, and influence regional social, environmental and economic outcomes. A novel system dynamics model is developed to forecast a business as usual (BAU) and green-building scenario framed around the recently introduced Australian Affordable Housing Bond Aggregator (AHBA) policy framework. The purpose of the model is to examine how a mandate on green building standards together with the use of green versus conventional bonds impact social, environmental and economic outcomes of the framework over a 20-year horizon, within the South East Queensland metropolitan area of Australia. The study determined that the green building framework would deliver 2.37% less housing overall due to higher initial capital costs. However, offers substantial long-term energy and water efficiency benefits, improved affordability for tenants due to reduced utility costs, deferred water and energy infrastructure benefits, and considerable contributions towards Australia’s carbon emission reduction targets. Tenants are expected to benefit from a 45–59% reduction in energy and a 27% decrease in water bills. Moreover, a shift to affordable green building policy could contribute to 2.9% of Australia’s Conference of Parties (2030) carbon emissions reduction target if applied nationally. The study concludes with a sensitivity analysis to reveal policy refinement recommendations that would yield the benefits of green affordable housing without sacrificing the small reduction in housing stock delivered by the proposed program.

A reliable simulation of the manganese cycle in water supply system is essential for timely and effective manganese treatment strategies. A data-driven model estimating manganese levels in the treated water was developed through a threshold-based approach using raw water and supernatant return manganese levels. This data-driven model and a three-dimensional manganese cycle model for the source reservoir were integrated to simulate the overall manganese variations from the lake to the treated water. A decision support system based on the developed data-driven model was established to allow operators to react to situations that could result in elevated levels of manganese in the treated water. Scenario analyses were completed to examine the manganese variations in the reservoir, raw water and treated water due to different precipitations and wind conditions and to allow the identification of optimal strategies for manganese management from the reservoir to the water supply system.

Shifting climate patterns are causing extreme drought and flooding across the globe. This combined with the world’s burgeoning population and insatiable thirst for water requires water service providers to think differently about the limited resources they manage. In Australia, the severe drought at the beginning of the century caused dams to fall to record levels. In response, many state governments invested heavily in rain-independent supplies such as desalination to augment and diversify traditional sources. However, extreme rainfall soon followed the drought, filled reservoirs and caused flooding in many locations leaving billions of dollars worth of damage and new water infrastructure standing idle. This is the case in Sydney, where the new desalination plant is still not used and the potential for major flooding has raised concerns over the safety of the large population downstream of the dam. This paper explores the growing need to understand the relationship between drought, flooding and infrastructure optimisation. The paper focuses on Sydney to illustrate the application of a system dynamics model. The new model explores options for raising the dam wall, offering airspace to assist flood protection, in contrast to options to lower the dam full supply level and utilise idle desalination capacity to fill the water security gap created. The illustrative results, using publicly available data, find that by lowering the dam water levels and operating desalination, significant flood protection can be achieved at a similar cost to raising the dam wall. The paper demonstrates the importance of optimising existing and new water resources for multiple purposes and how system dynamics modelling can assist water service providers in these complex investigations.

Water efficiency measures and alternative supply sources alleviate peak water demand on urban water supply networks. Consequently, they also provide benefits to water service providers, in terms of augmentation deferrals and reduced sized infrastructure. However, while these benefits are acknowledged in the literature, they have not been thoroughly investigated and quantified. This paper empirically demonstrates how the installation of different potable water saving measures would affect the design of urban water supply networks. Peak day water demand profiles were developed for the baseline scenario, which represented the typical building code mandated for new dwellings constructed in Queensland, Australia, and for households fitted with water saving measures. The core novel feature of this study relates to the use of an innovative bottom-up approach to the development of demand profiles based on smart meters enabling comprehensive water end use datasets (i.e. demand in shower, tap, etc.) to be obtained. Hydraulic model runs were conducted for various water savings scenarios across different planning horizons to determine the scheduling of augmentations in a water supply study area. The results of the model runs showed deferred and eliminated augmentations, as well as reductions in infrastructure sizing for the water savings scenarios compared to the baseline scenario. Financial analysis (i.e. NPV) on trunk main augmentation requirements over 50 year asset life cycles indicated that savings of between $1,574,289 (11.4%) and $7,030,796 (51%) could be achieved by incorporating water efficiency and potable source substitution measures in new infill developments in the study region.

Increasingly, the water sector is exploring the value of applying demand management strategies to reduce peak water use through behavioural and technical solutions. Literature suggests that using behavioural interventions may be a useful approach in changing the daily peak demand patterns to reduce the pressure on network pumping energy costs during peak use times. There is a lack of studies, however, that have investigated the role of social based marketing or behavioural intervention studies on specifically reducing and shifting residential peak diurnal daily water end-use demand. This concept is modelled in this current study through the application of longitudinal experimental end-use data to predict how reduced demand through behaviour change can impact on overall peak residential demand. Notwithstanding the acknowledged limitations of the study, results illustrate a range of potential peak hour flow savings that can be realised from reducing total demand, or shifting the peak demand in households. The study provides preliminary evidence that water businesses can use demand-side strategies to also achieve efficiencies in the distribution of urban water (e.g. reduced energy for pumping in pressurised water system, pipe augmentation deferrals, peak energy demands).

Severe storms and tropical cyclones bring destructive winds and heavy rain. While building structural performance has significantly improved in the last few decades due to higher regulatory requirements, some non-structural elements, such as windows, external doors, roof coverings and attachments such as guttering, fascia and eaves, remain subject to minor failure, causing loss of amenity and damage to structural building components over time. Enhancing the performance of buildings has become imperative to mitigating the impacts of tropical cyclones and storm events. Damage investigations conducted after tropical cyclones and severe storms have consistently revealed that windows and external glazed doors are affected by wind-driven rain, causing leakage into the cavity and interior of the building. This research study focuses on repeated water ingress through windows and external glazed doors. Wind-driven rain can penetrate undamaged windows and external doors, gaps around the window seals or doors, and waterproofing elements, thereby allowing water to enter buildings. A qualitative expert interview research approach was applied to identify several factors affecting the performance of openings (windows and external glazed doors). Subsequently, a Bayesian Network model was developed according to the determined parameters and expert workshops. The Bayesian Network scenario analysis enabled the researchers to identify the best combination of management interventions to enhance the performance of openings to water ingress from tropical cyclones and severe storms. The study findings provide evidence-based support for industry and government authorities to develop effective strategies for enhancing the performance of openings subject to wind-driven rain from tropical cyclones and severe storms.

The green bond market is emerging as an impactful financing mechanism in climate change mitigation efforts. Studies investigating this market have revealed the notion of a ‘green premium’ or ‘greenium’ within green bond pricing, including insights into influential characteristics and drivers that govern it. However, methodological heterogeneity among these studies has resulted in general ambiguity regarding a consensus over the existence of the green premium. This research addresses this gap through a systematic literature review with the aim of establishing a consensus on the existence, or nonexistence, of a green premium in the green bond market. The review examines studies published between 2007 and 2019. A ranking of the green bond characteristics most likely to exhibit a green premium is organised, including a framework of driving factors. The findings confirm a consensus on the existence of a green premium within 56% of primary and 70% of secondary market studies, particularly for those green bonds that are government issued, investment grade, and that follow defined green bond governance and reporting procedures. The green premium varies widely for the primary market; however, an average greenium of −1 to −9 basis points on the secondary market is observed. Overall, our findings highlight the crucial role of strengthening environmental preferences amongst bond market participants; including implications for bond pricing theory, by suggesting that future bond pricing should consider noneconomic motives of investors, such as environmental preferences; and, for future growth implications as a catalyst towards the financing of climate change mitigation efforts globally.

Understanding the complexities around managing the end-of-life (EoL) residential solar photovoltaic (PV) and battery energy storage systems (BESS) is a precursor to a better decision-making process that mitigates unintended product life-cycle impacts. In this paper, a participatory Systems Thinking approach was utilised to build a causal loop diagram (CLD) for this system based on the collective knowledge of stakeholders. The developed CLD was categorised into three sub-systems: (1) waste flows; (2) regulatory aspects; and (3) industry strategies and government incentives. Two main system archetypes were identified in a reflection to the CLD, namely fixes that fail and drifting goals. The identified feedback loops indicate the need to introduce a comprehensive national product stewardship scheme, complimentary landfill restrictions, and provide sufficient incentives to industries for promoting recovery activities within residential PV panels and BESS sectors. An effective waste management system for these renewable energy technologies is most effective if industries are required to participate through regulation which also specifies certain targets, such as product and material recovery rates and establishes a sustainable funding model to meet operational requirements and future needs. The increasingly prohibitive overseas waste export market will require local industries and governments to collaboratively improve domestic recycling capability and capacity. In this light, the failure to build an effective EoL management system for residential PV and BESS will result in valuable and hazardous materials in both technologies to be disposed of in the landfill, stockpiled or illegally dumped; consequently, reinforcing unintended and adverse environmental impacts.

Climate change mitigation strategies are multifaceted and require collaboration among a range of stakeholder groups. The objective of this paper was to develop an overarching Renewable Energy and Energy Conservation Area Policy (REECAP) framework. The framework was developed based on a comprehensive literature review, in which seven principles for Renewable Energy and Energy Conservation Policies were identified. The paper also includes a case study to demonstrate an application of the REECAP framework. The novelty of the framework stems from its integration of carbon-energy-cash flows among different decision-making spheres, scales and area specific characteristics. The framework provides a mathematical understanding of how energy strategies can be transformed and optimised in a cost-effective manner by integrating stakeholders under a shared vision.

Seawater reverse osmosis (SWRO) desalination is a proven technology for augmenting water supply in remote areas with limited access to fresh water resources. Lowering the energy consumption of SWRO is crucial in such areas where the source of energy is often reliant on transported liquid fuels. This study presents a life cycle cost model for two diluted desalination processes using water reuse to dilute seawater, and reduce energy consumption. The economic model details the life cycle capital and operational costs of i) a baseline SWRO desalination, ii) alternative osmotic dilution desalination in a forward osmosis (FO)–reverse osmosis (RO) hybrid system, and iii) a mixed dilution desalination process in an ultrafiltration (UF)–RO system. The results reveal a 4–5% lower total water cost associated with the two alternative processes compared to conventional SWRO. Sensitivity analysis revealed that the FO–RO system is an economically viable alternative to standalone RO if a water flux ≥6 L·m−2 h−1 and a recovery rate >25% can be achieved. The sensitivity analysis showed that diluted desalination with UF–RO was economically preferable to SWRO even when the water flux and recovery rate of UF meet only minimum thresholds (18 L·m−2 h−1 and 55%, respectively).

TiO2 nanotubes (TNTs) are a promising bone/dental implant surface modification strategy with enhanced bioactivity and local therapeutic functions. However, inconsistencies related to the understanding of the influence of various TNTs characteristics on Ti implant functions, and the multi-factorial inter-dependence of such characteristics, requires an in-depth Quality by Design (QbD) analysis towards optimizing TNTs-modified implants. To this end, an extensive systematic literature search was undertaken to identify the various TNTs characteristics that may influence implant performance. Subsequently, in order to facilitate a QbD analysis, an expert questionnaire survey was carried out to determine the perceived contribution of various TNTs characteristics on an implant's biological, physicochemical, and mechanical performance. To achieve this goal, the Quality Function Deployment method was employed using symmetrical triangular fuzzy numbers to translate qualitative expert opinion into meaningful quantitative information. The results show that pore diameter, inter-nanotube distance and wall thickness are the TNTs characteristics with the most influential effects on the overall implant performance. This pioneering study evaluates perceived importance of various parameters contributing to TNTs functionality, and represents a step forward in the implementation of QbD strategies towards optimizing nano-engineered Ti implants.

Cyanobacteria harmful blooms (CyanoHABs) in lakes and reservoirs represent a major risk for water authorities globally due to their toxicity and economic impacts. Anticipating bloom occurrence and understanding the main drivers of CyanoHABs are needed to optimize water resources management. An extensive review of the application of CyanoHABs forecasting and predictive models was performed, and a summary of the current state of knowledge, limitations and research opportunities on this topic is provided through analysis of case studies. Two modelling approaches were used to achieve CyanoHABs anticipation; process-based (PB) and data-driven (DD) models. The objective of the model was a determining factor for the choice of modelling approach. PB models were more frequently used to predict future scenarios whereas DD models were employed for short-term forecasts. Each modelling approach presented multiple variations that may be applied for more specific, targeted purposes. Most models reviewed were site-specific. The monitoring methodologies, including data frequency, uncertainty and precision, were identified as a major limitation to improve model performance. A lack of standardization of both model output and performance metrics was observed. CyanoHAB modelling is an interdisciplinary topic and communication between disciplines should be improved to facilitate model comparisons. These shortcomings can hinder the adoption of modelling tools by practitioners. We suggest that water managers should focus on generalising models for lakes with similar characteristics and where possible use high frequency monitoring for model development and validation.

Coral reefs are among the most fragile ecosystems that provide essential services to local Small Island Developing States (SIDS) communities. As such, exploring the characteristics and interactions shaping regime shifts of coral reefs is of paramount importance in managing system pressures; enhancing resilience; aiding their regeneration and recovery process; and restoring habitat complexity. However, understanding the dynamics of coral reef ecosystems regime shift requires employing an approach capable of dealing with systems being affected by multiple climatic and socio-economic non-climatic pressures as well as an effective treatment of systemic embedded uncertainties. This study applies Fuzzy Cognitive Mapping (FCM) in a participatory stepwise and systematic procedure to reflect dynamic casualties and temporal changes of coral reef ecosystem regime change over a long-time perspective. This mapping technique allows conceptualising dynamic models to represent causalities and modelling input values to simulate fluctuations within a complex temporal system. Port Resolution on Tanna Island in Vanuatu was selected as the case study region representative of Pacific-SIDS geography and human communities. As an initial outcome and an indicator of multidisciplinary of this study, twenty-seven principal influential factors and their corresponding causal relationships were identified. Subsequently, the coral reef regime shift was analysed under four main plausible scenarios representing major climatic and non-climatic trajectories. The results indicate that climate change factors play pivotal roles in the regime shift of the coral reef ecosystem globally. At the focal scale of this study, the tourism industry and coral fisheries are the most vulnerable services provided by coral reefs. As such, coupled local management interventions and global efforts in mitigating the adverse impacts of climate change is likely to yield better coral reef ecosystem services at a local community level.

The successful diffusion of rooftop solar photovoltaics (SPV) in residential sector depends on the decisions of bounded rational households to accept this technology. Such heterogeneous decision is formed by socio-demographic, economic and technical predictors interwoven with a variety of behavioural traits. Over the past years, these exogenous and endogenous predictors have served as the starting point of a large body of studies seeking to understand the heterogeneity of the behaviour and to predict the diffusion rate. The present systematic review primarily identifies the predictors of 173 original quantitative and qualitative studies on the residential adoption behaviour of SPV, solar home system (SHS) and SPV coupled with battery energy storage system (SPV-BESS) technologies. For the designated predictors, the study proposes a customised taxonomy aimed at in-depth understanding of knowledge in an organised and coherent framework. Extracted from 133 of the 173 initially reviewed papers, the last objective of the review further expands on current understanding of the predictors by exploring their correlations with the behaviour. The upshot of the review unfolds a total of 333 predictors that sought to investigate households' attitude, knowledge, tendency, awareness, willingness, intention and adoption decisions towards the uptake of SPV, SHS, and SPV-BESS. The identified predictors are classified into individual, social and information dimensions, with 20 categories and 20 subcategories. An exhaustive comparison on the frequency use of predictors denotes more popularity of social predictors led by ‘age’ and ‘income’, and ‘financial knowledge’ at the category dimension. However, the synthesised results of the vote-counting method applied to a total of 239 factors suggest a lower correlation of several traditional determinants (e.g., age, gender) whereas certain attitudinal traits categorised as individual demonstrate significant correlation with the behaviour.

Planning and decision-making vastly benefit from a holistic and systematic understanding of the long-term impacts of climate change and other non-climatic stressors on the health and resilience of coral reef ecosystems, and the efficacy of adaptation strategies and management interventions on mitigating these impacts and maintaining ecosystem condition and associated ecosystem service. This study reports on an approach to modelling coral reef stressors and possible adaptation interventions using the coral reef ecosystem of Port Resolution on Tanna Island, Vanuatu as the case study serving as a microcosm of endangered Pacific Small Island Developing States (SIDS). A novel participatory modelling framework was developed and followed in a stepwise manner to integrate local and long-term climate change pressures by coupling structural analysis and the Bayesian Network (BN) techniques. The BN model was quantified through an advanced consolidated data-induced, evidence-based, and expert-driven approach that incorporated: (1) projections of future climate conditions and changing human activities; (2) the influences of multiple stressors including physical environmental and sociological factors; and (3) spatial variability in the key processes and variables. The first and second phases conceptualised the whole system by providing a graphical presentation of system variables within the Driver-Pressure-State-Impact (DPSI) framework using the structural analysis technique. In the third phase, the BN technique was used to integrate the outcomes of multidisciplinary assessments and analysis with experts’ opinion. The BN modelling phase was completed based on evidence extracted from literature which reported the results of regional and downscaled climate models, GIS-based analysis, parametrised data obtained from the region, and tacit knowledge elicited from experts. The validated model was employed to anticipate the future health and resilience condition of coral reefs under different sets of climatic trajectories and adaptive responses scenarios. The results predict the risks to the health and resilience of the Port Resolution coral reef system from the adverse impacts of climate change and harmful human activities and the possible success of adaptations strategies. A sobering conclusion was that despite the current satisfactory condition of coral reefs in the case study zone, their health and resilience would be severely threatened by 2070 in the absence of implementing adaptation strategies and associated sustainable management interventions.

This paper details a system dynamics model developed to simulate proposed changes to water governance through the integration of supply, demand and asset management processes. To effectively accomplish this, interconnected feedback loops in tariff structures, demand levels and financing capacity are included in the model design, representing the first comprehensive life-cycle modelling of potable water systems. A number of scenarios were applied to Australia's populated South-east Queensland region, demonstrating that introducing temporary drought pricing (i.e. progressive water prices set inverse with availability), in conjunction with supply augmentation through rain-independent sources, is capable of efficiently providing water security in the future. Modelling demonstrated that this alternative tariff structure reduced demand in scarcity periods thereby preserving supply, whilst revenues are maintained to build new water supply infrastructure. In addition to exploring alternative tariffs, the potential benefits of using adaptive pressure-retarded osmosis desalination plants for both potable water and power generation was explored. This operation of these plants for power production, when they would otherwise be idle, shows promise in reducing their net energy and carbon footprints. Stakeholders in industry, government and academia were engaged in model development and validation. The constructed model displays how water resource systems can be reorganised to cope with systemic change and uncertainty. System dynamics model integrates supply, demand and financial dimensions.Diverse supply source portfolios that are grid connected alleviates water scarcity.Desalination reduces need for restrictions compared to a rain-dependent portfolio.Pressure retarded osmosis technology integration into desalination component of water supply networks for renewable energy.Scarcity pricing is an effective strategy for reducing demand while simultaneously generating the additional revenues.

In order to reduce the total cost of a dual source drinking water treatment plant operation, a comprehensive hybrid prediction model was built to estimate the necessary chemicals dosage and pumping energy costs for alternative source selection scenarios. Correlations between the water quality parameters and the required treatment chemicals were estimated using historical data and the expected pH variations associated with each chemical addition, which was based on the Caldwell-Lawrence diagram. The pumping energy costs were also estimated using a data-driven approach that was based on historical plant data. The research has practical implications for water treatment operators seeking to minimize plant operational costs through selecting raw water intake volumes for their treatment plant based on multiple source options and offtake tower gate levels. Future research seeks to better link current and future water treatment dosage cost predictions directly to water quality measurements taken from vertical profiling systems. Prediction model built to estimate treatment costs for a dual source drinking WTP.Model optimises source selection proportions based on treatment and pumping costs.Model aids WTP plant operators to alter the source selection strategy in extreme events.Model utilisation for source selection optimization delivers life cycle monetary savings.

Manganese monitoring and removal is essential for water utilities in order to avoid supplying discoloured water to consumers. Traditional manganese monitoring in water reservoirs consists of costly and time-consuming manual lake samplings and laboratory analysis. However, vertical profiling systems can automatically collect and remotely transfer a range of physical parameters that affect the manganese cycle. In this study, a manganese prediction model was developed, based on the profiler's historical data and weather forecasts. The model effectively forecasted seven-day ahead manganese concentrations in the epilimnion of Advancetown Lake (Queensland, Australia). The manganese forecasting model was then operationalised into an automatically updated decision support system with a user-friendly graphical interface that is easily accessible and interpretable by water treatment plant operators. The developed tool resulted in a reduction in traditional expensive monitoring while ensuring proactive water treatment management. DSS developed for Mn forecasting and proactive treatment in water reservoirs.DSS enabled significant reduction in Mn sampling and laboratory testing costs.Graphical user interface enabled proactive Mn water treatment by operators.

Smart metering technology enables the capture of high resolution water consumption data.Intelligent algorithms autonomously categorise single and combined water end use events.Hybrid combination of HMM, ANN and DTW for pattern recognition problem.Expert system developed to autonomously disaggregate water use into end use categories. Over half of the world's population will live in urban areas in the next decade, which will impose significant pressure on water security. The advanced management of water resources and their consumption is pivotal to maintaining a sustainable water future. To contribute to this goal, the aim of this study was to develop an autonomous and intelligent system for residential water end-use classification that could interface with customers and water business managers via a user-friendly web-based application. Water flow data collected directly from smart water metres connected to dwellings includes both single (e.g., a shower event occurring alone) and combined (i.e., an event that comprises several overlapping single events) water end use events. The authors recently developed an intelligent application called Autoflow which served as a prototype tool to solve the complex problem of autonomously categorising residential water consumption data into a registry of single and combined events. However, this first prototype application achieved overall recognition accuracy of 85%, which is not sufficient for a commercial application. To improve this accuracy level, a larger dataset consisting of over 82,000 events from over 500 homes in Melbourne and South-east Queensland, Australia, were employed to derive a new single event recognition method employing a hybrid combination of Hidden Markov Model (HMM), Artificial Neural Networks (ANN) and the Dynamic Time Warping (DTW) algorithm. The classified single event registry was then used as the foundations of a sophisticated hybrid ANN-HMM combined event disaggregation module, which was able to strip apart concurrently occurring end use events. The new hybrid model's recognition accuracy ranged from 85.9% to 96.1% for single events and 81.8-91.5% for combined event disaggregation, which was a 4.9% and 8.0% improvement, respectively, when compared to the first prototype model. The developed Autoflow tool has far-reaching implications for enhanced urban water demand planning and management, sustained customer behaviour change through more granular water conservation awareness, and better customer satisfaction with water utility providers.

Three phase battery energy storage (BES) installed in the residential low voltage (LV) distribution network can provide functions such as peak shaving and valley filling (i.e. charge when demand is low and discharge when demand is high), load balancing (i.e. charge more from phases with lower loads and discharge more to phases with higher loads) and management of distributed renewable energy generation (i.e. charge when rooftop solar photovoltaics are generating). To accrue and enable these functions an intelligent scheduling system was developed. The scheduling system can reliably schedule the charge and discharge cycles and operate the BES in real time. The scheduling system is composed of three integrated modules: (1) a load forecast system to generate next-day load profile forecasts; (2) a scheduler to derive an initial charge and discharge schedule based on load profile forecasts; and (3) an online control algorithm to mitigate forecast error through continuous schedule adjustments. The scheduling system was applied to an LV distribution network servicing 128 residential customers located in an urban region of South East Queensland, Australia.

Heat pump water heaters have been increasingly used to promote energy savings in residential buildings. Nonetheless, their performance may vary significantly due to different site-specific characteristics and technical specifications. To examine this issue, this research study sought to identify key performance parameters for air source heat pump water heaters (ASHPWH) in residential buildings in Australia by using a sensitivity analysis through theoretical models calibrated with empirical measurements. ASHPWH were analysed taking into account their energy performance (i.e. annual energy consumption and energy intensity) and level of service (i.e. compliance with recommended hot water temperatures). The assessment encompassed sensitivity analyses for key performance parameters related to the technical specification of ASHPWH (i.e. coefficients of performance, water heating capacities, hot water storage tank insulation, size and set-point temperature) and site-specific characteristics (i.e. weather conditions, cold water supply temperatures, hot water set-point temperatures and consumption patterns, and the time-distribution of energy according to the electricity tariffs). The findings indicate that the energy performance and the level of service of ASHPWH are mostly influenced by their coefficient of performance and site-specific electricity tariff, respectively. Therefore, careful consideration of ASHPWH technical specifications based on site-specific characteristics should be used to underpin the development of energy efficiency programmes for residential water heaters.