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Domestic water heating accounts for 15% to 27% of the total energy consumption in buildings in Australia. Over the past two decades, the latent heat thermal energy storage (LHTES) system has been widely investigated as a way to reduce fossil fuel consumption and increase the share of renewable energy in solar water heating. However, the research has concentrated on the geometric optimisation of the LHTES heat exchanger for the past few years, and this might not be sufficient for commercialisation. Moreover, recent review papers mainly discussed the development of a particular heat-transfer improvement technique. This paper presents perspectives on various solar hot water systems using LHTES to shift focus to on-demand performance studies, as well as structure optimisation studies for faster commercialisation. Future challenges are also discussed. Since the topic is an active area of research, this paper focuses on references that showcase the overall performance of LHTES-assisted solar hot water systems and cannot include all published work in the discussion. This perspective paper provides directional insights to researchers for developing an energy-efficient solar hot water system using LHTES. [ABSTRACT FROM AUTHOR]

Accurate short-term power load forecasting is crucial to maintaining a balance between energy supply and demand, thus minimizing operational costs. However, the intrinsic uncertainty and non-linearity of load data substantially impact the accuracy of forecasting results. To mitigate the influence of these uncertainties and non-linearity in electric load data on the forecasting results, we propose a hybrid network that integrates variational mode decomposition with a temporal convolutional network (TCN) and a bidirectional gated recurrent unit (BiGRU). This integrated approach aims to enhance the accuracy of short-term power load forecasting. The method was validated on load datasets from Singapore and Australia. The MAPE of this paper's model on the two datasets reached 0.42% and 1.79%, far less than other models, and the R2 reached 98.27% and 97.98, higher than other models. The experimental results show that the proposed network exhibits a better performance compared to other methods, and could improve the accuracy of short-term electricity load forecasting. [ABSTRACT FROM AUTHOR]

Voltage rise caused by high levels of distributed generation is manifesting as voltage regulation challenges for many electricity network service providers. In this environment it would be ideal to reduce supply voltage magnitudes, however, many network operators are hesitant to do so due to concerns related to consumer appliance performance at reduced supply voltage magnitudes. Voltage regulation requirements are defined by network standards and network service providers must ensure voltages remain within specified limits. Through an evaluation of domestic appliance performance when supplied at various voltage magnitudes, this paper examines the impact of varying voltage levels on residential appliances. Equipment energy demand, operation and actuation were monitored for each applied voltage magnitude. While no equipment failures were recorded, appliance behaviour varied significantly with applied voltage magnitude. Individual appliance conservation voltage reduction (CVR) factors have also been established. The results highlight the importance of good voltage regulation and provide substantiated appliance performance figures for future studies. The outcomes of this paper allow electricity network service providers to understand the implications of supply voltage magnitude on domestic appliance performance, whether it be understating of the impact of higher voltage magnitudes caused by distributed generation or implications of reducing voltage magnitudes to provide headroom for distributed generation integration. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal pre-cooling problem for air conditioners (AC) used in Internet of Things (IoT)-enabled smart homes while ensuring that user-defined thermal comfort can be achieved. The proposed strategy utilises renewable energy generation periods and moves some of the air conditioning loads to these periods to reduce the electricity demand. In particular, we propose a multi-stage approach which maximises the utilisation of renewable energy at the first stage to satisfy air conditioning loads, and then schedules residual energy consumption of these loads to low price periods at the second stage. The proposed approach is investigated for the temperature and renewable generation data of NSW, Australia, over the period 2012–2013. It is shown that the approach developed can significantly reduce the energy consumption and cost associated with AC operation for nearly all days in summer when cooling is required. Specifically, the proposed approach was found to achieve a 24% cost saving in comparison to the no pre-cooling case for the highest average temperature day in January, 2013. The analysis also demonstrated that the proposed scheme performed better when the thermal insulation levels in the smart home are higher. However, the optimal pre-cooling scheme can still achieve reduced energy costs under lower thermal insulation conditions compared to the no pre-cooling case. [ABSTRACT FROM AUTHOR]

• Presenting a set of mathematical models for green flowshop scheduling problems. • Considering common carbon reduction policies and their corresponding scenarios. • Providing analysis of models and insights to design a framework for green flowshops. • Industries can use our cost-effective strategies to meet limits mandated by government. • Government can explore our integrated framework to gauge impacts of carbon policies. In this paper we consider, from an environmental policy-maker perspective, how carbon reduction policies impact the economic competitiveness of the manufacturing sector. Specifically, we focus on flowshop scheduling – which typically aims to minimize makespan for purely economic objectives – and consider how three common carbon reduction policies – namely, taxes on emissions, baselines on emissions, and emissions trading schemes – can create competitive green flowshops that balance minimization of makespan and carbon emissions. The goal is to enable policy-makers to understand how to set policies and control parameters to achieve environmental objectives while ensuring global economic competitiveness of industry. We initially present a set of mixed-integer linear programming (MILP) models for flowshop scheduling problems operating in a regulated environment in which each carbon reduction policy is adopted. We then introduce a bi-objective scheduling framework for the corresponding problem to obtain alternative solutions under each policy. These models and their computational results however, are not the main focus of the study, but are presented as a means to demonstrate how green policies co-exist with economic objectives, with policy-makers in control of the balance. To this end, based on financial data from Australia's carbon emissions profile, we provide a policy-oriented analysis of the models, and some managerial insights into the effect of scheduling strategies on carbon emissions under different reduction policies. These insights offer support to both environmental policy-makers and corporate production and sustainability managers to determine whether it is technically feasible and profitable to replace traditional scheduling strategies with environmentally friendly scheduling strategies. [ABSTRACT FROM AUTHOR]

• Direct and indirect effects of sectors on net energy consumption are analysed. • Level effect is identified as the major driver for increasing energy consumption. • Manufacturing has the largest energy consumption reduction potential. • How to decrease net energy consumption has been offered to policy-makers. This paper provides a comprehensive analysis of Australian net energy consumption between 2004–05 and 2014–15. Results from environmentally-extended input-output (EEIO) analysis show that the Transport sector has the largest direct effect on net energy consumption in industrial sectors, which decreased by about 35% for net energy consumption per million $AUD in the period. The Export sector has the largest direct net energy consumption while Households consumption results in the largest net energy consumption embodied in different categories of Final demand. The structural decomposition analysis (SDA) decomposes the change of net energy consumption into five drivers, in which net energy intensity mainly reduces Australian net energy consumption by about 8000 Petajoules, while the level effect of Final demand increases it by about 10,000 Petajoules. Analysis of forward and backward linkages highlights the Manufacturing sector as the key industrial sector with the largest energy consumption reduction potential via minor changes in its input and Final demand. This indicates that more attention should be given to the reduction of energy demand from the consumption patterns of Households consumption, the improvement of energy intensity, and the application of cleaner technologies in the Transport and Manufacturing sectors. The Australian Environmental-Economic Accounts is combined with Australian input-output tables to construct the EEIO tables for net energy consumption. The combination of economic and environmental data sets provides a depth of understanding their potential to inform environmental policy decisions. The novelty of the research is the combination of economic and energy data sets, the application of EEIO model, the implementation of the additive SDA method, and the use of forward and backward linkages for the Australian energy system. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the method and results of a survey of 27 of the 33 Australian universities teaching engineering education in late 2007, undertaken by The Natural Edge Project (hosted by Griffith University and the Australian National University) and supported by the National Framework for Energy Efficiency. This survey aimed to ascertain the extent of energy efficiency (EE) education, and to identify preferred methods to assist in increasing the extent to which EE education is embedded in engineering curriculum. In this paper the context for the survey is supported by a summary of the key results from a variety of surveys undertaken over the last decade internationally. The paper concludes that EE education across universities and engineering disciplines in Australia is currently highly variable and ad hoc. Based on the results of the survey; this paper highlights a number of preferred options to support educators to embed sustainability within engineering programs, and future opportunities for monitoring EE, within the context of engineering education for sustainable development (EESD). [Copyright &y& Elsevier]

Moisture-related issues in buildings can accelerate the decay of building materials and increase occurrence of mould, resulting in significant economic and health implications. Transient hygrothermal risks assessment proved to be the most reliable approach, yet its application at early design stages is impeded by the high number of assumptions, input and skills required to be robustly applied. Climate-based indicators can assist in determining effective prevention design strategies based on the magnitude of the potential hygrothermal risks. However, in Australia, the National Construction Code relies on a climate zoning originally developed for thermal and energy efficiency provisions, clustering locations that present different moisture profiles. This paper investigates the suitability of the existing climate zones for hygrothermal risk indications by calculating the moisture index for several representative cites across Australia. Results reveal the current climate mapping to be unsuitable and too coarse to reliably provide robust indications for moisture management. Further, a new characterization of Australian climates for hygrothermal assessment purposes is proposed as a first step towards a better representation of moisture risks within the building code. [ABSTRACT FROM AUTHOR]

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 tickbox 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. [ABSTRACT FROM AUTHOR]

Lead is a persistent pollutant and the subject of many environmental studies, yet, in Australia, the extent of atmospheric lead emissions from the use of leaded petrol is unquantified. This paper details the first comprehensive account of leaded petrol sales and its lead concentrations over the 70 years of use in Australia. The resulting atmospheric lead emissions are calculated to provide the most complete understanding of the volume of lead released to the Australian continent from the consumption of leaded petrol. Atmospheric emissions of lead to the entire Australian continent from leaded petrol are calculated to total 240,510 tonnes over seven decades of use, peaking at 7869 tonnes in 1974. Total emissions for individual states and territories range from 1745 to 67,893 tonnes, with New South Wales responsible for the largest emissions. The effect of regulations on allowable concentrations of tetraethyl-lead additives are observed in the reduction of lead emissions in New South Wales and Victoria. The consequences to human health and the environment of leaded petrol consumption in Australia's populous cities are examined against historical air quality data and blood lead levels. [ABSTRACT FROM AUTHOR]

• Bottom-up estimation tool to develop electric public transport (EPT) charging loads. • Uses GTFS public data and can be scaled to hundreds of cities/states globally. • EPT could add 17% to critical peak demand on some distribution zone substations. Battery electrified public transit (BEPT), including buses and passenger ferries, is a promising solution to transport-related climate emissions and urban air pollution, but introduces potentially challenging large, coincident demand in the low-voltage distribution network. This paper presents a tool for estimating the energy and charging demand of electrified public transit using public data that is available in over 150 cities/states globally and demonstrates it in two case studies. The tool applies heuristic vehicle scheduling to existing public General Transit Feed Specification data to model the charging profiles and electricity consumption of BEPT for various charging regimes across different geographical scales. For the case study of New South Wales, Australia, the impacts of BEPT are most significant at the low-voltage network, where adding a battery-electric bus depot was found to increase the annual critical peak demand at the local zone substation in Summer by up to 17% while exacerbating demand during the daily evening peak period by 20–30%. Across the entire state of NSW, a full transition to BEPT increases annual electricity consumption by 1.28–1.34% and peak daily demand by 1–3%. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, our goal is to examine the unit root null hypothesis in energy consumption for Australian states and territory. We consider sectoral energy consumption for Australia and its six states and one territory using time series data for the period 1973–2007. This is the first study that does this. Generally, except for some cases in South Australia, we find strong support that shocks to energy consumption have a temporary effect on energy consumption in Australia. [Copyright &y& Elsevier]

Abstract: A two-storey rammed earth building was built on the Thurgoona Campus of Charles Sturt University in Albury-Wodonga, Australia, in 1999. The building is novel both in the use of materials and equipment for heating and cooling. The climate at Wodonga can be characterised as hot and dry, so the challenge of providing comfortable working conditions with minimal energy consumption is considerable. This paper describes an evaluation of the building in terms of measured thermal comfort and energy use. Measurements, confirmed by a staff questionnaire, found the building was too hot in summer and too cold in winter. Comparison with another office building in the same location found that the rammed earth building used more energy for heating. The thermal performance of three offices in the rammed earth building was investigated further using simulation to predict office temperatures. Comparisons were made with measurements made over typical weeks in summer and winter. The validated model has been used to investigate key building parameters and strategies to improve the thermal comfort and reduce energy consumption in the building. Simulations showed that improvements could be made by design and control strategy changes. [Copyright &y& Elsevier]

• The impacts of COVID lockdown on six aged care communities' energy and peak demand are studied. • The lockdown period was compared with the same period of similar temperature in previous years. • The impacts of COVID lockdown varied enormously in different climate regions. • Effectiveness and limitations of existing energy KPIs are discussed. In late March to April 2020 residential aged care facilities (RAC) in Australia were under COVID-19 lockdown. This paper explores whether the resultant restrictions on entry and exit and elimination of site group activities within RACs impacted on the total electricity use, peak demand and electrical load profiles. Six RACs in four different climate zones are analysed, comparing historical electricity load and demand profiles with the lockdown period. The facilities in warm regions showed largest reductions in energy use and peak demands. There was a peak demand timing shift in temperate regions and hot regions' changes were negligible for energy use or peak demands. This study revealed the limitations of using aggregate data as the key performance indicator (KPI) – energy use per bed. Also, KPIs in relation to cooling degree days (or total cooling and heating degree days) have been examined and are not recommended for temperate regions or temperate seasons. The potential CO 2 emission reduction from onsite renewable generation is quantified. Further research is to investigate energy use changes at circuit levels under lockdowns, and develop more nuanced KPIs that include the rate of energy use and the timing of that use. [ABSTRACT FROM AUTHOR]

Reliance on new and alternative water supply sources is a desirable option for upgrading existing and ageing urban water system infrastructure that is no longer able to cater for steadily increasing water demand. This transformation will increasingly involve the use of decentralised, more complex and energy intensive urban water systems. Modelling capability that takes a holistic systems approach to optimize the dynamic interactions between water and energy is needed to evaluate the performance of fit-for-purpose water supply systems for the urban sector. This paper presents a simulation-optimisation model to concurrently simulate and optimize an urban water supply system based on minimum energy use when water of alternative quality is supplied to different users on a 'fit-for-purpose' basis. The model uses a System Dynamics approach to simulate the energy use of different water supply systems in the form of stocks and flows combined with a Genetic Algorithm (GA) technique to optimize energy use while satisfying all the water demands. The comprehensive model framework is built on a Matlab/Simulink® platform. Life Cycle Energy Assessment (LCEA) is used to generate the embodied energy use variables which are input to the simulation-optimisation model. The model is sufficiently flexible to accommodate water supply systems of variable spatial scales and analyse water and energy use at variable time scales. The application of the modelling framework on the Aurora urban development estate, Australia, shows that the model produces essential information about the water supply and energy use intensity according to specified criteria. • A modelling framework for 'fit-for-purpose' urban water supply systems is presented. • The framework consists of three modules: 1) LCEA ii) SDM iii) Optimisation. • All relevant infrastructure, processes and sclaes were considered. • The modeling framework was applied it to a new greenfield development site. • Both operational and embodied energy use are significant. [ABSTRACT FROM AUTHOR]

Common property (CP) is a significant consumer of electricity in apartment buildings. Although some apartments in Australia have adopted shared microgrid configurations to offset grid consumption, the characteristics and load patterns of CP are rarely discussed due to lack of available data. As common areas normally constitute part of owner corporations, energy distribution in these premises requires attention. This paper presents empirical analysis of the CP load connected to shared solar and battery storage for three apartment complexes located in Perth Australia. Load patterns for CP over a defined dataset period were analyzed, and grid usage reduction was examined by implementing and comparing three energy allocation strategies based on surplus energy utilization. The findings indicated significant grid usage reduction for CP load in different apartments after implementation of three strategies. Instantaneous consumption decreased 72%, and surplus allocation strategy reduced 91%, while consumption-based allocation reduced 76%, of grid electricity. Moreover, consumption-based allocation offered improved cost benefits compared to the other two strategies. The results further revealed the usefulness of energy allocation and effectiveness of surplus energy utilization. Based on outcomes, the strategies provide consolidation with conventional energy trading mechanisms and broadly link to the virtual power plant concept for coordinating energy flows between multiple generators. [ABSTRACT FROM AUTHOR]

Common property (CP) is a significant consumer of electricity in apartment buildings. Although some apartments in Australia have adopted shared microgrid configurations to offset grid consumption, the characteristics and load patterns of CP are rarely discussed due to lack of available data. As common areas normally constitute part of owner corporations, energy distribution in these premises requires attention. This paper presents empirical analysis of the CP load connected to shared solar and battery storage for three apartment complexes located in Perth Australia. Load patterns for CP over a defined dataset period were analyzed, and grid usage reduction was examined by implementing and comparing three energy allocation strategies based on surplus energy utilization. The findings indicated significant grid usage reduction for CP load in different apartments after implementation of three strategies. Instantaneous consumption decreased 72%, and surplus allocation strategy reduced 91%, while consumption-based allocation reduced 76%, of grid electricity. Moreover, consumption-based allocation offered improved cost benefits compared to the other two strategies. The results further revealed the usefulness of energy allocation and effectiveness of surplus energy utilization. Based on outcomes, the strategies provide consolidation with conventional energy trading mechanisms and broadly link to the virtual power plant concept for coordinating energy flows between multiple generators. [ABSTRACT FROM AUTHOR]

Despite the global context, only 6% of Australia's total energy consumption was derived from renewables, while 86.3% of electricity was generated from fossil fuels. However, this trend has been disrupted by the recent decommissioning and closure of a fleet of ageing coal power plants, along with the country's international commitments to reduce fossil fuel emissions. Currently, Australia is at a crucial stage of its transition to renewable energy adoption. With the purpose of providing an updated depiction of renewable energy, related policy and the way ahead in the Australian context, this research paper presents a systematic review of contemporary literature from the perspectives of energy potential, utilisation and policy incentives. The research analysed a total of 118 documents using an interpretivist epistemological lens; emergent findings offer an evidence-based, lucid account of advancements in the renewable energy market. Polynomial regression analysis is also conducted on secondary data obtained from the literature to predict trends in the electricity generated by different sources of renewable energy. The work also identifies several areas that require attention, including: (1) the most recent time-varying feed-in tariffs (TV FiTs) which offer the innate potential to alter consumer attitudes toward power usage at peak times, thereby improving grid security; (2) the increasingly popular integration of waste-to-energy with other sources of renewable energy emerges as a viable complementary solution to meet energy demands; (3) the increasing utilisation of subsidised geothermal energy installations which are set to rise exponentially in the future; and (4) the use of Public Private Partnerships (PPP) for developing renewable energy generation infrastructure and how this investment may facilitate the national transition to renewable energy adoption. This research contributes to the existing body of knowledge by raising awareness of the current state of renewable energy in Australia, along with proposing pragmatic recommendations for overcoming any challenges posed. As a comprehensive reference, this study provides practitioners and policymakers with thorough, reliable and collated information on Australia's current renewable energy position. [ABSTRACT FROM AUTHOR]

• Development of a new enthalpy-based Climatic Cooling Potential index. • Critical evaluation of using enthalpy to evaluate Climatic Cooling Potential. • Practical significance of the enthalpy-based Climatic Cooling Potential. • Critical discussion of climatic effects on applicability of Ventilative Cooling. • Spatiotemporal analysis of the Climatic Cooling Potential in Australia. Ventilative Cooling is a promising technique to improve the energy efficiency of buildings through reduced need for mechanical cooling to maintain thermal comfort. To assess the feasibility of Ventilative Cooling in a specific location, it is useful for designers to be able to evaluate the climate potential for cooling. This paper describes a new and enhanced version of the Climatic Cooling Potential (CCP) method, which is a simplified method to evaluate the climate potential for passive cooling that does not require detailed knowledge of the building characteristics. The proposed Enthalpy Climatic Cooling Potential (CCP h) integrates humidity into the climate evaluation, using the enthalpy difference between indoors and outdoors, instead of the temperature difference. It accounts for latent heat, providing a better estimate of locations where the use of outside air will be beneficial from an energy perspective. The methods were compared by mapping the Ventilative Cooling potential for the Australian climate, using weather data from 391 weather stations across Australia. Results showed that most of the highly populated cities in the south of Australia have a climate suitable for Ventilative Cooling. This is true even in the summer period, although the area where the climate is beneficial is significantly larger during the shoulder seasons and winter. The analysis showed that using the CCP index, rather than the new CCP h index, can lead to an under-estimation of the cooling potential during dry winters and an over-estimation during humid summers, due to the contribution of the latent heat. [ABSTRACT FROM AUTHOR]