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Abstract 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.

Abstract 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.

Abstract Accurate rooftop solar energy potential characterization is critically important for promoting the wide penetration of renewable energy in high-density cities. However, it has been a long-standing challenge due to the complex building shading effects and diversified rooftop availabilities. To overcome the challenge, this study proposed a novel 3D-geographic information system (GIS) and deep learning integrated approach, in which a 3D-GIS-based solar irradiance analyzer was developed to predict dynamic rooftop solar irradiance by taking shading effects of surrounding buildings into account. A deep learning framework was developed to identify the rooftop availabilities. Experimental validations have shown their high accuracies. As a case study, a real urban region of Hong Kong was used. The results showed that the annual solar energy potential of the entire building group was reduced by 35.7% due to the shading effect and the reduced rooftop availability. The reductions of individual buildings varied from 13.4% to 74.5%. In spite of the substantial reductions of the annual solar energy, the shading effect could only slightly reduce the peak solar power. In fact, the annual solar energy reduction could be five times higher than the peak solar power reduction. Further analysis showed that simple addition of the respective solar energy potential reductions, caused by the shading effect and the rooftop availability, tends to highly overestimate the total reduction by up to 26%. For this reason, their impacts cannot be considered separately but as joint effects. The integrated approach provides a viable means to accurately characterize rooftop solar energy potential in urban regions, which can help facilitate solar energy applications in high-density cities.

Abstract Accurate rooftop solar energy potential characterization is critically important for promoting the wide penetration of renewable energy in high-density cities. However, it has been a long-standing challenge due to the complex building shading effects and diversified rooftop availabilities. To overcome the challenge, this study proposed a novel 3D-geographic information system (GIS) and deep learning integrated approach, in which a 3D-GIS-based solar irradiance analyzer was developed to predict dynamic rooftop solar irradiance by taking shading effects of surrounding buildings into account. A deep learning framework was developed to identify the rooftop availabilities. Experimental validations have shown their high accuracies. As a case study, a real urban region of Hong Kong was used. The results showed that the annual solar energy potential of the entire building group was reduced by 35.7% due to the shading effect and the reduced rooftop availability. The reductions of individual buildings varied from 13.4% to 74.5%. In spite of the substantial reductions of the annual solar energy, the shading effect could only slightly reduce the peak solar power. In fact, the annual solar energy reduction could be five times higher than the peak solar power reduction. Further analysis showed that simple addition of the respective solar energy potential reductions, caused by the shading effect and the rooftop availability, tends to highly overestimate the total reduction by up to 26%. For this reason, their impacts cannot be considered separately but as joint effects. The integrated approach provides a viable means to accurately characterize rooftop solar energy potential in urban regions, which can help facilitate solar energy applications in high-density cities.

Performance analysis of solar cogeneration system with different integration strategies for potable water and domestic hot water production

Performance analysis of solar cogeneration system with different integration strategies for potable water and domestic hot water production

Abstract This article experimentally demonstrates a novel, microgrid control algorithm based on a two-layer economic model predictive control framework that was previously developed by the authors. This algorithm is applied to an isolated microgrid with a solar photovoltaic system, a battery bank and a gasoline-fuelled generator. The control system performance is experimentally compared to a baseline algorithm over 5 min and 10 h periods, while an experimentally validated model is used to compare performance over a full year. The results indicate that applying the proposed, two-layer economic model predictive control algorithm can reduce operating costs and CO 2 emissions by 5%–10% relative to conventional, rule based methods, and by 10%–15% if improved solar and demand forecasts are available. Furthermore, the proposed two-level algorithm can achieve reductions of up to 5% compared with current state-of-the-art methods which only attempt to optimize performance in the energy management system.

Abstract This article experimentally demonstrates a novel, microgrid control algorithm based on a two-layer economic model predictive control framework that was previously developed by the authors. This algorithm is applied to an isolated microgrid with a solar photovoltaic system, a battery bank and a gasoline-fuelled generator. The control system performance is experimentally compared to a baseline algorithm over 5 min and 10 h periods, while an experimentally validated model is used to compare performance over a full year. The results indicate that applying the proposed, two-layer economic model predictive control algorithm can reduce operating costs and CO 2 emissions by 5%–10% relative to conventional, rule based methods, and by 10%–15% if improved solar and demand forecasts are available. Furthermore, the proposed two-level algorithm can achieve reductions of up to 5% compared with current state-of-the-art methods which only attempt to optimize performance in the energy management system.

Geoenergy sources will continue to be mainstays of the world’s energy mix for many years to come, but the technological and business realities behind these energy sources are changing in two fundamental ways. First, with much of the world’s “easy oil” already consumed, the companies behind geoenergy will have to use increasingly sophisticated technologies to find and deliver these energy sources to the market. Second, the expectations placed upon the geoenergy sector by many of its stakeholders have grown considerably with regards to environmental stewardship, safety, and human welfare. In the face of these kinds of challenges, the industry will require an increasing degree of technological and commercial sophistication to continue to be a part of the world’s sustainable energy mix. Blockchain has emerged as a promising innovation that could potentially play an important role in delivering the kinds of technological and commercial capabilities that the geoenergy sector will need to achieve these ends. In spite of the myriad ways that blockchain could potentially improve the efficiency and sustainability of the geoenergy industry, however, the technology is still evolving, and a few barriers stand in the way of its widespread deployment. This paper puts forward case study evidence from the Intel Corporation and the Energistics Consortium showing what the geoenergy sector can learn about blockchain from other industries, and highlights that the absence of data standards and interoperability has contributed to blockchain’s failure to deliver significant value in the geoenergy domain thus far.