• Canada
• Research dataclose
• Research softwareclose
• Other research productsclose
• Education and Research Archive close
• Energy Research close

Software energy consumption is a performance related non-functional requirement that complicates building software on mobile devices today. Energy hogging applications (apps) are a liability to both the end-user and software developer. Measuring software energy consumption is non-trivial, requiring both equipment and expertise, yet researchers have found that software energy consumption can be modelled. Prior works have hinted that with more energy measurement data we can make more accurate energy models. This data, however, was expensive to extract because it required energy measurement of running test cases (rare) or time consuming manually written tests. In this paper, we show that automatic random test generation with resource-utilization heuristics can be used successfully to build accurate software energy consumption models. Code coverage, although well-known as a heuristic for generating and selecting tests in traditional software testing, performs poorly at selecting energy hungry tests. We propose an accurate software energy model, GreenScaler, that is built on random tests with CPU-utilization as the test selection heuristic. GreenScaler not only accurately estimates energy consumption for randomly generated tests, but also for meaningful developer written tests. Also, the produced models are very accurate in detecting energy regressions between versions of the same app. This is directly helpful for the app developers who want to know if a change in the source code, for example, is harmful for the total energy consumption. We also show that developers can use GreenScaler to select the most energy efficient API when multiple APIs are available for solving the same problem. Researchers can also use our test generation methodology to further study how to build more accurate software energy models.

The development of a cost structure for energy storage systems (ESS) has received limited attention. In this study, we developed data-intensive techno-economic models to assess the economic feasibility of ESS. The ESS here includes pump hydro storage (PHS) and compressed air energy storage (CAES). The costs were developed using data-intensive bottom-up models. Scale factors were developed for each component of the storage systems. The life cycle costs of energy storage were estimated for capacity ranges of 98-491 MW, 81-404 MW, and 60-298 MW for PHS, conventional CAES (C-CAES), and adiabatic CAES (A-CAES), respectively, to ensure a market-driven price can be achieved. For CAES systems, costs were developed for storage in salt caverns hard rock caverns, and porous formations. The results show that the annual life cycle storage cost is $220-400 for PHS, $215-265 for C-CAES, and $375-480 per kW-year for A-CAES. The levelised cost of electricity is $69-121 for PHS, $58-70 for C-CAES, and $96-121 per MWh for A-CAES. C-CAES is economically attractive at all capacities, PHS is economically attractive at higher capacities, and A-CAES is not attractive at all. The developed information is helpful in making investment decision related to large energy storage systems.

A planet once flourishing with ecological biodiversity is now experiencing catastrophic changes as it undergoes a severe exploitation of its natural resources. Such a level of exploitation is predominantly caused by various but linked human-centric or anthropocentric forces. Everything we do as humans has an effect on the planet, and many human activities have grave and at times, unforeseeable effects. At present, we overexploit the Earth’s resources constantly – with the flick of a switch we utilize fossil fuels that power electricity; with a trip in the car we emit greenhouse gases; with a purchase at the grocery store we use excessive packaging – and the extent to which the Earth’s resources are being used to meet the demand of a large and growing human population has created severe exploitation. This feeding frenzy has led to the current prognosis: an astronomical number of environmental disasters and projected global temperatures that cannot sustain plant, animal, or human life in the future. The widespread consequences of human activities such as major wildlife extinction, rising sea levels, air pollution, and irreversible global warming look to perpetuate until the Earth is uninhabitable. As one population among many at extreme risk of major die-off, it is crucial that we explore what remedial options we have left. These ecologically catastrophic changes are only characteristic of our relatively recent history as humanity’s recent answers to fundamental survival questions have trended towards overlooking environmental sustainability. I have come to understand agriculture, from its ancient form to the current industrial and mass-scale variety, as one game-changing initiation if not the origin of massive human exploitation of the Earth’s resources. Thus, both industrial and ancient agriculture will be the focus of my research. Through the exploration of recent historical and scientific research surrounding agriculture, I will provide insight into how we made our way to the current crisis, what prevents us from changing our unsustainable behaviour, and how we can look within ourselves and at the external complex system in which we live, to change the current prognosis and come home to a sustainable way of life on this planet.

There is a growing realization among scientists and policy makers that an increased understanding of today's environmental issues requires international collaboration and data synthesis. Meta-analyses have served this role in ecology for more than a decade, but the different experimental methodologies researchers use can limit the strength of the meta-analytic approach. Considering the global nature of many environmental issues, a new collaborative approach, which we call coordinated distributed experiments (CDEs), is needed that will control for both spatial and temporal scale, and that encompasses large geographic ranges. Ecological CDEs, involving standardized, controlled protocols, have the potential to advance our understanding of general principles in ecology and environmental science.

An image from a peripheral blood smear showing a thick smear with an abundant amount of red blood cells and platelets often seen in patients with polycythemia vera (50X oil immersion).

The demand for hydrogen in conventional and unconventional oil refining industries is considerable. Currently, the predominant source of hydrogen is from fossil fuel production pathways, in particular, steam methane reforming (SMR), which incurs a significant greenhouse gas (GHG) emissions footprint. Thus, alternative environmentally benign sources of hydrogen will be needed in oil refinery complexes the world over, if their greenhouse gas (GHG) emissions footprint is to be reduced materially. In this paper, an integrated data-intensive techno-economic model is developed to provide a credible estimate of hydropower-hydrogen production costs in Western Canada. The minimum hydrogen production cost for the hydropower-hydrogen plant amounts to $2.43/kg H2 – this corresponds to an electrolyser farm with 90 units of a 3496 kW (760 Nm3/h) rated electrolyser. This cost is competitive with SMR/SMR coupled with carbon capture and sequestration (CCS) production costs, which vary from $1.87/kg H2 to $2.60/ kg H2. This point is buttressed by the fact that if existing hydropower plants are used (hence negating hydropower capital costs), the minimum production cost amounts to $1.18/ kg H2. Hydrogen from hydro power, under the techno-economic conditions considered here, is competitive compared to SMR.

With the increasing concern of climate change and greenhouse gas emissions, innovative solutions to produce energy via renewable sources are needed. Geothermal energy has the potential to provide heating and cooling to residential and commercial buildings, however, its implementation has been stunted due to high initial costs, longer payback periods, and lesser return on investment. Helical steel piles, mainly used as structural foundations for buildings, have the potential to act as in-ground heat exchangers, producing higher efficiencies than conventional borehole systems at a lower cost. Eight helical steel piles, fitted with plastic tubing for fluid circulation, have been installed in an experimental site in Waterloo, Ontario. Cooling and heating tests have been conducted on the novel system to evaluate the capacity, power consumption and coefficient of performance. This paper presents the results of the peak and steady state capacity tests as well as the limitations experienced. Part of Proceedings of the Canadian Society for Mechanical Engineering International Congress 2022.

Renewable energy technology-based power generation is considered to be environmentally friendly and to have a low life cycle greenhouse gas emissions footprint. However, the life cycle water footprint of renewable energy technology-based power generation needs to be assessed. The objective of this study is to develop life cycle water footprints for renewable energy technology-based power generation pathways. Water demand is evaluated through consumption and withdrawals coefficients developed in this study. Sixty renewable energy technology-based power generation pathways were developed for a comprehensive comparative assessment of water footprints. The pathways were based on the use of biomass, nuclear, solar, wind, hydroelectricity, and geothermal as the source of energy. During the complete life cycle, power generation from bio-oil extracted from wood chips, a biomass source, was found to have the highest water demand footprint and wind power the lowest. During the complete life cycle, the water demand coefficients for biomass-based power generation pathways range from 260 – 1289 litres of water per kilowatt hour and for nuclear energy pathways from 0.48 – 179 litres of water per kilowatt hour. The water demand for power generation from solar energy-based pathways ranges from 0.02 – 4.39 litres of water per kilowatt hour, for geothermal pathways from 0.04 – 1.94 litres of water per kilowatt hour, and for wind from 0.005 – 0.104 litres of water per kilowatt hour. A sensitivity analysis was conducted with varying conversion efficiencies to evaluate the impact of power plant performance on water demand. Cooling systems used in power generation plants were also studied and include once-through, recirculating, dry, and hybrid cooling. When only the power generation stage is considered, hydroelectricity and nuclear power generation with once-through cooling systems showed the highest water consumption (68 litres of water per kilowatt hour) and water withdrawals coefficients (178 litres of water per kilowatt hour), respectively.

Hydrothermal processing, a thermochemical approach, is an excellent method of converting energy-rich biomass into useful products. This approach offers the advantage of handling biomass with relatively high moisture content by precluding an energy-intensive pretreatment step. Hydrothermal processing is of world-wide interest in view of depleting fossil-fuel reserves and increased environmental greenhouse gas emissions. There is potential to develop this novel technology at demonstration scale. This paper reviews the three hydrothermal technologies, namely hydrothermal liquefaction, gasification and carbonization, to provide insight into the likelihood of commercialization. The study discusses the role of different process parameters that have key impacts on the quality and yield of the desired products. This study also identifies the gaps in the literature including the need to establish a baseline to develop key process models and to perform a techno-economic assessment to get a better sense of the viability of the technology in future.