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Gender considerations, such as the division of household labour and the coordination of everyday household practices, are important for the energy transition of households. Household labour involves everyday practices (e.g. cooking, laundry and caring for others) and practices of energy household labour ('e.g'. managing digital technologies and energy systems). Emerging smart energy technologies require energy flexibility and efficiency, thereby introducing new forms of household labour can have implications for the household which are not well understood. Through a literature review and some empirical insights from a European Horizon 2020 project, mental aspects of energy household labour are identified: practices of coordination and multitasking, remembering and anticipating, and powerful emotional labour which shapes the practices. Smart technologies and energy systems add more physical and mental labour to households due to the need for additional coordination and change of practices. This additional demand for coordination can exacerbate existing gender inequalities in the division of household labour: technological strategies and designs need to engage with this and reduce new burdens. Considerations for future research are proposed and a gender-sensitive framework for understanding the transition of energy household labour is outlined. 'Practice relevance' Despite the balancing of more stereotypical gender roles in everyday life at home, the mental load of household labour still overburdens women. Technology design and strategies for its domestication need to recognise this load and adopt more gender-sensitive ways of supporting the mental aspects of household labour performed for the organisation of home and provide appropriate digital literacy opportunities for those who use them, without undermining their contribution in the process. Technology-assisted support for the mental aspects of household labour should allow for better negotiations and distribution of household labour required for a successful energy transition, without at the same time adding extra work for both men and women. The initial empirical insights call for a gender-sensitive framework for investigating the emerging practices that this involves, for the energy transition of households.

Demand Response (DR) schemes are effective tools to maintain a dynamic balance in energy markets with higher integration of fluctuating renewable energy sources. DR schemes can be used to harness residential devices' flexibility and to utilize it to achieve social and financial objectives. However, existing DR schemes suffer from low user participation as they fail at taking into account the users' requirements. First, DR schemes are highly demanding for the users, as users need to provide direct information, e.g. via surveys, on their energy consumption preferences. Second, the user utility models based on these surveys are hard-coded and do not adapt over time. Third, the existing scheduling techniques require the users to input their energy requirements on a daily basis. As an alternative, this paper proposes a DR scheme for user-oriented direct load-control of residential appliances operations. Instead of relying on user surveys to evaluate the user utility, we propose an online data-driven approach for estimating user utility functions, purely based on available load consumption data, that adaptively models the users' preference over time. Our scheme is based on a day-ahead scheduling technique that transparently prescribes the users with optimal device operation schedules that take into account both financial benefits and user-perceived quality of service. To model day-ahead user energy demand and flexibility, we propose a probabilistic approach for generating flexibility models under uncertainty. Results on both real-world and simulated datasets show that our DR scheme can provide significant financial benefits while preserving the user-perceived quality of service. 10 pages plus 1 page references, 11 figures, conference: ACM e-Energy 2018

Accelerated local deployments of renewable energy sources and energy storage units, as well as increased overall flexibility in local demand and supply through active user involvement and smart energy solutions, open up new opportunities (e.g., self-sufficiency and CO2 neutrality through local renewables) and yet pose new challenges (e.g., how to maintain the security of supply and get the best yield) to market players in the lower parts of the energy system (including prosumers, energy communities, aggregators, and distribution system operators (DSOs)). One way to cope with the challenges requires "logical" reorganization of the energy system bottom-up as a number of nested (maximally) self-sufficient and interacting cells with their own local (i.e. within a cell) energy management and trading capabilities. This change necessitates effective IT-based solutions. Towards this goal, we propose a unified Flexibility Modeling, Management, and Trading System (FMTS) that generalizes flexibility modeling, management, and intra-cell trading in such cellular energy systems. Our system offers different flexibility provisioning options (Machine Learning based, and Model Predictive Control based), activation mechanisms (indirect and direct device-control), and trading schemes (e.g. flexibility contracts, market-based trading) and suits different cellular system use-cases. In this paper, we introduce the FMTS, overview its core functionality and components, and explain how it practically manages, prices, and trades flexibility from a diverse variety of loads. We then introduce the real-world FMTS instances developed in the GOFLEX project1 and present experimental results that demonstrate significantly increased flexibility capacities, user gains, and balance between demand and supply when an FMTS instance is used in the simulated cellular energy system setting.

In Europe, the perennial C4 grass miscanthus is currently mainly cultivated for energy generation via combustion. In recent years, anaerobic digestion has been identified as a promising alternative utilization pathway. Anaerobic digestion produces a higher-value intermediate (biogas), which can be upgraded to biomethane, stored in the existing natural gas infrastructure and further utilized as a transport fuel or in combined heat and power plants. However, the upgrading of the solid biomass into gaseous fuel leads to conversion-related energy losses, the level of which depends on the cultivation parameters genotype, location, and harvest date. Thus, site-specific crop management needs to be adapted to the intended utilization pathway. The objectives of this paper are to quantify (i) the impact of genotype, location and harvest date on energy yields of anaerobic digestion and combustion and (ii) the conversion losses of upgrading solid biomass into biogas. For this purpose, five miscanthus genotypes (OPM 3, 6, 9, 11, 14), three cultivation locations (Adana, Moscow, Stuttgart), and up to six harvest dates (August-March) were assessed. Anaerobic digestion yielded, on average, 35% less energy than combustion. Genotype, location, and harvest date all had significant impacts on the energy yield. For both, this is determined by dry matter yield and ash content and additionally by substrate-specific methane yield for anaerobic digestion and moisture content for combustion. Averaged over all locations and genotypes, an early harvest in August led to 25% and a late harvest to 45% conversion losses. However, each utilization option has its own optimal harvest date, determined by biomass yield, biomass quality, and cutting tolerance. By applying an autumn green harvest for anaerobic digestion and a delayed harvest for combustion, the conversion-related energy loss was reduced to an average of 18%. This clearly shows that the delayed harvest required to maintain biomass quality for combustion is accompanied by high energy losses through yield reduction over winter. The pre-winter harvest applied in the biogas utilization pathway avoids these yield losses and largely compensates for the conversion-related energy losses of anaerobic digestion.

AbstractWhat allows a planet to be both within a potentially habitable zone and sustain habitability over long geologic time? With the advent of exoplanetary astronomy and the ongoing discovery of terrestrial‐type planets around other stars, our own solar system becomes a key testing ground for ideas about what factors control planetary evolution. Mars provides the solar system's longest record of the interplay of the physical and chemical processes relevant to habitability on an accessible rocky planet with an atmosphere and hydrosphere. Here we review current understanding and update the timeline of key processes in early Mars history. We then draw on knowledge of exoplanets and the other solar system terrestrial planets to identify six broad questions of high importance to the development and sustaining of habitability (unprioritized): (1) Is small planetary size fatal? (2) How do magnetic fields influence atmospheric evolution? (3) To what extent does starting composition dictate subsequent evolution, including redox processes and the availability of water and organics? (4) Does early impact bombardment have a net deleterious or beneficial influence? (5) How do planetary climates respond to stellar evolution, e.g., sustaining early liquid water in spite of a faint young Sun? (6) How important are the timescales of climate forcing and their dynamical drivers? Finally, we suggest crucial types of Mars measurements (unprioritized) to address these questions: (1) in situ petrology at multiple units/sites; (2) continued quantification of volatile reservoirs and new isotopic measurements of H, C, N, O, S, Cl, and noble gases in rocks that sample multiple stratigraphic sections; (3) radiometric age dating of units in stratigraphic sections and from key volcanic and impact units; (4) higher‐resolution measurements of heat flux, subsurface structure, and magnetic field anomalies coupled with absolute age dating. Understanding the evolution of early Mars will feed forward to understanding the factors driving the divergent evolutionary paths of the Earth, Venus, and thousands of small rocky extrasolar planets yet to be discovered.

Background: Food wastes are an abundant resource that can be effectively valorised by hydrothermal liquefaction to produce bio-fuels. The objective of the European project WASTE2ROAD is to demonstrate the complete value chain from waste collection to engine tests. The principle of hydrothermal liquefaction is well known but there are still many factors that make the science very empirical. Most experiments in the literature are performed on batch reactors. Comparison of results from batch reactors with experiments with continuous reactors are rare in the literature. Methods: Various food wastes were transformed by hydrothermal liquefaction. The resources used and the products from the experiments have been extensively analysed. Two different experimental reactors have been used, a batch reactor and a continuous reactor. This paper presents a dataset of fully documented experiments performed in this project, on food wastes with different compositions, conditions and solvents. The data set is extended with data from the literature. The data was analysed using machine learning analysis and regression techniques. Results: This paper presents experimental results on various food wastes as well as modelling. Aqueous phase recycling allows the re-use of some of the solubilised organics, but this paper shows that after some recycles, the yield is principally in the char yield and not so much in the oil yield. The experimental results were further used to attempt to establish a link between batch and continuous experiments. The molecular weight of bio-oil from continuous experiments appear higher than that of batch experiments. This may be due to the configuration of our reactor. Conclusions: This paper shows how the use of regression models help with understanding the results, and the importance of process variables and resource composition. A novel data analysis technique gives an insight on the accuracy that can be obtained from these models.