• Energy Research
• 2016-2025close
• 7. Clean energy close
• 13. Climate action close
• 1. No poverty close
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This edited volume examines the opportunities to think, do, and/or create jointly afforded by digital storytelling. The contributors discuss digital storytelling in the context of educational programs, teaching anthropology, and ethnographic research involving a variety of populations and subjects that will appeal to researchers and practitioners engaged with qualitative methods and pedagogies that rely on media technology.

AbstractElevated CO2 concentration has been reported to decrease grain nutrient concentrations and thus worsen nutritional deficiency and hidden hunger. One nutritional aspect is mineral content, yet mineral bioavailability can be limited by the presence of phytic acid. Given that future climate scenarios predict elevated global temperature driven by elevated atmospheric CO2 concentrations, we used Temperature by Free‐Air CO2 Enrichment (T‐FACE) field experiments to investigate whether elevated temperature alters the effects of elevated CO2 on grain mineral concentrations, grain mineral yields, and their bioavailability in a range of wheat and rice genotypes. We found that the negative effects of elevated CO2 were compensated for by positive effects of elevated temperature. As a result, the combined elevated CO2 and elevated temperature increased concentrations of some minerals by up to ~15% in both rice and wheat relative to control conditions. Moreover, the combined elevated CO2 and elevated temperature did not significantly change total yields of some minerals despite lower grain yields. The combined CO2 and temperature elevation increased phytic acid concentration in rice by 18.1% but decreased it in wheat by 3.5%. The mineral bioavailability, estimated as the mole ratio of phytic acid to minerals in rice and wheat grains, was limited by the combined CO2 and temperature elevation in only a few cases. Our results indicate that under future climate conditions of elevated temperature and CO2, the nutritional quality of rice and wheat with respect to minerals may remain unchanged.

Abstract. Flooding in the Amazon basin is frequently attributed to modes of large-scale climate variability, but little attention is paid to how these modes influence the timing and duration of floods despite their importance to early warning systems and the significant impacts that these flood characteristics can have on communities. In this study, river discharge data from the Global Flood Awareness System (GloFAS 2.1) and observed data at 58 gauging stations are used to examine whether positive or negative phases of several Pacific and Atlantic indices significantly alter the characteristics of river flows throughout the Amazon basin (1979–2015). Results show significant changes in both flood magnitude and duration, particularly in the north-eastern Amazon for negative El Niño–Southern Oscillation (ENSO) phases when the sea surface temperature (SST) anomaly is positioned in the central tropical Pacific. This response is not identified for the eastern Pacific index, highlighting how the response can differ between ENSO types. Although flood magnitude and duration were found to be highly correlated, the impacts of large-scale climate variability on these characteristics are non-linear; some increases in annual flood maxima coincide with decreases in flood duration. The impact of flood timing, however, does not follow any notable pattern for all indices analysed. Finally, observed and simulated changes are found to be much more highly correlated for negative ENSO phases compared to the positive phase, meaning that GloFAS struggles to accurately simulate the differences in flood characteristics between El Niño and neutral years. These results have important implications for both the social and physical sectors working towards the improvement of early warning action systems for floods.

Abstract With promising benefits such as traffic emission reduction, traffic congestion alleviation, and parking problem solving, Electric Vehicle (EV)-sharing systems have attracted large attentions in recent years. Different from other business modes, customers in sharing economy systems are usually price sensitive. Therefore, it is possible to shift the usage of shared EVs through a well-designed Dynamic Pricing Scheme (DPS), with the objective of maximizing the system operator's total profit. In this study, we propose a novel DPS for a large-scale EV-sharing network to address the EV unbalancing issue and satisfy the vehicle-grid-integration (VGI) service based on accurate station-level demand prediction. The proposed DPS is formulated as a complex optimization problem, which includes two Price Adjustment Level (PAL) decision variables for every origin-destination pair of stations. The two PALs are employed to affect the EV-sharing demand and travel time between each station pair, respectively. Physical and operational constraints from both EV demand and VGI service aspects are also included in the proposed model. Two case study are conducted to validate the effectiveness of the proposed method.

Facile sol–gel synthesis of Mo:BiVO4 thin films with optimized morphology results in reduced surface recombination and enhanced hole transfer efficiency.

Abstract The price effect of the rising share of renewable electricity, which is called ‘merit-order-effect’, leads to noticeable changes in the German power industry and debates about the electricity market design. This paper estimates the merit-order-effect induced by variable renewable energy in the German-Austrian electricity sector with a multivariate regression model. The research focus lies on the impact of the estimated effects on the marketability of variable renewable electricity generation. The results show a systematic decline of the average market revenues for wind and photovoltaic plants in the period from January 2011 to December 2013. Current market data shows a continuation of this trend into 2016. According to the German long term goals for the use of renewables, wind and solar power will play a crucial role in the future electricity generation mix. If investments in these technologies will be profitable without any regulatory remuneration mechanisms in addition to the market revenues, depends on the further cost degression and the development of the merit-order-effect.

Lithium-ion batteries exhibit a dynamic voltage behaviour depending nonlinearly on current and state of charge. The modelling of lithium-ion batteries is therefore complicated and model parametrisation is often time demanding. Grey-box models combine physical and data-driven modelling to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling. Differential equations given by physical laws and NODEs can be combined in a single modelling framework. Here we demonstrate the use of NODEs for grey-box modelling of lithium-ion batteries. A simple equivalent circuit model serves as a basis and represents the physical part of the model. The voltage drop over the resistor–capacitor circuit, including its dependency on current and state of charge, is implemented as a NODE. After training, the grey-box model shows good agreement with experimental full-cycle data and pulse tests on a lithium iron phosphate cell. We test the model against two dynamic load profiles: one consisting of half cycles and one dynamic load profile representing a home-storage system. The dynamic response of the battery is well captured by the model.