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In recent years, prices for photovoltaics have fallen steadily and the demand for sustainable energy has increased. Consequentially, the assessment of roof surfaces in terms of their suitability for PV (Photovoltaic) installations has continuously gained in importance. Several types of assessment approaches have been established, ranging from sampling to complete census or aerial image analysis methodologies. Assessments of rooftop photovoltaic potential are multi-stage processes. The sub-task of examining the photovoltaic potential of individual rooftops is crucial for exact case study results. However, this step is often time-consuming and requires lots of computational effort especially when some form of intelligent classification algorithm needs to be trained. This often leads to the use of sampled rooftop utilization factors when investigating large-scale areas of interest, as data-driven approaches usually are not well-scalable. In this paper, a novel neighbourhood-based filtering approach is introduced that can analyse large amounts of irradiation data in a vectorised manner. It is tested in an application to the city of Giessen, Germany, and its surrounding area. The results show that it outperforms state-of-the-art image filtering techniques. The algorithm is able to process high-resolution data covering 1 km2 within roughly 2.5 s. It successfully classifies rooftop segments which are feasible for PV installations while omitting small, obstructed or insufficiently exposed segments. Apart from minor shortcomings, the approach presented in this work is capable of generating per-rooftop PV potential assessments at low computational cost and is well scalable to large scale areas.

Abstract Solutions containing hydrofluoric acid (HF), hydrochloric acid (HCl), and hydrogen peroxide (H2O2) were investigated as novel acidic, NOx-free etching mixtures for texturing of monocrystalline silicon wafers. High etch rates of up to 13.3 nm s−1 were observed at room temperature, which are comparable to the etch rates of KOH-IPA solutions. The silicon surface was investigated by scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM), indicating pyramidal textures for diamond wire and SiC-slurry sawn as well as saw-damage etched (polished) wafers. Non-stirred baths generate random pyramidal structures while constantly stirred solutions generate novel random inverted pyramidal surface structures. The light trapping efficiency of wafers etched by the HF-HCl-H2O2 solutions was compared by UV/vis-reflectivity measurements to KOH/i-propanol specimens indicating lower reflectivities for the HF-HCl-H2O2-treated samples. Using the ‘wafer ray tracer’ (pvlighthouse.com) the light absorption properties of monomodal and random inverted pyramid structures were simulated and compared to well-known random and monomodal textures for PERC solar cells, clearly indicating the best performance for random inverted pyramids. Besides, simulation of a PERC solar cell on a roof top at our university was performed, indicating improved performance, especially for random inverted pyramid textures.

This paper presents the design of an extended Kalman filter (EKF) as a state-of-charge (SoC) estimator for a lithium–titanate (LTO) battery cell. The design is based on a Thevenin model of the equivalent battery electrical circuit with two parallel resistive-capacitive (RC) elements for the emulation of electrolyte polarization effects. The effectiveness of the proposed SoC estimator is verified through computer simulations on the comprehensive nonlinear battery simulation model featuring battery parameter vs. SoC variations, which is subject to highly dynamic loading regimes.

Judged by their negative nutrient balances, low soil cover and low productivity, the predominant agro-pastoral farming systems in the Sudano-Sahelian zone of West Africa are highly unsustainable for crop production intensification. With kaolinite as the main clay type, the cation exchange capacity of the soils in this region, often less than 1 cmolc kg−1soil, depends heavily on the organic carbon (Corg) content. However, due to low carbon sequestration and to the microbe, termite and temperature-induced rapid turnover rates of organic material in the present land-use systems, Corg contents of the topsoil are very low, ranging between 1 and 8 g kg-1 in most soils. For sustainable food production, the availability of phosphorus (P) and nitrogen (N) has to be increased considerably in combination with an improvement in soil physical properties. Therefore, the adoption of innovative management options that help to stop or even reverse the decline in Corg typically observed after cultivating bush or rangeland is of utmost importance. To maintain food production for a rapidly growing population, targeted applications of mineral fertilisers and the effective recycling of organic amendments as crop residues and manure are essential. Any increase in soil cover has large effects in reducing topsoil erosion by wind and water and favours the accumulation of wind-blown dust high in bases which in turn improves P availability. In the future decision support systems, based on GIS, modelling and simulation should be used to combine (i) available fertiliser response data from on-station and on-farm research, (ii) results on soil productivity restoration with the application of mineral and organic amendments and (iii) our present understanding of the cause-effect relationships governing the prevailing soil degradation processes. This will help to predict the effectiveness of regionally differentiated soil fertility management approaches to maintain or even increase soil Corg levels.

AbstractMicrobial carbon (C) use efficiency (CUE) delineates the proportion of organic C used by microorganisms for anabolism and ultimately influences the amount of C sequestered in soils. However, the key factors controlling CUE remain enigmatic, leading to considerable uncertainty in understanding soil C retention and predicting its responses to global change factors. Here, we investigate the global patterns of CUE estimate by stoichiometric modeling in surface soils of natural ecosystems, and examine its associations with temperature, precipitation, plant‐derived C and soil nutrient availability. We found that CUE is determined by the most limiting resource among these four basic environmental resources within specific climate zones (i.e., tropical, temperate, arid, and cold zones). Higher CUE is common in arid and cold zones and corresponds to limitations in temperature, water, and plant‐derived C input, while lower CUE is observed in tropical and temperate zones with widespread limitation of nutrients (e.g., nitrogen or phosphorus) in soil. The contrasting resource limitations among climate zones led to an apparent increase in CUE with increasing latitude. The resource‐specific dependence of CUE implies that soils in high latitudes with arid and cold environments may retain less organic C in the future, as warming and increased precipitation can reduce CUE. In contrast, oligotrophic soils in low latitudes may increase organic C retention, as CUE could be increased with concurrent anthropogenic nutrient inputs. The findings underscore the importance of resource limitations for CUE and suggest asymmetric responses of organic C retention in soils across latitudes to global change factors.

Smart charging of Electric Vehicles (EVs) reduces operating cost, allows more sustainable battery usage, and promotes the rise of electric mobility. In addition, bidirectional charging and improved connectivity enable efficient power grid support. Today, however, uncoordinated charging, e.g., governed by users’ habits, is still the norm. Thus, the impact of upcoming smart charging applications is mostly unexplored. We aim to estimate the expenses inherent with smart charging, e.g., battery aging costs, and give suggestions for further research. Using typical onboard sensor data we concisely model and validate an EV battery. We then integrate the battery model into a realistic smart charging use case and compare it with measurements of real EV charging. The results show that i) the temperature dependence of battery aging calls for precise thermal models for charging power greater than 7 kW, ii) disregarding battery aging underestimates EVs’ operating cost by approx. 30%, and iii) the profitability of Vehicle-to-Grid (V2G) services based on bidirectional power flow, e.g., energy arbitrage , depends on battery aging costs and the electricity price spread.

Abstract The impact of a twisted turbulator in a parabolic solar collector on the improvement of thermal–hydraulic performance (THP), as well as energy and exergy Efficiency of MgO-Cu/water hybrid nanofluid (HNF) is numerically evaluated. The main goal of this study is to use a twisted turbulator with pitch ratios (γ) of 1, 1.5, 2, and 2.5 for Reynolds numbers (Re) of 8000 to 32,000 and volume fractions (ϕ) of 1, 2, and 3% of MgO and Cu nanoparticles. Numerical simulation findings are reported in the form of graphs of average Nusselt number (Nuave), pressure drop (Δp), THP, energy Efficiency ( η c ) , and exergy Efficiency ( η ex ) . It can be concluded that Nuave and Δp depends on ϕ and Re and augments linearly with their values. Moreover, Nuave and Δp increases significantly by increasing the pitch ratio of the turbulator. The results revealed that the maximum values of augmentation of η c and η ex are respectively 23.79% and 21.15% by raising Re from 8000 to 32000. In a SC with a turbulator with γ = 2 and ϕ = 3%, η c is raised by 24.16% by increasing Re from 8000 to 32000; while, in the case of γ = 2.5 and ϕ = 3%, η c is enhanced by 18.2%.