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This paper studies the long-term energy management of a microgrid coordinating hybrid hydrogen-battery energy storage. We develop an approximate semi-empirical hydrogen storage model to accurately capture the power-dependent efficiency of hydrogen storage. We introduce a prediction-free two-stage coordinated optimization framework, which generates the annual state-of-charge (SoC) reference for hydrogen storage offline. During online operation, it updates the SoC reference online using kernel regression and makes operation decisions based on the proposed adaptive virtual-queue-based online convex optimization (OCO) algorithm. We innovatively incorporate penalty terms for long-term pattern tracking and expert-tracking for step size updates. We provide theoretical proof to show that the proposed OCO algorithm achieves a sublinear bound of dynamic regret without using prediction information. Numerical studies based on the Elia and North China datasets show that the proposed framework significantly outperforms the existing online optimization approaches by reducing the operational costs and loss of load by around 30% and 80%, respectively. These benefits can be further enhanced with optimized settings for the penalty coefficient and step size of OCO, as well as more historical references. Submitted to Applied Energy

Life in soil is a key driver of important ecosystem processes, such as the recycling of carbon and nutrients. In current intensive agricultural soils, however, richness and abundance of many groups of soil organisms are often reduced, which may threaten soil health and sustainable agriculture in the long run. Therefore, a switch to alternative agricultural practices (e.g., minimal tillage) that are less detrimental or even stimulate soil life has been suggested as a way to increase sustainable food production. Although we understand how some of these practices impact specific species or functional groups in soils, it is necessary to get a more complete overview to understand which practices can be used in agriculture to improve soil biodiversity. Here, we present a systematic literature review identifying which practices are studied as alternatives to current, intensive practices for four soil taxonomic groups encompassing a range of trophic groups and functions in the soil ecosystem: nematodes, earthworms, bacteria and fungi. Further, we review how these alternative practices impact the abundance and diversity of these four taxonomic groups, as well as for the 14 functional groups identified and retrieved from the review. We found that a total of 23 alternative agricultural practices, grouped into 10 groups of practices, were studied for the four target taxonomic groups. Three groups of practices, 'fertilization’, ‘soil cover’ and ‘tillage’ were studied for all taxa. In general, alternative agricultural practices had positive impacts on the species richness in the four taxonomic groups and on the abundance of organisms in the functional groups. However, there were some exceptions. For example, organic fertilizers reduced the abundance of epigeic earthworms, while enhancing the abundance of endogeic and anecic earthworms. There was only one alternative practice, i.e., the use of cover crops, that was neutral to positive for the abundance of all functional groups across all taxa. Our review revealed that there are gaps in the literature, as practices that are commonly studied for aboveground biodiversity, such as field margins or flower strips, are not studied well across taxonomic and functional groups and need to be further studied to improve our understanding of the impact of alternative practices on soil life. We conclude that alternative agricultural practices are promising to enhance soil biodiversity. However, as some practices have specific impacts on taxonomic groups in the soil, we may require careful application and combinations of alternative agricultural practices to stimulate multiple groups.

We present a modeling and optimization framework to design powertrains for a family of electric vehicles, focusing on the concurrent sizing of their motors and batteries. Whilst tailoring these component modules to each individual vehicle type can minimize energy consumption, it can result in high production costs due to the variety of component modules to be realized for the family of vehicles, driving the Total Costs of Ownership (TCO) high. Against this backdrop, we explore modularity and standardization strategies whereby we jointly design unique motor and battery modules to be installed in all the vehicles in the family, using a different number of these modules when needed. Such an approach results in higher production volumes of the same component module, entailing significantly lower manufacturing costs due to Economy-of-Scale (EoS) effects, and hence a potentially lower TCO for the family of vehicles. To solve the resulting one-size-fits-all problem, we instantiate a nested framework consisting of an inner convex optimization routine which jointly optimizes the modules' sizes and the powertrain operation of the entire family, for given driving cycles and modules' multiplicities. Likewise, we devise an outer loop comparing each configuration to identify the minimum-TCO solution with global optimality guarantees. Finally, we showcase our framework on a case study for the Tesla vehicle family in a benchmark design problem, considering the Model S, Model 3, Model X, and Model Y. Our results show that, compared to an individually tailored design, the application of our concurrent design optimization framework achieves a significant reduction of the production costs for a minimal increase in operational costs, ultimately lowering the family TCO in the benchmark design problem by 3.5\%. 17 pages, 17 figures, 7 tables

1H and 13C nuclear magnetic resonance spectra were recorded on a Bruker Avance III 400 or Magritek Spinsolve 60 spectrometer at 293 K. Chemical shifts are reported as δ in parts per million (ppm) and referenced to the chemical shift of the residual solvent resonances (CDCl3: 1H: δ = 7.26 ppm, 13C: δ = 77.16 ppm). Polymer molecular weight and dispersity were determined using a Malvern Viscotek GPCmax size exclusion chromatograph instrument fitted with a Viscotek TDA 305 detector unit equipped with refractive index and light scattering detectors. Samples were dissolved in tetrahydrofuran at a concentration of approximately 1 mg mL-1 and eluted through a guard column and two Agilent PLGel 5 µm mixed C columns (300 x 7.5 mm) at a flow rate of 1 ml.min-1; the elution pathlength was heated to 30 °C for the duration. Molecular weights were calibrated against known poly(methyl acrylate) standards. Differential scanning calorimetry was conducted using a TA Instruments Discovery 2500. Samples were analysed in non-hermetic aluminium pans and compared against an empty reference pan of the same type. Loaded sample masses were between 3 and 10 mg. Samples were subjected to two complete heat/cool cycles from -50 °C to 150 °C (-85 °C to 150 °C for lower Tg samples) and both heating and cooling rates were set at 10 °C min-1. UV/Vis transmittance and absorption spectra were measured with a PerkinElmer Lambda 750 spectrophotometer. Transmittance spectra of films were measured using wavelength scan with a resolution of 1 nm at a scan speed of 267 nm/min and a slit width of 2 nm. Samples were directly mounted to the sample holder. Solution spectroscopy was carried out on solutions in THF in quartz SUPRASIL® cuvettes (10 mm pathlength). Absorption spectra of luminophore solutions were taken using a wavelength scan with a resolution of 0.5 nm at a scan speed of 141.20 nm/min and a slit width of 2 nm. A reference sample of THF in an identical cuvette was used to apply a 100% transmission correction. Steady-state PL spectroscopy was performed on a Fluorolog-3 spectrophotometer (Horiba Jobin Yvon). Solid-state emission spectra were recorded using the front-face configuration. Solution emission spectra were recorded using the right-angle configuration, over 10 averaged scans. The excitation and emission slits were adjusted so that the maximum PL intensity was within the range of linear response of the detector and were kept the same between samples if direct comparison between the emission intensity was required. Emission and excitation spectra were corrected for the wavelength response of the system and the intensity of the lamp profile over the excitation range, respectively, using correction factors supplied by the manufacturer. Photoluminescence quantum yields (ΦPL) were measured using a Quanta-phi integrating sphere (Horiba Jobin Yvon) mounted on the Fluorolog-3 spectrophotometer. The UC emission and phosphorescence spectra, threshold intensity (I_th), UC quantum yield (UC) and lifetime measurements were performed using an FLS1000 time-correlated single photon counting (TCSPC) spectrometer (Edinburgh Instruments Ltd.). The samples were excited with a 532 nm laser (MGL-III-532, 200mW). To determine I_th, the laser power was adjusted using a Thorlabs PM100A Power Meter Console combined with a S120VC Si photodiode power sensor (range: 200-1100 nm) before the measurement, across the 5 to 8000 mW cm-2. The ΦUC was measured with an integrating sphere (SNS125 5-inch sphere, three windows, International Light Technologies). The sample was placed at the center of the sphere using a sample holder. A baffle is placed in front of the observation window, which blocks any scattering and reflection of the laser from the sample surface. The angle of the sample holder is adjustable. The normal direction of the sample holder is 22.5˚ to the excitation beam line, which leads the reflection of the laser to the inner surface of the sphere. The laser power was measured with a photodiode before each ΦUC measurement. Both the emission of the sample (380-500 nm) and scattering of the laser beam (530-534 nm) were measured. A neutral density filter (O.D.=3.0) was placed before the excitation beam for the scattering intensity measurements. Six data sets were collected to calculate the ΦUC of each sample: 1. sample in the path of the beam – “in fluorescence”; 2. sample in scattering; 3. sample facing away from beam – “out of fluorescence”, 4. sample out of scattering; 5. empty sphere fluorescence; 6. empty sphere scattering. Fluorescence decay measurements were performed using the multi-channel scaling (MCS) method on a the FLS1000 TCSPC spectrometer. The emission decay was recorded using a photomultiplier tube (PMT-980) equipped with TCC2 counting electronics. For the upconversion lifetime measurements, a wavelength of 440 nm was selected, and a short-pass filter (cut-off at 500 nm, Thorlabs) was placed in front of the detector. For the phosphorescence lifetimes, a wavelength of 660 nm was selected, and a long-pass filter (cut-off 550 nm, Thorlabs) was used. The instrument response function (IRF) was measured using Ludox® colloidal silica solution (a SiO2 particle suspension solution) and using a neutral density filter (O.D.=3) to attenuate the laser intensity. The pulse repetition rate was adjusted to ensure the full decay was detected within the time window. Data-fitting was carried out by tail fitting to each emission decay trace using a multiexponential decay function within the FAST software package (Edinburgh Instruments Ltd.). The goodness of fit was evaluated using the reduced chi-square statistics (χ2) and the randomness of the residuals. Please also see the readme file for more details on data collection and file organisation.