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The injection of green hydrogen and biomethane is currently seen as the next step towards the decarbonization of the gas sector in several countries. However, the introduction of these gases in existent infrastructure has energetic, material and operational implications that should be carefully looked at. With regard to a fully blown green gas grid, transport and distribution will require adaptations. Furthermore, the adequate performance of end-use equipment connected to the grid must be accounted for. In this paper, a technical analysis of the energetic, material and operational aspects of hydrogen and biomethane introduction in natural gas infrastructure is performed. Impacts on gas transmission and distribution are evaluated and an interchangeability analysis, supported by one-dimensional Cantera simulations, is conducted. Existing gas infrastructure seems to be generally fit for the introduction of hydrogen and biomethane. Hydrogen content up to 20% by volume appears to be possible to accommodate in current infrastructure with only minor technical modifications. However, at the Distribution System Operator (DSO) level, the introduction of gas quality tracking systems will be required due to the distributed injection nature of hydrogen and biomethane. The different tolerances for hydrogen blending of consumers, depending on end-use equipment, may be critical during the transition period to a 100% green gas grid as there is a risk of pushing consumers off the grid.

A mathematical model and results of calculations of combustion of boron-containing particles of the condensed phase in channels with distributed injection of air are presented. Combustion in channels with discrete and continuously distributed injection of air is considered. Basic effects of the air-to-fuel ratio, place of air injection, and air temperature at the channel inlet on the combustion efficiency are analyzed. Conditions that ensure enhancement of the efficiency of particle combustion in the channel owing to distributed injection of air are determined. The data obtained in the present study can be used at the stage of design and experimental investigations of promising propulsion systems with the use of boron-containing high-energy-density materials as a fuel.

A mathematical model of combustion of boron particles in a ram-rocket engine is developed. The boron combustion efficiency for one-stage and two-stage injection of air into the afterburning chamber is calculated. It is demonstrated that two-stage injection of air sometimes allows the time of complete combustion of boron particles to be significantly reduced (by a factor of 1.5–3); thus, the fuel combustion efficiency in the ram-rocket engine can be increased. The simulated results are consistent with available experimental data.

The injection of apparent power to self-consumption buses generates voltage variations during network operation, which, when properly monitored, could support voltage regulation and control. In this paper, we propose a linear sensitivity modelling, quite useful for studies of voltage regulation with distributed energy resources (DER). This modelling consists of two analytical improvement steps: first, a full formulation for the voltage deviations, and second, the influence of line capacitance as Q-injections at the points of common couplings (PCCs). Our proposal is based on the linear topological sensitivity of an existing network (as a function of the line parameters X, R, and Bc), branch power flow (active–reactive power (P-Q)), and power injections at the PCCs. Here, the linear sensitivity algorithm is applied to a modified IEEE 33-bus distribution system to demonstrate its extended effectiveness to voltage monitoring and control scenarios. Its application estimates and compensates in a better way the voltage deviations with regard to the operating desired voltage (|V|op) constraints, using distributed power injections at the PCCs. Numerical results always showed a curtailment of the relative error against common simplifications of the electrical modelling in steady-state, thus simulating two critical scenarios of operation production–consumption for the existing system response. The proposed algorithm was validated considering as reference the voltage profile outputs of the load flow analysis, using the Newton–Raphson method via DIgSILENT, in terms of its accuracy, silhouette shape along the feeder and with regard to the application of reactive compensation as an analytical case study for voltage improvement in active distribution networks.

Organic semiconductors are carbon-based materials that combine optoelectronic properties with simple fabrication and the scope for tuning by changing their chemical structure 1 – 3 . They have been successfully used to make organic light-emitting diodes 2 , 4 , 5 (OLEDs, now widely found in mobile phone displays and televisions), solar cells 1 , transistors 6 and sensors 7 . However, making electrically driven organic semiconductor lasers is very challenging 8 , 9 . It is difficult because organic semiconductors typically support only low current densities, suffer substantial absorption from injected charges and triplets, and have additional losses due to contacts 10 , 11 . In short, injecting charges into the gain medium leads to intolerable losses. Here we take an alternative approach in which charge injection and lasing are spatially separated, thereby greatly reducing losses. We achieve this by developing an integrated device structure that efficiently couples an OLED, with exceptionally high internal-light generation, with a polymer distributed feedback laser. Under the electrical driving of the integrated structure, we observe a threshold in light output versus drive current, with a narrow emission spectrum and the formation of a beam above the threshold. These observations confirm lasing. Our results provide an organic electronic device that has not been previously demonstrated, and show that indirect electrical pumping by an OLED is a very effective way of realizing an electrically driven organic semiconductor laser. This provides an approach to visible lasers that could see applications in spectroscopy, metrology and sensing. An electrically driven organic semiconductor laser is achieved by integrating a device structure that efficiently couples an organic light-emitting diode, with extremely high internal-light generation, with a polymer distributed feedback laser.

The storage of fluids in the subsurface is critical for a broad spectrum of applications including managed aquifer recharge, storage of liquefied carbon dioxide and hydrogen, geothermal heat extraction and exploitation of hydrocarbon. It is surprising then, that there has been relatively little measurement of the vertical distribution of poroelastic storage in geologic formations as compared with permeability. We present experiments in which fluid was injected into an important regional aquifer and the depth‐dependent strain response measured using fiber optic distributed acoustic sensing. The formation expansion and contraction in response to fluid injection were several 100 nanostrain. Strain, and the implied storage distribution, was highly localized in specific strata and demonstrated complex, hydromechanical behavior. This new window into fluid‐geomechanical coupling undermines some typically use models and observations currently in practice, but provides potential for complete representation and prediction of fluid storage in the subsurface. Plain Language Summary A fiber optic cable was used to measure strain in response to water injection and withdrawal in a sandstone aquifer. The strain was found to be focused in limited strata within the aquifer. In some strata, the formation expanded with injection, and in others the formation contracted with injection. This complex behavior is attributed to the interaction between fluid flow and mechanical strain in the porous rock and is contrary to common models of water storage which predict a proportionate expansion of pore space with injection. These results provide important insight into understanding transient flow response to impulse and oscillatory pressure loading of the well. Key Points Strain and flow direction was correlated or anti‐correlated with injection head, depending on injection interval Injection and strain behavior was not simply correlated to geophysical or lithologic logs Dynamic injection/withdrawal tests are of limited predictive value to managed aquifer recharge

We propose and fabricate a monolithically integrated dual-mode semiconductor laser (DML) based on optical amplified feedback, where the adjustable optical self-injection feedback could induce dual-wavelength lasing, and the sub-millimeter total cavity length provides access to be microwave source. When keeping the injection current of semiconductor optical amplifier (SOA) be constant, inject different current for the segment of distributed feedback laser (DFB), we have achieved tunable microwave signal with different ranges of 10 GHz and 18 GHz respectively, which significantly simplifies the system configuration and reduces the footprint, power consumption and cost. Besides, through a special current injection scheme for the two-segment semiconductor laser, whole wavelength tuning with fixed wavelength spacing can also be realized. It provides a convenient and low-cost photonic solution for flexible and tunable microwave sources.

This paper investigates the frequency restoration problem of islanded microgrids when subjected to malicious data injection. A distributed event-triggered resilient control method based on hidden layer is proposed to mitigate the impact of false data injection (FDI) attacks on microgrids. Unlike traditional estimation-based or compensation-based approaches, the proposed method does not interfere the system during normal operations. Furthermore, this method does not rely on attack detection technology, reducing the complexity of the system. In addition, a dynamic threshold is introduced to dynamically adjust the event-triggered mechanism (ETM) over time, further reducing the number of triggers. The stability of islanded microgrids is analyzed using Lyapunov theory, and the Zeno behavior is avoided. The effectiveness and superiority of this control scheme are verified by simulation examples.

Distributed generation (DG) is becoming increasingly important due to the serious environmental pollution caused by conventional fossil-energy-based generation and the depletion of non-renewable energy. As the flexible resources in the active distribution network (ADN), battery energy system (BES) and responsive load (RL) are all able to assist renewable DG integration in day-ahead dispatch. In addition, the security and economic level can be significantly improved by adjusting network topology. Therefore, in this paper, a coordinated day-ahead scheduling method incorporating topology reconfiguration, BES optimization and load response is presented to minimize the total day-ahead operational costs in the ADN. Linearized current injection models are presented for renewable DG, RL and BES based on the linear power flow model, and an extensible linear switching operations calculation (ELSOC) method is proposed to address the network reconfiguration. Thus, a mixed integer linear programming (MILP) model is proposed for optimal coordinated operation of an ADN. The correctness and effectiveness of the proposed method are demonstrated by simulations on a modified test system. In addition, the combined scenario and Monte-Carlo method is used to handle the uncertainties of loads and DGs, and the results of different uncertainties can further verify the feasibility of the proposed model.

False data injection attacks (FDIAs) on sensor networks involve injecting deceptive or malicious data into the sensor readings that cause decision-makers to make incorrect decisions, leading to serious consequences. With the ever-increasing volume of data in large-scale sensor networks, detecting FDIAs in large-scale sensor networks becomes more challenging. In this paper, we propose a framework for the distributed detection of FDIAs in large-scale sensor networks. By extracting the spatiotemporal correlation information from sensor data, the large-scale sensors are categorized into multiple correlation groups. Within each correlation group, an autoregressive integrated moving average (ARIMA) is built to learn the temporal correlation of cross-correlation, and a consistency criterion is established to identify abnormal sensor nodes. The effectiveness of the proposed detection framework is validated based on a real dataset from the U.S. smart grid and simulated under both the simple FDIA and the stealthy FDIA strategies.