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Wave Energy Converters (WECs) need to be deployed in large numbers in an array layout in order to have a significant power production. Each WEC has an impact on the incoming wave field, by diffracting, reflecting and radiating waves. Simulating the wave transformations within and around a WEC array is complex; it is difficult, or in some cases impossible, to simulate both these near-field and far-field wake effects using a single numerical model, in a time- and cost-efficient way in terms of computational time and effort. Within this research, a generic coupling methodology is developed to model both near-field and far-field wake effects caused by floating (e.g., WECs, platforms) or fixed offshore structures. The methodology is based on the coupling of a wave-structure interaction solver (Nemoh) and a wave propagation model. In this paper, this methodology is applied to two wave propagation models (OceanWave3D and MILDwave), which are compared to each other in a wide spectrum of tests. Additionally, the Nemoh-OceanWave3D model is validated by comparing it to experimental wave basin data. The methodology proves to be a reliable instrument to model wake effects of WEC arrays; results demonstrate a high degree of agreement between the numerical simulations with relative errors lower than 5 % and to a lesser extent for the experimental data, where errors range from 4 % to 17 % .

High reactivity coke is beneficial for achieving low carbon emission blast furnace ironmaking. Therefore, the preparation of highly reactive ferro-coke has aroused widespread attention. However, the effects of the particle size of iron ore on the pyrolysis behaviour of a coal-iron ore briquette are still unclear. In this study, the effect of three particle sizes (0.50–1.00 mm, 0.25–0.50 mm and <0.74 mm) of iron ore on the thermal and kinetic behaviours of coal-iron ore briquettes were investigated by non-isothermal kinetic analysis. The results showed that the synergistic effect of iron ore and coal during coking mainly occurred during the later reaction stage (850–1100 °C) and smaller particle sizes of iron ore have a stronger synergistic effect. The addition of iron ore had little effect on T0 (the initial temperature) and Tp (the temperature at the maximum conversion rate) of briquette pyrolysis, however itgreatly affected the conversion rate and Tf (the final temperature) of the briquettes. T0 decreased with the decrease of iron ore particle sizes, while Tp and Tf showed opposite trends. After adding iron ore into the coal briquette, the reaction kinetics at all stages of the coal-iron ore briquettes changed. The weighted apparent activation energy of the caking coal (JM) briquette was 35.532 kJ/mol, which is lower than that of the coal-iron ore briquettes (38.703–55.627 kJ/mol). In addition, the weighted apparent activation energy gradually increased with decreasing iron ore particle sizes.

With the development of distributed energy resources, the low voltage distribution network (LVDN) is supposed to be the integrator of small distributed energy sources. This makes the users in LVDNs multifarious, which leads to more complex modeling. Additionally, data acquisition could be tricky due to rising privacy concerns. These impose severe demands on control schemes in LVDNs that the classical centralized control might not be able to fulfill. To tackle this, a model-free control approach with distributed decision-making architecture is proposed in this paper. Employing statistical methods and game theory, individual users in LVDNs achieve local optimum autonomously. Comparing to conventional approaches applied in LVDNs, the proposed approach is able to achieve active control with less communication burden and computational resources. The paper proves the convergence to the Nash Equilibrium (NE) and uses player compatible relations to form the specific equilibrium. A variant of the log-linear trial and error learning process is applied in a novel “suggest-convince” mechanism to implement the proposed approach. In the case study, a 103 nodes test network based on a real Belgian semiurban LVDN is illustrated. The proposed approach is validated and analyzed with practical load profiles on the 103 nodes network. In addition to that, centralized control is implemented as a benchmark to show the performance of the proposed approach by comparing it with the classical optimization result. The results demonstrate that the proposed approach is able to achieve player compatible equilibrium in an expected way, resulting in a good approximation to the local optimum.

The renewable biomass material obtained from rice husk, a low-cost agricultural waste, was used as a precursor to synthesize a highly porous graphene-based carbon as electrode material for supercapacitors. Activated graphene-based carbon (AGC) was obtained by a two-step chemical procedure and exhibited a very high specific surface area (SSA) of 3292 m2 g−1. The surface morphology of the synthesized materials was studied using scanning and transmission electron microscopy (SEM, TEM). Furthermore, the AGC was modified with nickel hydroxide Ni(OH)2 through a simple chemical precipitation method. It was found that the most significant increase in capacitance could be reached with Ni(OH)2 loadings of around 9 wt.%. The measured specific capacitance of the pure AGC supercapacitor electrodes was 236 F g−1, whereas electrodes from the material modified with 9 wt.% Ni(OH)2 showed a specific capacitance of up to 300 F g−1 at a current density of 50 mA g−1. The increase in specific capacitance achieved due to chemical modification was, therefore 27%.

In this study, uncertainty and sensitivity analyses were performed with MELCOR 2.2.18 to study the hydrogen generation (figure-of-merit (FoM)) during the in-vessel phase of a severe accident in a light water reactor. The focus of this work was laid on a large generation-III pressurized water reactor (PWR) and a double-ended hot leg (HL) large break loss of coolant accident (LB-LOCA) without a safety injection (SI). The FPT-1 Phebus integral experiment emulating LOCA was studied, where the experiment outcomes were applied for the plant scale modelling. The best estimate calculations were supplemented with an uncertainty analysis (UA) based on 400 input-decks and Latin hypercube sampling (LHS). Additionally, the sensitivity analysis (SA) utilizing the linear regression and linear and rank correlation coefficients was performed. The study was prepared with a new open-source MELCOR sensitivity and uncertainty tool (MelSUA), which was supplemented with this work. The FPT-1 best-estimate model results were within the 10% experimental uncertainty band for the final FoM. It was shown that the hydrogen generation uncertainties in PWR were similar to the FPT-1, with the 95% percentile being covered inside a ~50% band and the 50% percentile inside a ~25% band around the FoM median. Two different power profiles for PWR were compared, indicating its impact on the uncertainty but also on the sensitivity results. Despite a similar setup, different uncertainty parameters impacted FoM, showing the difference between scales but also a significant impact of boundary conditions on the sensitivity analysis.

Power-to-X is an upcoming sector-coupling technology that can play a role in the decarbonisation of energy systems. The aim of this study was to widen the current knowledge of strengths, weaknesses, opportunities, and threats (SWOT) of this innovative technology in the Danish context by utilizing the analytic hierarchy process (AHP) to evaluate and compare perception of academic and industrial experts. The results of this analysis indicate that the external factors such as current policy framework are more important than the internal technology related factors. Further, positive factors predominate negative ones, with academic experts indicating strengths as the most important category and practitioners’ opportunities. All experts consider the country being a P2X knowledge hub as one of the most important factors, and in the given context of the Danish energy system, wind developments and Danish industrial environment, seizing this opportunity could be the biggest enabler for P2X success.

In the current paper, a novel technique is represented to control the liquid petrochemical and petroleum products passing through a transmitting pipe. A simulation setup, including an X-ray tube, a detector, and a pipe, was conducted by Monte Carlo N Particle-X version (MCNPX) code to examine a two-by-two mixture of four diverse petroleum products (ethylene glycol, crude oil, gasoline, and gasoil) in various volumetric ratios. As the feature extraction system, twelve time characteristics were extracted from the received signal, and the most effective ones were selected using correlation analysis to present reasonable inputs for neural network training. Three Multilayers perceptron (MLP) neural networks were applied to indicate the volume ratio of three kinds of petroleum products, and the volume ratio of the fourth product can be feasibly achieved through the results of the three aforementioned networks. In this study, increasing accuracy was placed on the agenda, and an RMSE < 1.21 indicates this high accuracy. Increasing the accuracy of predicting volume ratio, which is due to the use of appropriate characteristics as the neural network input, is the most important innovation in this study, which is why the proposed system can be used as an efficient method in the oil industry.

The increasing share of converter-based renewable energy sources in the power system has forced the system operators to demand voltage support from the converters in case of faults. In the case of symmetric faults, all the phases have equal voltage support, but in the case of asymmetric faults, selective voltage support is required. The grid codes define the voltage support required in the case of symmetric/asymmetric faults, which is the reactive current injection in the respective sequence proportional to its voltage dip, but studies confirm that it does not result in a minimum unbalance factor in the case of asymmetric faults. The unbalance factor is an indication of the level of imbalance voltage among the phases. Moreover, it also results in fluctuated active power injection in the case of asymmetric faults, which causes dc link voltage fluctuations, and the power reversal may also occur due to such fluctuations, which leads to higher protection costs for the dc link. In order to (1) enhance the uniformity of voltage among different phases in the case of asymmetric faults and (2) minimize the real power fluctuations in such conditions, a novel control scheme is presented in this paper. It optimally distributes the negative sequence current phasor into its active and reactive components to achieve the minimum voltage unbalance factor. It also confirms the minimum real power fluctuations by adjusting the positive and negative sequence current phasors. The proposed scheme also ensures the current limit of the converter. The proposed scheme is developed in Matlab/Simulink and tested under different faulty conditions. The results confirm the better performance of the proposed scheme against the grid code recommendation under different faulty conditions.