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The energy transition in Germany takes part in decentral structures. With the ongoing integration of Renewable Energy Sources (RES) into the electricity supply system, supply-side is therefore becoming increasingly decentral and volatile due to the specific generation characteristics. A rather inflexible demand-side, on the other hand, increases the effort to gain the necessary equilibrium between generation and consumption. This paper discusses how consumer behaviour can be influenced by real-time pricing to align demand with generation. Therefore, a combination of two different approaches is used, (I) The Cellular Approach (CA) and (II) Agent Based Modelling (ABM). A model is set up considering a regional energy market, where regional electricity products can be traded peer-to-peer regarding each consumer’s preferences. The observation is made for a whole distribution grid including all types of consumers. The investigations show that energy purchases can be stimulated individually by a flexible pricing mechanism and met preferences. Moreover, benefits occur for the whole region and potentials arise to smooth the exchange balance to the superordinate grid level. Running the model for one entire year in a conservative generation scenario, hours of oversupply could be reduced by 18% and the consumption of green electricity generated regionally could be increased by over 125 MWh within the region itself, in comparison to a base scenario.

Biodiesel as an alternative to diesel fuel produced from vegetable oils or animal fats has attracted more and more attention because it is renewable and environmentally friendly. Compared to conventional diesel fuel, biodiesel has slightly lower performance in engine combustion due to the lower calorific value that leads to lower power generated. This study investigates the effect of multi-walled carbon nanotubes (MWCNTs) as an additive to the rice bran methyl ester (RBME). Artificial neural network (ANN) and response surface methodology (RSM) was used for predicting the calorific value. The interaction effects of parameters such as dosage of MWCNTs, size of MWCNTs and reaction time on the calorific value of RBME were studied. Comparison of RSM and ANN performance was evaluated based on the correlation coefficient (R2), the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the average absolute deviation (AAD) showed that the ANN model had better performance (R2 = 0.9808, RMSE = 0.0164, MAPE = 0.0017, AAD = 0.173) compare to RSM (R2 = 0.9746, RMSE = 0.0170, MAPE = 0.0028, AAD = 0.279). The optimum predicted of RBME calorific value that is generated using the cuckoo search (CS) via lévy flight optimization algorithm is 41.78 (MJ/kg). The optimum value was obtained using 64 ppm of < 7 nm MWCNTs blending for 60 min. The predicted calorific value was validated experimentally as 41.05 MJ/kg. Furthermore, the experimental results have shown that the addition of MWCNTs was significantly increased the calorific value from 36.87 MJ/kg to 41.05 MJ/kg (11.6%). Also, the addition of MWCNTs decreased flashpoint (−18.3%) and acid value (−0.52%). As a conclusion, adding MWCNTs as an additive had improved the physicochemical properties characteristics of RBME. To our best knowledge, no research has yet been performed on the effect of multi-walled carbon nanotubes-additive in physicochemical property of rice brand methyl ester application so far.

Optimization-based design tools for energy systems often require a large set of parameter assumptions, e.g., about technology efficiencies and costs or the temporal availability of variable renewable energies. Understanding the influence of all these parameters on the computed energy system design via direct sensitivity analysis is not easy for human decision-makers, since they may become overloaded by the multitude of possible results. We thus propose transferring an approach from explaining complex neural networks, so-called locally interpretable model-agnostic explanations (LIME), to this related problem. Specifically, we use variations of a small number of interpretable, high-level parameter features and sparse linear regression to obtain the most important local explanations for a selected design quantity. For a small bottom-up optimization model of a grid-connected building with photovoltaics, we derive intuitive explanations for the optimal battery capacity in terms of different cloud characteristics. For a larger application, namely a national model of the German energy transition until 2050, we relate path dependencies of the electrification of the heating and transport sector to the correlation measures between renewables and thermal loads. Compared to direct sensitivity analysis, the derived explanations are more compact and robust and thus more interpretable for human decision-makers.

Polymer electrolyte membrane fuel cells (PEMFCs) for household applications utilize H2 produced from natural gas via steam reforming followed by a water gas shift (WGS) unit. The H2-rich gas contains CO2 and small amounts of CO, which is a poison for PEMFCs. Today, CO is mostly converted by addition of O2 and preferential oxidation, but H2 is then also partly oxidized. An alternative is selective CO methanation, studied in this work. CO2 methanation is then a highly unwanted reaction, consuming additional H2. The kinetics of CO methanation in CO2/H2 rich gases were studied with a home-made Ru catalyst in a fixed bed reactor at 1 bar and 160–240 °C. Both CO and CO2 methanation can be well described by a Langmuir Hinshelwood approach. The rate of CO2 methanation is slow compared to CO. CO2 is directly converted to methane, i.e., the indirect route via reverse water gas shift (WGS) and subsequent CO methanation could be excluded by the experimental data and in combination with kinetic considerations. Pore diffusion may affect the CO conversion (>200 °C). The kinetic equations were applied to model an adiabatic fixed bed methanation reactor of a fuel cell appliance.

The accurate determination of the electrical properties of photovoltaic devices is of utmost importance to predict and optimize their overall optoelectronic performance. For example, the minority carrier lifetime and the carrier diffusion length have a strong relationship with the carrier recombination rate. Additionally, parasitic resistances have an important effect on the fill factor of a solar cell. Within this context, the alternating current (AC) and direct current (DC) electrical characteristics of Si-based metal–insulator–semiconductor (MIS) Schottky barrier diodes with the basic structure Al/Si/TiO2/NiCr were studied, aiming at using them as photovoltaic devices. The basic diode structure was modified by adding nanostructured porous silicon (nanoPS) layers and by infiltrating silver nanoparticles (AgNPs) into the nanoPS layers, leading to Al/Si+nanoPS/TiO2/NiCr and Al/Si+nanoPS+AgNPs/TiO2/NiCr structures, respectively. The AC electrical properties were studied using a combination of electrochemical impedance spectroscopy and Mott–Schottky analysis, while the DC electrical properties were determined from current–voltage measurements. From the experimental results, an AC equivalent circuit model was proposed for the three different MIS Schottky barrier diodes under study. Additionally, the most significant electrical parameters were calculated. The results show a remarkable improvement in the performance of the MIS Schottky barrier diodes upon the addition of hybrid nanoPS layers with embedded Ag nanoparticles, opening the way to their use as photovoltaic devices.

The aim of this study is to perform a review of the state-of-the-art of the reactors available in the literature, which are used for solid–gas reactions or thermal decomposition processes around 1000 °C that could be further implemented for thermochemical energy storage in CSP (concentrated solar power) plants, specifically for SPT (solar power tower) technology. Both direct and indirect systems can be implemented, with direct and closed systems being the most studied ones. Among direct and closed systems, the most used configuration is the stacked bed reactor, with the fixed bed reactor being the most frequent option. Out of all of the reactors studied, almost 70% are used for solid–gas chemical reactions. Few data are available regarding solar efficiency in most of the processes, and the available information indicates relatively low values. Chemical reaction efficiencies show better values, especially in the case of a fluidized bed reactor for solid–gas chemical reactions, and fixed bed and rotary reactors for thermal decompositions.