Performance assessment of stacked air-cathode microbial fuel cells under series and parallel electrical connections

Microbial fuel cells (MFCs) are playing an important role in the context of sustainable energy development. They represent a sustainable approach to harvest electricity from biodegradable materials. However, harvesting energy from MFCs represents a critical issue because of the low output voltage and power produced. Realizing stacked configurations may involve an increase in MFCs performances in terms of output voltage, current and electric power. In this paper, two stacked configurations under different electrical connection modes have been designed, developed, modeled and tested. The stacked MFCs consist of 4 reactors (28 mL x4) that are connected in series, and parallel-series modes. Three different tests have been carried out, which involves: 1) performing the polarization and power curves by applying decreasing resistances; 2) assessment of the electric behavior of each reactor over time at a fixed resistance, 3) performing the polarization and power curves by applying increasing resistances. Moreover, a numerical model for predicting the transient behavior of the electrical quantities for one reactor, has been developed and validated by using the experimental data. As expected, the results highlighted that the parallel-series configuration assures the highest volumetric power density compared to the series configuration, reaching the maximum value of 1248.5 mW/m3 (139.8 µW) at 0.291 mA. Eventually, by comparing the numerical and the experimental data, it has been demonstrated that the developed model is able to predict the reactor’s electrical trend with a good accuracy.

Wind Farm Power Curves and Power Distributions

There seems to be an agreement in the scientific community [...]

Multivariate Wind Turbine Power Curve Model Based on Data Clustering and Polynomial LASSO Regression

Wind turbine performance monitoring is a complex task because of the non-stationary operation conditions and because the power has a multivariate dependence on the ambient conditions and working parameters. This motivates the research about the use of SCADA data for constructing reliable models applicable in wind turbine performance monitoring. The present work is devoted to multivariate wind turbine power curves, which can be conceived of as multiple input, single output models. The output is the power of the target wind turbine, and the input variables are the wind speed and additional covariates, which in this work are the blade pitch and rotor speed. The objective of this study is to contribute to the formulation of multivariate wind turbine power curve models, which conjugate precision and simplicity and are therefore appropriate for industrial applications. The non-linearity of the relation between the input variables and the output was taken into account through the simplification of a polynomial LASSO regression: the advantages of this are that the input variables selection is performed automatically. The k-means algorithm was employed for automatic multi-dimensional data clustering, and a separate sub-model was formulated for each cluster, whose total number was selected by analyzing the silhouette score. The proposed method was tested on the SCADA data of an industrial Vestas V52 wind turbine. It resulted that the most appropriate number of clusters was three, which fairly resembles the main features of the wind turbine control. As expected, the importance of the different input variables varied with the cluster. The achieved model validation error metrics are the following: the mean absolute percentage error was in the order of 7.2%, and the average difference of mean percentage errors on random subsets of the target data set was of the order of 0.001%. This indicates that the proposed model, despite its simplicity, can be reliably employed for wind turbine power monitoring and for evaluating accumulated performance changes due to aging and/or optimization.

Smart Charging of Electric Vehicles Considering SOC-Dependent Maximum Charging Powers

The aim of this work is to schedule the charging of electric vehicles (EVs) at a single charging station such that the temporal availability of each EV as well as the maximum available power at the station are considered. The total costs for charging the vehicles should be minimized w.r.t. time-dependent electricity costs. A particular challenge investigated in this work is that the maximum power at which a vehicle can be charged is dependent on the current state of charge (SOC) of the vehicle. Such a consideration is particularly relevant in the case of fast charging. Considering this aspect for a discretized time horizon is not trivial, as the maximum charging power of an EV may also change in between time steps. To deal with this issue, we instead consider the energy by which an EV can be charged within a time step. For this purpose, we show how to derive the maximum charging energy in an exact as well as an approximate way. Moreover, we propose two methods for solving the scheduling problem. The first is a cutting plane method utilizing a convex hull of the, in general, nonconcave SOC–power curves. The second method is based on a piecewise linearization of the SOC–energy curve and is effectively solved by branch-and-cut. The proposed approaches are evaluated on benchmark instances, which are partly based on real-world data. To deal with EVs arriving at different times as well as charging costs changing over time, a model-based predictive control strategy is usually applied in such cases. Hence, we also experimentally evaluate the performance of our approaches for such a strategy. The results show that optimally solving problems with general piecewise linear maximum power functions requires high computation times. However, problems with concave, piecewise linear maximum charging power functions can efficiently be dealt with by means of linear programming. Approximating an EV’s maximum charging power with a concave function may result in practically infeasible solutions, due to vehicles potentially not reaching their specified target SOC. However, our results show that this error is negligible in practice.

Wind Energy Potential on A Highrise Building: A Preliminary Study

Indonesia has an abundant renewable energy source. One of them is wind energy resources. Unfortunately, Indonesia's wind energy resource is not fully utilized, especially for application in high-rise buildings. The paper investigates the potential of energy production from the horizontal-axis wind turbine (HAWT) and the vertical-axis wind turbine (VAWT) on the rooftop of a university building in Indonesia. The wind speed data were measured on the rooftop of the building for seven months. The data was analyzed using Weibull distribution. Based on the probability density function of the Weibull distribution, the potential energy production was calculated using the power curves from the manufacturer. Comparing energy production between HAWTs and VAWTs has shown that VAWTs can produce more energy than HAWTs. Using six turbines, VAWTs can produce 48,476 kWh. On the other hand, with four turbines, HAWTs can produce 41,729 kWh. The reason is that VAWT requires shorter distance requirements for inter-turbine and between rows. Therefore, VAWT can use more turbines than HAWT in the limited area. In conclusion, VAWT for high-rise buildings is more preferred because VAWT can generate more energy. Further study should investigate the optimal configuration with varying the wind direction and quantifying the wake effect on power output.

Laser Polishing of Additive Manufactured Aluminium Parts by Modulated Laser Power

In this study a new approach to laser polishing with periodic modulated laser power in the kilohertz regime is introduced. By varying the modulation frequency and modulation time, different periodic laser power curves with varying minimum, peak and average laser power can be created. The feasibility of the method is shown by polishing of vertical built AlSi10Mg L-PBF parts with an initial roughness of Ra = 12.22 µm. One polishing pass revealed a decreasing surface roughness with increasing energy density on the surface up to Ra = 0.145 µm. An increasing energy density results in a rising remelting depth between 50 and 255 µm and a rising relative porosity of 0.3% to 4.6%. Furthermore, the thermal process stability, analysed by the melt pool length in scanning direction, reveals a steadily increasing melt pool dimension due to component heating. Multiple laser polishing passes offers a further reduced surface roughness, especially at higher modulation frequencies and provides an improved orientation independent roughness homogeneity. The process stability regarding varying initial surface roughness revealed an almost constant relative roughness reduction rate with an achievable roughness variation after two polishing passes between Ra = 0.13–0.26 µm from an initial state of Ra = 8.0−19.2 µm.

REVIEW OF STUDIES ON THE CFD-BEM APPROACH FOR ESTIMATING POWER LOSSES OF ICED-UP WIND TURBINES

The application of computational fluid dynamics (CFD) in wind turbine design and analysis is becoming increasingly common in research on wind energy, resulting in a better knowledge of the aerodynamic behaviour of rotors. Due to the deformation of blade airfoils on account of icing, a significant drop in aerodynamic performance brings wind turbines to lose considerable portions of their productivity. Estimating power degradation due to icing via 3D simulation, although it is essential to capture the three-dimensional turbulence effects, is very costly in computational resources despite technological development it then becomes unfeasible when it comes to different operation scenarios to estimate icing originated power losses. The Quasi-3D simulation based on the CFD-BEM method is a practical alternative for generating wind turbines power curves. It showed effectiveness in predicting performance up to a certain level. More than few studies in the literature have adopted this approach to generate the power curve for both clean (un-iced) and iced-up wind turbines. However, the methodology was not adequately presented and discussed for wind turbine icing. This paper reviews the results of almost all the up-to-date published papers that approached this method, summarizing the findings and federates the research in that field to conclude with concrete facts and details that advance research in this domain.