Distributed energy resources management with cyber-physical SCADA in the context of future smart grids

In the energy management of a small power system, the scheduling of the generation units is a crucial problem for which adequate methodologies can maximize the performance of the energy supply. This paper proposes an innovative methodology for distributed energy resources management. The optimal operation of distributed generation, demand response and storage resources is formulated as a mixed-integer linear programming model (MILP) and solved by a deterministic optimization technique CPLEX-based implemented in General Algebraic Modeling Systems (GAMS). The paper deals with a vision for the grids of the future, focusing on conceptual and operational aspects of electrical grids characterized by an intensive penetration of DG, in the scope of competitive environments and using artificial intelligence methodologies to attain the envisaged goals. These concepts are implemented in a computational framework which includes both grid and market simulation.

In vitro transference and molecular characterization of bla TEM genes in bacteria isolated from Portuguese ready-to-eat foods

The principal aim of this study was to investigate the possibility of transference to Escherichia coli of β-lactam resistance genes found in bacteria isolated from ready-to-eat (RTE) Portuguese traditional food. From previous screenings, 128 β-lactam resistant isolates (from different types of cheese and of delicatessen meats), largely from the Enterobacteriaceae family were selected and 31.3% of them proved to transfer resistance determinants in transconjugation assays. Multiplex PCR in donor and transconjugant isolates did not detect bla CTX, bla SHV and bla OXY, but bla TEM was present in 85% of them, while two new TEMs (TEM-179 and TEM-180) were identified in two isolates. The sequencing of these amplicons showed identity between donor and transconjugant genes indicating in vitro plasmid DNA transfer. These results suggest that if there is an exchange of genes in natural conditions, the consumption of RTE foods, particularly with high levels of Enterobacteriaceae, can contribute to the spread of antibiotic resistance.

Biocomplementation of SVE to achieve clean-up goals in soils contaminated with toluene and xylene

Soil vapor extraction (SVE) and bioremediation (BR) are two of the most common soil remediation technologies. Their application is widespread; however, both present limitations, namely related to the efficiencies of SVE on organic soils and to the remediation times of some BR processes. This work aimed to study the combination of these two technologies in order to verify the achievement of the legal clean-up goals in soil remediation projects involving seven different simulated soils separately contaminated with toluene and xylene. The remediations consisted of the application of SVE followed by biostimulation. The results show that the combination of these two technologies is effective and manages to achieve the clean-up goals imposed by the Spanish Legislation. Under the experimental conditions used in this work, SVE is sufficient for the remediation of soils, contaminated separately with toluene and xylene, with organic matter contents (OMC) below 4 %. In soils with higher OMC, the use of BR, as a complementary technology, and when the concentration of contaminant in the gas phase of the soil reaches values near 1 mg/L, allows the achievement of the clean-up goals. The OMC was a key parameter because it hindered SVE due to adsorption phenomena but enhanced the BR process because it acted as a microorganism and nutrient source.

CuxSnSx+1 (x = 2, 3) thin films grown by sulfurization of metallic precursors deposited by dc magnetron sputtering

We report the results of the growth of Cu-Sn-S ternary chalcogenide compounds by sulfurization of dc magnetron sputtered metallic precursors. Tetragonal Cu2SnS3 forms for a maximum sulfurization temperature of 350 ºC. Cubic Cu2SnS3 is obtained at sulfurization temperatures above 400 ºC. These results are supported by XRD analysis and Raman spectroscopy measurements. The latter analysis shows peaks at 336 cm-1, 351 cm-1 for tetragonal Cu2SnS3, and 303 cm-1, 355 cm-1 for cubic Cu2SnS3. Optical analysis shows that this phase change lowers the band gap from 1.35 eV to 0.98 eV. At higher sulfurization temperatures increased loss of Sn is expected in the sulphide form. As a consequence, higher Cu content ternary compounds like Cu3SnS4 grow. In these conditions, XRD and Raman analysis only detected orthorhombic (Pmn21) phase (petrukite). This compound has Raman peaks at 318 cm-1, 348 cm-1 and 295 cm-1. For a sulfurization temperature of 450 ºC the samples present a multi-phase structure mainly composed by cubic Cu2SnS3 and orthorhombic (Pmn21) Cu3SnS4. For higher temperatures, the samples are single phase and constituted by orthorhombic (Pmn21) Cu3SnS4. Transmittance and reflectance measurements were used to estimate a band gap of 1.60 eV. For comparison we also include the results for Cu2ZnSnS4 obtained using similar growth conditions.

Influence of selenization pressure on the growth of Cu2ZnSnSe4 films from stacked metallic layers

Cu2ZnSnSe4 (CZTSe) is a p-type semiconductor with a high absorption coefficient, 104 to 105 cm-1, and is being seen as a possible replacement for Cu(In,Ga)Se2 in thin film solar cells. Yet, there are some fundamental properties of CZTSe that are not well known, one of them is its band gap. In order to resolve its correct value it is necessary to improve the growth conditions to ensure that single phase crystalline thin films are obtained. One of the problems encountered when growing CZTSe is the loss of Sn through evaporation of SnSe. Stoichiometric films are then difficult to obtain and usually there are other phases present. One possible way to overcome this problem is to increase the pressure of growth of CZTSe. This can be done by introducing an atmosphere of an inert gas like Ar or N2. In this work we report the results of morphological, structural and optical studies of the properties of CZTSe thin films grown by selenization of DC magnetron sputtered metallic layers under different Ar pressures. The films are analysed by SEM/EDS, Raman scattering and XRD.

Laxity dynamics and LLF schedulability analysis on multiprocessor platforms

LLF (Least Laxity First) scheduling, which assigns a higher priority to a task with a smaller laxity, has been known as an optimal preemptive scheduling algorithm on a single processor platform. However, little work has been made to illuminate its characteristics upon multiprocessor platforms. In this paper, we identify the dynamics of laxity from the system’s viewpoint and translate the dynamics into LLF multiprocessor schedulability analysis. More specifically, we first characterize laxity properties under LLF scheduling, focusing on laxity dynamics associated with a deadline miss. These laxity dynamics describe a lower bound, which leads to the deadline miss, on the number of tasks of certain laxity values at certain time instants. This lower bound is significant because it represents invariants for highly dynamic system parameters (laxity values). Since the laxity of a task is dependent of the amount of interference of higher-priority tasks, we can then derive a set of conditions to check whether a given task system can go into the laxity dynamics towards a deadline miss. This way, to the author’s best knowledge, we propose the first LLF multiprocessor schedulability test based on its own laxity properties. We also develop an improved schedulability test that exploits slack values. We mathematically prove that the proposed LLF tests dominate the state-of-the-art EDZL tests. We also present simulation results to evaluate schedulability performance of both the original and improved LLF tests in a quantitative manner.

Relativistic time effects in financial dynamics

Financial time series have a complex dynamic nature. Many techniques were adopted having in mind standard paradigms of time flow. This paper explores an alternative route involving relativistic effects. It is observed that the measuring perspective influences the results and that we can have different time textures.

Dynamic analysis of earthquake phenomena by means of pseudo phase plane

This paper analyses earthquake data in the perspective of dynamical systems and its Pseudo Phase Plane representation. The seismic data is collected from the Bulletin of the International Seismological Centre. The geological events are characterised by their magnitude and geographical location and described by means of time series of sequences of Dirac impulses. Fifty groups of data series are considered, according to the Flinn-Engdahl seismic regions of Earth. For each region, Pearson’s correlation coefficient is used to find the optimal time delay for reconstructing the Pseudo Phase Plane. The Pseudo Phase Plane plots are then analysed and characterised.

Complex dynamics of financial indices

This paper presents a novel method for the analysis of nonlinear financial and economic systems. The modeling approach integrates the classical concepts of state space representation and time series regression. The analytical and numerical scheme leads to a parameter space representation that constitutes a valid alternative to represent the dynamical behavior. The results reveal that business cycles can be clearly revealed, while the noise effects common in financial indices can elegantly be filtered out of the results.

Optimal controllers with complex order derivatives

This paper studies the optimization of complex-order algorithms for the discrete-time control of linear and nonlinear systems. The fundamentals of fractional systems and genetic algorithms are introduced. Based on these concepts, complexorder control schemes and their implementation are evaluated in the perspective of evolutionary optimization. The results demonstrate not only that complex-order derivatives constitute a valuable alternative for deriving control algorithms, but also the feasibility of the adopted optimization strategy.

Fractional order modelling of fractional-order holds

Discrete time control systems require sample- and-hold circuits to perform the conversion from digital to analog. Fractional-Order Holds (FROHs) are an interpolation between the classical zero and first order holds and can be tuned to produce better system performance. However, the model of the FROH is somewhat hermetic and the design of the system becomes unnecessarily complicated. This paper addresses the modelling of the FROHs using the concepts of Fractional Calculus (FC). For this purpose, two simple fractional-order approximations are proposed whose parameters are estimated by a genetic algorithm. The results are simple to interpret, demonstrating that FC is a useful tool for the analysis of these devices.

Dynamical behaviour of multi-particle large-scale systems

Collective behaviours can be observed in both natural and man-made systems composed of a large number of elemental subsystems. Typically, each elemental subsystem has its own dynamics but, whenever interaction between individuals occurs, the individual behaviours tend to be relaxed, and collective behaviours emerge. In this paper, the collective behaviour of a large-scale system composed of several coupled elemental particles is analysed. The dynamics of the particles are governed by the same type of equations but having different parameter values and initial conditions. Coupling between particles is based on statistical feedback, which means that each particle is affected by the average behaviour of its neighbours. It is shown that the global system may unveil several types of collective behaviours, corresponding to partial synchronisation, characterised by the existence of several clusters of synchronised subsystems, and global synchronisation between particles, where all the elemental particles synchronise completely.

Optimal virtual cluster-based multiprocessor scheduling

Scheduling of constrained deadline sporadic task systems on multiprocessor platforms is an area which has received much attention in the recent past. It is widely believed that finding an optimal scheduler is hard, and therefore most studies have focused on developing algorithms with good processor utilization bounds. These algorithms can be broadly classified into two categories: partitioned scheduling in which tasks are statically assigned to individual processors, and global scheduling in which each task is allowed to execute on any processor in the platform. In this paper we consider a third, more general, approach called cluster-based scheduling. In this approach each task is statically assigned to a processor cluster, tasks in each cluster are globally scheduled among themselves, and clusters in turn are scheduled on the multiprocessor platform. We develop techniques to support such cluster-based scheduling algorithms, and also consider properties that minimize total processor utilization of individual clusters. In the last part of this paper, we develop new virtual cluster-based scheduling algorithms. For implicit deadline sporadic task systems, we develop an optimal scheduling algorithm that is neither Pfair nor ERfair. We also show that the processor utilization bound of us-edf{m/(2m−1)} can be improved by using virtual clustering. Since neither partitioned nor global strategies dominate over the other, cluster-based scheduling is a natural direction for research towards achieving improved processor utilization bounds.

Provably good multiprocessor scheduling with resource sharing

We present a 12(1 + 3R/(4m)) competitive algorithm for scheduling implicit-deadline sporadic tasks on a platform comprising m processors, where a task may request one of R shared resources.

Uncertainty propagation in inverse reliability-based design of composite structures

An approach for the analysis of uncertainty propagation in reliability-based design optimization of composite laminate structures is presented. Using the Uniform Design Method (UDM), a set of design points is generated over a domain centered on the mean reference values of the random variables. A methodology based on inverse optimal design of composite structures to achieve a specified reliability level is proposed, and the corresponding maximum load is outlined as a function of ply angle. Using the generated UDM design points as input/output patterns, an Artificial Neural Network (ANN) is developed based on an evolutionary learning process. Then, a Monte Carlo simulation using ANN development is performed to simulate the behavior of the critical Tsai number, structural reliability index, and their relative sensitivities as a function of the ply angle of laminates. The results are generated for uniformly distributed random variables on a domain centered on mean values. The statistical analysis of the results enables the study of the variability of the reliability index and its sensitivity relative to the ply angle. Numerical examples showing the utility of the approach for robust design of angle-ply laminates are presented.