Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

Electric vehicle fleets and smart grids are two growing technologies. These technologies provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, the comparison of several evolutionary algorithms, genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution are shown in order to evaluate the proposed architecture. The proposed solution is presented to prevent the overload of the power grid

Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

Electric vehicle fleets and smart grids are two growing technologies. These technologies provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, the comparison of several evolutionary algorithms, genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution are shown in order to evaluate the proposed architecture. The proposed solution is presented to prevent the overload of the power grid

Monitoring and Fault Location Sensor Network for Underground Distribution Lines

One of the fundamental tasks of electric distribution utilities is guaranteeing a continuous supply of electricity to their customers. The primary distribution network is a critical part of these facilities because a fault in it could affect thousands of customers. However, the complexity of this network has been increased with the irruption of distributed generation, typical in a Smart Grid and which has significantly complicated some of the analyses, making it impossible to apply traditional techniques. This problem is intensified in underground lines where access is limited. As a possible solution, this paper proposes to make a deployment of a distributed sensor network along the power lines. This network proposes taking advantage of its distributed character to support new approaches of these analyses. In this sense, this paper describes the aquiculture of the proposed network (adapted to the power grid) based on nodes that use power line communication and energy harvesting techniques. In this sense, it also describes the implementation of a real prototype that has been used in some experiments to validate this technological adaptation. Additionally, beyond a simple use for monitoring, this paper also proposes the use of this approach to solve two typical distribution system operator problems, such as: fault location and failure forecasting in power cables.Ministerio de Economía y Competitividad, Government of Spain project Sistema Inteligente Inalámbrico para Análisis y Monitorización de Líneas de Tensión Subterráneas en Smart Grids (SIIAM) TEC2013-40767-RMinisterio de Educación, Cultura y Deporte, Government of Spain, for the funding of the scholarship Formación de Profesorado Universitario 2016 (FPU 2016

Increasing the Efficiency of Rule-Based Expert Systems Applied on Heterogeneous Data Sources

Nowadays, the proliferation of heterogeneous data sources provided by different research and innovation projects and initiatives is proliferating more and more and presents huge opportunities. These developments create an increase in the number of different data sources, which could be involved in the process of decisionmaking for a specific purpose, but this huge heterogeneity makes this task difficult. Traditionally, the expert systems try to integrate all information into a main database, but, sometimes, this information is not easily available, or its integration with other databases is very problematic. In this case, it is essential to establish procedures that make a metadata distributed integration for them. This process provides a “mapping” of available information, but it is only at logic level. Thus, on a physical level, the data is still distributed into several resources. In this sense, this chapter proposes a distributed rule engine extension (DREE) based on edge computing that makes an integration of metadata provided by different heterogeneous data sources, applying then a mathematical decomposition over the antecedent of rules. The use of the proposed rule engine increases the efficiency and the capability of rule-based expert systems, providing the possibility of applying these rules over distributed and heterogeneous data sources, increasing the size of data sets that could be involved in the decision-making process

Random Generation of Arbitrary Waveforms for Emulating Three-Phase Systems

This paper describes an apparatus for generating a signal representative of steady-state and transient disturbances in three-phase waveforms of an ac electrical system as described in IEEE Std 1159-09. It can be configured as a synthesizer of randomly distorted signals for different applications: for testing the effects of disturbed grid on equipment and to generate patterns of electrical disturbances for the training of artificial neural networks, which are used for measuring power quality tasks. For the first purpose, voltage and current amplifiers are added in the output stage, which allows the generation of disturbed signals at grid level.Comisión Interministerial de Ciencia y Tecnología DPI2006-15467-C02-01Comisión Interministerial de Ciencia y Tecnología DPI2006-15467-C02-0

Heterogeneous data source integration for smart grid ecosystems based on metadata mining

The arrival of new technologies related to smart grids and the resulting ecosystem of applications andmanagement systems pose many new problems. The databases of the traditional grid and the variousinitiatives related to new technologies have given rise to many different management systems with several formats and different architectures. A heterogeneous data source integration system is necessary toupdate these systems for the new smart grid reality. Additionally, it is necessary to take advantage of theinformation smart grids provide. In this paper, the authors propose a heterogeneous data source integration based on IEC standards and metadata mining. Additionally, an automatic data mining framework isapplied to model the integrated information.Ministerio de Economía y Competitividad TEC2013-40767-

In vitro modeling of dysfunctional glial cells in neurodegenerative diseases using human pluripotent stem cells

Most neurodegenerative diseases are characterized by a complex and mostly still unresolved pathology. This fact, together with the lack of reliable models, have precluded the development of effective therapies counteracting the disease progression. In the past few years, several studies have evidenced that lack of proper functionality of glial cells (astrocytes, microglia and oligodendrocytes) has a key role in the pathology of several neurodegenerative conditions including Alzheimer´s disease, amyotrophic lateral sclerosis and multiple sclerosis among others. However, this glial dysfunction is poorly modelled by available animal models, and we hypothesize that patientderived cells can serve as a better platform where to study this glial dysfunction. In this sense, human pluripotent stem cells (hPSCs) has revolutionized the field allowing the generation of disease-relevant neural cell types that can be used for disease modelling, drug screening and, possibly, cell transplantation purposes. In the case of the generation of oligodendrocytes (OLs) from hPSCs, we have developed a fast and robust protocol to generate surface antigen O4-positive (O4+) and myelin basic protein-positive OLs from hPSCs in only 22 days, including from patients with multiple sclerosis or amyotrophic lateral sclerosis. The generated cells resemble primary human OLs at the transcriptome level and can myelinate neurons in vivo. Using in vitro OLneuron co-cultures, effective myelination of neurons can also be demonstrated. This platform is being translated as well to the generation of the other glial cell types, allowing the derivation of patient-specific glial cells where to model disease-specific dysfunction. This methodology can be used for elucidating pathogenic pathways associated with neurodegeneration and to identify therapeutic targets susceptible of drug modulation, contributing to the development of novel and effective drugs for these devastating disorders.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Supported by PI18/01557 (to AG) and P18/1556 (to JV) grants from ISCiii of Spain co-financed by FEDER funds from European Union, and PI-0276-2018 grant (to JAGL) from Consejeria de Salud of Junta de Andalucia. JAGL held a postdoctoral contract from the I Research Plan Propio of the University of Malaga. CV and KE were supported by IWT-SBO-150031-iPSCAF and the Thierry Lathran Foundation grant – ALS-OL, and KN by FWO1166518

Electricity clustering framework for automatic classification of customer loads

Clustering in energy markets is a top topic with high significance on expert and intelligent systems. The main impact of is paper is the proposal of a new clustering framework for the automatic classification of electricity customers’ loads. An automatic selection of the clustering classification algorithm is also highlighted. Finally, new customers can be assigned to a predefined set of clusters in the classificationphase. The computation time of the proposed framework is less than that of previous classification tech- niques, which enables the processing of a complete electric company sample in a matter of minutes on a personal computer. The high accuracy of the predicted classification results verifies the performance of the clustering technique. This classification phase is of significant assistance in interpreting the results, and the simplicity of the clustering phase is sufficient to demonstrate the quality of the complete mining framework.Ministerio de Economía y Competitividad TEC2013-40767-RMinisterio de Economía y Competitividad IDI- 2015004

Increasing the Efficiency of Rule-Based Expert Systems Applied on Heterogeneous Data Sources

Nowadays, the proliferation of heterogeneous data sources provided by different research and innovation projects and initiatives is proliferating more and more and presents huge opportunities. These developments create an increase in the number of different data sources, which could be involved in the process of decision-making for a specific purpose, but this huge heterogeneity makes this task difficult. Traditionally, the expert systems try to integrate all information into a main database, but, sometimes, this information is not easily available, or its integration with other databases is very problematic. In this case, it is essential to establish procedures that make a metadata distributed integration for them. This process provides a “mapping” of available information, but it is only at logic level. Thus, on a physical level, the data is still distributed into several resources. In this sense, this chapter proposes a distributed rule engine extension (DREE) based on edge computing that makes an integration of metadata provided by different heterogeneous data sources, applying then a mathematical decomposition over the antecedent of rules. The use of the proposed rule engine increases the efficiency and the capability of rule-based expert systems, providing the possibility of applying these rules over distributed and heterogeneous data sources, increasing the size of data sets that could be involved in the decision-making process

Expression and Functional Study of Extracellular BMP Antagonists during the Morphogenesis of the Digits and Their Associated Connective Tissues

The purpose of this study is to gain insight into the role of BMP signaling in the diversification of the embryonic limb mesodermal progenitors destined to form cartilage, joints, and tendons. Given the importance of extracellular BMP modulators in in vivo systems, we performed a systematic search of those expressed in the developing autopod during the formation of the digits. Here, we monitored the expression of extracellular BMP modulators including: Noggin, Chordin, Chordin-like 1, Chordin-like 2, Twisted gastrulation, Dan, BMPER, Sost, Sostdc1, Follistatin, Follistatin-like 1, Follistatin-like 5 and Tolloid. These factors show differential expression domains in cartilage, joints and tendons. Furthermore, they are induced in specific temporal patterns during the formation of an ectopic extra digit, preceding the appearance of changes that are identifiable by conventional histology. The analysis of gene regulation, cell proliferation and cell death that are induced by these factors in high density cultures of digit progenitors provides evidence of functional specialization in the control of mesodermal differentiation but not in cell proliferation or apoptosis. We further show that the expression of these factors is differentially controlled by the distinct signaling pathways acting in the developing limb at the stages covered by this study. In addition, our results provide evidence suggesting that TWISTED GASTRULATION cooperates with CHORDINS, BMPER, and NOGGIN in the establishment of tendons or cartilage in a fashion that is dependent on the presence or absence of TOLLOID