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Currently, in many data landscapes, the information is distributed across various sources and presented in diverse formats. This fragmentation can pose a significant challenge to the efficient application of analytical methods. In this sense, distributed data mining is mainly based on clustering or classification techniques, which are easier to implement in distributed environments. However, the solution to some problems is based on the usage of mathematical equations or stochastic models, which are more difficult to implement in distributed environments. Usually, these types of problems need to centralize the required information, and then a modelling technique is applied. In some environments, this centralization may cause an overloading of the communication channels due to massive data transmission and may also cause privacy issues when sending sensitive data. To mitigate this problem, this paper describes a general-purpose distributed analytic platform based on edge computing for distributed networks. Through the distributed analytical engine (DAE), the calculation process of the expressions (that requires data from diverse sources) is decomposed and distributed between the existing nodes, and this allows sending partial results without exchanging the original information. In this way, the master node ultimately obtains the result of the expressions. The proposed solution is examined using three different computational intelligence algorithms, i.e., genetic algorithm, genetic algorithm with evolution control, and particle swarm optimization, to decompose the expression to be calculated and to distribute the calculation tasks between the existing nodes. This engine has been successfully applied in a case study focused on the calculation of key performance indicators of a smart grid, achieving a reduction in the number of communication messages by more than 91% compared to the traditional approach. [ABSTRACT FROM AUTHOR]

Short-term electric power forecasting is a tool of great interest for power systems, where the presence of renewable and distributed generation sources is constantly growing. Specifically, this type of forecasting is essential for energy management systems in buildings, industries and microgrids for optimizing the operation of their distributed energy resources under different criteria based on their expected daily energy balance (the consumption–generation relationship). Under this situation, this paper proposes a complete framework for the short-term multistep forecasting of electric power consumption and generation in smart grids and microgrids. One advantage of the proposed framework is its capability of evaluating numerous combinations of inputs, making it possible to identify the best technique and the best set of inputs in each case. Therefore, even in cases with insufficient input information, the framework can always provide good forecasting results. Particularly, in this paper, the developed framework is used to compare a whole set of rule-based and machine learning techniques (artificial neural networks and random forests) to perform day-ahead forecasting. Moreover, the paper presents and a new approach consisting of the use of baseline models as inputs for machine learning models, and compares it with others. Our results show that this approach can significantly improve upon the compared techniques, achieving an accuracy improvement of up to 62% over that of a persistence model, which is the best of the compared algorithms across all application cases. These results are obtained from the application of the proposed methodology to forecasting five different load and generation power variables for the Savona Campus at the University of Genova in Italy. [ABSTRACT FROM AUTHOR]

The mission of distribution system operators (DSOs) is to operate and manage distribution networks in a safe and secure manner. DSOs are also responsible for developing distribution grids to ensure the long-term ability of a system to deliver high-quality services to users and other stakeholders of the electric power system. Traditionally, DSOs have carried out their mission through adequate network operation and planning. However, the profound transformation of the energy system that is currently taking place worldwide creates new challenges for DSOs to carry out their responsibilities in a cost-efficient and secure manner. A significant number of renewable energy sources (RESs) are already connected, and more are expected in the future. Furthermore, the number of electric vehicles (EVs) and public charging stations will see a major increase in the coming years. These trends are coupled with an exponential technological evolution that enables decentralized energy sources to be connected at lower voltages and, at the same time, enables customers to interact with the market in response to grid conditions. [ABSTRACT FROM AUTHOR]

In the last few decades, the Smart Grid paradigm presence has increased within power systems. These new kinds of networks demand new Operations and Planning approaches, following improvements in the quality of service. In this sense, the role of the Distribution Management System, through its Outage Management System, is essential to guarantee the network reliability. This system is responsible for minimizing the consequences arising from a fault event (or network failure). Obviously, knowing where the fault appears is critical for a good reaction of this system. Therefore, several fault location techniques have been proposed. However, most of them provide individual results, associated with specific testbeds, which make the comparison between them difficult. Due to this, a review of fault location methods has been done in this paper, analyzing them for their use on underground distribution lines. Specifically, this study is focused on an impedance-based method because their requirements are in line with the typical instrumentation deployed in distribution networks. This work is completed with an exhaustive analysis of these methods over a PSCAD™ X4 implementation of the standard IEEE Node Test Feeder, which truly allows us to consistently compare the results of these location methods and to determine the advantages and drawbacks of each of them. [ABSTRACT FROM AUTHOR]

Nowadays, the presence of renewable generation systems and mobile loads (i.e., electric vehicle) spread throughout the distribution network is increasing. The problem is that this type of system introduces an added difficulty since they present a strong dependence on the meteorology and the mobility needs of the users. This problem forces the distribution system operators to seek tools that make it possible to balance the relationship between consumption and generation. In this sense, automated demand response systems are an appropriate solution that allow the operator to request specific reductions in customers' consumption, offering a discount to the customer and avoiding network congestion. This paper analyzes the implementation and architecture of a demand response solution based on OpenADR standard and its possible integration with a building management system through a use case. As will be analyzed, a key part of the architecture is the measurement system based on smart meters acting as sensors. This is the base of the auditing system which makes it possible to verify compliance with the consumption reduction agreements. Additionally, this study is completed with a parallel auditing system which makes it possible to verify compliance with the consumption reduction agreements. All of the proposed demand response cycle is implemented as a proof of concept in a classroom in the Escuela Politécnica Superior at the University of Seville, which makes it possible to identify the advantages of this architecture in the ambit of connection between distribution network and buildings. [ABSTRACT FROM AUTHOR]

Nowadays, Distribution System Operators are increasing the digitalization of their smart grids, making it possible to measure and manage their state at any time. However, with the massive eruption of change-distributed generation (e.g., renewable resources, electric vehicles), the grid operation have become more complex, requiring specific technologies to balance it. In this sense, the demand-side management is one of its techniques; the demand response is a promising approach for providing Flexibility Services (FSs) and complying with the regulatory directives of the energy market. As a solution, this paper proposes the use of the OpenADR (Open Automated Demand Response) standard protocol in combination with a Decentralized Permissioned Market Place (DPMP) based on Blockchain. On one hand, OpenADR hierarchical architecture based on distributed nodes provides communication between stakeholders, adding monitoring and management services. Further, this architecture is compatible with an aggregator schema that guarantees the compliance with the strictest regulatory framework (i.e., European market). On the other hand, DPMP is included at different levels of this architecture, providing a global solution to Flexibility Service Providers (FSP) that can be adapted depending on the regulation of a specific country. As a proof of concept, this paper shows the result of a real experimental case, which implements a Capacity Bidding Program where the OpenADR protocol is used as a communication method to control and monitor energy consumption. In parallel, the proposed DPMP based on Blockchain makes it possible to manage the incentives of FSs, enabling the integration of local and global markets. [ABSTRACT FROM AUTHOR]

The complexity of power systems is rising mainly due to the expansion of renewable energy generation. Due to the enormous variability and uncertainty associated with these types of resources, they require sophisticated planning tools so that they can be used appropriately. In this sense, several tools for the simulation of renewable energy assets have been proposed. However, they are traditionally focused on the simulation of the generation process, leaving the operation of these systems in the background. Conversely, more expert SCADA operators for the management of renewable power plants are required, but their training is not an easy task. SCADA operation is usually complex, due to the wide set of information available. In this sense, simulation or co-simulation tools can clearly help to reduce the learning curve and improve their skills. Therefore, this paper proposes a useful simulator based on a JavaScript engine that can be easily connected to any renewable SCADAs, making it possible to perform different simulated scenarios for novel operator training, as if it were a real facility. Using this tool, the administrators can easily program those scenarios allowing them to sort out the lack of support found in setting up facilities and training of novel operator tasks. Additionally, different renewable energy generation models that can be implemented in the proposed simulator are described. Later, as a use example of this tool, a study case is also performed. It proposes three different wind farm generation facility models, based on different turbine models: one with the essential generation turbine function obtained from the manufacturer curve, another with an empirical model using monotonic splines, and the last one adding the most important operational states, making it possible to demonstrate the usefulness of the proposed simulation tool. [ABSTRACT FROM AUTHOR]

In this paper a novel model is being proposed and considered by ENEL—the largest electric utility in Chile—and analyzed thoroughly, whereby electric power control and energy management for a 60-apartments' residential building is presented as an example of the utility's green energy program, part of its Smart Grid Transformation plan to install grid-tied distributed generation (DG) systems, namely microgrids, with solar generation and energy storage in Santiago, Chile. The particular tariffs scheme analysis shown is part of the overall projected tentative benefits of adopting the new scheme, which will require the utility's customers to adapt their consumption behavior to the limited supply of renewable energy by changing energy consumption habits and schedules in a way that maximizes the capacity and efficiency of the grid-tied microgrid with energy storage. The change in behavior entails rescheduling power consumption to hours where the energy supply capacity in the DG system is higher and price is lower as well as curtailing their power needs in certain hourly blocks so as to maximize DG system's efficiency and supply capacity. Nevertheless, the latter presents a problem under the perspective of ENEL's renewable energy sources (RES) integration plan with the electric utility's grid supply, which, up until now and due to current electric tariffs law, has not had a clear solution. Under said scenario, a set of strategies based on energy homeostasis principles for the coordination and control of the electricity supply versus customers' demand has been devised and tested. These strategies which consider various scenarios to conform to grid flexibility requirements by ENEL, have been adapted for the specific needs of these types of customers while considering the particular infrastructure of the network. Thus, the microgrid adjusts itself to the grid in order to complement the grid supply while seeking to maximize green supply capacity and operational efficiency, wherein the different energy users and their energy consumption profiles play a crucial role as "active loads", being able to respond and adapt to the needs of the grid-connected microgrid while enjoying economic benefits. Simulation results are presented under different tariff options, system's capacity and energy storage alternatives, in order to compare the proposed strategies with the actual case of traditional grid's electricity distribution service, where no green energy is present. The results show the advantage of the proposed tariffs scheme, along with power control and energy management strategies for the integration of distributed power generation within ENEL's Smart Grid Transformation in Chile. [ABSTRACT FROM AUTHOR]

Electric vehicle fleets and smart grids are two growing technologies. These technologies have 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, a comparison of several evolutionary algorithms—namely genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution—is shown, in order to evaluate the proposed architecture. The proposed solution is presented as a means to prevent overload of the power grid. [ABSTRACT FROM AUTHOR]

Nowadays, microgrids are gaining importance in electric power generation and distribution environments due to their flexibility, versatility, scalability and the possibility of supplying ancillary services when connected to the grid. They allow for the customization of electric supply for very different types of consumers. Therefore, a new control model for power and energy management based on homeostaticity of electric power systems (EPS) is presented, which has been already analyzed and approved by ENEL Chile in its developmental stage. ENEL, the largest electric utility in the country, is interested in incorporating smart microgrids in the electricity distribution market, as part of a worldwide policy. Such microgrids are to be installed in buildings serviced by ENEL. To demonstrate the model's utility, a Simulink model of a real microgrid is used, which is comprised of PV generation, energy storage, an air conditioning (AC) equipment and thermal storage of the building upon which the microgrid is installed. The behavior of every element is simulated, including the dynamic thermal model of the building in order to optimize energy management and power supply versus consumption. The behavior of the whole system is analyzed under different environmental profiles and energy consumption patterns using the proposed homeostaticity system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objectives: Cytokines signaling pathway genes represent a key component of the genetic network implicated in the pathogenesis of immunoglobulin-A vasculitis (IgAV), an inflammatory vascular pathology. Interleukin (IL)17A is described as a genetic risk locus for some autoimmune diseases, such as giant cell arteritis and spondyloarthritis. Accordingly, we aimed to determine the potential influence of IL17A on the pathogenesis of IgAV., Methods: Five IL17A tag polymorphisms (rs4711998, rs8193036, rs3819024, rs2275913 and rs7747909), which cover the major variability of this gene, were genotyped in 360 Caucasian patients with IgAV and 1,003 sex and ethnically matched healthy controls using TaqMan probes., Results: No statistically significant differences between patients with IgAV and healthy controls were observed when each IL17A genetic variant was analysed independently. Similarly, no statistically significant differences between patients with IgAV and healthy controls were found when the five IL17A polymorphisms were evaluated combined conforming haplotypes. In addition, there were no statistically significant differences in genotype, allele and haplotype frequencies of IL17A when patients with IgAV were stratified according to the age at disease onset or to the presence/absence of gastrointestinal or renal manifestations., Conclusions: Our results do not support an influence of IL17A on the pathogenesis of IgAV.

Objectives: Interferon regulatory factor 5 (IRF5) is a major regulator of type I interferon induction and is also critical to produce pro-inflammatory cytokines. An influence of IRF5 genetic variants on the increased risk of immune-mediated diseases has been described. Accordingly, we aimed to evaluate the implication of IRF5 in the pathogenesis of Immunoglobulin-A vasculitis (IgAV), an inflammatory vascular pathology., Methods: Three tag genetic variants (rs2004640, rs2070197 and rs10954213), representative of 3 different haplotype blocks within IRF5, were genotyped in 372 Caucasian patients with IgAV and 876 sex and ethnically matched healthy controls by TaqMan assays., Results: No significant differences in the genotype and allele frequencies between patients with IgAV and healthy controls were observed when each IRF5 polymorphism was evaluated independently. Likewise, no significant differences between patients with IgAV and healthy controls were found when we assessed the three IRF5 polymorphisms combined, conforming haplotypes. In addition, there were no significant differences in genotype, allele and haplotype frequencies of IRF5 when patients with IgAV were stratified according to the age at disease onset or to the presence/absence of gastrointestinal or renal manifestations., Conclusions: Our results do not support an influence of IRF5 on the pathogenesis of IgAV.

The genetic component of Immunoglobulin-A (IgA) vasculitis is still far to be elucidated. To increase the current knowledge on the genetic component of this vasculitis we performed the first genome-wide association study (GWAS) on this condition. 308 IgA vasculitis patients and 1,018 healthy controls from Spain were genotyped by Illumina HumanCore BeadChips. Imputation of GWAS data was performed using the 1000 Genomes Project Phase III dataset as reference panel. After quality control filters and GWAS imputation, 285 patients and 1,006 controls remained in the datasets and were included in further analysis. Additionally, the human leukocyte antigen (HLA) region was comprehensively studied by imputing classical alleles and polymorphic amino acid positions. A linkage disequilibrium block of polymorphisms located in the HLA class II region surpassed the genome-wide level of significance (OR = 0.56, 95% CI = 0.46-0.68). Although no polymorphic amino acid positions were associated at the genome-wide level of significance, P-values of potential relevance were observed for the positions 13 and 11 of HLA-DRB1 (P = 6.67E-05, P = 1.88E-05, respectively). Outside the HLA, potential associations were detected, but none of them were close to the statistical significance. In conclusion, our study suggests that IgA vasculitis is an archetypal HLA class II disease.

Objectives: Data from a small series suggested that the Interleukin 1 beta (IL1ß) rs16944 polymorphism may be associated with severe renal involvement and persistent renal damage (renal sequelae) in Henoch-Schönlein purpura (HSP). To confirm this association, we assessed the largest cohort of Caucasian HSP patients ever considered for genetic studies., Methods: 338 Spanish HSP patients and 635 sex and ethnically matched controls were recruited in this study. All patients were required to have had at least 6 months' follow-up. Patients and controls were genotyped for IL1β rs16944 by TaqMan genotyping assay., Results: No differences between IL1β rs16944 genotype or allele frequencies were found either in the case/control study or when HSP patients were stratified according to the age at disease onset, presence of nephritis or gastrointestinal manifestations. Nevertheless, 4 (25%) of the 16 HSP patients who developed severe renal manifestations carried the TT genotype versus 29 (9%) of 322 who did not develop this complication (p=0.01, OR=5.48, 95% CI: 1.01-28.10). Accordingly, patients carrying the mutant T allele had an increased risk of developing severe nephropathy (p=0.016, OR=2.35, 95% CI: 1.09-5.07). Additionally, a significant increase of the TT genotype was observed in patients with persistent renal damage when compared with those patients without this complication (25% versus 8.6%, respectively; p=0.0035, OR=4.90, 95% CI: 1.26- 18.51). Moreover, renal sequelae were more common in patients carrying the mutant T allele (p=0.0076, OR=2.20, 95% CI: 1.17-4.14)., Conclusions: Our results support that the IL1ß rs16944 polymorphism may be a potential marker of severe renal manifestations and renal sequelae in HSP.