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Simultaneous Plasmonic Measurement of Refractive Index and Temperature Based on a D-Type Fiber Sensor With Gold Wires
Get PDFThis paper presents an optical fiber sensor, that uses surface plasmon resonance on metallic wires to directly and simultaneously measure both the refractive index and the temperature. The sensor is constituted by gold wires on a D-type fiber engineered, using numerical simulations based on the finite-element method to support plasmon modes with strong dependencies to either one of the measured parameters. In particular, the influence of the temperature on the structure of the plasmon modes results from contributions from the thermooptic effect in the fiber core and sensing layer, and phononelectron scattering along with electron-electron scattering in the metal wire. The performance of the sensor is evaluated in terms of its sensitivity and resolution
Advanced voltage control for smart microgrids using distributed energy resources
No full textLarge scale integration of distributed generation (DG), particularly based on variable renewable energy sources (RES), in low voltage (LV) distribution networks brings significant challenges to operation. This paper presents a new methodology for mitigating voltage problems in LV networks, in a future scenario with high integration of distributed energy resources (DER), taking advantage of these resources based on a smart grid type architecture. These resources include dispersed energy storage systems, controllable loads of residential clients under demand side management (DSM) actions and microgeneration units. The algorithm developed was tested in a real Portuguese LV network and showed good performance in controlling voltage profiles while being able to integrate all energy from renewable sources and minimizing the energy not supplied
Evolvix BEST Names for semantic reproducibility across code2brain interfaces
Get PDFNames in programming are vital for understanding the meaning of code and big data. We define code2brain (C2B) interfaces as maps in compilers and brains between meaning and naming syntax, which help to understand executable code. While working toward an Evolvix syntax for general-purpose programming that makes accurate modeling easy for biologists, we observed how names affect C2B quality. To protect learning and coding investments, C2B interfaces require long-term backward compatibility and semantic reproducibility (accurate reproduction of computational meaning fromcoder-brains to reader-brains by code alone). Semantic reproducibility is often assumed until confusing synonyms degrade modeling in biology to deciphering exercises. We highlight empirical naming priorities from diverse individuals and roles of names in different modes of computing to show how naming easily becomes impossibly difficult. We present the Evolvix BEST (Brief, Explicit, Summarizing, Technical) Names concept for reducing naming priority conflicts, test it on a real challenge by naming subfolders for the Project Organization Stabilizing Tool system, and provide naming questionnaires designed to facilitate C2B debugging by improving names used as keywords in a stabilizing programming language. Our experiences inspired us to develop Evolvix using a flipped programming language design approach with some unexpected features and BEST Names at its core
An overview of Demand Response: Key-elements and international experience
No full textThe increasing penetration of renewable energy sources (RES) in power systems intensifies the need of enhancing the flexibility in grid operations in order to accommodate the uncertain power output of the leading RES such as wind and solar generation. Utilities have been recently showing increasing interest in developing Demand Response (DR) programs in order to match generation and demand in a more efficient way. Incentive and price-based DR programs aim at enabling the demand side in order to achieve a range of operational and economic advantages, towards developing a more sustainable power system structure. The contribution of the presented study is twofold. First, a complete and up-to-date overview of DR enabling technologies, programs and consumer response types is presented. Furthermore, the benefits and the drivers that have motivated the adoption of DR programs, as well as the barriers that may hinder their further development, are thoroughly discussed. Second, the international DR status quo is identified by extensively reviewing existing programs in different regions
Performance Comparison of Grid-Faulty Control Schemes for Inverter-Based Industrial Microgrids
Get PDFFusion-Based Variational Image Dehazing
No full textWe propose a novel image-dehazing technique based on the minimization of two energy functionals and a fusion scheme to combine the output of both optimizations. The proposed fusion-based variational image-dehazing (FVID) method is a spatially varying image enhancement process that first minimizes a previously proposed variational formulation that maximizes contrast and saturation on the hazy input. The iterates produced by this minimization are kept, and a second energy that shrinks faster intensity values of well-contrasted regions is minimized, allowing to generate a set of difference-of-saturation (DiffSat) maps by observing the shrinking rate. The iterates produced in the first minimization are then fused with these DiffSat maps to produce a haze-free version of the degraded input. The FVID method does not rely on a physical model from which to estimate a depth map, nor it needs a training stage on a database of human-labeled examples. Experimental results on a wide set of hazy images demonstrate that FVID better preserves the image structure on nearby regions that are less affected by fog, and it is successfully compared with other current methods in the task of removing haze degradation from faraway regions
Modelling the relationship between heating energy use and indoor temperatures in residential buildings through Artificial Neural Networks considering occupant behavior
No full textThe heating energy demand stated in energy performance certificates (EPC) and in other instruments used in the of evaluation of building's energy performance is usually determined assuming very specific (reference) indoor behavioral/heating patterns. Particularly, they tend to assume that households heat (nearly) the entire house to a "comfort" temperature during (nearly) all the heating season. However, several field studies have shown that there are major niches of the housing stock which do not follow this pattern (even the majority, in some geographical areas). Considering this matter, it would be interesting to build models able to estimate heating energy use values resultant from occupation and heating patterns different from those considered as "reference". This work aimed at producing tools to assess the relationship between heating energy use and indoor temperatures at different levels of occupant behavior (in terms of where, when and at what temperature households heat their dwellings). This relationship was expressed through models while still takes advantage of the information from the certificates. The work developed artificial neural networks (ANN) that characterize the relationship between heating energy use, indoor temperatures and the heating energy demand under reference conditions (typically available from energy rating/certificates) in the residential buildings, for different occupant behaviors heating patterns. Theoretically, these models can be applicable to any national geographical context. The data for building the ANNs was obtained from dynamic thermal building simulations using ESP-r, considering a large number of housing types and hypothetical occupation and heating patterns (i.e., which parts of the house are heated, when and at what temperature). From the analysis performed, it was possible to conclude that the developed ANN models proved to perform well (R-2 > 0.93) in estimating either heating energy use or indoor temperature, both at an individual and at the building stock level. This work may have important contributions in the energy planning practices regarding the residential building stock
Dynamic Price Vector Formation Model-Based Automatic Demand Response Strategy for PV-Assisted EV Charging Stations
No full textA real-time price (RTP)-based automatic demand response (ADR) strategy for PV-assisted electric vehicle (EV) Charging Station (PVCS) without vehicle to grid is proposed. The charging process is modeled as a dynamic linear program instead of the normal day-ahead and real-time regulation strategy, to capture the advantages of both global and real-time optimization. Different from conventional price forecasting algorithms, a dynamic price vector formation model is proposed based on a clustering algorithm to form an RTP vector for a particular day. A dynamic feasible energy demand region (DFEDR) model considering grid voltage profiles is designed to calculate the lower and upper bounds. A deduction method is proposed to deal with the unknown information of future intervals, such as the actual stochastic arrival and departure times of EVs, which make the DFEDR model suitable for global optimization. Finally, both the comparative cases articulate the advantages of the developed methods and the validity in reducing electricity costs, mitigating peak charging demand, and improving PV self-consumption of the proposed strategy are verified through simulation scenarios
Neuroimmune expression in hip osteoarthritis: a systematic review
Get PDFBackground: Neuroimmune axis is central in the physiopathology of hip osteoarthritis (OA), but its specific pathways are still unclear. This systematic review aims to assess the nervous and immune system profile of patients with hip osteoarthritis (OA) when compared to healthy controls. Methods: A systematic review followed PRISMA guidelines was conducted. A two-step selection process was completed, and from 609 references 17 were included. The inclusion criteria were: original articles on adult patients with hip OA, with assessment of neuroimmune expression. Articles with other interventions prior to analysis and those without a control group were excluded. Results: Thirty-nine relevant neuroimmune markers were identified, with assessments in bone, cartilage, synovial membrane, synovial fluid, whole blood, serum and/or immune cells. GM-CSF, IFN-gamma, IL-1 alpha, IL-6, IL-8, IL-1 and TNF-alpha presented variable expression among tissues studied when compared between hip OA and controls. VEGFs and TGF-beta isoforms showed similar tendencies among tissues and studies. On nervous expression, CGRP, Tuj-1 and SP were increased in synovial membrane. Overall, patients with hip OA presented a higher number of overexpressed markers. Conclusions: For the first time a systematic review on neuroimmune expression in patients with hip OA found an upregulation of neuroimmune markers, with deregulated balance between pro and anti-inflammatory cytokines. However, no clear systematic pattern was found, and few information is available on nervous expression. This highlights the importance of future research with clear methodologies to guide the management of these patients