Evaluation of the Electric Vehicle Impact in the Power Demand Curve in a Smart Grid Environment

Smart grids with an intensive penetration of distributed energy resources will play an important role in future power system scenarios. The intermittent nature of renewable energy sources brings new challenges, requiring an efficient management of those sources. Additional storage resources can be beneficially used to address this problem; the massive use of electric vehicles, particularly of vehicle-to-grid (usually referred as gridable vehicles or V2G), becomes a very relevant issue. This paper addresses the impact of Electric Vehicles (EVs) in system operation costs and in power demand curve for a distribution network with large penetration of Distributed Generation (DG) units. An efficient management methodology for EVs charging and discharging is proposed, considering a multi-objective optimization problem. The main goals of the proposed methodology are: to minimize the system operation costs and to minimize the difference between the minimum and maximum system demand (leveling the power demand curve). The proposed methodology perform the day-ahead scheduling of distributed energy resources in a distribution network with high penetration of DG and a large number of electric vehicles. It is used a 32-bus distribution network in the case study section considering different scenarios of EVs penetration to analyze their impact in the network and in the other energy resources management.

Enantiomeric fraction evaluation of pharmaceuticals in environmentalmatrices by liquid chromatography-tandem mass spectrometry

The interest for environmental fate assessment of chiral pharmaceuticals is increasing and enantioselective analytical methods are mandatory. This study presents an enantioselective analytical method for the quantification of seven pairs of enantiomers of pharmaceuticals and a pair of a metabolite. The selected chiral pharmaceuticals belong to three different therapeutic classes, namely selective serotonin reuptake inhibitors (venlafaxine, fluoxetine and its metabolite norfluoxetine), beta-blockers (alprenolol, bisoprolol, metoprolol, propranolol) and a beta2-adrenergic agonist (salbutamol). The analytical method was based on solid phase extraction followed by liquid chromatography tandem mass spectrometry with a triple quadrupole analyser. Briefly, Oasis® MCX cartridges were used to preconcentrate 250 mL of water samples and the reconstituted extracts were analysed with a Chirobiotic™ V under reversed mode. The effluent of a laboratory-scale aerobic granular sludge sequencing batch reactor (AGS-SBR) was used to validate the method. Linearity (r2 > 0.99), selectivity and sensitivity were achieved in the range of 20–400 ng L−1 for all enantiomers, except for norfluoxetine enantiomers which range covered 30–400 ng L−1. The method detection limits were between 0.65 and 11.5 ng L−1 and the method quantification limits were between 1.98 and 19.7 ng L−1. The identity of all enantiomers was confirmed using two MS/MS transitions and its ion ratios, according to European Commission Decision 2002/657/EC. This method was successfully applied to evaluate effluents of wastewater treatment plants (WWTP) in Portugal. Venlafaxine and fluoxetine were quantified as non-racemic mixtures (enantiomeric fraction ≠ 0.5). The enantioselective validated method was able to monitor chiral pharmaceuticals in WWTP effluents and has potential to assess the enantioselective biodegradation in bioreactors. Further application in environmental matrices as surface and estuarine waters can be exploited.

Alternative chelating agents: Evaluation of the ready biodegradability and complexation properties

The ready biodegradability of four chelating agents, N,N -(S,S)-bis[1-carboxy-2-(imidazol-4-yl)ethyl]ethylenediamine (BCIEE), N - ethylenedi-L-cysteine (EC), N,N -bis (4-imidazolymethyl)ethylenediamine (EMI) and 2,6-pyridine dicarboxylic acid (PDA), was tested according to the OECD guideline for testing of chemicals. PDA proved to be a readily biodegradable substance. However, none of the other three compounds were degraded during the 28 days of the test. Chemical simulations were performed for the four compounds in order to understand their ability to complex with some metal ions (Ca, Cd, Co, Cu, Fe, Mg, Mn, Ni, Pb, Zn) and discuss possible applications of these chelating agents. Two different conditions were simulated: (i) in the presence of the chelating agent and one metal ion, and (ii) in the simultaneous presence of the chelating agent and all metal ions with an excess of Ca. For those compounds that were revealed not to be readily biodegradable (BCIEE, EC and EMI), applications were evaluated where this property was not fundamental or even not required. Chemical simulations pointed out that possible applications for these chelating agents are: food fortification, food process, fertilizers, biocides, soil remediation and treatment of metal poisoning. Additionally, chemical simulations also predicted that PDA is an efficient chelating agent for Ca incrustations removal, detergents and for pulp metal ions removal process.

Performance evaluation of a cooperative reputation system for vehicular delay-tolerant networks

In the last decade, both scientific community and automotive industry enabled communications among vehicles in different kinds of scenarios proposing different vehicular architectures. Vehicular delay-tolerant networks (VDTNs) were proposed as a solution to overcome some of the issues found in other vehicular architectures, namely, in dispersed regions and emergency scenarios. Most of these issues arise from the unique characteristics of vehicular networks. Contrary to delay-tolerant networks (DTNs), VDTNs place the bundle layer under the network layer in order to simplify the layered architecture and enable communications in sparse regions characterized by long propagation delays, high error rates, and short contact durations. However, such characteristics turn contacts very important in order to exchange as much information as possible between nodes at every contact opportunity. One way to accomplish this goal is to enforce cooperation between network nodes. To promote cooperation among nodes, it is important that nodes share their own resources to deliver messages from others. This can be a very difficult task, if selfish nodes affect the performance of cooperative nodes. This paper studies the performance of a cooperative reputation system that detects, identify, and avoid communications with selfish nodes. Two scenarios were considered across all the experiments enforcing three different routing protocols (First Contact, Spray and Wait, and GeoSpray). For both scenarios, it was shown that reputation mechanisms that punish aggressively selfish nodes contribute to increase the overall network performance.

Sustainability and economic evaluation of microalgae grown in brewery wastewater

This article evaluates the sustainability and economic potential of microalgae grown in brewery wastewater for biodiesel and biomass production. Three sustainability and two economic indicators were considered in the evaluation within a life cycle perspective. For the production system the most efficient process units were selected. Results show that harvesting and oil separation are the main process bottlenecks. Microalgae with higher lipid content and productivity are desirable for biodiesel production, although comparable to other biofuel’s feedstock concerning sustainability. However, improvements are still needed to reach the performance level of fossil diesel. Profitability reaches a limit for larger cultivation areas, being higher when extracted biomass is sold together with microalgae oil, in which case the influence of lipid content and areal productivity is smaller. The values of oil and/or biomass prices calculated to ensure that the process is economically sound are still very high compared with other fuel options, especially biodiesel.

Sustainability Evaluation of Biodiesel Produced from Microalgae Chlamydomonas sp Grown in Brewery Wastewater

This study performs a sustainability evaluation of biodiesel from microalga Chlamydomonas sp. grown in 20 % (v/v) of brewery’s wastewater, blended with pentose sugars (xylose, arabinose or ribose resulting from the hydrolysis of brewer’s spent grains (BSG). The life cycle steps considered for the study are: microalgae cultivation, biomass processing and lipids extraction at the brewery site, and its conversion to biodiesel at a dedicated external biofuel’s plant. Three sustainability indicators (LCEE, FER and GW) were considered and calculated using experimental data. Literature data was used, whenever necessary, to complement life cycle data, thus allowing a more accurate sustainability evaluation. A comparative analysis of the biodiesel life cycle steps was also conducted, with the main goal of identifying which steps need to be improved. Results show that biomass processing, especially cell harvesting, microalgae cultivation, and lipids extraction are the main process bottlenecks. It is also analysed the influence on the microalgae biodiesel sustainability of adding each pentose sugar to the cultivation media, concluding that it strongly influences the biomass and lipid productivity. In particular, the addition of xylose is preferable in terms of lipid productivity, but from a sustainability point of view, ribose is the best, though the difference from xylose is not significant. Nevertheless, culture without pentose addition presents the best sustainability results.

Information literacy skills of portuguese LIS students: some topics on evaluation of resources credibility

This paper focuses on the Portuguese results from an international survey on LIS students’ information literacy skills. The results’ analysis will be grounded on a literature review on the criteria application to evaluate information and determine the credibility by undergraduate students. The guidelines for the information evaluation, especially regarding credibility aspect, on three main information literacy frameworks will be presented. After an overall presentation of the main results, the analysis of the Portuguese survey results will focus on issues related to information evaluation skills, namely on criteria to assess information credibility and on difficulties to apply them.

A Data Mining Framework for Electric Load Profiling

This paper consists in the characterization of medium voltage (MV) electric power consumers based on a data clustering approach. It is intended to identify typical load profiles by selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The best partition is selected using several cluster validity indices. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ behavior. The data-mining-based methodology presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partitions. To validate our approach, a case study with a real database of 1.022 MV consumers was used.

Consumers Performance Evaluation of the Participation in Demand Response Programs Using Baseline Methods

Demand response has gain increasing importance in the context of competitive electricity markets environment. The use of demand resources is also advantageous in the context of smart grid operation. In addition to the need of new business models for integrating demand response, adequate methods are necessary for an accurate determination of the consumers’ performance evaluation after the participation in a demand response event. The present paper makes a comparison between some of the existing baseline methods related to the consumers’ performance evaluation, comparing the results obtained with these methods and also with a method proposed by the authors of the paper. A case study demonstrates the application of the referred methods to real consumption data belonging to a consumer connected to a distribution network.

Analysis of Consumption Data to Detect Commercial Losses Using Performance Evaluation Methods in a Smart Grid

Electric power networks, namely distribution networks, have been suffering several changes during the last years due to changes in the power systems operation, towards the implementation of smart grids. Several approaches to the operation of the resources have been introduced, as the case of demand response, making use of the new capabilities of the smart grids. In the initial levels of the smart grids implementation reduced amounts of data are generated, namely consumption data. The methodology proposed in the present paper makes use of demand response consumers’ performance evaluation methods to determine the expected consumption for a given consumer. Then, potential commercial losses are identified using monthly historic consumption data. Real consumption data is used in the case study to demonstrate the application of the proposed method.

Intake of milk, but not total dairy, yogurt, or cheese, is negatively associated with the clustering of cardiometabolic risk factors in adolescents

Epidemiologic studies have reported an inverse association between dairy product consumption and cardiometabolic risk factors in adults, but this relation is relatively unexplored in adolescents. We hypothesized that a higher dairy product intake is associated with lower cardiometabolic risk factor clustering in adolescents. To test this hypothesis, a cross-sectional study was conducted with 494 adolescents aged 15 to 18 years from the Azorean Archipelago, Portugal. We measured fasting glucose, insulin, total cholesterol, high-density lipoprotein cholesterol, triglycerides, systolic blood pressure, body fat, and cardiorespiratory fitness. We also calculated homeostatic model assessment and total cholesterol/high-density lipoprotein cholesterol ratio. For each one of these variables, a z score was computed using age and sex. A cardiometabolic risk score (CMRS) was constructed by summing up the z scores of all individual risk factors. High risk was considered to exist when an individual had at least 1 SD from this score. Diet was evaluated using a food frequency questionnaire, and the intake of total dairy (included milk, yogurt, and cheese), milk, yogurt, and cheese was categorized as low (equal to or below the median of the total sample) or “appropriate” (above the median of the total sample).The association between dairy product intake and CMRS was evaluated using separate logistic regression, and the results were adjusted for confounders. Adolescents with high milk intake had lower CMRS, compared with those with low intake (10.6% vs 18.1%, P = .018). Adolescents with appropriate milk intake were less likely to have high CMRS than those with low milk intake (odds ratio, 0.531; 95% confidence interval, 0.302-0.931). No association was found between CMRS and total dairy, yogurt, and cheese intake. Only milk intake seems to be inversely related to CMRS in adolescents.