A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

The energy management system is executed in microgrids for optimal integration of distributed energy resources (DERs) into the power distribution grids. To this end, various strategies have been more focused on cost reduction, whereas effectively both economic and technical indices/factors have to be considered simultaneously. Therefore, in this paper, a two-layer optimization model is proposed to minimize the operation costs, voltage fluctuations, and power losses of smart microgrids. In the outer-layer, the size and capacity of DERs including renewable energy sources (RES), electric vehicles (EV) charging stations and energy storage systems (ESS), are obtained simultaneously. The inner-layer corresponds to the scheduled operation of EVs and ESSs using an integrated coordination model (ICM). The ICM is a fuzzy interface that has been adopted to address the multi-objectivity of the cost function developed based on hourly demand response, state of charges of EVs and ESS, and electricity price. Demand response is implemented in the ICM to investigate the effect of time-of-use electricity prices on optimal energy management. To solve the optimization problem and load-flow equations, hybrid genetic algorithm (GA)-particle swarm optimization (PSO) and backward-forward sweep algorithms are deployed, respectively. One-day simulation results confirm that the proposed model can reduce the power loss, voltage fluctuations and electricity supply cost by 51%, 40.77%, and 55.21%, respectively, which can considerably improve power system stability and energy efficiency.</jats:p

A survey of metabolic syndrome in first-degree relatives (fathers) of patients with polycystic ovarian syndrome

Objectives: Women with polycystic ovarian syndrome (PCOS) are at twice the risk of developing metabolic syndrome, compared to women from the general population. The aim of this study was to assess the prevalence of metabolic syndrome in the first-degree relatives (fathers) of patients suffering from PCOS.Design: This was a case control study.Setting and subjects: The study was conducted on 34 fathers of women with PCOS who presented at gynaecological clinics in Shiraz, Iran (as the case group), and 34 fathers of healthy women (as the control group).Outcomes measures: Metabolic syndrome was determined according to Adult Treatment Panel III (ATP III) and International Diabetes Federation (IDF) indices. A blood sample was obtained to assay serum insulin, blood sugar, testosterone and lipoproteins. The data were analysed using independent t-test, Fisher’s exact test and the chisquare test.Results: According to the ATP III index, the prevalence of metabolic syndrome was 29.35% in the fathers of the PCOS patients and 8.8% in the fathers of women in the control group (p-value &lt; 0.05). According to the IDF index, this rate was 17.41 in the fathers of patients with PCOS (p-value &lt; 0.05). According to the quantitative insulin sensitivity check and homeostasis model insulin resistance indices, the prevalence of insulin resistance, hypertension, type 2 diabetes and hypercholesterolaemia was higher in the fathers of patients with PCOS than in the control group, but the difference was not significant (p-value &gt; 0.05).Conclusion: The fathers of the women with PCOS were at a higher risk of developing metabolic syndrome, hypertension, dyslipidaemia, impaired glucose tolerance and diabetes.Keywords: metabolic disorders, polycystic ovarian syndrome, Insulin resistance, impaired glucose toleranc

Active Power Sharing and Frequency Restoration in an Autonomous Networked Microgrid

© 1969-2012 IEEE. Microgrid (MG) concept is considered as the best solution for future power systems, which are expected to receive a considerable amount of power through renewable energy resources and distributed generation units. Droop control systems are widely adopted in conventional power systems and recently in MGs for power sharing among generation units. However, droop control causes frequency fluctuations, which leads to poor power quality. This paper deals with frequency fluctuation and stability concerns of f-P droop control loop in MGs. Inspired from conventional synchronous generators, virtual damping is proposed to diminish frequency fluctuation in MGs. Then, it is demonstrated that the conventional frequency restoration method inserts an offset to the phase angle, which is in conflict with accurate power sharing. To this end, a novel control method, based on phase angle feedback, is proposed for frequency restoration in conjunction with a novel method for adaptively tuning the feedback gains to preserve precise active power sharing. Nonlinear stability analysis is conducted by drawing the phase variations of the nonlinear second-order equation of the δ-P droop loop and it is proved that the proposed method improves the stability margin of f-P control loop. Simulation results demonstrate the effectiveness of the proposed method

A universal model for power converters of battery energy storage systems utilizing the impedance-shaping concepts

Battery energy storage systems (BESSs) render different services in microgrids (MGs) depending on the MG connection mode. In the grid-connected mode, the BESS optimally injects/absorbs power, operated by a power converter controlled as the grid-feeding voltage source converter (GFD-VSC). In the islanded mode, the BESS may work as distributed slack bus controlled by the grid-forming VSC (GFM-VSC). However, for performing with a desirable performance, the GFD-VSC and the GFM-VSC demand different grid characteristics. Specifically, the GFD-VSC has a grid-synchronization issue and may become unstable in weak grids with high inductive impedance. Contrarily, the GFM-VSC reveals better performance when connected to the MG through a feeder with high inductive impedance. Besides, switching between different operating modes may cause undesirable transients. This issue can be addressed by the impedance shaping concept that is realized through the control design of the power converter. However, there is limited flexibility for impedance shaping using conventional PI-based controllers. To this end, in this paper, a universal controller is proposed, which is developed based on the impedance shaping concept. Also, sliding mode control (SMC) is used to overcome the undesirable transients due to switching between operating modes. The effectiveness of the control structure is evaluated in islanded operation, under weak grid connection, and FRT transients in MATLAB/Simulink

Battery energy storage systems (BESSs) and the economy-dynamics of microgrids: Review, analysis, and classification for standardization of BESSs applications

Existing literature on microgrids (MGs) has either investigated the dynamics or economics of MG systems. Accordingly, the important impacts of battery energy storage systems (BESSs) on the economics and dynamics of MGs have been studied only separately due to the different time constants of studies. However, with the advent of modern complicated microgrids, BESSs are bridging these two domains. Thus, there is a need to study how these two are related in conjunction with each other. Looking at both the economics and dynamics of MGs and exploring their links will help researchers develop joint Econo-dynamics models, notably in the context of digital twin technology, that could be the future trend toward net-zero emission systems. This paper reviews, analyses, and classifies BESSs applications based on their time constants. The classified BESS applications are: 1) synthetic inertia response; 2) primary frequency support to compensate for the slow response micro-sources; 3) real-time energy management for covering intermittent renewables; 4) economic dispatch for improving steady-state performance, and 5) slack bus realization. Research gaps and future trends have been discussed throughout the paper and are summarized in the future trend section

Adaptive Neural Network for a Stabilizing Shunt Active Power Filter in Distorted Weak Grids

Harmonics destructively impact the performance and stability of power systems. This paper proposes the development of a stable shunt active power filter (SAPF) for harmonics mitigation. The proper and stable operation of the SAPF control system requires the determination of the current reference, phase angle synchronization, and DC-link voltage regulation. This paper uses an artificial neural network (ANN) and one of its sub-methods, the adaptive linear neuron (ADALINE), to determine the current reference. However, determining the current reference requires providing a stable phase angle, which is a fundamental challenge in distorted grids because harmonics created in the grid cause phase angle synchronization problems, due to malfunction of the conventional phase-locked loop (PLL). These things considered, the weak grid connection imposes an instability issue due to the poor performance of the conventional PLL when the grid impedance is high. In this paper, a robust synchronous filter (RSF) is adopted, which separates the harmonic from the main component to provide harmonics-free signals for the PLL. Using RSF, a robust synchronizer quasi-static filter (RSQSF) PLL model is designed, which is effective in dealing with harmonics in weak-grid conditions. MATLAB Simulink was used to check the validation and effectiveness of the proposed control structure. The results show a reduction in harmonics generated in the grid by 86.7% for nonlinear load with a balanced source, 84% for nonlinear load with an unbalanced source under grid impedance, and 80.46% for the nonlinear load with an unbalanced source under weak-grid conditions

The Problem of Mixing up of Leishmania Isolates in the Laboratory: Suggestion of ITS1 Gene Sequencing for Verification of Species

Background: Leishmaniasis is endemic in Iran. Different species of Leishmania (L.) parasites are causative agents of this disease. Correct identification of Leishmania species is important for clinical studies,prevention, and control of the diseases. Mix up of Leishmania isolates is possible in the laboratory, so there is need for verification of species for isolates of uncertain identity. Different methods may be used for this purpose including isoenzyme electrophoresis and molecular methods. The isoenzyme lectrophoresis, due to its drawbacks, is feasible only in specialized laboratories while molecular methods may be more feasible. The aim of this research was to study the application of the internal transcribedspacer 1 (ITS1) sequencing method, in comparison to isoenzyme electrophoresis method, for verification of Leishmania species.Methods: Six Leishmania isolates were received from different research institutions in Iran. The species of these isolates were known by donating institution according to their isoenzyme profile. The species of these isolates were re-identified in Pasteur Institute of Iran by PCR amplification of ITS1 followed bysequencing and comparison of these sequences with Leishmania sequences in GenBank. Isoenzyme electrophoresis was performed for confirmation of the results of ITS1.Results: ITS1 sequence showed that some isolates were mixed up or contaminated with Crithidia. Isoenzyme electrophoresis confirmed the results of ITS1 sequences.Conclusion: ITS1 sequencing is relatively more feasible than the traditional isoenzyme electrophoresismethod and is suggested for verification of Leishmania species

Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics

In [1], we have explored the theoretical aspects of feature selection and evolutionary algorithms. In this chapter, we focus on optimization algorithms for enhancing data analytic process, i.e., we propose to explore applications of nature-inspired algorithms in data science. Feature selection optimization is a hybrid approach leveraging feature selection techniques and evolutionary algorithms process to optimize the selected features. Prior works solve this problem iteratively to converge to an optimal feature subset. Feature selection optimization is a non-specific domain approach. Data scientists mainly attempt to find an advanced way to analyze data n with high computational efficiency and low time complexity, leading to efficient data analytics. Thus, by increasing generated/measured/sensed data from various sources, analysis, manipulation and illustration of data grow exponentially. Due to the large scale data sets, Curse of dimensionality (CoD) is one of the NP-hard problems in data science. Hence, several efforts have been focused on leveraging evolutionary algorithms (EAs) to address the complex issues in large scale data analytics problems. Dimension reduction, together with EAs, lends itself to solve CoD and solve complex problems, in terms of time complexity, efficiently. In this chapter, we first provide a brief overview of previous studies that focused on solving CoD using feature extraction optimization process. We then discuss practical examples of research studies are successfully tackled some application domains, such as image processing, sentiment analysis, network traffics / anomalies analysis, credit score analysis and other benchmark functions/data sets analysis

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

Background: Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. Methods: In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question "Will a MCI patient convert to dementia somewhere in the future" to the question "Will a MCI patient convert to dementia in a specific time window". Results: The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set. Conclusions: Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.FCT under the Neuroclinomics2 project [PTDC/EEI-SII/1937/2014, SFRH/BD/95846/2013]; INESC-ID plurianual [UID/CEC/50021/2013]; LASIGE Research Unit [UID/CEC/00408/2013

Adults' Awareness of Faces Follows Newborns' Looking Preferences

From the first days of life, humans preferentially orient towards upright faces, likely reflecting innate subcortical mechanisms. Here, we show that binocular rivalry can reveal face detection mechanisms in adults that are surprisingly similar to inborn face detection mechanism. We used continuous flash suppression (CFS), a variant of binocular rivalry, to render stimuli invisible at the beginning of each trial and measured the time upright and inverted stimuli needed to overcome such interocular suppression. Critically, specific stimulus properties previously shown to modulate looking preferences in neonates similarly modulated adults' awareness of faces presented during CFS. First, the advantage of upright faces in overcoming CFS was strongly modulated by contrast polarity and direction of illumination. Second, schematic patterns consisting of three dark blobs were suppressed for shorter durations when the arrangement of these blobs respected the face-like configuration of the eyes and the mouth, and this effect was modulated by contrast polarity. No such effects were obtained in a binocular control experiment not involving CFS, suggesting a crucial role for face-sensitive mechanisms operating outside of conscious awareness. These findings indicate that visual awareness of faces in adults is governed by perceptual mechanisms that are sensitive to similar stimulus properties as those modulating newborns' face preferences