Hybrid stochastic/robust flexible and reliable scheduling of secure networked microgrids with electric springs and electric vehicles

Electric spring (ES) as a novel concept in power electronics has been developed for the purpose of dealing with demand-side management. In this paper, to conquer the challenges imposed by intermittent nature of renewable energy sources (RESs) and other uncertainties for constructing a secure modern microgrid (MG), the hybrid distributed operation of ESs and electric vehicles (EVs) parking lot is suggested. The proposed approach is implemented in the context of a hybrid stochastic/robust optimization (HSRO) problem, where the stochastic programming based on unscented transformation (UT) method models the uncertainties associated with load, energy price, RESs, and availability of MG equipment. Also, the bounded uncertainty-based robust optimization (BURO) is employed to model the uncertain parameters of EVs parking lot to achieve the robust potentials of EVs in improving MG indices. In the subsequent stage, the proposed non-linear problem model is converted to linear approximated counterpart to obtain an optimal solution with low calculation time and error. Finally, the proposed power management strategy is analyzed on 32-bus test MG to investigate the hybrid cooperation of ESs and EVs parking lot capabilities in different cases. The numerical results corroborate the efficiency and feasibility of the proposed solution in modifying MG indices.© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Insulin resistance and coronary artery disease in non-diabetic patients: Is there any correlation?

Background: Cardiovascular diseases are the most common causes of death in the world and type 2 diabetes is one of them because it is highly prevalent and doubles heart disease risk. Some studies suggest that insulin resistance is associated with coronary artery disease in non-diabetics. The aim of this study was to evaluate the association of insulin resistance (IR) and coronary artery disease (CAD) in non-diabetic patients. Methods: In this cross-sectional study, from September 2014 to July 2015, 120 patients referring to Shahid Beheshti Hospital of Qom were evaluated. Their medical history, baseline laboratory studies, BMI and GFR were recorded. After 8 hours of fasting, blood samples were taken from the patients at 8 am, including fasting glucose and insulin level. We estimated insulin resistance using the homeostatic model assessment index of IR (HOMA-IR). Finally, we evaluated the association between IR and CAD. Results: Totally, 120 patients were assigned to participate in this study, among them, 50 patients without CAD and 70 with coronary artery stenosis. Insulin resistance (HOMA-IR> 2.5) was positive in 59 (49.3%) patients and negative in 61 (50.7%) patients. Hence, the correlation between IR and CAD was not statistically significant (P=0.9). Conclusions: In this study, although the correlation was not found between insulin resistance and coronary heart disease, among men, we found a significant association between coronary heart disease and insulin resistance

Predicting Renal Failure Progression in Chronic Kidney Disease Using Integrated Intelligent Fuzzy Expert System

Background. Chronic kidney disease (CKD) is a covert disease. Accurate prediction of CKD progression over time is necessary for reducing its costs and mortality rates. The present study proposes an adaptive neurofuzzy inference system (ANFIS) for predicting the renal failure timeframe of CKD based on real clinical data. Methods. This study used 10-year clinical records of newly diagnosed CKD patients. The threshold value of 15 cc/kg/min/1.73 m2 of glomerular filtration rate (GFR) was used as the marker of renal failure. A Takagi-Sugeno type ANFIS model was used to predict GFR values. Variables of age, sex, weight, underlying diseases, diastolic blood pressure, creatinine, calcium, phosphorus, uric acid, and GFR were initially selected for the predicting model. Results. Weight, diastolic blood pressure, diabetes mellitus as underlying disease, and current GFR(t) showed significant correlation with GFRs and were selected as the inputs of model. The comparisons of the predicted values with the real data showed that the ANFIS model could accurately estimate GFR variations in all sequential periods (Normalized Mean Absolute Error lower than 5%). Conclusions. Despite the high uncertainties of human body and dynamic nature of CKD progression, our model can accurately predict the GFR variations at long future periods

Factors Contributing to the Longevity of Ghurid Dynasty

The political power of Ghurids is recognized during the governance era of Shansabaneyah dynasty.  They have ruled from 401 to 612 AH after which their successors ruled in Indian subcontinent for long years.  The current study conducts a descriptive-analytical investigation in the field of historical literature in order to assess and evaluate the causes of longevity in political and military power of Ghurids and their political establishment in the eastern lands of the Islamic Caliphate. Results of this study demonstrated that these factors in turn contributed to the long-term sustainability Ghurids. Offensive and defensive power of Ghurids, geographic spread, the relative popularity of Shansabaneyah among Ghurids, financial and economic power of Ghurids, their conversion to Islam, the support from Abbasid Caliphate, the governance of the family, the presence of multiple centers and the circumstances surrounding were among the most important factors

Bi-level fuzzy stochastic-robust model for flexibility valorizing of renewable networked microgrids

Publisher Copyright: © 2022 The Author(s)This paper presents a new bi-level multi-objective model to valorize the microgrid (MG) flexibility based on flexible power management system. It considers the presence of renewable and flexibility resources including demand response program (DRP), energy storage system and integrated unit of electric spring with electric vehicles (EVs) parking (IUEE). The proposed bi-level model in the upper-level maximizes expected flexibility resources profit subject to flexibility constraints. Also, in the lower level, minimizing MG energy cost and voltage deviation function based on the Pareto optimization technique is considered as the objective functions; it is bounded by the linearized AC optimal power flow constraints, renewable and flexibility resources limits, and the MG flexibility restrictions. In the following, the proposed bi-level model using Karush–Kuhn–Tucker (KKT) technique is converted to a single-level counterpart, and the fuzzy decision-making method is employed to achieve the best compromise solution. Further, hybrid stochastic-robust programming models uncertain parameters of the proposed model, so that stochastic programming models uncertainties associated with demand, energy price, and the maximum renewables active generation. Also, to capture the flexible potential capabilities of the IUEE, robust optimization models the EVs’ parameters uncertainty. Finally, numerical results confirm the proposed model could jointly improve operation, economic and flexibility conditions of the MG and turned it to a flexi-optimized-renewable MG.Peer reviewe

Risk-averse and flexi-intelligent scheduling of microgrids based on hybrid Boltzmann machines and cascade neural network forecasting

Funding Information: This research has received funding by the Finnish public funding agency for research, Business Finland, under the project Reliable 6G for Energy Vertical Applications)REEVA-Project 10278/31/2022). Publisher Copyright: © 2023 The AuthorsThe future of energy flexibility in microgrids (MGs) is steering towards a highly granular control of the end-user customers. This calls for more highly accurate uncertainty forecasting and optimal management of risk and flexibility options. This paper presents a novel data-driven model to optimize the operation of MGs based on a risk-averse flexi-intelligent energy management system (RFEMS), considering the rising challenge of global climate change. It considers the presence of renewables, a diesel generator, and flexibility resources (FRs) containing a demand response program (DRP), distributed electric vehicles (EVs), and electric springs (ESs). In the first phase, the proposed model, by means of a novel hybrid deep-learning (DL) model, forecasts uncertain parameters associated with wind and solar generations, load demand, and day-ahead energy market price. The architecture of the proposed hybrid forecasting model is composed of several stacked restricted Boltzmann machines and a cascade neural network. Inthe second phase, the MG operator (MGO), based on the obtained uncertainty forecasting results, in the context of a hybrid risk-controlling model, optimizes the MG operation using the provided demand-side flexibility. The proposed optimization problem is linearized stochastic programming with robust concepts, subject to AC optimal power flow constraints, MG flexibility restrictions, and operating limits of local resources. Finally, the efficiency of the proposed RFEMS by using real German datasets on a 33-bus test MG is analyzed. Numerical results demonstrate the superior performance of the proposed forecasting model over several hybrid DL models. In particular, the root mean square error (RMSE) index for wind, solar, load, and price forecasting is improved by 53.35%, 73.24%, 80.24%, and 58.1%, respectively. Further analysis of the proposed RFEMS reveals that operating indices in two 33-bus and 69-bus test networks are significantly improved. It paves pathways to risk-averse, flexible, and economic operationof smart active distribution networks.Peer reviewe

Helicobacter pylori and Migraine: Is Eradication of Helicobacter pylori Effective in Relief of Migraine Headache?

Background: Association between Helicobacter pylori (HP) infection and migraine and the effect of HP eradication on relief of migraine headache have been studied but the results are controversial. Objectives: To evaluate the effect of HP eradication in treatment of patients affected by migraine. Materials and Methods: Eighty consecutive HP infected patients affected by migraine without aura were enrolled in this clinical trial. They have referred to an endoscopy clinic for work-up of HP infection from October 2013 to November 2014. Patients were randomly assigned in 2 groups using 2 different regimens; Group A: migraine treatment and a 14-day triple therapy for HP infection and Group B: migraine treatment without HP eradication. The mean duration (hour), headache severity (MIDAS) and the frequency (per month) of clinical headache attacks were calculated upon enrollment in the study and at 6 months and 12 months after treatment. All data were analyzed using SPSS version 16. Comparison of categorical variables across the groups was performed using Chi-square test. Results: In group A, HP infection was eradicated in 34 of 40 patients (85%). After treatment in eradicated patients compared with the control group there was significant decrease in severity and frequency (but not in duration) of the migraine attacks at 6 months (p<0.001) and significant decrease in intensity, frequency and duration of the migraine attacks at 12 months (p<0.001). Conclusion: HP should be considered and examined in migranous patients and eradication treatment can be beneficial for relief of clinical attacks

Hybrid planning of distributed generation and distribution automation to improve reliability and operation indices

This paper intends to give an effective hybrid planning of distributed generation and distribution automation in distribution networks aiming to improve the reliability and operation indices. The distribution automation platform consists of automatic voltage and VAR control and automatic fault management systems. The objective function minimizes the sum of the expected daily investment, operation, energy loss and reliability costs. The scheme is constrained by linearized AC optimal power flow equations and planning model of sources and distribution automation. A stochastic programming approach is also implemented in this paper based on a hybrid method of Monte Carlo simulation and simultaneous backward method to model uncertainty parameters of the understudy model including load, energy price and availability of network equipment. Finally, the proposed strategy is implemented on an IEEE 69-bus radial distribution network and different case studies are presented to demonstrate the economic and technical benefits of the investigated model. By allocating the optimal places for sources and distribution automation across the distribution network and extracting the optimal performance, the proposed scheme can simultaneously enhance economic, operation, and reliability indices in the distribution system compared to power flow studies.©2022 Elsevier. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

Risk-averse and flexi-intelligent scheduling of microgrids based on hybrid Boltzmann machines and cascade neural network forecasting

The future of energy flexibility in microgrids (MGs) is steering towards a highly granular control of the end-user customers. This calls for more highly accurate uncertainty forecasting and optimal management of risk and flexibility options. This paper presents a novel data-driven model to optimize the operation of MGs based on a risk-averse flexi-intelligent energy management system (RFEMS), considering the rising challenge of global climate change. It considers the presence of renewables, a diesel generator, and flexibility resources (FRs) containing a demand response program (DRP), distributed electric vehicles (EVs), and electric springs (ESs). In the first phase, the proposed model, by means of a novel hybrid deep-learning (DL) model, forecasts uncertain parameters associated with wind and solar generations, load demand, and day-ahead energy market price. The architecture of the proposed hybrid forecasting model is composed of several stacked restricted Boltzmann machines and a cascade neural network. In the second phase, the MG operator (MGO), based on the obtained uncertainty forecasting results, in the context of a hybrid risk-controlling model, optimizes the MG operation using the provided demand-side flexibility. The proposed optimization problem is linearized stochastic programming with robust concepts, subject to AC optimal power flow constraints, MG flexibility restrictions, and operating limits of local resources. Finally, the efficiency of the proposed RFEMS by using real German datasets on a 33-bus test MG is analyzed. Numerical results demonstrate the superior performance of the proposed forecasting model over several hybrid DL models. In particular, the root mean square error (RMSE) index for wind, solar, load, and price forecasting is improved by 53.35%, 73.24%, 80.24%, and 58.1%, respectively. Further analysis of the proposed RFEMS reveals that operating indices in two 33-bus and 69-bus test networks are significantly improved. It paves pathways to risk-averse, flexible, and economic operation of smart active distribution networks.</p