The power system and microgrid protection-a review

In recent years, power grid infrastructures have been changing from a centralized power generation model to a paradigm where the generation capability is spread over an increasing number of small power stations relying on renewable energy sources. A microgrid is a local network including renewable and non-renewable energy sources as well as distributed loads. Microgrids can be operated in both grid-connected and islanded modes to fill the gap between the significant increase in demand and storage of electricity and transmission issues. Power electronics play an important role in microgrids due to the penetration of renewable energy sources. While microgrids have many benefits for power systems, they cause many challenges, especially in protection systems. This paper presents a comprehensive review of protection systems with the penetration of microgrids in the distribution network. The expansion of a microgrid affects the coordination and protection by a change in the current direction in the distribution network. Various solutions have been suggested in the literature to resolve the microgrid protection issues. The conventional coordination of the protection system is based on the time delays between relays as the primary and backup protection. The system protection scheme has to be changed in the presence of a microgrid, so several protection schemes have been proposed to improve the protection system. Microgrids are classified into different types based on the DC/AC system, communication infrastructure, rotating synchronous machine or inverter-based distributed generation (DG), etc. Finally, we discuss the trend of future protection schemes and compare the conventional power systems

Microgrid working conditions identification based on cluster analysis – a case study from Lambda Microgrid

This article presents the application of cluster analysis (CA) to data proceeding from a testbed microgrid located at Sapienza University of Rome. The microgrid consists of photovoltaic (PV), battery storage system (BESS), emergency generator set, and different types of load with a real-time energy management system based on supervisory control and data acquisition. The investigation is based on the area-related approach - the CA algorithm considers the input database consisting of data from all measurement points simultaneously. Under the investigation, different distance measures (Euclidean, Chebyshev, or Manhattan), as well as an approach to the optimal number of cluster selections. Based on the investigation, the four different clusters that represent working conditions were obtained using methods to define an optimal number of clusters. Cluster 1 represented time with high PV production; cluster 2 represented time with relatively low PV production and when BESS was charged; cluster 3 represents time with relatively high PV production and when BESS was charged; cluster 4 represents time without PV production. Additionally, after the clustering process, a deep analysis was performed in relation to the working condition of the microgrid

Energy management of virtual power plant considering distributed generation sizing and pricing

UID/EMS/00667/2019The energy management of virtual power plants faces some fundamental challenges that make it complicated compared to conventional power plants, such as uncertainty in production, consumption, energy price, and availability of network components. Continuous monitoring and scaling of network gain status, using smart grids provides valuable instantaneous information about network conditions such as production, consumption, power lines, and network availability. Therefore, by creating a bidirectional communication between the energy management system and the grid users such as producers or energy applicants, it will afford a suitable platform to develop more efficient vector of the virtual power plant. The paper is treated with optimal sizing of DG units and the price of their electricity sales to achieve security issues and other technical considerations in the system. The ultimate goal in this study to determine the active demand power required to increase system loading capability and to withstand disturbances. The effect of different types of DG units in simulations is considered and then the efficiency of each equipment such as converters, wind turbines, electrolyzers, etc., is achieved to minimize the total operation cost and losses, improve voltage profiles, and address other security issues and reliability. The simulations are done in three cases and compared with HOMER software to validate the ability of proposed model.publishersversionpublishe

Operation and Planning of Energy Hubs Under Uncertainty - a Review of Mathematical Optimization Approaches

Co-designing energy systems across multiple energy carriers is increasingly attracting attention of researchers and policy makers, since it is a prominent means of increasing the overall efficiency of the energy sector. Special attention is attributed to the so-called energy hubs, i.e., clusters of energy communities featuring electricity, gas, heat, hydrogen, and also water generation and consumption facilities. Managing an energy hub entails dealing with multiple sources of uncertainty, such as renewable generation, energy demands, wholesale market prices, etc. Such uncertainties call for sophisticated decision-making techniques, with mathematical optimization being the predominant family of decision-making methods proposed in the literature of recent years. In this paper, we summarize, review, and categorize research studies that have applied mathematical optimization approaches towards making operational and planning decisions for energy hubs. Relevant methods include robust optimization, information gap decision theory, stochastic programming, and chance-constrained optimization. The results of the review indicate the increasing adoption of robust and, more recently, hybrid methods to deal with the multi-dimensional uncertainties of energy hubs

Socioeconomic - related inequalities in overweight and obesity: findings from the PERSIAN cohort study

BackgroundOverweight and obesity are major health concerns worldwide, with adverse health consequences during the life span. This study measured socioeconomic inequality in overweight and obesity among Iranian adults.MethodsData were extracted from 129,257 Iranian adults (aged 35years and older) participated in the Prospective Epidemiologic Research Studies in IrAN (PERSIAN) in 14 provinces of Iran in 2014. Socioeconomic-related inequality in overweight and obesity was estimated using the Concentration Index (C-n). The C-n further decomposed to find factors explaining the variability within the Socioeconomic related inequality in overweight and obesity.ResultsOf the total number of participants, 1.98, 26.82, 40.76 and 30.43% had underweight, normal weight, overweight and obesity respectively. The age-and sex standardized prevalence of obesity was higher in females than males (39.85% vs 18.79%). People with high socioeconomic status (SES) had a 39 and 15% higher chance of being overweight and obese than low SES people, respectively. The positive value of C-n suggested a higher concentration of overweight (0.081, 95% confidence interval [CI]; 0.074-0.087) and obesity (0.027, 95% CI; 0.021-0.034) among groups with high SES. There was a wide variation in socioeconomic-related inequality in overweight and obesity rate across 14 provinces. The decomposition results suggested that SES factor itself explained 66.77 and 89.07% of the observed socioeconomic inequalities in overweight and obesity among Iranian adults respectively. Following SES, province of residence, physical activity, using hookah and smoking were the major contributors to the concentration of overweight and obesity among the rich.ConclusionsOverall, we found that overweight and obesity is concentrated among high SES people in the study population. . Accordingly, it seems that intersectional actions should be taken to control and prevent overweight and obesity among higher socioeconomic groups. Keywords:Socioeconomic Factors; Inequality; Concentration index; overweight and obesity; PERSIAN; Ira

Operation and planning of energy hubs under uncertainty - A review of mathematical optimization approaches

Co-designing energy systems across multiple energy carriers is increasingly attracting attention of researchers and policy makers, since it is a prominent means of increasing the overall efficiency of the energy sector. Special attention is attributed to the so-called energy hubs, i.e., clusters of energy communities featuring electricity, gas, heat, hydrogen, and also water generation and consumption facilities. Managing an energy hub entails dealing with multiple sources of uncertainty, such as renewable generation, energy demands, wholesale market prices, etc. Such uncertainties call for sophisticated decision-making techniques, with mathematical optimization being the predominant family of decision-making methods proposed in the literature of recent years. In this paper, we summarize, review, and categorize research studies that have applied mathematical optimization approaches towards making operational and planning decisions for energy hubs. Relevant methods include robust optimization, information gap decision theory, stochastic programming, and chance-constrained optimization. The results of the review indicate the increasing adoption of robust and, more recently, hybrid methods to deal with the multi-dimensional uncertainties of energy hubs.Web of Science117228720

Socioeconomic inequalities in prevalence, awareness, treatment and control of hypertension: evidence from the PERSIAN cohort study

Background Elevated blood pressure is associated with cardiovascular disease, stroke and chronic kidney disease. In this study, we examined the socioeconomic inequality and its related factors in prevalence, Awareness, Treatment and Control (ATC) of hypertension (HTN) in Iran. Method The study used data from the recruitment phase of The Prospective Epidemiological Research Studies in IrAN (PERSIAN). A sample of 162,842 adults aged > = 35 years was analyzed. HTN was defined according to the Joint National Committee)JNC-7(. socioeconomic inequality was measured using concentration index (Cn) and curve. Results The mean age of participants was 49.38(SD = +/- 9.14) years and 44.74% of the them were men. The prevalence of HTN in the total population was 22.3%(95% CI: 20.6%; 24.1%), and 18.8%(95% CI: 16.8%; 20.9%) and 25.2%(95% CI: 24.2%; 27.7%) in men and women, respectively. The percentage of awareness treatment and control among individuals with HTN were 77.5%(95% CI: 73.3%; 81.8%), 82.2%(95% CI: 70.2%; 81.6%) and 75.9%(95% CI: 70.2%; 81.6%), respectively. The Cn for prevalence of HTN was -0.084. Two factors, age (58.46%) and wealth (32.40%), contributed most to the socioeconomic inequality in the prevalence of HTN. Conclusion The prevalence of HTN was higher among low-SES individuals, who also showed higher levels of awareness. However, treatment and control of HTN were more concentrated among those who had higher levels of SES, indicating that people at a higher risk of adverse event related to HTN (the low SES individuals) are not benefiting from the advantage of treatment and control of HTN. Such a gap between diagnosis (prevalence) and control (treatment and control) of HTN needs to be addressed by public health policymakers

Global variations in diabetes mellitus based on fasting glucose and haemogloblin A1c

Fasting plasma glucose (FPG) and haemoglobin A1c (HbA1c) are both used to diagnose diabetes, but may identify different people as having diabetes. We used data from 117 population-based studies and quantified, in different world regions, the prevalence of diagnosed diabetes, and whether those who were previously undiagnosed and detected as having diabetes in survey screening had elevated FPG, HbA1c, or both. We developed prediction equations for estimating the probability that a person without previously diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa. The age-standardised proportion of diabetes that was previously undiagnosed, and detected in survey screening, ranged from 30% in the high-income western region to 66% in south Asia. Among those with screen-detected diabetes with either test, the agestandardised proportion who had elevated levels of both FPG and HbA1c was 29-39% across regions; the remainder had discordant elevation of FPG or HbA1c. In most low- and middle-income regions, isolated elevated HbA1c more common than isolated elevated FPG. In these regions, the use of FPG alone may delay diabetes diagnosis and underestimate diabetes prevalence. Our prediction equations help allocate finite resources for measuring HbA1c to reduce the global gap in diabetes diagnosis and surveillance.peer-reviewe

Analyzing and Optimizing the Emission Impact of Intersection Signal Control in Mixed Traffic

Signalized intersections are one of the typical bottlenecks in urban transport systems that have reduced speeds and which have substantial vehicle emissions. This study aims to analyze and optimize the impacts of signal control on the emissions of mixed traffic flow (CO, HC, and NOx) containing both heavy- and light-duty vehicles at urban intersections, leveraging high-resolution field emission data. An OBEAS-3000 (Manufacturer: Xiamen Tongchuang Inspection Technology Co., Ltd., Xiamen, China.) vehicle emission testing device was used to collect microscopic operating characteristics and instantaneous emission data of different vehicle types (light- and heavy-duty vehicles) under different operating conditions. Based on the collected data, the VSP (Vehicle Specific Power) model combined with the VISSIM traffic simulation platform was used to quantitatively analyze the impact of signal control on traffic emissions. Heavy-duty vehicles contribute to most of the emissions regardless of the low proportion in the traffic flows. Afterward, a model is proposed for determining the optimal signal control at an intersection for a specific percentage of heavy-duty vehicles based on the conversion of emission factors of different types of vehicles. Signal control is also optimized based on conventional signal timing, and vehicle emissions are calculated. In the empirical analysis, the changes in CO, HC, and NOx emissions of light- and heavy-duty vehicles before and after conventional signal control optimization are quantified and compared. After the signal control optimization, the CO, HC, and NOx emissions of heavy-duty vehicles were reduced. The CO and HC emissions of light-duty vehicles were reduced, but the NOx emissions of light-duty vehicles remained unchanged. The emissions of vehicles after optimized signal control based on vehicle conversion factors are reduced more significantly than those after conventional optimized signal control. This study provides a scientific basis for developing traffic management measures for energy saving and emission reduction in transport systems with mixed traffic

A stochastic bilevel model for the energy hub manager problem

A bilevel stochastic programming problem (BSPP) model of the decision-making of an energy hub manager is presented. Hub managers seek ways to maximize their profit by selling electricity and heat. They have to make decisions about: (1) the level of involvement in forward contracts, electricity pool markets, and natural gas networks and (2) the electricity and heat offering prices to the clients. These decisions are made under uncertainty of pool prices, demands as well as the prices offered by rival hub managers. On the other hand, the clients try to minimize the total cost of energy procurement. This two-agent relationship is presented as a BSPP in which the hub manager is placed in the upper level and the clients in the lower one. The bilevel scheme is converted to its equivalent single-level scheme using the Karush-Kuhn-Tucker optimality conditions although there are two bilinear products related to electricity and heat. The heat bilinear product is replaced by a heat price-quota curve and the electricity bilinear product is linearized using the strong duality theorem. In addition, conditional value at risk is used to reduce the unfavorable effects of the uncertainties. The effectiveness of the proposed model is evaluated in various simulations of a realistic case study