Optimized Solar Photovoltaic Generation in a Real Local Distribution Network

Remarkable penetration of renewable energy in electric networks, despite its valuable opportunities, such as power loss reduction and loadability improvements, has raised concerns for system operators. Such huge penetration can lead to a violation of the grid requirements, such as voltage and current limits and reverse power flow. Optimal placement and sizing of Distributed Generation (DG) are one of the best ways to strengthen the efficiency of the power systems. This paper builds a simulation model for the local distribution network based on obtained load profiles, GIS information, solar insolation, feeder and voltage settings, and define the optimization problem of solar PVDG installation to determine the optimal siting and sizing for different penetration levels with different objective functions. The objective functions include voltage profile improvement and energy loss minimization and the considered constraints include the physical distribution network constraints (AC power flow), the PV capacity constraint, and the voltage and reverse power flow constraints.Comment: To be published (Accepted) in: Proceedings of the IEEE PES Innovative Smart Grid Technologies Conference (ISGT), Washington D.C., USA, 201

Time-Series Analysis of Photovoltaic Distributed Generation Impacts on a Local Distributed Network

Increasing penetration level of photovoltaic (PV) distributed generation (DG) into distribution networks will have many impacts on nominal circuit operating conditions including voltage quality and reverse power flow issues. In U.S. most studies on PVDG impacts on distribution networks are performed for west coast and central states. The objective of this paper is to study the impacts of PVDG integration on local distribution network based on real-world settings for network parameters and time-series analysis. PVDG penetration level is considered to find the hosting capacity of the network without having major issues in terms of voltage quality and reverse power flow. Time-series analyses show that distributed installation of PVDGs on commercial buses has the maximum network energy loss reduction and larger penetration ratios for them. Additionally, the penetration ratio thresholds for which there will be no power quality and reverse power flow issues and optimal allocation of PVDG and penetration levels are identified for different installation scenarios.Comment: To be published (Accepted) in: 12th IEEE PES PowerTech Conference, Manchester, UK, 201

Modeling Cascading Failures in Power Systems in the Presence of Uncertain Wind Generation

One of the biggest threats to the power systems as critical infrastructures is large-scale blackouts resulting from cascading failures (CF) in the grid. The ongoing shift in energy portfolio due to ever-increasing penetration of renewable energy sources (RES) may drive the electric grid closer to its operational limits and introduce a large amount of uncertainty coming from their stochastic nature. One worrisome change is the increase in CFs. The CF simulation models in the literature do not allow consideration of RES penetration in studying the grid vulnerability. In this dissertation, we have developed tools and models to evaluate the impact of RE penetration on grid vulnerability to CF. We modeled uncertainty injected from different sources by analyzing actual high-resolution data from North American utilities. Next, we proposed two CF simulation models based on simplified DC power flow and full AC power flow to investigate system behavior under different operating conditions. Simulations show a dramatic improvement in the line flow uncertainty estimation based on the proposed model compared to the simplified DC OPF model. Furthermore, realistic assumptions on the integration of RE resources have been made to enhance our simulation technique. The proposed model is benchmarked against the historical blackout data and widely used models in the literature showing similar statistical patterns of blackout size

Investigation of Peripheral Blood Mononuclear Cells Phagocytosis in Allergic Asthma Mice Model

The respiratory system is exposed to the potentially harmful environment agents. More importantly, respiratory system infection is an important risk factor for inflammation and some pathogens can be main responsible of asthma. Phagocytosis is a main mechanism to eliminate of microbial infection. Phagocytic clearance may control asthma pathogenesis. In asthma, cytokines balance may be changed, therefore we investigated possible change in phagocytes in the present study.14 male Balb/c mice were divided into two control and asthmatic group. Asthma model in mice was produced by ovalbumin. Peripheral blood mononuclear cells were separated and reduction nitro blue tetrazolium and latex bead florescence phagocytosis tests were done.There was no significant difference in phagocytosis and NBT reduction test between asthmatic and control groups (P≤0.05).Airway inflammation and unbalancing of cytokines in asthma might modulate phagocytosis function. Therefore, asthmatic patient might be more susceptible to airway infection but there was not any notable changes in phagocytosis

Predictability of International Stock Returns with Sum of the Parts and Equity Premiums under Regime Shifts

This research consists of two essays. The first essay entitled” Stock Return Forecasting with Sum-of-the-Parts Methodology: Evidence from Around the World”, examines forecasting ability of stock returns by employing the sum-of-the-parts (SOP) modeling technique introduced by Ferreira and Santa-Clara (2011).This approach decomposes return into three components of growth in price-earnings ratio, earnings growth, and dividend-price ratio. Each component is forecasted separately and fitted values are used in forecast model to predict stock return. We conduct a series of one-step ahead recursive forecasts for a wide range of developed and emerging markets over the period February 1995 through November 2014. Decomposed return components are forecasted separately using a list of financial variables and the fitted values from the best estimators are used according to out-of-sample performance. Our findings show that the SOP method with financial variables outperforms the historical sample mean for the majority of countries. Second essay entitled,” Equity Premium Predictability under Regime Shifts: International Evidence”, utilizes the modified version of the dividend-price ratio that alleviates some econometric concerns in the literature regarding the non-stationary and persistent predictor when forecasting international equity premium across different regimes. We employ Markov switching technique to address the issue of non-linearity between the equity premium and the predictor. The results show different patterns of equity premium predictability over the regimes across countries by the modified ratio as predictor. In addition, transition probability analysis show the adverse effect of financial crisis on regime transition probabilities by increasing the probability of switching between regimes post-crisis 2007 implying higher risk perceived by investors as a result of uncertainty inherent in regime transitions

A data mining algorithm for determination of influential factors on the hospitalization of patients subject to chronic obstructive pulmonary disease

Background: The present study is on the development of a data mining algorithm for finding the influential factors on the hospitalization of patients subject to chronic obstructive pulmonary disease.Materials and Methods: This is a descriptive analytical study conducted cross sectionally in 2017 on a research community of 150 people with disease symptoms referred to clinics and hospitals across Tehran (Iran). The people were surveyed by a self-designed questionnaire, including queries on life style and family information. The sampling was simple intuitive from previously published studies. The modeling of the data was based on the CRISP method. The C5 decision tree algorithm was used and the data was analyzed by RapidMiner software. Results: The common symptoms of the patients were found to be shortness of breath, cough, chest pain, sputum, continuous cold, and cyanogens. Besides, the family history, smoking, and exposure to allergic agents were other influential factors on the disease. After accomplishment of this study, the results were consulted with the experts of the field.Conclution: It is concluded that data mining can be applied for excavation of knowledge from the gathered data and for determination of the effective factors on patient conditions. Accordingly, this model can successfully predict the disease status of any patient from its symptoms