This paper aims at enhancing the resilience of a photovoltaic-based microgrid equipped with battery storage, supplying a typical commercial building. When extreme weather conditions such as hurricane, tsunami and similar events occur, leading to islanding of the microgrid from the main power grid, it is not expected that the microgrid would be taken out of service for a long time. At the same time, it is not cost effective to make the electrical system to be absolutely reliable to provide service for the customers. The main contribution of this paper lies in its ability to determine the optimal point between the operational cost and grid resilience. In other words, this work proposes an optimal management system of battery energy storage in a way to enhance the resilience of the proposed microgrid while maintaining its operational cost at a minimum level. The optimization is achieved by solving a linear optimization programming problem while the Conditional Value at Risk (CVaR) is incorporated in the objective function. The CVaR is used to account for the uncertainty in the intermittent PV system generated power and that in the electricity price. Simulation analyses are carried out in MATLAB to evaluate the performance of the proposed method. Results reveal that the commercial building microgrid resilience is improved remarkably at a slight increase in the operational cost.

The inclusion of plug-in electrical vehicles (PEVs) in microgrids not only could bring benefits by reducing the on-peak demand, but could also improve the economic efficiency and increase the environmental sustainability. Therefore, in this paper a two stage energy management strategy for the contribution of PEVs in demand response (DR) programs of commercial building microgrids is addressed. The main contribution of this work is the incorporation of the uncertainty of electricity prices in a model predictive control (MPC) based plan for energy management optimization. First, the optimization problem considers the operation of PEVs and wind power in order to optimize the energy management in the commercial building. Second, the total charged power reference which is computed for PEVs in this stage is sent to the PEVs control section so that it could be allocated to each PEV. Therefore, the power balance can be achieved between the power supply and the load in the proposed microgrid building while the operational cost is minimized. The predicted values for load demand, wind power, and electricity price are forecasted by a seasonal autoregressive integrated moving average (SARIMA) model. In addition, the conditional value at risk (CVaR) is used for the uncertainty in the electricity prices. In the end, the results confirm that the PEVs can effectively contribute in the DR programs for the proposed microgrid model. Index Terms-Demand response (DR), model predictive control (MPC), conditional value at risk (CVaR), plug-in electric vehicles (PEV), wind power, commercial building microgrids. I. NOMENCLATURE

This paper addresses the wind farm contribution in frequency control during the integration in the power grid. In the proposed model, the wind farm utilizes inertia control and droop control techniques with the purpose of improving the frequency regulation. In order to achieve optimal results, all the parameters of the controllers for the different units in the power grid are obtained by using a particle swarm optimization algorithm (PSO) and by introducing a modified objective function instead of a conventional objective function e.g., Integral Time-weighted Absolute Error (ITAE). Also, different constraints such as reheat turbine, time delay, governor dead band and generation rate constraint (GRC) are considered for thermal and hydro units with the aim of studying a more realistic power system, which is the main contribution of this paper when compared to the other works in this field. It is shown that, in case of a perturbation in power demand, the system frequency will recover quickly and effectively in comparison with the traditional approaches. In addition, a sensitivity test is carried out in a single power grid area in order to examine the effectiveness of the proposed approach. Then, the system is extended to a multi-area power system using a multi-terminal HVDC for further investigation of the suggested strategy. Simulation results are presented in order to assess the performance of the proposed approach in the power system.

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The requirement for reliable electrical energy supply increases continuously because of its vital role in our lives. However, events due to various factors in the power grid can cause energy supply to be interrupted. One of these factors is human error and thus human reliability analysis is a serious element in the industry. The first step is to identify the roots of human error, on which there has been limited research in this area. In this paper, the potential and actual causes of human error in maintenance teams of power transmission system protection are identified and predicted within a framework of human factors analysis and classification system method. Then, human error factors are ranked to help improve human reliability. The proposed method is implemented in the Fars Electricity Maintenance Company.

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Background: Female sex workers (FSWs) are amongst the most susceptible groups to acquire Human papillomavirus (HPV) infection and consequently, to develop cervical intraepithelial neoplasia and cervical cancer. This is the first systematic review and meta-analysis to provide estimates of the pooled prevalence of HPV infection and distribution of HPV types among FSWs across the world.Methods: Five computerized databases were searched for relevant studies published since the inception date of databases to September 2019. The pooled HPV prevalence was calculated by the random effect model described by DerSimonian-Laird. Subgroup analysis was performed to identify the probable source of heterogeneity. The meta-analysis was performed using the "Metaprop" function in the R package Meta.Results: Sixty-six studies involving 22938 FSWs from 34 countries were included in this meta-analysis, and the pooled HPV prevalence was 41.97% (95% confidence interval (CI): 37.99%-46.05%). HPV-16 (10.42%, 95% CI: 8.61%-12.55%), HPV-52 (7.76%, 95% CI: 5.75%-10.39%), and HPV-53 (6.09%, 95% CI: 4.55%-8.12%) were the most common high-risk HPV types identified among FSWs. Geographically, the highest and the lowest prevalence of HPV were obtained from Hungary (82.35%, 95%CI: 65.90%-91.85%) and Mexico (13.30%, 95%CI: 1.67%-58.06%), respectively.Conclusion: Due to the high prevalence of HPV infection, particularly with high-risk types, FSWs have a great susceptibility to the development of cervical and vaginal cancers. Furthermore, they can transmit their infection to their clients, which may result in a high prevalence of HPV and incidence of HPV-associated malignancies among the general population.

Due to the importance of electrical grid reliability, analysis and evaluation of human error in the maintenance of electrical networks should be also considered seriously. The root causes of these errors must be identified and prioritized to plan for human error reduction. One of the objectives of the present study is to identify and predict these roots for power transmission maintenance groups from organizational, job position, communication, individual, and supervision aspects along with the relationships between these factors. In particular, this paper demonstrates that supervisor behavior as an external factor has a significant effect on maintenance personnel error. For this reason, special attention has been paid to identifying and controlling human factors from a supervisory point of view in this study. This paper also provides a method for detecting the extent of the expected influence of these roots on each personnel, since human error has a random nature. This is done based on the law of mathematical expectation. Finally, a method is suggested to rank roots based on greater effectiveness and evaluate personnel with higher error expectations. The proposed method is a combination of intermediate methods, Shannon entropy, and technique for order of preference by similarity to ideal solution (TOPSIS). The origins of the four human errors between 2014 and 2018 related to the two experts of Fars Electricity Maintenance Contractor Company are compared by the proposed method.