A hybrid Jaya algorithm for reliability–redundancy allocation problems

© 2017 Informa UK Limited, trading as Taylor & Francis Group. This article proposes an efficient improved hybrid Jaya algorithm based on time-varying acceleration coefficients (TVACs) and the learning phase introduced in teaching–learning-based optimization (TLBO), named the LJaya-TVAC algorithm, for solving various types of nonlinear mixed-integer reliability–redundancy allocation problems (RRAPs) and standard real-parameter test functions. RRAPs include series, series–parallel, complex (bridge) and overspeed protection systems. The search power of the proposed LJaya-TVAC algorithm for finding the optimal solutions is first tested on the standard real-parameter unimodal and multi-modal functions with dimensions of 30–100, and then tested on various types of nonlinear mixed-integer RRAPs. The results are compared with the original Jaya algorithm and the best results reported in the recent literature. The optimal results obtained with the proposed LJaya-TVAC algorithm provide evidence for its better and acceptable optimization performance compared to the original Jaya algorithm and other reported optimal results

Demand Response Modeling in Microgrid Operation: a Review and Application for Incentive-Based and Time-Based Programs

© 2018 Elsevier Ltd During recent years, with the advent of restructuring in power systems as well as the increase of electricity demand and global fuel energy prices, challenges related to implementing demand response programs (DRPs) have gained remarkable attention of independent system operators (ISOs) and customers, aiming at the improvement of attributes of the load curve and reduction of energy consumption as well as benefiting customers. In this paper, different types of DRPs are modeled based on price elasticity of the demand and the concept of customer benefit. Besides, the impact of implementing DRPs on the operation of grid-connected microgrid (MG) is analyzed. Moreover, several scenarios are presented in order to model uncertainties interfering MG operations including failure of generation units and random outages of transmission lines and upstream line, error in load demand forecasting, uncertainty in production of renewable energies (wind and solar) based distributed generation units, and the possibility that customers do not respond to scheduled interruptions. Simulations are conducted for two principal categories of DRP including incentive-based programs and time-based programs on an 11-bus MG over a 24-h period and also a 14-bus MG over a period of 336 h (two weeks). Simulation results indicate the effects of DRPs on total operation costs, customer's benefit, and load curve as well as determining optimal use of energy resources in the MG operation. In this regard, prioritizing of DRPs on the MG operation is required

A review on the virtual power plant: Components and operation systems

© 2016 IEEE. Due to the high penetration of Distributed Generations (DGs) in the network and the presently involving competition in all electrical energy markets, Virtual Power Plant (VPP) as a new concept has come into view, with the intention of dealing with the increasing number of DGs in the system and handling effectively the competition in the electricity markets. This paper reviews the VPP in terms of components and operation systems. VPP fundamentally is composed of a number of DGs including conventional dispatchable power plants and intermittent generating units along with possible flexible loads and storage units. In this paper, these components are described in an all-inclusive manner, and some of the most important ones are pointed out. In addition, the most important anticipated outcomes of the two types of VPP, Commercial VPP (CVPP) and Technical VPP (TVPP), are presented in detail. Furthermore, the important literature associated with Combined Heat and Power (CHP) based VPP, VPP components and modeling, VPP with Demand Response (DR), VPP bidding strategy, and participation of VPP in electricity markets are briefly classified and discussed in this paper

A novel reliability oriented bi-objective unit commitment problem

© 2017 IEEE. This paper presents a new solution to unit commitment for single-objective and multi-objective frameworks. In the first step, the total expected energy not supplied (TEENS) is proposed as a separate reliability objective function and at the next step, the multi-objective Pareto front strategy is implemented to simultaneously optimize the cost and reliability objective functions. Additionally, an integer based codification of initial solutions is added to reduce the dimension of ON/OFF status variables and also to eliminate the negative influence of penalty factor. The modified invasive weed optimization (MIWO) algorithm is also developed to optimally solve the proposed problem. The obtained solutions are compared with results in the literature which confirms the applicability and superiority of the proposed algorithm for a 10-unit system and 24-hour scheduling horizon

A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data

© 2017 Elsevier Ltd Nowadays, due to some environmental restrictions and decrease of fossil fuel sources, renewable energy sources and specifically wind power plants have a major part of energy generation in the industrial countries. To this end, the accurate forecasting of wind power is considered as an important and influential factor for the management and planning of power systems. In this paper, a novel intelligent method is proposed to provide an accurate forecast of the medium-term and long-term wind power by using the uncertain data from an online supervisory control and data acquisition (SCADA) system and the numerical weather prediction (NWP). This new method is based on the particle swarm optimization (PSO) algorithm and applied to train the Type-2 fuzzy neural network (T2FNN) which is called T2FNN-PSO. The presented method combines both of fuzzy system's expert knowledge and the neural network's learning capability for accurate forecasting of the wind power. In addition, the T2FNN-PSO can appropriately handle the uncertainties associated with the measured parameters from SCADA system, the numerical weather prediction and measuring tools. The proposed method is applied on a case study of a real wind farm. The obtained simulation results validate effectiveness and applicability of the proposed method for a practical solution to an accurate wind power forecasting in a power system control center

CFA optimizer: A new and powerful algorithm inspired by Franklin's and Coulomb's laws theory for solving the economic load dispatch problems

Copyright © 2018 John Wiley & Sons, Ltd. This paper presents a new efficient algorithm inspired by Franklin's and Coulomb's laws theory that is referred to as CFA algorithm, for finding the global solutions of optimal economic load dispatch problems in power systems. CFA is based on the impact of electrically charged particles on each other due to electrical attraction and repulsion forces. The effectiveness of the CFA in different terms is tested on basic benchmark problems. Then, the quality of the CFA to achieve accurate results in different aspects is examined and proven on economic load dispatch problems including 4 different size cases, 6, 10, 15, and 110-unit test systems. Finally, the results are compared with other inspired algorithms as well as results reported in the literature. The simulation results provide evidence for the well-organized and efficient performance of the CFA algorithm in solving great diversity of nonlinear optimization problems

Dynamic performance improvement of an ultra-lift Luo DC–DC converter by using a type-2 fuzzy neural controller

© 2018 Due to the uncertainty associated with the structure and electrical elements of DC–DC converters and the nonlinear performance of these modules, designing an effective controller is highly complicated and also technically challenging. This paper employs a new control approach based on type-2 fuzzy neural controller (T2FNC) in order to improve the dynamic response of an ultra-lift Luo DC–DC converter under different operational conditions. The proposed controller can rapidly stabilize the output voltage of converter to expected values by tuning the converter switching duty cycle. This controller can tackle the uncertainties associated with the structure of converters, measured control signals and measuring devices. Moreover, a new intelligent method based on firefly algorithm is applied to tune the parameters of T2FNC. In order to demonstrate the effectiveness of the proposed control approach, the proposed controller is compared to PI and fuzzy controllers under different operational conditions. Results validate efficiency of proposed T2FNC

The acute effect of maximal exercise on plasma beta-endorphin levels in fibromyalgia patients

Background: This study aimed to investigate the effect of strenuous exercise on β-endorphine (β-END) level in fibromyalgia (FM) patients compared to healthy subjects. Methods: We enrolled 30 FM patients and 15 healthy individuals. All study participants underwent a treadmill exercise test using modified Bruce protocol (M.Bruce). The goal of the test was achieving at least 70 of the predicted maximal heart rate (HRMax). The serum levels of β-END were measured before and after the exercise program. Measurements were done while heart rate was at least 70 of its predicted maximum. Results: The mean � the standard deviation (SD) of exercise duration in the FM and control groups were 24.26 � 5.29 and 29.06 � 3.26 minutes, respectively, indicating a shorter time to achieve the goal heart rate in FM patients (P < 0.003). Most FM patients attained 70 HRMax at lower stages (stage 2 and 3) of M.Bruce compared to the control group (70 versus 6.6, respectively; P < 0.0001). Compared to healthy subjects, FM patients had lower serum β-END levels both in baseline and post-exercise status (Mean � SD: 122.07 � 28.56 μg/ml and 246.55 � 29.57 μg/ml in the control group versus 90.12 � 20.91 μg/ml and 179.80 � 28.57 μg/ml in FM patients, respectively; P < 0.001). Conclusions: We found that FM patients had lower levels of β-END in both basal and post-exercise status. Exercise increased serum the β-END level in both groups but the average increase in β-END in FM patients was significantly lower than in the control group. � The Korean Pain Society, 2016

Static Var Compensator allocation considering transient stability, voltage profile and losses

© 2017 IEEE. The purpose of this paper is to determine the optimal location, size and controller parameters of Static Var Compensator (SVC) to simultaneously improve static and dynamic objectives in a power system. Four goals are considered in this paper including transient stability, voltage profile, SVC investment cost and power loss reduction. Along with the SVC allocation for improving the system transient stability, an additional controller is used and adjusted to improve the SVC performance. Also, an estimated annual load profile including three load levels is utilized to accurately find the optimal location and capacity of SVC. By considering three load levels, the cost of power losses in the power system is decreased significantly. The combination of the active power loss cost and SVC investment cost is considered as a single objective to obtain an accurate and practical solution, while the improvement of transient stability and voltage profile of the system are considered as two separate objectives. The problem is therefore formulated as a multi-objective optimization problem, and Multi Objective Particle Swarm Optimization (MOPSO) algorithm is utilized to find the best solutions. The suggested technique is verified on a 10-generator 39-bus New England test system. The results of the nonlinear simulation indicate that the optimal sizing, location and controller parameters setting of SVC can improve significantly both static and dynamic performance of the system

First report of mobile tigecycline resistance gene tet(X4)-harbouring multidrug-resistant Escherichia coli from wastewater in Norway

The mobile tigecycline resistance gene tet(X4), conferring resistance to all tetracyclines, is largely reported from China, however the global spread of such a novel resistance mechanism is a concern for preserving the efficacy of these last-resort antibiotics. The aim of our study was to determine the genetic basis of resistance in a tigecycline-resistant Escherichia coli strain (2-326) isolated from sewage in Bergen, Norway, using whole-genome sequencing (WGS).publishedVersio