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A group of distributed generators (DGs) systems including wind, solar, diesel, energy storage (ES), etc., that are under a central management and control is often considered as virtual power plant (VPP) concept. One of the components of a VPP is ES, whose presence and participation in the electricity market can create business opportunities. In this paper, a new mathematical-based strategy for identifying different types of trading situations considering VPPs effects is proposed in the electricity market to obtain maximum benefit. Also VPP trading between energy and ancillary services is considered and analysed. The presented model considers all limitations of the VPP including network constrains and the structure of VPPs. The optimal management of distributed energy units determines the state of charge (SoC) or discharge of ES resources and the amount of intermittent load for the day ahead electricity market. By implementing the proposed model on the microgrid (MG), two different modes of trading for VPPs are examined and the changes of efficiency related to energy storages are analysed. In order to solve the issue of optimal operation strategy, an intelligent approach based on differential evolution (DE) algorithm is used. The obtained simulation results of both modes are compared with those VPP without energy storage. The results show notable profits in both modes.

To control a smart-island (which comprises three distributed generation (DG) units with their autonomous converters), based on a nonlinear backstepping control (BSC) scheme, an adaptive reference signal, and a state observer are designed to improve the steady-state performance of the system. An adaptive reference value has been designed and used instead of a constant reference signal to improve the system's steady-state performance, e.g., total harmonic distortion (THD), peak value, and effective value in the voltage control mode. The presented adaptive reference is changed based on the error between the reference signal and the output voltage signal to tend the error to zero. Compared to the classical backstepping controllers, the reference signal is pursued with a fast response and a low steady-state of loads alternations. To demonstrate the efficiency, authenticity, and compatibility of the proposed control strategy, offline digital time-domain simulation studies are carried out on a master-slave organized inverter-based microgrids in various load variations like linear and nonlinear loads in MATLAB/Simulink software environment. The obtained results are compared with previously reported techniques. Moreover, the obtained simulation results are verified by performing laboratory-based experimental tests based on digital signal processing (DSP), which validate the proposed control strategy's accuracy, authenticity, and effectiveness under different conditions.

Today, due to the several benefits of using the subway as a clean transportation system and also its expansion in many cities around the world, electrification to urban and suburban railway systems...

Automation in power distribution systems and supervisory control and data acquisition (SCADA), which perform network switching automatically and remotely, allows distribution companies to flexibly control distribution power grids. Cross-section switches also has a significant role in the automation in distribution systems, in that the operational optimization of these switches is able to enhance the supply power quality and reliability indicators, and can be a prosperous solution to increase the reliability, efficiency and overall service quality in services to customers. In this regard, in this work, the genetic optimization algorithm (GOA) approach which integrated the Steepest Descend Technique (SDT) is proposed and enhanced based on the features of the mentioned issue to sketch the optimal location and control of automatic and manual cross-section switches and protection relay systems in distribution power systems. The GOA is able to search globally that can prevent the result from locally convergence, also, GOA gives superior primary solutions for the SDT. Thus, the SDT can search locally with higher performance which increase the solutions' accuracy. Therefore, an optimization formulation is proposed to improve the value-based reliability of the suggested layout considering the cost of customer downtime and the costs related to segmentation of switches and relay protection devices. Also, a distributed generation (DG) system in distribution networks is considered based on the islanded state of generation units. The effectiveness of the optimal suggested procedure is evaluated and represented via performing a practical test system in the distribution network of Ahvaz city in Iran. The results show that using proposed method and by optimally allocating switches maneuver, energy losses without switches are reduced from 310.17 (MWh) to 254.2 (MWh), and also by using DG, losses are reduced from 554.01 to 533.61 which confirms the ability and higher accuracy of the proposed method to improve reliability indices.

Since Smart-Islands (SIs) with advanced cyber-infrastructure are incredibly vulnerable to cyber-attacks, increasing attention needs to be applied to their cyber-security. False data injection attacks (FDIAs) by manipulating measurements may cause wrong state estimation (SE) solutions or interfere with the central control system performance. There is a possibility that conventional attack detection methods do not detect many cyber-attacks; hence, system operation can interfere. Research works are more focused on detecting cyber-attacks that target DC-SE; however, due to more widely uses of AC SIs, investigation on cyber-attack detection in AC systems is more crucial. In these regards, a new mechanism to detect injection of any false data in AC-SE based on signal processing technique is proposed in this paper. Malicious data injection in the state vectors may cause deviation of their temporal and spatial data correlations from their ordinary operation. The suggested detection method is based on analyzing temporally consecutive system states via wavelet singular entropy (WSE). In this method, to adjust singular value matrices and wavelet transforms' detailed coefficients, switching surface based on sliding mode controller are decomposed; then, by applying the stochastic process, expected entropy values are calculated. Indices are characterized based on the WSE in switching level of current and voltage for cyber-attack detection. The proposed detection method is applied to different case studies to detect cyber-attacks with various types of false data injection, such as amplitude, and vector deviation signals. The simulation results confirm the high-performance capability of the proposed FDIA detection method. This detection method's significant characteristic is its ability in fast detection (10 ms from the attack initiation); besides, this technique can achieve an accuracy rate of over 96.5%.

Introduction: Studies report that mobility changes could be present in early stages of Alzheimer’s disease (AD) or even in previous stages, such as mild cognitive impairment (MCI). The use of motor tests, involving dual task, could facilitate screening and differentiation between the elderly with AD and MCI. The goal of this study is to design and validate dual-tasks based on Kinect sensor in a virtual reality environment to evaluate cognitive disorders in the elderly. Materials and Methods: This was a validating study with the statistical society including the psychologists who were expert in the field of AD treatment, cognitive disorders, and the educational technologies, among whom 10 people were purposefully selected. A package of dual tasks based on Kinect sensor in a virtual reality environment was developed and evaluated for assessing cognitive disorders in geriatrics. The tools used in this study included a three dimensional game made by Unity, a Kinect sensor, and a virtual reality headset. To determine the content validity of the software, a questionnaire was prepared by the technical team concerning the software content, and then was filled by the psychologists. The software validity was determined using the Kappa coefficient. Results: Applying the revisions based on the comments received, the experts’ opinions were analyzed and resulted in the Kappa coefficient of 93.4%. Conclusion: The software developed seems to have the acceptable content validity. Dual tasks based on the Kinect sensor may be used to accurately evaluate the cognitive function in the elderly.

Due to the widespread use of electric motors in various industries, it is very important to have optimally designed motors in that they have high efficiency and lower negative effects on the quality of the power grid. Therefore, in this paper, the effects of winding type (wide and concentrated) on ripple torque in internal permanent magnet motor (IPMM) are investigated. In order to reduce the ripple torque and to increase the average torque, by making optimal holes in the rotor surface and using the sensitivity analysis method, the structure of the IPMM is improved. In this method, the number, dimensions and location of holes are optimized using the sensitivity analysis approach, which reduces the ripple torque of the motor. Using a concentrated winding instead of a wide winding, the toothed ripple torque is reduced by approximately 75% while maintaining the average torque value. Also, by making holes in the rotor surface and optimizing them using the finite element technique and sensitivity analysis, it is demonstrated that the amount of ripple torque by 20%. In the proposed approach, it is proved that in the concentrated winding, in addition to reducing the spatial harmonics, the average amount of torque can also be improved. Obtained results of the simulation confirm the effectiveness of the proposed method.

Renewable energy is investigated as a solution for environmental pollution and mitigating energy tension. Overall, there are two basic subjects in technical and economic evaluation of Renewable Energy Sources (RES) including lack of a general indicator for multi-criteria assessment and difficult to quantify analysis indicators. In this paper, an analytical approach for assessing the technical and economic performance of Power Quality (PQ) changes strategy using Flexible AC Transmission System (FACTS) devices is presented. In addition, an optimisation-based power quality change method for delivering differentiated power quality in grids integrated with renewable generations is proposed. The suggested method is based on the evaluation of financial losses due to several critical power quality phenomena, the cost of different mitigation solutions, and the payback owing to the adoption of particular solution. Furthermore, this accounts for different customers’ requirements and provides differentiated levels of power quality across the grid. In order to verify the functionality of the suggested method, effectiveness of proposed model is applied to the IEEE 118-bus test case, and compared with achieved outcomes. The simulation results present both the financial and technical benefits of the optimal mitigation plan. The developed assessment model could provide a reference for investment decision making and subsidy policy optimizing.

Hybrid renewable system is a particular type of energy systems which can be used as Distributed Generation (DG) resources to reduce network losses and increase its efficiency. Overall, at design phase, there are two major constraints: first, availability, and second, the cost of equipment. In this paper, considering these constraints and using DGs as Renewable Energy Sources (RES) including wind turbines and photovoltaics, an intelligent method based on multi-objective particle swarm optimization is utilized. Besides, battery bank has been used as a backup unit and energy storage of the hybrid system to reduce the volatility of RESs. The purposes of this paper are: to provide a comprehensive analysis on new structures of AC and DC systems, and then, to determine the capacity and optimal design with hybrid RESs in a smart microgrid to increase the availability and reduce network costs. In order to demonstrate the possibility of proposed approach, an optimized method is designed and implemented in two scenarios (Basic, and Maximum Renewable). Effectiveness of the proposed approach is applied over a real study case. By comparing the proposed method with multi-objective genetic algorithm, simulation results show that the proposed method has effective performance in reducing costs and improving availability.

Optimal design, control and management of distributed generations (DGs) such as wind, photovoltaic (PV), and diesel generators can increase the system stability and resilience of the future smart microgrids (MGs). Also, the integration of wind and solar energy can reduce pollution and greenhouse gas emissions. However, the control, sizing and placement of DG in the power system poses a significant challenge. This study proposes a multi-objective Gray Wolf optimization (MOGWO) model for optimal placement of DG in a smart MG. The objectives include minimizing the economic costs, greenhouse gas emissions, and pollution of the entire system. The IEEE 30 system is used to validate the proposed model. Simulation outcomes show that the suggested plan is cost effective and can find the optimal DG placement with the minimum air pollution.

Qualitative advancement in DC microgrids (MGs) has provided the use of various loads with special conditions and characteristics in the power grid. Optimal control of such grids requires the introduction of new tools and methods in the fields of power electronics. In this research work, by merging sliding mode and backstepping control approaches, the stability of DC-MG system using boost converter and constant power load (CPL) with nonlinear disturbance observer (NDO) is guaranteed. In this regards, first, the linear model of the canonical brunovsky form of the system is extracted to solve the problem of nonlinearity of the system, due to the existence of CPL and the problem of non-minimum phase of the boost converter. Then, using a nonlinear observer, the disturbance is estimated. Also, by combining sliding mode control and backstepping control, the desired control technique is implemented, and the overall stability of the system is achieved. In order to verify the effectiveness of the proposed stabilizing technique, the simulation is performed in MATLAB software package and the results confirm the superiority of dynamic proficiency and higher robustness of the suggested controller compared to other controllers.

Today, in various leading power utilities in developing countries, achieving optimal operational energy management and planning, taking into account the costs reduction of generation, transmission and distribution of electricity, and also reducing the emission of an environmental pollutant becomes more and more important. Optimal use of renewable energy sources (RESs) is an effective way to achieve these goals. In this regard, in this research article, an improved multi-objective differential evolutionary (IMODE) optimization algorithm is suggested and performed to dispatch electricity generations in a smart microgrid (MG) system, taking into account economy and emission as competitive issues. In this paper, a nonlinear equation of multi-objective optimization issue with various equality and inequality limitations is formulated in order to lower the total operational costs of the MG considering environmental pollution effects simultaneously. In order to address the issue of optimal operation of the MG in single-objective and multi-objective forms, an intelligent method according to the improved differential evolutionary (IDE) optimization is utilized and performed and the proposed algorithm is implemented on different problems. First, it is assumed that there is no limit to the exchange of power overhead, and secondly, the limitation of power exchange with the upstream grid is considered. In multi-objective mode, these two modes are also considered. In order to show the impact of renewable energy on the cost, in the third part of the simulations, the operation is solved with maximum participation of renewable energy sources. In the final section, the sensitivity analysis on the number of populations in this problem is performed. The obtained results of the simulation are compared to differential evolutionary (DE) and particle swarm optimization (PSO) techniques. The effectiveness of the suggested multi-operational energy management method is confirmed by applying a study case system.

In this study, an auxiliary damping controller based on a robust controller considering the active and reactive power control loops for a doubly-fed induction generator for wind farms is proposed. The presented controller is able to improve the inter-area oscillation damping. In addition, the proposed controller applies only one accessible local signal as the input; however, it can improve the inter-area oscillation damping and, consequently the system stability for the various working conditions and uncertainties. The oscillatory modes of the system are appointed using the linear analysis. Then, the controller’s parameters are determined using the robust control approaches ($${H}_{\infty }/{H}_{2})$$ H ∞ / H 2 ) with the pole placement and linear matrix inequality method. The results of the modal analysis and time-domain simulations confirm that the controller develops the inter-area oscillation damping under the various working conditions and uncertainties.

In Smart Island (SI) systems, operators of power distribution system usually utilize actual-time measurement information as the Advanced Metering Infrastructure (AMI) to have an accurate, efficient, advanced control and monitor of whole their system. SI system can be vulnerable to complicated information integrity attacks such as False Data Injection Attack (FDIA) on some equipment including sensors and controllers, which can generate misleading operational decision in the system. Hence, lack of detailed research in the evaluation of power system that links the FDIAs with system stability is felt, and it will be important for both assessment of the effect of cyber-attack and taking preventive protection measures. In this regards, time-frequency-based differential approach is proposed for SI cyber-attack detection according to non-stationary signal assessment. In this paper, non-stationary signal processing approach of Hilbert-Huang Transform (HHT) is performed for the FDIA detection in several case studies. Since various critical case studies with a small FDIA in data where accurate and efficient detection can be a challenge, the simulation results confirm the efficiency of HHT approach and the proposed detection frame is compared with shallow model. In this research, the configuration of the SI test case is developed in the MATLAB software with several Distributed Generations (DGs). As a result, it is found that the HHT approach is completely efficient and reliable for FDIA detection target in AC-SI. The simulation results verify that the proposed model is able to achieve accuracy rate of 93.17% and can detect FDIAs less than 50 ms from cyber-attack starting in different kind of scenarios.

Due to the growing use of Renewable Energy Sources (RES) in many countries, the need for an accurate and detailed analysis from the technical and economic view point seems to be necessary. Power Quality (PQ) can also be another considerable matter that needs to be carefully evaluated. In this regards, in this article, an analytical and detailed approach is presented to evaluate the technical and economic performance of the strategy for PQ changes. In addition, an optimization-based PQ change strategy is presented to provide a distinct PQ in integrated grids with renewable generations. The proposed strategy is based on the assessment of financial losses due to changes in the quality of power and the cost estimation of various reduction approaches and repayment solutions. This approach presents various customer requirements and various levels of PQ in the power grid. In order to evaluate the performance of the proposed method, the effectiveness of the presented model has been utilised in an actual case study, and the results have been obtained and analysed. The results of the simulation provide both technical and financial advantages to obtain optimal change strategy.

Ancillary services is used to refer to a variety of operations beyond generation and transmission which are requested to maintain grid stability, security and reliability of power system. These ser...

Nowadays, the role of cyber-physical systems (CPSs) is of paramount importance in power system security since they are more vulnerable to different cyber-attacks. Detection of cyber-attacks on a direct current microgrid (DC-MG) has become a pivotal issue due to the increasing use of them in various electrical engineering applications, from renewable power generations to the distribution of electricity and power system of public transportation and subway electric network. In this study, a novel strategy was provided to diagnose possible false data injection attacks (FDIA) in DC-MGs to enhance the cyber-security of electrical systems. Accordingly, to diagnose cyber-attacks in DC-MG and to identify the FDIA to distributed energy resource (DER) unit, a new procedure of wavelet transform (WT) and singular value decomposition (SVD) based on deep machine learning was proposed. Additionally, this paper presents a developed selective ensemble deep learning (DL) approach using the gray wolf optimization (GWO) algorithm to identify the FDIA in DC-MG. In the first stage, in the paper, to gather sufficient data within the ordinary performance required for the training of the DL network, a DC-MG was operated and controlled with no FDIAs. In the information generation procedure, load changing was considered to have diagnosing datasets for cyber-attack and load variation schemes. The obtained simulation results were compared with the new Shallow model and Hilbert Huang Transform methods, and the results confirmed that the presented approach could more precisely and robustly identify multiple forms of FDIAs with more than 95% precision.

Cyber-physical threats as false data injection attacks (FDIAs) in islanded smart microgrids (ISMGs) are typical accretion attacks, which need urgent consideration. In this regard, this paper proposes a novel cyber-attack detection model to detect FDIAs based on singular value decomposition (SVD) and fast Fourier transform (FFT). Since new research are mostly focusing on FDIAs detection in DC systems, paying attention to AC systems attack detection is also necessary, hence, AC state estimation (SE) have been used in SI analysis and in considering renewable energy sources effect. Whenever malicious data are added into the system state vectors, vectors’ temporal and spatial datum relations might drift from usual operating conditions. In this approach, switching surface based on sliding mode controllers is dialyzed to regulate detailed FFT’s coefficients to calculate singular values. Indexes are determined according to the composition of FFT and SVD in voltage/current switching surface to distinguish the potential cyber-attack. This protection layout is presented for cyber-attack detection and is studied in various types of FDIA forms like amplitude and vector derivation of signals, which exchanged between agents such as smart sensor, control units, smart loads, etc. The prominent advantage of the proposed detection layout is to reduce the time (less than 10 milliseconds from the attack outset) in several kinds of case studies. The proposed method can detect more than 96% accuracy from 2967 sample tests. The performances of the method are carried out on AC-ISMG in MATLAB/Simulink environment.

In this paper, a new prediction model is introduced based on hybrid forecast engine and new feature selection. In this model, the load signal is filtered by feature selection to filter out the best candidates. Then, the proposed forecast engine is predicted the output of feature selection. In this model, the weights of proposed forecast engine are optimised by an intelligent algorithm to increase its accuracy. Effectiveness of the proposed method is applied over real-world engineering test case and compared with other different well-known methods. Obtained results proof the validity of the proposed method.

This paper proposes a framework to extract appropriate locational marginal prices for each type of reserve (up-/down-going reserves at both generation- and demand-sides). The proposed reserve pricing scheme accounts for the lost opportunity of selling the convertible products (energy and reserve). The fair prices can be obtained for capacity reserves applying this framework, since this framework assigns the same prices to the same services provided at the same location. The proposed reserve pricing scheme provides all the market participants with the appropriate signals to modify their offers according to the system operator requirements. The pricing problem is decomposed to different hourly sub-problems considering the bounding constraints. To show the effectiveness of the proposed algorithm, it is applied to the IEEE reliability test system and the results are discussed.

Fuzzy Logic revolution started during 1965 by Prof Lotfi A. Zadeh [1], [2] have evolved uncertainty and indeterminacy analytics forever. Sentiment analysis of published content on social networks is an essential key to analyze and predict social engagement polarities determined by provider's attitude and viewpoint toward a subject, being a profile, topic or single post. This paper uses precious works of Prof Lotfi A. Zadeh to propose a quantitative sentiment calculation method by introducing fuzziness and context dynamism on language-specific lexicons found on published short informal textual content on three holistic contexts of owner, user and the post itself.

Distributed Generations (DGs) have a productive capacity of tens of kilowatts to several megawatts, which are used to produce electrical energy at close proximity to consumers, which of the types of DGs can be named solar cells and Photovoltaics (PVs), fuel cells, micro turbines, wind power plants, and etc. If such kinds of power plants are connected to the network in optimal places, they will have several positive effects on the system, such as reducing network losses, improving the voltage profile, and increasing network reliability. The lack of optimal placement of DGs in the network will increase the costs of energy production and losses in transmission lines. Therefore, it is necessary to optimize the location of such DGs in the network so that the number of DGs, installation locations, and their capacity are determined to which the maximum reduction in network losses occurs. Besides, by applying an appropriate objective function, the evolutionary algorithm can find the optimal location of renewable units with respect to the constraints of the issue. In this paper, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm are used to address the placement of wind and photovoltaic generators simultaneously in two states: With and without considering the effects of greenhouse gas emission. In this regard, first, an analytical method for optimal DG (wind and PV) placement is presented, then, the proposed approach is applied over a real study case, and the simulation carried out using the MATLAB program, hence, the placement problem was solved using GA and PSO and implemented in the IEEE 33-bus radial distribution system. The obtained results were compared and analyzed. The results of the simulation show the improvement of the voltage profile and the reduction of losses in the network.

Minimizing total system failure could improve the reliability of power network in order to optimize power system operation. In this way, reliability analysis has been proposed by researchers to tackle the mentioned problem. This paper investigates an analytical methodology for reliability assessment and failure analysis techniques in an actual distributed power system. We use reliability analysis to evaluate system design and gathering outage data in this paper. Modelling and simulation of our assumed system are implemented in electrical transient analyzer program (ETAP) software. The results of theoretical/practical reliability and failure analysis including mean time between failure, mean time to repair, availability, system average interruption frequency index, system average interruption duration index, consumer average interruption duration index, average service availability index, average service unavailability index, expected energy not supplied, expected interruption costs, and interrupted energy assessment rate are compared with the summary of reliability assessment simulation. The capability and effectiveness of reliability evaluation are demonstrated according to the simulation results through ETAP which obtained by applying it to this power system.

Using blockchain technology as one of the new methods to enhance the cyber and physical security of power systems has grown in importance over the past few years. Blockchain can also be used to improve social welfare and provide sustainable energy for consumers. In this article, the effect of distributed generation (DG) resources on the transmission power lines and consequently fixing its conjunction and reaching the optimal goals and policies of this issue to exploit these resources is investigated. In order to evaluate the system security level, a false data injection attack (FDIA) is launched on the information exchanged between independent system operation (ISO) and under-operating agents. The results are analyzed based on the cyber-attack, wherein the loss of network stability as well as economic losses to the operator would be the outcomes. It is demonstrated that cyber-attacks can cause the operation of distributed production resources to not be carried out correctly and the network conjunction will fall to a large extent, with the elimination of social welfare, the main goals and policies of an independent system operator as an upstream entity are not fulfilled. Besides, the contracts between independent system operators with distributed production resources are not properly closed. In order to stop malicious attacks, a secured policy architecture based on blockchain is developed to keep the security of the data exchanged between ISO and under-operating agents. The obtained results of the simulation confirm the effectiveness of using blockchain to enhance the social welfare for power system users. Besides, it is demonstrated that ISO can modify its polices and use the potential and benefits of distributed generation units to increase social welfare and reduce line density by concluding contracts in accordance with the production values given.

Most loads in the power distribution system of Tehran Metro (Subway) are inductive and lead to poor power factor (PF) especially in Lighting and Power Substation. One of the methods of PF correction is adding or producing the capacitive components within the circuit to eliminate the effect of inductive loads. Optimal capacitor placement (OCP), with the objects of power system voltage profile improvement, PF correction, loss reduction, and line reactive power decrease are of particular importance in power system control and planning. These objects depend on how the capacitive components are installation and to achieve them, since the capacitor placement is nonlinear problem and has some equally and inequality constraint. This article investigates OCP in the actual power distribution grid of Tehran Metro (Line 2) in the presence of nonlinear loads. The placement problem is solved using Genetic Algorithms as implemented in the Electrical Transient Analyser Program software. Results (capacity release, total generation, loading, demand, power losses and number of capacitor banks, costs and annual benefits) are obtained and analysed. This study and simulation shows that by OCP can calculate reduce annual losses and release capacity of equipment in power distribution grid of Tehran Metro such as cables and transformers from reactive power and that will maximize profits. © 2016 Wiley Periodicals, Inc. Complexity, 2016