Optimal adaptive protection of smart grids using high-set relays and smart selection of relay tripping characteristics considering different network configurations and operation modes

Much attention has been paid to optimizing smart grids (SGs) and microgrids (MGs) protection schemes. The SGs' operation in different operating modes (especially grid-connected and islanded conditions) and various system configurations (such as the outage of each of the distribution generations) adversely influence the protection system. The adaptive protection schemes using different setting groups are suitable and reliable solutions to achieve a fast protective system. However, the literature shows a research gap in developing optimized adaptive protection schemes, focusing on constraint reduction, besides the optimal selection of time-current characteristics for direction overcurrent relays (DOCRs) and high-set relays (HSRs). This research aims to fill such a research gap. The power system analyses, such as power flow and short circuit studies, are done in DIgSILENT, and the genetic algorithm (GA) is used to find the optimum solutions. Test results of the IEEE 38-bus distribution system illustrate the advantages of this study compared to existing ones. The comparative test results emphasize that 31.78% and 21.62% decrement in time of the protective scheme in different topologies for the distribution networks of the IEEE 38-bus and IEEE 14-bus test systems could be achievable by simultaneously optimizing relay characteristics and HSRs compared to existing approaches.Much attention has been paid to optimizing smart grids (SGs) and microgrids (MGs) protection schemes. The SGs' operation in different operating modes (especially grid-connected and islanded conditions) and various system configurations (such as the outage of each of the distribution generations) adversely influence the protection system. The adaptive protection schemes using different setting groups are suitable and reliable solutions to achieve a fast protective system. However, the literature shows a research gap in developing optimized adaptive protection schemes, focusing on constraint reduction, besides the optimal selection of time-current characteristics for direction overcurrent relays (DOCRs) and high-set relays (HSRs). This research aims to fill such a research gap. The power system analyses, such as power flow and short circuit studies, are done in DIgSILENT, and the genetic algorithm (GA) is used to find the optimum solutions. Test results of the IEEE 38-bus distribution system illustrate the advantages of this study compared to existing ones. The comparative test results emphasize that 31.78% and 21.62% decrement in time of the protective scheme in different topologies for the distribution networks of the IEEE 38-bus and IEEE 14-bus test systems could be achievable by simultaneously optimizing relay characteristics and HSRs compared to existing approaches

Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment

COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19’s spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications

Optimal communication-aided protection of meshed smart grids considering stability constraints of distributed generations incorporating optimal selection of relay characteristics

The optimized protective systems for smart grids and microgrids have received much attention in recent years. However, besides other selectivity constraints, less attention has been paid to synchronous generators’ stability concerns. The stability challenges are intensified in meshed grids compared to radial configurations. The literature review shows that a knowledge gap exists in introducing a communication-aided protective scheme for meshed smart grids, considering the stability constraints. This study tries to fill such a research gap by proposing an optimal protection system using the communication links between directional overcurrent relays on both sides of protection zones/distribution lines. Assigning the optimum standard characteristics to protective relays is another contribution. The introduced study is applied to the distribution portion of the IEEE 30-bus test system. The proposed method is implemented in DIgSILENT and MATLAB to perform the power system simulations and solve the optimization problem. The comparative test results infer that the proposed communication-aided scheme results in a 49.57% improvement in speed of the protection system, while there is no stability constraint violation compared to conventional communication-free schemes. Test results highlight the advantages of this research to meet the stability constraints of distributed generations and selectivity constraints simultaneously

Optimized protection coordination of microgrids considering power quality-based voltage indices incorporating optimal sizing and placement of fault current limiters

Several solutions, such as fault current limiters (FCLs), have been utilized to mitigate the challenges of smart grids and ADNs’ protection schemes. However, less attention has been paid to simultaneously optimizing the protection coordination and power quality (PQ) concerns, incorporating the FCLs. There is a research gap in the literature in developing a method for optimal placement and impedance value of FCLs to optimize the speed of the protection systems, voltage sag energy index, and voltage sag duration. This research aims to fill such a research gap by proposing a new multi-objective optimization problem based on the optimum placement and impedance value of FCL. The proposed method is examined by implementing the distribution network of the IEEE 30-bus test system. MATLAB and DIgSILENT are linked online to simulate the protection of the smart grid and PQ voltage-based analyses and find the best solutions by the genetic algorithm (GA). The comparative analyses infer that a 13.13% improvement in criteria, e.g., operating time of relays, voltage sag energy, and voltage sag duration, is achieved by optimal placement of FCLs, beside other decision variables

Optimal Techno-economic Sequence-based Set of Diagnostic Tests for Distribution Transformers Using Genetic Algorithm

The diagnostic measurement and tests of transformers are essential. Also, the costs of diagnostic tests are considerable. Hence, proposing a method to determine an economic-technical sequence-bases set of diagnostic tests for transformers is useful and interesting. In this paper, a new method is proposed to determine the optimal sequence-based set of diagnostic tests for distribution transformers. A new objective function based on the branch and bound concept is developed in this paper. The proposed optimization problem is solved by using the Genetic Algorithm (GA). The statistical data regarding the experimental diagnostic tests for more than 20 distribution transformers of South-West Power System Company (Pivdenno-Zakhidna Power System) located in Ukraine have been used. The usage of the actual statistical data of distribution transformers is one of the most important contributions of this paper. The comparison of the obtained optimum test results and those of a typical conventional non-optimum sequence of diagnostic tests illustrate the advantages of the proposed method. By applying the proposed method, it is achievable to perform the comprehensive diagnostic tests with the minimum required costs

Optimization of the scheduling and operation of prosumers considering the loss of life costs of battery storage systems

The deployment of the energy storage systems by prosumers is steadily growing due to uncertainties of renewable energy resources. There is a knowledge gap about the techno-economic analysis of the loss of life (LOL) cost of the battery storage systems (BSSs) in the optimal day-ahead scheduling of the residential and commercial prosumers. This paper tries to fill such a research gap by proposing a novel optimization method for the dayahead scheduling of prosumers, which considers the BSS LOL costs. The comparison of the proposed prosumers’ optimal scheduling method and other existing methods illustrates the techno-economic advantages of the introduced method. Different kinds of BSS technologies, i.e. Li-ion and Lead-acid, are studied in this paper. The proposed method is examined under different weather conditions and corresponding output power of renewable distributed generations. The sensitivity analysis is carried out to investigate how the changes in the BSS LOL cost affect the prosumers’ optimal costs and the operational decisions. The sensitivity analysis results infer that the effectiveness of the proposed method is highlighted when the BSS LOL costs are increased

Stochastic operation and scheduling of energy hub considering renewable energy sources’ uncertainty and N-1 contingency

The optimization of energy systems, including various kinds of energies, based on the concepts of energy hub (EH) has received a great deal of attention. Different methods have been introduced for optimization of EH’s operation, which considered the uncertainties of distributed renewable energy sources (DRESs). However, a knowledge gap exists in developing a stochastic optimization method, which comprehensively considers the uncertainties of DRESs and different configurations of EH due to outage of sub-systems. In existing methods, it has been assumed that the EH’s sub-systems are ideal and they are in-service every time. Therefore, most of the introduced methods have been developed based on the base configuration of EH, while all sub-systems are available. This paper tries to fill such a knowledge gap by proposing a new stochastic optimization method considering different configurations of EH due to N-1 contingency as well as DRESs’ uncertainties. The comparison of test results with other available deterministic and probabilistic methods that did not concern the different EH’s configurations illustrates the advantages of the proposed method. Test results show that more than 9 % inaccuracy might occur due to non-consideration of different EH’s configurations

Reliability Evaluation of Smart Microgrids Considering Cyber Failures and Disturbances under Various Cyber Network Topologies and Distributed Generation’s Scenarios

Smart microgrids (SMGs), as cyber–physical systems, are essential parts of smart grids. The SMGs’ cyber networks facilitate efficient system operation. However, cyber failures and interferences might adversely affect the SMGs. The available studies about SMGs have paid less attention to SMGs’ cyber–physical features compared to other subjects. Although a few current research works have studied the cyber impacts on SMGs’ reliability, there is a research gap about reliability evaluation simultaneously concerning all cyber failures and interferences under various cyber network topologies and renewable distributions scenarios. This article aims to fill such a gap by developing a new Monte Carlo simulation-based reliability assessment method considering cyber elements’ failures, data/information transmission errors, and routing errors under various cyber network topologies. Considering the microgrid control center (MGCC) faults in comparion to other failures and interferences is one of the major contributions of this study. The reliability evaluation of SMGs under various cyber network topologies, particularly based on an MGCC’s redundancy, highlights this research’s advantages. Moreover, studying the interactions of uncertainties for cyber systems and distributed generations (DGs) under various DG scenarios is another contribution. The proposed method is applied to a test system using actual historical data. The comparative test results illustrate the advantages of the proposed method

Analytical reliability evaluation method of smart micro-grids considering the cyber failures and information transmission system faults

The reliability of smart micro-grids (SMGs), as a cyber-physical system (CPS), might be influenced by cyber failures and information transmission faults. Several Monte Carlo simulation (MCS)-based approaches have been reported to assess the reliability of SMGs and smart grids. On the other hand, analytical reliability assessment methods have been presented in some research works, while the cyber system has not been concerned. However, the literature shows a research gap in developing an accurate and fast reliability evaluation method for SMGs based on the unavailability of cyber elements and information transmission faults. This article tries to fill the discussed gap by adding the analytical modelling of cyber-physical interdependencies and information transmission faults to available analytical methods, focusing on physical uncertainties. Comparing the proposed model with existing MCS-based and analytical reliability evaluation methods illustrates the advantages of this research. Test results show that less than 5.7% expected energy not supplied (EENS) error occurs by the proposed method, which would be much faster than MCS-based ones. Moreover, the sensitivity analyses highlight the impacts of the cyber network topologies on the cyber-physical interdependencies. © 2022 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.The reliability of smart micro-grids (SMGs), as a cyber-physical system (CPS), might be influenced by cyber failures and information transmission faults. Several Monte Carlo simulation (MCS)-based approaches have been reported to assess the reliability of SMGs and smart grids. On the other hand, analytical reliability assessment methods have been presented in some research works, while the cyber system has not been concerned. However, the literature shows a research gap in developing an accurate and fast reliability evaluation method for SMGs based on the unavailability of cyber elements and information transmission faults. This article tries to fill the discussed gap by adding the analytical modelling of cyber-physical interdependencies and information transmission faults to available analytical methods, focusing on physical uncertainties. Comparing the proposed model with existing MCS-based and analytical reliability evaluation methods illustrates the advantages of this research. Test results show that less than 5.7% expected energy not supplied (EENS) error occurs by the proposed method, which would be much faster than MCS-based ones. Moreover, the sensitivity analyses highlight the impacts of the cyber network topologies on the cyber-physical interdependencies. © 2022 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology

Multi-year load growth-based optimal planning of grid-connected microgrid considering long-term load demand forecasting: A case study of Tehran, Iran

Although much efforts have been devoted to the optimal design of the energy systems, there is a research gap about the multi-year load growth-based optimal planning of microgrids. This paper tries to fill such a research gap by developing a novel method for the optimal design of the grid-connected microgrids based on the longterm load demand forecasting. The multilayer perceptron artificial neural network is used for time-series load prediction. The impacts of the annual load growth are analyzed under various cases based on the consideration and determination methods of yearly load growth. The proposed method is applied to an actual microgrid in Tehran, Iran, using HOMER (Hybrid Optimization of Multiple Energy Resources) software. The load modeling’s capabilities of HOMER software, as a well-known software for the optimal design of energy systems, are used, which have received less attention. Since most existing research works in Iran focused on the off-grid operating mode, the study of an actual microgrid under grid-connected operating mode is one of the most contributions of this paper. The comparison of the obtained results and other available methods illustrate the impacts of the adequately precise estimation of annual load growth in the design of energy systems