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Hamed Hashemi-Dezaki was born in Borujen, Iran, in 1986. He received B.Sc., M.Sc., and Ph.D. degrees in electrical engineering from the Amirkabir University of Technology, Tehran, Iran, in 2008, 2010, and 2015, respectively. Since 2016, he has been an Assistant Professor with the Department of Electrical and Computer Engineering at the University of Kashan, Kashan, Iran. His research interests include the smart grid, power system protection, power system reliability, power system optimization, and high voltage.
This paper is concerned with secure state estimation of non-linear systems under malicious cyber-attacks. The application of target tracking over a wireless sensor network is investigated. The existence of rotational manoeuvre in the target movement introduces non-linear behaviour in the dynamic model of the system. Moreover, in wireless sensor networks under cyber-attacks, erroneous information is spread in the whole network by imperilling some nodes and consequently their neighbours. Thus, they can deteriorate the performance of tracking. Despite the development of target tracking techniques in wireless sensor networks, the problem of rotational manoeuvring target tracking under cyber-attacks is still challenging. To deal with the model non-linearity due to target rotational manoeuvres, an unscented Kalman filter is employed to estimate the target state variables consisting of the position and velocity. A diffusion-based distributed unscented Kalman filtering combined with a trust-based scheme is applied to ensure robustness against the cyber-attacks in manoeuvring target tracking applications over a wireless sensor network with secured nodes. Simulation results demonstrate the effectiveness of the proposed strategy in terms of tracking accuracy, while random attacks, false data injection attacks, and replay attacks are considered.
Mahdieh Adeli; Majid Hajatipour; Mohammad Javad Yazdanpanah; Mohsen Shafieirad; Hamed Hashemi‐Dezaki. Distributed trust‐based unscented Kalman filter for non‐linear state estimation under cyber‐attacks: The application of manoeuvring target tracking over wireless sensor networks. IET Control Theory & Applications 2021, 1 .
AMA StyleMahdieh Adeli, Majid Hajatipour, Mohammad Javad Yazdanpanah, Mohsen Shafieirad, Hamed Hashemi‐Dezaki. Distributed trust‐based unscented Kalman filter for non‐linear state estimation under cyber‐attacks: The application of manoeuvring target tracking over wireless sensor networks. IET Control Theory & Applications. 2021; ():1.
Chicago/Turabian StyleMahdieh Adeli; Majid Hajatipour; Mohammad Javad Yazdanpanah; Mohsen Shafieirad; Hamed Hashemi‐Dezaki. 2021. "Distributed trust‐based unscented Kalman filter for non‐linear state estimation under cyber‐attacks: The application of manoeuvring target tracking over wireless sensor networks." IET Control Theory & Applications , no. : 1.
The thermal analysis of power cables to improve their lifetime and reliability is essential. Also, the current harmonics intensify the excessive heat condition of power cables. Although several research works have been introduced for harmonic-based thermal analysis of power cables, there is a research gap about developing a thermal analysis of power cable focusing on the even current harmonics. This paper tries to such a research gap by proposing a new harmonic-based thermal analysis of electric arc furnace (EAF) power cables, which considers even harmonics. The impacts of ambient conditions and the performance of air-forced ventilation of cable tunnels are studied in this article. The proposed method is applied to an actual steel company located in Iran. The simulation test results based on the finite element method (FEM) in COMSOL Multiphysics are compared with actual measured values to validate the proposed method. Test results illustrate the advantages of the proposed method. Moreover, different paradigms for the cyclic operation of EAFs on the thermal behavior of power cables have been analyzed. Simulation results infer that 4.8% inaccuracy (about 3 ̊C) occurs in the thermal analysis due to neglecting the current harmonics. In addition, approximately 41% improvement is achievable by appropriate forced convection. Test results also imply that the maximum temperature of power cables could be effectively controlled by changes in the operation and scheduling of EAFs. Around 10% decrement in the cable system temperature could be obtained by 10 min increment time interval between two operating cycles of the EAF. Simulation results illustrate the advantages of the proposed study.
Ahmadreza Jamali-Abnavi; Hamed Hashemi-Dezaki; Abdorrasoul Ahmadi; Ehsan Mahdavimanesh; Mohammad-Jafar Tavakoli. Harmonic-based thermal analysis of electric arc furnace's power cables considering even current harmonics, forced convection, operational scheduling, and environmental conditions. International Journal of Thermal Sciences 2021, 170, 107135 .
AMA StyleAhmadreza Jamali-Abnavi, Hamed Hashemi-Dezaki, Abdorrasoul Ahmadi, Ehsan Mahdavimanesh, Mohammad-Jafar Tavakoli. Harmonic-based thermal analysis of electric arc furnace's power cables considering even current harmonics, forced convection, operational scheduling, and environmental conditions. International Journal of Thermal Sciences. 2021; 170 ():107135.
Chicago/Turabian StyleAhmadreza Jamali-Abnavi; Hamed Hashemi-Dezaki; Abdorrasoul Ahmadi; Ehsan Mahdavimanesh; Mohammad-Jafar Tavakoli. 2021. "Harmonic-based thermal analysis of electric arc furnace's power cables considering even current harmonics, forced convection, operational scheduling, and environmental conditions." International Journal of Thermal Sciences 170, no. : 107135.
Many studies have been devoted to developing the optimized protection schemes of smart grids. However, there is a research gap about studying the transient stability constraints in smart grids’ optimized protection schemes. Also, there is a major challenge in the transient stability-oriented protection system's speed due to increased coordination constraints and transient stability for synchronous distributed generations (DGs). This paper tries to fill such a research gap by developing a new transient stability-oriented method for optimized settings of directional overcurrent relays (DOCRs), smartly selecting relay curves in double-inverse relays equipped with high-set (HS) relays. This study's main advantage is achieving a fast and optimal protection system, while all transient stability constraints have been concerned. The proposed method has been studied in the IEEE 33-bus test system, while the GA and PSO algorithms solve the optimization problem. The comparative analysis of the test results with those of other available methods illustrates that 33.7% improvement in the sum of DOCRs’ operating time appears, while there is no stability constraint violation. The validation of speed and both coordination and stability constraints violations using the DIgSILENT simulations is another contribution of this research work.
Ali Narimani; Hamed Hashemi-Dezaki. Optimal stability-oriented protection coordination of smart grid’s directional overcurrent relays based on optimized tripping characteristics in double-inverse model using high-set relay. International Journal of Electrical Power & Energy Systems 2021, 133, 107249 .
AMA StyleAli Narimani, Hamed Hashemi-Dezaki. Optimal stability-oriented protection coordination of smart grid’s directional overcurrent relays based on optimized tripping characteristics in double-inverse model using high-set relay. International Journal of Electrical Power & Energy Systems. 2021; 133 ():107249.
Chicago/Turabian StyleAli Narimani; Hamed Hashemi-Dezaki. 2021. "Optimal stability-oriented protection coordination of smart grid’s directional overcurrent relays based on optimized tripping characteristics in double-inverse model using high-set relay." International Journal of Electrical Power & Energy Systems 133, no. : 107249.
The energy hubs (EHs) have been developed as multi-carrier systems to enhance the energy efficiency, reliability, and economic profits. Also, the residential sector could be techno-economically optimized by applying the residential EH (REH) scheme. The system uncertainties are one of the most crucial problems that might adversely affect the EHs and REHs. The stochastic nature of renewable energy sources’ output power as supply-side’s uncertainties in REHs have received more attention than modelling and studies of demand-side ones, e.g., plug-in hybrid electric vehicle (PHEV) uncertainties. Although several types of research, particularly Monte Carlo simulation-based studies, have been reported for probabilistic optimal operation of REHs, there is a research gap about proposing a fast method considering both PHEVs and RES uncertainties simultaneously. This study aims to investigate the stochastic energy management of REHs, considering intermittencies in the PHEVs and the RESs using the two-point estimation method (2PEM). In this paper, the uncertainties in the PHEVs, i.e., arrival time, driving distance, and departure time, are concerned. The consideration of different charging modes of PHEVs, such as vehicle-to-grid and grid-to-vehicle modes, is another contribution of this article. The comparative results are verified with the current methods, and the proposed method’s efficiency is illustrated.
Pouria Emrani-Rahaghi; Hamed Hashemi-Dezaki; Arezoo Hasankhani. Optimal stochastic operation of residential energy hubs based on plug-in hybrid electric vehicle uncertainties using two-point estimation method. Sustainable Cities and Society 2021, 72, 103059 .
AMA StylePouria Emrani-Rahaghi, Hamed Hashemi-Dezaki, Arezoo Hasankhani. Optimal stochastic operation of residential energy hubs based on plug-in hybrid electric vehicle uncertainties using two-point estimation method. Sustainable Cities and Society. 2021; 72 ():103059.
Chicago/Turabian StylePouria Emrani-Rahaghi; Hamed Hashemi-Dezaki; Arezoo Hasankhani. 2021. "Optimal stochastic operation of residential energy hubs based on plug-in hybrid electric vehicle uncertainties using two-point estimation method." Sustainable Cities and Society 72, no. : 103059.
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.
Mehrdad Aslani; Hamed Hashemi-Dezaki; Abbas Ketabi. Reliability Evaluation of Smart Microgrids Considering Cyber Failures and Disturbances under Various Cyber Network Topologies and Distributed Generation’s Scenarios. Sustainability 2021, 13, 5695 .
AMA StyleMehrdad Aslani, Hamed Hashemi-Dezaki, Abbas Ketabi. Reliability Evaluation of Smart Microgrids Considering Cyber Failures and Disturbances under Various Cyber Network Topologies and Distributed Generation’s Scenarios. Sustainability. 2021; 13 (10):5695.
Chicago/Turabian StyleMehrdad Aslani; Hamed Hashemi-Dezaki; Abbas Ketabi. 2021. "Reliability Evaluation of Smart Microgrids Considering Cyber Failures and Disturbances under Various Cyber Network Topologies and Distributed Generation’s Scenarios." Sustainability 13, no. 10: 5695.
The reliability of the smart grid is adversely affected due to system uncertainties. Also, the steadily growing deployment of renewable distributed generation (DG) units increases the uncertainties of smart grids. Hence, it is essential to concern the uncertainties in the field of reliability evaluation of smart grids. Although the Monte Carlo simulation (MCS) has received a significant deal of consideration in the literature, there is a research gap in using the clustering algorithms to assess smart grids' reliability. This article aims to fill such a research gap by proposing a new reliability assessment method, using various clustering algorithms. The benefits from the proposed method's accuracy and fast computation are highlighted, while optimal operation, optimal short‐term planning, and repetitive problems should be studied. In this paper, the performance and accuracy of various classic (k‐means, fuzzy c‐means, and k‐medoids) and metaheuristic (genetic algorithm, particle swarm optimization, differential evolutionary, harmony search, and artificial bee colony) clustering algorithms are studied. Comparing different scenario reduction algorithms in the proposed reliability evaluation method is one of the most contributions. The proposed method is applied to two realistic test systems. Test results infer that the proposed method is adequately precise, while the required computation time is less than MCS‐based approaches. Test results for both test systems imply that the accurate expected energy not supplied (EENS) with less than 2.1% is achievable applying the proposed method. The fuzzy c‐means clustering algorithm results in the best accuracy among the studied classic and nonclassic (metaheuristic) algorithms.
Mehran Memari; Ali Karimi; Hamed Hashemi‐Dezaki. Reliability evaluation of smart grid using various classic and metaheuristic clustering algorithms considering system uncertainties. International Transactions on Electrical Energy Systems 2021, 31, e12902 .
AMA StyleMehran Memari, Ali Karimi, Hamed Hashemi‐Dezaki. Reliability evaluation of smart grid using various classic and metaheuristic clustering algorithms considering system uncertainties. International Transactions on Electrical Energy Systems. 2021; 31 (6):e12902.
Chicago/Turabian StyleMehran Memari; Ali Karimi; Hamed Hashemi‐Dezaki. 2021. "Reliability evaluation of smart grid using various classic and metaheuristic clustering algorithms considering system uncertainties." International Transactions on Electrical Energy Systems 31, no. 6: e12902.
The smart grid reliability is dramatically affected due to system uncertainties. Although much efforts have been devoted to developing the Monte Carlo simulation (MCS)-based or analytical methods for reliability-based optimal allocation distributed generation (DGs) and protective devices (PDs), there is a research gap about developing the probabilistic scenario-based optimization methods. This paper proposes a novel stochastic scenario-based reliability evaluation method for optimal allocation of smart grids’ PDs and DGs. The scenario reduction is applied using the k-means algorithm and modified system state, including the clusters of renewable-based DGs. The malfunction of PDs is concerned, which is one of the most important contributions of the introduced method. The introduced clustering-based reliability evaluation method is applied to IEEE 33-bus test system. Test results infer that around 10% inaccuracy occurs in deterministic approaches without considering uncertainties of DGs and PDs. Obtained test results also imply that the impacts of renewable DGs’ uncertainties are more considerable than eventual malfunctions of PDs. The MCS-based methods are used to verify the precision of the introduced method. Moreover, by comparing the introduced method with other available analytical methods, it is shown that the obtained results are 1.2% more precise than current analytical ones.
Mohammad-Reza Yaghoubi-Nia; Hamed Hashemi-Dezaki; Abolfazl Halvaei Niasar. Optimal stochastic scenario-based allocation of smart grids’ renewable and non-renewable distributed generation units and protective devices. Sustainable Energy Technologies and Assessments 2021, 44, 101033 .
AMA StyleMohammad-Reza Yaghoubi-Nia, Hamed Hashemi-Dezaki, Abolfazl Halvaei Niasar. Optimal stochastic scenario-based allocation of smart grids’ renewable and non-renewable distributed generation units and protective devices. Sustainable Energy Technologies and Assessments. 2021; 44 ():101033.
Chicago/Turabian StyleMohammad-Reza Yaghoubi-Nia; Hamed Hashemi-Dezaki; Abolfazl Halvaei Niasar. 2021. "Optimal stochastic scenario-based allocation of smart grids’ renewable and non-renewable distributed generation units and protective devices." Sustainable Energy Technologies and Assessments 44, no. : 101033.
Although it is essential to investigate the impacts of the analytical modeling of plug-in hybrid electric vehicles (PHEVs) for reliability and adequacy evaluation of smart grids, this issue has received less attention. This article tries to develop a new analytical reliability and adequacy model of smart grids, considering the precise model of PHEVs. Until now, no solution has been reported to determine the appropriate number of PHEVs’ states in the analytical reliability evaluation methods. In this paper, a novel framework is developed to determine the suitable number of PHEVs’ states that simultaneously guarantees the speed and accuracy of the reliability calculations. The Monte Carlo simulation (MCS) is used to validate the proposed reliability evaluation method of smart grids using the developed analytical model of PHEVs. In addition, the impacts of different stochastic parameters on the adequacy evaluation of PHEVs, such as distance driven, departure time, and arrival time are studied by performing a variety of sensitivity analyses under different charging scenarios. The proposed method is applied to an actual electric distribution network of Kashan, which is located in the Isfahan province distribution company. The comparative test results illustrate the advantages of the introduced analytical reliability evaluation framework. One point that claims attention is the importance of distance driven’s impact and its state reduction in comparison to the impact of arrival time or departure time. Test results imply that reliability calculations of smart grids could be accelerated significantly by investigating the suitable number of discretized states of PHEVs’ characteristics.
Ali-Mohammad Hariri; Maryam A. Hejazi; Hamed Hashemi-Dezaki. Investigation of impacts of plug-in hybrid electric vehicles’ stochastic characteristics modeling on smart grid reliability under different charging scenarios. Journal of Cleaner Production 2020, 287, 125500 .
AMA StyleAli-Mohammad Hariri, Maryam A. Hejazi, Hamed Hashemi-Dezaki. Investigation of impacts of plug-in hybrid electric vehicles’ stochastic characteristics modeling on smart grid reliability under different charging scenarios. Journal of Cleaner Production. 2020; 287 ():125500.
Chicago/Turabian StyleAli-Mohammad Hariri; Maryam A. Hejazi; Hamed Hashemi-Dezaki. 2020. "Investigation of impacts of plug-in hybrid electric vehicles’ stochastic characteristics modeling on smart grid reliability under different charging scenarios." Journal of Cleaner Production 287, no. : 125500.
The concept of the residential energy hub (REH) including electrical and thermal energy storage system has been developed as a good scheme for optimizing the residential energy systems. Although the optimal operation of REHs has received a great deal of attention, there is a research gap to propose a stochastic-based optimization model, which simultaneously concerns the uncertainties of renewable energy resources and electricity prices. This article tries to fill such a research gap by introducing a probabilistic scenario-based model that concerns the renewable energy sources’ uncertainties as well as electricity price's uncertainties. The proposed scenario-based model for optimization of REH operation cost is validated by the comparison of test results and Monte Carlo simulation (MCS)-based ones. It has been concluded that less than 2% inaccuracy occurs due to the simplifying of the proposed model in comparison with simulation-based models like MCS. Also, the proposed model is compared with other available ones. The study of seasonal heat and power demand changes is one of the most crucial contributions of this paper. Furthermore, test results illustrated that the PHEV and heat storage system could act as appropriate solutions to decrease the operation costs of REH.
Pouria Emrani-Rahaghi; Hamed Hashemi-Dezaki. Optimal Scenario-based Operation and Scheduling of Residential Energy Hubs Including Plug-in Hybrid Electric Vehicle and Heat Storage System Considering the Uncertainties of Electricity Price and Renewable Distributed Generations. Journal of Energy Storage 2020, 33, 102038 .
AMA StylePouria Emrani-Rahaghi, Hamed Hashemi-Dezaki. Optimal Scenario-based Operation and Scheduling of Residential Energy Hubs Including Plug-in Hybrid Electric Vehicle and Heat Storage System Considering the Uncertainties of Electricity Price and Renewable Distributed Generations. Journal of Energy Storage. 2020; 33 ():102038.
Chicago/Turabian StylePouria Emrani-Rahaghi; Hamed Hashemi-Dezaki. 2020. "Optimal Scenario-based Operation and Scheduling of Residential Energy Hubs Including Plug-in Hybrid Electric Vehicle and Heat Storage System Considering the Uncertainties of Electricity Price and Renewable Distributed Generations." Journal of Energy Storage 33, no. : 102038.
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.
Jamal Faraji; Hamed Hashemi-Dezaki; Abbas Ketabi. Stochastic operation and scheduling of energy hub considering renewable energy sources’ uncertainty and N-1 contingency. Sustainable Cities and Society 2020, 65, 102578 .
AMA StyleJamal Faraji, Hamed Hashemi-Dezaki, Abbas Ketabi. Stochastic operation and scheduling of energy hub considering renewable energy sources’ uncertainty and N-1 contingency. Sustainable Cities and Society. 2020; 65 ():102578.
Chicago/Turabian StyleJamal Faraji; Hamed Hashemi-Dezaki; Abbas Ketabi. 2020. "Stochastic operation and scheduling of energy hub considering renewable energy sources’ uncertainty and N-1 contingency." Sustainable Cities and Society 65, no. : 102578.
There is a knowledge gap about the development of optimal coordination of microgrids, which considers all N-1 contingencies by using the smart selection of standard relay characteristics. This paper tries to fill such a knowledge gap by contributing to introduce a novel method to optimize the coordination of microgrids’ directional overcurrent relays (DOCRs), which smartly selects the time-current characteristic of relays and considers all system topologies. Achieving an optimal protection scheme of microgrids without any selectivity constraint under various topologies is the main purpose of this research. The proposed objective function of total operating time of DOCRs under various system topologies is linearized as the TDSs. The hybrid heuristic-linear programming algorithms (HHLPAs) are used to solve the mixed-integer non-linear programming (MINLP) problem. The decrease in the number of heuristic algorithm’s decision variables improves the performance of the proposed HHLPAs. The soft computing-based comparison of the hybrid genetic algorithm-linear programming (GA-LP) and the hybrid particle swarm optimization-linear programming (PSO-LP) is another contribution of this paper. About 80% decrease in the DOCRs’ operating time has been achieved by applying the proposed smart selection of standard relay characteristics (normally inverse, very inverse, and extremely inverse) in comparison to use of just normally inverse curve based on existing methods. The satisfaction of coordination constraints of optimum relay settings is validated based on the DIgSILENT protection simulations.
Amir Mohammad Entekhabi-Nooshabadi; Hamed Hashemi-Dezaki; Seyed Abbas Taher. Optimal microgrid’s protection coordination considering N-1 contingency and optimum relay characteristics. Applied Soft Computing 2020, 98, 106741 .
AMA StyleAmir Mohammad Entekhabi-Nooshabadi, Hamed Hashemi-Dezaki, Seyed Abbas Taher. Optimal microgrid’s protection coordination considering N-1 contingency and optimum relay characteristics. Applied Soft Computing. 2020; 98 ():106741.
Chicago/Turabian StyleAmir Mohammad Entekhabi-Nooshabadi; Hamed Hashemi-Dezaki; Seyed Abbas Taher. 2020. "Optimal microgrid’s protection coordination considering N-1 contingency and optimum relay characteristics." Applied Soft Computing 98, no. : 106741.
Prosumer microgrids (PMGs) are considered as active users in smart grids. These units are able to generate and sell electricity to aggregators or neighbor consumers in the prosumer market. Although the optimal scheduling and operation of PMGs have received a great deal of attention in recent studies, the challenges of PMG’s uncertainties such as stochastic behavior of load data and weather conditions (solar irradiance, ambient temperature, and wind speed) and corresponding solutions have not been thoroughly investigated. In this paper, a new energy management systems (EMS) based on weather and load forecasting is proposed for PMG’s optimal scheduling and operation. Developing a novel hybrid machine learning-based method using adaptive neuro-fuzzy inference system (ANFIS), multilayer perceptron (MLP) artificial neural network (ANN), and radial basis function (RBF) ANN to precisely predict the load and weather data is one of the most important contributions of this article. The performance of the forecasting process is improved by using a hybrid machine learning-based forecasting method instead of conventional ones. The demand response (DR) program based on the forecasted data and considering the degradation cost of the battery storage system (BSS) are other contributions. The comparison of obtained test results with those of other existing approaches illustrates that more appropriate PMG’s operation cost is achievable by applying the proposed DR-based EMS using a new hybrid machine learning forecasting method.
Jamal Faraji; Abbas Ketabi; Hamed Hashemi-Dezaki; Miadreza Shafie-Khah; Joao P. S. Catalao. Optimal Day-Ahead Self-Scheduling and Operation of Prosumer Microgrids Using Hybrid Machine Learning-Based Weather and Load Forecasting. IEEE Access 2020, 8, 157284 -157305.
AMA StyleJamal Faraji, Abbas Ketabi, Hamed Hashemi-Dezaki, Miadreza Shafie-Khah, Joao P. S. Catalao. Optimal Day-Ahead Self-Scheduling and Operation of Prosumer Microgrids Using Hybrid Machine Learning-Based Weather and Load Forecasting. IEEE Access. 2020; 8 (99):157284-157305.
Chicago/Turabian StyleJamal Faraji; Abbas Ketabi; Hamed Hashemi-Dezaki; Miadreza Shafie-Khah; Joao P. S. Catalao. 2020. "Optimal Day-Ahead Self-Scheduling and Operation of Prosumer Microgrids Using Hybrid Machine Learning-Based Weather and Load Forecasting." IEEE Access 8, no. 99: 157284-157305.
Uncertainties of renewable energy sources (RESs) such as wind turbine (WT) and photovoltaic (PV) units are one of the considerable challenges of prosumer microgrids (PMGs) for the optimal day‐ahead operation. In this study, a new probabilistic scenario‐based method of optimal scheduling and operation of PMGs is developed. In this regard, different scenarios are generated using Monte Carlo Simulations (MCS). Furthermore, k‐means, k‐medoids, and differential evolution algorithms (DEA) are deployed to cluster the scenarios in the proposed method. A realistic commercial PMG in Iran is selected to apply the introduced method. The validity of the developed probabilistic optimization method for PMG operation is examined by comparing the results under various scenario reduction algorithms and MCS ones. The comparison of the obtained results and those of other existing deterministic methods highlights the advantages of the presented method. Furthermore, the sensitivity analyses are carried out to investigate the robustness of the developed method against the increase in the system uncertainty level. According to the test results, it is concluded that the k‐medoids algorithm has the best performance in comparison with the k‐means and the DEA‐based clustering under various conditions.
Jamal Faraji; Hamed Hashemi‐Dezaki; Abbas Ketabi. Optimal probabilistic scenario‐based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources. Energy Science & Engineering 2020, 8, 3942 -3960.
AMA StyleJamal Faraji, Hamed Hashemi‐Dezaki, Abbas Ketabi. Optimal probabilistic scenario‐based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources. Energy Science & Engineering. 2020; 8 (11):3942-3960.
Chicago/Turabian StyleJamal Faraji; Hamed Hashemi‐Dezaki; Abbas Ketabi. 2020. "Optimal probabilistic scenario‐based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources." Energy Science & Engineering 8, no. 11: 3942-3960.
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 day-ahead 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.
Jamal Faraji; Abbas Ketabi; Hamed Hashemi-Dezaki. Optimization of the scheduling and operation of prosumers considering the loss of life costs of battery storage systems. Journal of Energy Storage 2020, 31, 101655 .
AMA StyleJamal Faraji, Abbas Ketabi, Hamed Hashemi-Dezaki. Optimization of the scheduling and operation of prosumers considering the loss of life costs of battery storage systems. Journal of Energy Storage. 2020; 31 ():101655.
Chicago/Turabian StyleJamal Faraji; Abbas Ketabi; Hamed Hashemi-Dezaki. 2020. "Optimization of the scheduling and operation of prosumers considering the loss of life costs of battery storage systems." Journal of Energy Storage 31, no. : 101655.
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.
Mohammad Behdad Jamshidi; Ali Lalbakhsh; Jakub Talla; Zdenek Peroutka; Farimah Hadjilooei; Pedram Lalbakhsh; Morteza Jamshidi; Luigi La Spada; Mirhamed Mirmozafari; Mojgan Dehghani; Asal Sabet; Saeed Roshani; Sobhan Roshani; Nima Bayat-Makou; Bahare Mohamadzade; Zahra Malek; Alireza Jamshidi; Sarah Kiani; Hamed Hashemi-Dezaki; Wahab Mohyuddin. Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment. IEEE Access 2020, 8, 109581 -109595.
AMA StyleMohammad Behdad Jamshidi, Ali Lalbakhsh, Jakub Talla, Zdenek Peroutka, Farimah Hadjilooei, Pedram Lalbakhsh, Morteza Jamshidi, Luigi La Spada, Mirhamed Mirmozafari, Mojgan Dehghani, Asal Sabet, Saeed Roshani, Sobhan Roshani, Nima Bayat-Makou, Bahare Mohamadzade, Zahra Malek, Alireza Jamshidi, Sarah Kiani, Hamed Hashemi-Dezaki, Wahab Mohyuddin. Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment. IEEE Access. 2020; 8 ():109581-109595.
Chicago/Turabian StyleMohammad Behdad Jamshidi; Ali Lalbakhsh; Jakub Talla; Zdenek Peroutka; Farimah Hadjilooei; Pedram Lalbakhsh; Morteza Jamshidi; Luigi La Spada; Mirhamed Mirmozafari; Mojgan Dehghani; Asal Sabet; Saeed Roshani; Sobhan Roshani; Nima Bayat-Makou; Bahare Mohamadzade; Zahra Malek; Alireza Jamshidi; Sarah Kiani; Hamed Hashemi-Dezaki; Wahab Mohyuddin. 2020. "Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment." IEEE Access 8, no. : 109581-109595.
Energy management systems (EMSs) play an important role in the optimal operation of prosumers. As an essential segment of each EMS, the load forecasting (LF) block enhances the optimal utilization of renewable energy sources (RESs) and battery energy storage systems (BESSs). In this paper, a new optimal day-ahead scheduling and operation of the prosumer is proposed based on the two-level corrective LF. The proposed two-level corrective LF actions are developed through a very precise shortterm LF. In the first level, a time-series LF is applied using multi-layer perceptron artificial neural networks (MLP-ANNs). In order to improve the accuracy of the forecasted load data at the first level, the second level corrective LF is applied using feed-forward (FF) ANNs. The second stage prediction is initiated when the LF results violate the pre-defined criteria. The proposed method is applied to a prosumer under different cases (based on the consideration of BESS operation behaviors and cost) and various scenarios (based on the accuracy of the load data). The obtained optimal day-ahead operation results illustrate the advantages of the proposed method and its corrective forecasting process. The comparison of the obtained results and those of other available ones show the effectiveness of the proposed optimal operation of the prosumers. The advantages of the proposed method are highlighted while the BESS costs are considered.
Jamal Faraji; Abbas Ketabi; Hamed Hashemi-Dezaki; Miadreza Shafie-Khah; Joao P. S. Catalao. Optimal Day-Ahead Scheduling and Operation of the Prosumer by Considering Corrective Actions Based on Very Short-Term Load Forecasting. IEEE Access 2020, 8, 83561 -83582.
AMA StyleJamal Faraji, Abbas Ketabi, Hamed Hashemi-Dezaki, Miadreza Shafie-Khah, Joao P. S. Catalao. Optimal Day-Ahead Scheduling and Operation of the Prosumer by Considering Corrective Actions Based on Very Short-Term Load Forecasting. IEEE Access. 2020; 8 (99):83561-83582.
Chicago/Turabian StyleJamal Faraji; Abbas Ketabi; Hamed Hashemi-Dezaki; Miadreza Shafie-Khah; Joao P. S. Catalao. 2020. "Optimal Day-Ahead Scheduling and Operation of the Prosumer by Considering Corrective Actions Based on Very Short-Term Load Forecasting." IEEE Access 8, no. 99: 83561-83582.
Because of the importance of smart grids reliability, various stochastic-based simulation methodologies e.g. Monte Carlo simulation (MCS) have been developed. In contrast, the much less effort has been devoted in literature to develop the generalized analytical ones. In this paper, a new generalized analytical methodology is developed for smart grids’ reliability assessment. The main contributions of this article are proposing a new state matrix (S-matrix) method including the novel model for investigating the smart grid operating modes by using the segmentation concepts and graph theory; developing a new comprehensive model of PHEVs considering the all their uncertainties; and introduction of a novel integrating methodology of separate elements. The proposed method is applied to the IEEE 33-bus test system. The comparison of test results and those of MCS-based methods and also with other available analytical methods illustrate the accuracy of the proposed method. The sensitivity analyses results imply that the proposed method is not adversely affected due to changes in different uncertain parameters.
Ali-Mohammad. Hariri; Hamed. Hashemi-Dezaki; Maryam. A. Hejazi. A novel generalized analytical reliability assessment method of smart grids including renewable and non-renewable distributed generations and plug-in hybrid electric vehicles. Reliability Engineering & System Safety 2019, 196, 106746 .
AMA StyleAli-Mohammad. Hariri, Hamed. Hashemi-Dezaki, Maryam. A. Hejazi. A novel generalized analytical reliability assessment method of smart grids including renewable and non-renewable distributed generations and plug-in hybrid electric vehicles. Reliability Engineering & System Safety. 2019; 196 ():106746.
Chicago/Turabian StyleAli-Mohammad. Hariri; Hamed. Hashemi-Dezaki; Maryam. A. Hejazi. 2019. "A novel generalized analytical reliability assessment method of smart grids including renewable and non-renewable distributed generations and plug-in hybrid electric vehicles." Reliability Engineering & System Safety 196, no. : 106746.
Ali-Mohammad Hariri; Maryam A. Hejazi; Hamed Hashemi-Dezaki. Reliability optimization of smart grid based on optimal allocation of protective devices, distributed energy resources, and electric vehicle/plug-in hybrid electric vehicle charging stations. Journal of Power Sources 2019, 436, 1 .
AMA StyleAli-Mohammad Hariri, Maryam A. Hejazi, Hamed Hashemi-Dezaki. Reliability optimization of smart grid based on optimal allocation of protective devices, distributed energy resources, and electric vehicle/plug-in hybrid electric vehicle charging stations. Journal of Power Sources. 2019; 436 ():1.
Chicago/Turabian StyleAli-Mohammad Hariri; Maryam A. Hejazi; Hamed Hashemi-Dezaki. 2019. "Reliability optimization of smart grid based on optimal allocation of protective devices, distributed energy resources, and electric vehicle/plug-in hybrid electric vehicle charging stations." Journal of Power Sources 436, no. : 1.
There is a knowledge gap about how the load modeling of smart grid affects the accuracy of the analytical reliability evaluation process and its calculation time. This paper tries to fill such a research gap by contributing to developing a methodology which is used to quantify the time reduction of the smart grid reliability assessment provided by various load state reduction strategies. In this paper, four novel load state reduction strategies (the aggregation of identical load states and time/magnitude/time of use pricing-dependent ones) are developed. It is useful to determine the appropriate number of load states according to the trade-off between the computing time and calculation accuracy. The sensitivity analyses are performed to get insight into how the load modeling of smart grids is affected due to various DG technology scenarios and their different penetration levels. To illustrate the advantages of the proposed method, it is applied to IEEE 69-bus test system. The test results imply that a significant time reduction of the reliability assessment process has been achieved while the desired accuracy criteria have been satisfied.
Hamed Hashemi-Dezaki; Ali-Mohammad Hariri; Maryam A. Hejazi. Impacts of load modeling on generalized analytical reliability assessment of smart grid under various penetration levels of wind/solar/non-renewable distributed generations. Sustainable Energy, Grids and Networks 2019, 20, 100246 .
AMA StyleHamed Hashemi-Dezaki, Ali-Mohammad Hariri, Maryam A. Hejazi. Impacts of load modeling on generalized analytical reliability assessment of smart grid under various penetration levels of wind/solar/non-renewable distributed generations. Sustainable Energy, Grids and Networks. 2019; 20 ():100246.
Chicago/Turabian StyleHamed Hashemi-Dezaki; Ali-Mohammad Hariri; Maryam A. Hejazi. 2019. "Impacts of load modeling on generalized analytical reliability assessment of smart grid under various penetration levels of wind/solar/non-renewable distributed generations." Sustainable Energy, Grids and Networks 20, no. : 100246.
In this paper, a novel approach is proposed for investigating the critical elements of distribution systems based on the sensitivity analysis of reliability indices in terms of changes in the failure rate of various system elements. By using the proposed approach, it is possible to determine the impact of distribution network components based on their types (overhead lines, cable, transformers, substation, etc.) on the system reliability. Therefore, the appropriate strategies can be developed to improve the system reliability, corrective actions, and strict repair strategy for critical points. One of the advantages of the proposed method is its flexibility for the existing conditions of the distribution system and it doesn't require specific infrastructure or studies with widespread mathematical complexity. The proposed method is applied to the actual 20 kV distribution network of University of Kashan by using the DIgSILENT, MATLAB, and their interconnection. This feature (link between MATLAB and DIgSILENT) would be very interesting for the engineers of distribution companies, because its implementation is simple. The results illustrate the usefulness of the proposed approach in different situations.
Aminabbas Golshanfard; Hamed Hashemi-Dezaki. Sensitivity Analysis of Distribution System Reliability for Identifying the Critical Elements. 2019 27th Iranian Conference on Electrical Engineering (ICEE) 2019, 522 -526.
AMA StyleAminabbas Golshanfard, Hamed Hashemi-Dezaki. Sensitivity Analysis of Distribution System Reliability for Identifying the Critical Elements. 2019 27th Iranian Conference on Electrical Engineering (ICEE). 2019; ():522-526.
Chicago/Turabian StyleAminabbas Golshanfard; Hamed Hashemi-Dezaki. 2019. "Sensitivity Analysis of Distribution System Reliability for Identifying the Critical Elements." 2019 27th Iranian Conference on Electrical Engineering (ICEE) , no. : 522-526.