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The design of a solar PV system and its performance evaluation is an important aspect before going for a mass-scale installation and integration with the grid. The parameter evaluation of a solar PV model helps in accurate modeling and consequently efficient designing of the system. The parameters appear in the mathematical equations of the solar PV cell. A Chaos Induced Coyote Algorithm (CICA) to obtain the parameters in a single, double, and three diode model of a mono-crystalline, polycrystalline, and a thin-film solar PV cell has been proposed in this work. The Chaos Induced Coyote Algorithm for extracting the parameters incorporates the advantages of the conventional Coyote Algorithm by employing only two control parameters, making it easier to include the unique strategy that balances the exploration and exploitation in the search space. A comparison of the Chaos Induced Coyote Algorithm with some recently proposed solar photovoltaic cell parameter extraction algorithms has been presented. Analysis shows superior curve fitting and lesser Root Mean Square Error with the Chaos Induced Coyote Algorithm compared to other algorithms in a practical solar photovoltaic cell.
Shoeb Ahmad Khan; Shafiq Ahmad; Adil Sarwar; Mohd Tariq; Javed Ahmad; Mohammed Asim; Ahmed T. Soliman; Alamgir Hossain. Chaos Induced Coyote Algorithm (CICA) for Extracting the Parameters in a Single, Double, and Three Diode Model of a Mono-Crystalline, Polycrystalline, and a Thin-Film Solar PV Cell. Electronics 2021, 10, 2094 .
AMA StyleShoeb Ahmad Khan, Shafiq Ahmad, Adil Sarwar, Mohd Tariq, Javed Ahmad, Mohammed Asim, Ahmed T. Soliman, Alamgir Hossain. Chaos Induced Coyote Algorithm (CICA) for Extracting the Parameters in a Single, Double, and Three Diode Model of a Mono-Crystalline, Polycrystalline, and a Thin-Film Solar PV Cell. Electronics. 2021; 10 (17):2094.
Chicago/Turabian StyleShoeb Ahmad Khan; Shafiq Ahmad; Adil Sarwar; Mohd Tariq; Javed Ahmad; Mohammed Asim; Ahmed T. Soliman; Alamgir Hossain. 2021. "Chaos Induced Coyote Algorithm (CICA) for Extracting the Parameters in a Single, Double, and Three Diode Model of a Mono-Crystalline, Polycrystalline, and a Thin-Film Solar PV Cell." Electronics 10, no. 17: 2094.
Accurate prediction of wind power generation is complex due to stochastic component, but can play a significant role in minimizing operating costs, and improving reliability and security of a power system. This paper proposes a hybrid deep learning model to accurately forecast the very-short-term (5-min and 10-min) wind power generation of the Boco Rock Wind Farm in Australia. The model consists of a convolutional neural network, gated recurrent units (GRU) and a fully connected neural network. To improve performance, the hyper-parameters of the model are tuned using the Harris Hawks Optimization algorithm. The effectiveness of the proposed model is evaluated against other advanced models, including multilayer feedforward neural network (NN), recurrent neural network (RNN), long short-term memory (LSTM) and GRU. The forecasting model demonstrates around 38% and 24% higher accuracy as compared to the 5- and 10-min forecasting of the NN model, respectively.
Alamgir Hossain; Ripon K. Chakrabortty; Sondoss Elsawah; Evan Mac A Gray; Michael J. Ryan. Predicting Wind Power Generation using Hybrid Deep Learning with Optimization. IEEE Transactions on Applied Superconductivity 2021, PP, 1 -1.
AMA StyleAlamgir Hossain, Ripon K. Chakrabortty, Sondoss Elsawah, Evan Mac A Gray, Michael J. Ryan. Predicting Wind Power Generation using Hybrid Deep Learning with Optimization. IEEE Transactions on Applied Superconductivity. 2021; PP (99):1-1.
Chicago/Turabian StyleAlamgir Hossain; Ripon K. Chakrabortty; Sondoss Elsawah; Evan Mac A Gray; Michael J. Ryan. 2021. "Predicting Wind Power Generation using Hybrid Deep Learning with Optimization." IEEE Transactions on Applied Superconductivity PP, no. 99: 1-1.
In the last decade, there have been significant developments in the field of intelligent energy management systems (IEMSs), with various methods and new solutions proposed for managing the energy resources intelligently. An important issue related to finding the desired outcomes remains unexplored, i.e., how to determine key insights from the sparse academic literature in the age of digital publishing. To mitigate the issue, this study proposes a novel strategy to systematically survey the relevant studies by converting the sparse literature into visual presentations. We first apply a systematic approach called a PRISMA (Preferred Reporting Items for Systematic reviews and Meta-analyses) statement to provide the insights from the published literature of the past decade (2010–2020). Then, VOSviewer experiments are conducted to transform these sparse scholarly data into visual representations. In total, eighty-one papers published in high-impact journals are identified based on their scientific soundness and relevance, and a VOSviewer analysis is applied. The analysis revealed the existence of three research clusters focused on the following main thematic areas: the energy management in smart homes and smart grids (35 journal papers); the emerging concept of context-awareness (26 journal papers); and the role of privacy preservation in IEMSs (20 journal papers). This analysis uncovers the current state of IEMSs and explores existing issues, methods, findings, and gaps. Thus, future research directions have been recommended to fill the existing gaps. This systematic literature review is to assist both researchers and industry practitioners to understand the research gaps of previous studies.
Muhammad Ali; Krishneel Prakash; Alamgir Hossain; Hemanshu R. Pota. Intelligent energy management: Evolving developments, current challenges, and research directions for sustainable future. Journal of Cleaner Production 2021, 314, 127904 .
AMA StyleMuhammad Ali, Krishneel Prakash, Alamgir Hossain, Hemanshu R. Pota. Intelligent energy management: Evolving developments, current challenges, and research directions for sustainable future. Journal of Cleaner Production. 2021; 314 ():127904.
Chicago/Turabian StyleMuhammad Ali; Krishneel Prakash; Alamgir Hossain; Hemanshu R. Pota. 2021. "Intelligent energy management: Evolving developments, current challenges, and research directions for sustainable future." Journal of Cleaner Production 314, no. : 127904.
Automotive applications often experience conflicting-objective optimization problems focusing on performance parameters that are catered through precisely developed cost functions. Two such conflicting objectives which substantially affect the working of traction machine drive are maximizing its speed performance and minimizing its energy consumption. In case of an electric vehicle (EV) powertrain, drive energy is bounded by battery dynamics (charging and capacity) which depend on the consumption of drive voltage and current caused by driving cycle schedules, traffic state, EV loading, and drive temperature. In other words, battery consumption of an EV depends upon its drive energy consumption. A conventional control technique improves the speed performance of EV at the cost of its drive energy consumption. However, the proposed optimized energy control (OEC) scheme optimizes this energy consumption by using robust linear parameter varying (LPV) control tuned by genetic algorithms which significantly improves the EV powertrain performance. The analysis of OEC scheme is conducted on the developed vehicle simulator through MATLAB/Simulink based simulations as well as on an induction machine drive platform. The accuracy of the proposed OEC is quantitatively assessed to be 99.3% regarding speed performance which is elaborated by the drive speed, voltage, and current results against standard driving cycles.
S. Ali; Vivek Sharma; M. Hossain; Subhas Mukhopadhyay; Dong Wang. Optimized Energy Control Scheme for Electric Drive of EV Powertrain Using Genetic Algorithms. Energies 2021, 14, 3529 .
AMA StyleS. Ali, Vivek Sharma, M. Hossain, Subhas Mukhopadhyay, Dong Wang. Optimized Energy Control Scheme for Electric Drive of EV Powertrain Using Genetic Algorithms. Energies. 2021; 14 (12):3529.
Chicago/Turabian StyleS. Ali; Vivek Sharma; M. Hossain; Subhas Mukhopadhyay; Dong Wang. 2021. "Optimized Energy Control Scheme for Electric Drive of EV Powertrain Using Genetic Algorithms." Energies 14, no. 12: 3529.
In this work, the evaluation of the design and optimization of proposed offgrid hybrid microgrid systems for different load dispatch strategies is presented by assessing the component sizes, system responses and different cost analyses of the proposed system. This study optimizes the sizing of the Barishal and Chattogram (two popular divisions in Bangladesh) hybrid microgrid systems consisting of wind turbine, storage unit, solar PV, diesel generator and a load profile of 27.31 kW for five dispatch techniques: (i) generator order, (ii) cycle charging, (iii) load following, (iv) HOMER predictive dispatch and (v) combined dispatch strategy. The considered microgrids are optimized for the least CO 2 gas emission, Net Present Cost, and Levelized Cost of Energy. The two microgrids are analyzed for the five dispatch techniques using HOMER software, and subsequently, the power system performance and feasibility study of the microgrids are performed in MATLAB Simulink. The results in this research provide a guideline to estimate different component sizes and probable costing for the optimal operation of the proposed microgrids under various load dispatch conditions. The simulation results suggest that ‘Load Following’ is the best dispatch strategy for the proposed microgrids having a stable power system response with the lowest net present cost, levelized cost of energy, operating cost, and CO 2 emission rate. Additionally, the combined dispatch strategy is determined to be the worst dispatch technique for the proposed off-grid hybrid microgrid design having the maximum levelized cost of energy, net present cost, operating cost and CO 2 emission.
Fatin Ishraque; Sk. A. Shezan; M. M. Rashid; Ananta Bijoy Bhadra; Alamgir Hossain; Ripon K. Chakrabortty; Michael J. Ryan; Shahriar Rahman Fahim; Subrata K. Sarker; Sajal K. Das. Techno-Economic and Power System Optimization of a Renewable Rich Islanded Microgrid Considering Different Dispatch Strategies. IEEE Access 2021, 9, 77325 -77340.
AMA StyleFatin Ishraque, Sk. A. Shezan, M. M. Rashid, Ananta Bijoy Bhadra, Alamgir Hossain, Ripon K. Chakrabortty, Michael J. Ryan, Shahriar Rahman Fahim, Subrata K. Sarker, Sajal K. Das. Techno-Economic and Power System Optimization of a Renewable Rich Islanded Microgrid Considering Different Dispatch Strategies. IEEE Access. 2021; 9 ():77325-77340.
Chicago/Turabian StyleFatin Ishraque; Sk. A. Shezan; M. M. Rashid; Ananta Bijoy Bhadra; Alamgir Hossain; Ripon K. Chakrabortty; Michael J. Ryan; Shahriar Rahman Fahim; Subrata K. Sarker; Sajal K. Das. 2021. "Techno-Economic and Power System Optimization of a Renewable Rich Islanded Microgrid Considering Different Dispatch Strategies." IEEE Access 9, no. : 77325-77340.
A new multi-objective wind driven optimization algorithm is proposed to size a standalone photovoltaic system’s components to meet the load demand for a mobile network base station at a 1% loss of load probability or less with a minimum annual total life cost. To improve the sized model’s accuracy, a long short-term memory deep learning model is utilized to forecast the hourly performance of a photovoltaic module. The long-term memory model’s performance is compared with those obtained by a linear photovoltaic model and an artificial neural network model. The comparison is carried out based on the values of normalized root mean square error, normalized mean bias error, mean absolute percentage error, and the training and testing time. Accordingly, on the values obtained for these statistical errors, the long short-term memory model outperforms better than the linear model and the artificial neural network model based. In addition, a dynamic battery model is utilized to characterize the dynamic charging and discharging process. The findings show that the optimal number of the photovoltaic array and the capacity of the storage battery required to cover the load demand of a mobile network base station are 5.4 kWp and 2640 Ah/48 V, respectively. Besides, the annual total life cycle cost for the sized photovoltaic/battery configuration is 4028.33 AUD/year. The simulation time for the proposed method is 421.25 s. To generalize the sizing results for the mobile network base stations based on Sydney weather conditions, the photovoltaic array and storage battery ratios are calculated as 0.324 and 0.223, respectively. In addition, the cost of an energy unit generated by the optimized system is 0.254 AUD/kWh. Here, the results of the proposed method have been compared with those obtained by developed and recent benchmark published methods. The comparison outcomes show the effectiveness of the proposed method in terms of providing a high availability sized system at minimum cost within less simulation time than the other considered methods.
Ibrahim Anwar Ibrahim; Slaiman Sabah; Robert Abbas; M.J. Hossain; Hani Fahed. A novel sizing method of a standalone photovoltaic system for powering a mobile network base station using a multi-objective wind driven optimization algorithm. Energy Conversion and Management 2021, 238, 114179 .
AMA StyleIbrahim Anwar Ibrahim, Slaiman Sabah, Robert Abbas, M.J. Hossain, Hani Fahed. A novel sizing method of a standalone photovoltaic system for powering a mobile network base station using a multi-objective wind driven optimization algorithm. Energy Conversion and Management. 2021; 238 ():114179.
Chicago/Turabian StyleIbrahim Anwar Ibrahim; Slaiman Sabah; Robert Abbas; M.J. Hossain; Hani Fahed. 2021. "A novel sizing method of a standalone photovoltaic system for powering a mobile network base station using a multi-objective wind driven optimization algorithm." Energy Conversion and Management 238, no. : 114179.
The optimal operation of solar cells depends on the accurate determination of parameters in the Photovoltaic (PV) models, such as resistance and currents, which may vary due to unstable weathers conditions and equipment aging. The precise selection of these parameters resembles a multi-variable, nonlinear and multi-modal problem. Despite a few parameter extraction techniques being available to solve such a problem, more-accurate and advanced solutions still present a challenging research question. This paper therefore proposes an improved gaining-sharing knowledge (IGSK) algorithm to accurately and precisely extract the parameters of PV models. The improvement in the classical GSK algorithm is incorporated by introducing an adaptive mechanism to automatically adjust the value of the knowledge rate parameter. This adaptive mechanism ensures the balance between the number of dimensions updated by the junior gaining-sharing phase and the number of dimensions updated by the senior gaining-sharing phase. A bound-constraint handling method is also presented and a linear population size reduction technique is used to boost the speed of convergence and to maintain a tared-off between the exploration and exploitation properties. The efficacy of the proposed IGSK has been demonstrated by considering three different PV modules models, i.e., single diode, double diode, and PV modules and two other commercial ones (Thin Film ST40 and Mono-crystalline SM55). For those modules, the proposed IGSK receptively produces the following outcomes: 0.00098602188, 0.0009827277, 0.0024250749, 0.0017298137, and 0.016600603. The statistical obtained results demonstrate that the IGSK indicates competitive or even better performance on convergence speed, accuracy and reliability compared with other competing techniques. Therefore, the proposed approach is believed to be an effective and efficient alternative for parameter extraction of PV models.
Karam M. Sallam; Alamgir Hossain; Ripon K. Chakrabortty; Michael J. Ryan. An improved gaining-sharing knowledge algorithm for parameter extraction of photovoltaic models. Energy Conversion and Management 2021, 237, 114030 .
AMA StyleKaram M. Sallam, Alamgir Hossain, Ripon K. Chakrabortty, Michael J. Ryan. An improved gaining-sharing knowledge algorithm for parameter extraction of photovoltaic models. Energy Conversion and Management. 2021; 237 ():114030.
Chicago/Turabian StyleKaram M. Sallam; Alamgir Hossain; Ripon K. Chakrabortty; Michael J. Ryan. 2021. "An improved gaining-sharing knowledge algorithm for parameter extraction of photovoltaic models." Energy Conversion and Management 237, no. : 114030.
Providing an accurate and precise photovoltaic model is a vital stage prior to the system design, therefore, this paper proposes a novel algorithm, enhanced marine predators algorithm (EMPA), to identify the unknown parameters for different photovoltaic (PV) models including the static PV models (single-diode and double-diode) and dynamic PV model. In the proposed EMPA, the differential evolution operator (DE) is incorporated into the original marine predators algorithm (MPA) to achieve stable, and reliable performance while handling that nonlinear optimization problem of PV modeling. Three different real datasets are used to show the effectiveness of the proposed algorithm. In the first case study, the proposed algorithm is used to identify the unknown parameters of a single-diode and double-diode PV models. The root-mean-square error (RMSE) and standard deviation (STD) values for a single-diode are 7.7301e-04 and 5.9135e-07. Similarly for double diode are 7.4396e-04 and 3.1849e-05, respectively. In addition, the second case study is used to test the proposed model in identifying the unknown parameters of a double-diode PV model. Here, the proposed algorithm is compared with classical MPA in five scenarios at different operating conditions. In this case study, the RMSE and STD of the proposed algorithm are less than that obtained by the MPA algorithm. Moreover, the third case study is utilized to test the ability of the proposed model in identifying the parameters of a dynamic PV model. In this case study, the performance of the proposed algorithm is compared with the one obtained by MAP and heterogeneous comprehensive learning particle swarm optimization (HCLPSO) algorithms in terms of RMSE ± STD. The obtained value of RMSE ± STD by the proposed algorithm is 0.0084505±1.0971e-17, which is too small compared with that obtained by MPA and HCLPSO algorithms (0.0084505±9.6235e-14 and 0.0084505±2.5235e-9). The results show the proposed model’s superiority over the MPA and other recent proposed algorithms in data fitting, convergence rate, stability, and consistency. Therefore, the proposed algorithm can be considered as a fast, feasible, and a reliable optimization algorithm to identify the unknown parameters in static and dynamic PV models. The code of the dynamic PV models is available via this link: https://github.com/DAyousri/Identifying-the-parameters-of-the-integer-and-fractional-order-dynamic-PV-models?_ga=2.104793926.732834951.1616028563-1268395487.1616028563.
Mohamed Abd Elaziz; Sudhakar Babu Thanikanti; Ibrahim Anwar Ibrahim; Songfeng Lu; Benedetto Nastasi; Majed A. Alotaibi; Alamgir Hossain; Dalia Yousri. Enhanced Marine Predators Algorithm for identifying static and dynamic Photovoltaic models parameters. Energy Conversion and Management 2021, 236, 113971 .
AMA StyleMohamed Abd Elaziz, Sudhakar Babu Thanikanti, Ibrahim Anwar Ibrahim, Songfeng Lu, Benedetto Nastasi, Majed A. Alotaibi, Alamgir Hossain, Dalia Yousri. Enhanced Marine Predators Algorithm for identifying static and dynamic Photovoltaic models parameters. Energy Conversion and Management. 2021; 236 ():113971.
Chicago/Turabian StyleMohamed Abd Elaziz; Sudhakar Babu Thanikanti; Ibrahim Anwar Ibrahim; Songfeng Lu; Benedetto Nastasi; Majed A. Alotaibi; Alamgir Hossain; Dalia Yousri. 2021. "Enhanced Marine Predators Algorithm for identifying static and dynamic Photovoltaic models parameters." Energy Conversion and Management 236, no. : 113971.
Intelligent electronic devices (IEDs) along with advanced information and communication technology (ICT)-based networks are emerging in the legacy power grid to obtain real-time system states and provide the energy management system (EMS) with wide-area monitoring and advanced control capabilities. Cyber attackers can inject malicious data into the EMS to mislead the state estimation process and disrupt operations or initiate blackouts. A machine learning algorithm (MLA)-based approach is presented in this paper to detect false data injection attacks (FDIAs) in an IED-based EMS. In addition, stealthy construction of FDIAs and their impact on the detection rate of MLAs are analyzed. Furthermore, the impacts of natural disturbances such as faults on the system are considered, and the research work is extended to distinguish between cyber attacks and faults by using state-of-the-art MLAs. In this paper, state-of-the-art MLAs such as Random Forest, OneR, Naive Bayes, SVM, and AdaBoost are used as detection classifiers, and performance parameters such as detection rate, false positive rate, precision, recall, and f-measure are analyzed for different case scenarios on the IEEE benchmark 14-bus system. The experimental results are validated using real-time load flow data from the New York Independent System Operator (NYISO).
B. M. Ruhul Amin; M. J. Hossain; Adnan Anwar; Shafquat Zaman. Cyber Attacks and Faults Discrimination in Intelligent Electronic Device-Based Energy Management Systems. Electronics 2021, 10, 650 .
AMA StyleB. M. Ruhul Amin, M. J. Hossain, Adnan Anwar, Shafquat Zaman. Cyber Attacks and Faults Discrimination in Intelligent Electronic Device-Based Energy Management Systems. Electronics. 2021; 10 (6):650.
Chicago/Turabian StyleB. M. Ruhul Amin; M. J. Hossain; Adnan Anwar; Shafquat Zaman. 2021. "Cyber Attacks and Faults Discrimination in Intelligent Electronic Device-Based Energy Management Systems." Electronics 10, no. 6: 650.
Accurate forecasting of wind power generation plays a key role in improving the operation and management of a power system network and thereby its reliability and security. However, predicting wind power is complex due to the existence of high non-linearity in wind speed that eventually relies on prevailing weather conditions. In this paper, a novel hybrid deep learning model is proposed to improve the prediction accuracy of very short-term wind power generation for the Bodangora wind farm located in New South Wales, Australia. The hybrid model consists of convolutional layers, gated recurrent unit (GRU) layers and a fully connected neural network. The convolutional layers have the ability to automatically learn complex features from raw data while the GRU layers are capable of directly learning multiple parallel sequences of input data. The data sets of 5-min intervals from the wind farm are used in case studies to demonstrate the effectiveness of the proposed model against other advanced existing models, including long short-term memory, GRU, autoregressive integrated moving average and support vector machine, which are tuned to optimise outcome. To further evaluate the efficacy of the proposed model, another data set taken from the Capital wind farm in Australia is used. It is observed that the hybrid deep learning model exhibits superior performance in both the data sets over other forecasting models to improve the accuracy of wind power forecasting, numerically for the Bodangora wind farm, up to 1.59 per cent in mean absolute error, 3.73 per cent in root mean square error and 8.13 per cent in mean absolute percentage error.
Alamgir Hossain; Ripon K. Chakrabortty; Sondoss Elsawah; Michael J. Ryan. Very short-term forecasting of wind power generation using hybrid deep learning model. Journal of Cleaner Production 2021, 296, 126564 .
AMA StyleAlamgir Hossain, Ripon K. Chakrabortty, Sondoss Elsawah, Michael J. Ryan. Very short-term forecasting of wind power generation using hybrid deep learning model. Journal of Cleaner Production. 2021; 296 ():126564.
Chicago/Turabian StyleAlamgir Hossain; Ripon K. Chakrabortty; Sondoss Elsawah; Michael J. Ryan. 2021. "Very short-term forecasting of wind power generation using hybrid deep learning model." Journal of Cleaner Production 296, no. : 126564.
Economic dispatch is a critical problem in operation of power grids. A consensus-based algorithm was recently proposed to solve the economic dispatch problem in a distributed manner. In this paper, we propose a novel secure scheme for the consensus-based economic dispatch algorithm using the Paillier cryptosystem. This secure scheme ensures that not only the network transmitted information is protected from external malicious party but also the privacy information of each node remains intact. The proposed secure scheme has two features. First, it relies on the solution to the so-called structural consensus problem with time-varying network weights. Second, it contains a strategy for transmitting encrypted information and generating network weights with randomness, as well as treatment of the practical issues like quantization error and computation overflow/underflow. The performance in terms of cost optimization and privacy-preserving is verified by rigorous theoretical analysis and numerical simulation.
Yamin Yan; Zhiyong Chen; Vijay Varadharajan; Jahangir Hossain; Graham E. Town. Distributed Consensus-Based Economic Dispatch in Power Grids Using the Paillier Cryptosystem. IEEE Transactions on Smart Grid 2021, 12, 3493 -3502.
AMA StyleYamin Yan, Zhiyong Chen, Vijay Varadharajan, Jahangir Hossain, Graham E. Town. Distributed Consensus-Based Economic Dispatch in Power Grids Using the Paillier Cryptosystem. IEEE Transactions on Smart Grid. 2021; 12 (4):3493-3502.
Chicago/Turabian StyleYamin Yan; Zhiyong Chen; Vijay Varadharajan; Jahangir Hossain; Graham E. Town. 2021. "Distributed Consensus-Based Economic Dispatch in Power Grids Using the Paillier Cryptosystem." IEEE Transactions on Smart Grid 12, no. 4: 3493-3502.
The high-level penetration of renewable energy sources (RESs) is the main reason for shifting the conventional centralized power system control paradigm into distributed power system control. This massive integration of RESs faces two main problems: complex controller structure and reduced inertia. Since the system frequency stability is directly linked to the system’s total inertia, the renewable integrated system frequency control is badly affected. Thus, a fractional order controller (FOC)-based superconducting magnetic energy storage (SMES) is proposed in this work. The detailed modeling of SMES, FOC, wind, and solar systems, along with the power network, is introduced to facilitate analysis. The FOC-based SMES virtually augments the inertia to stabilize the system frequency in generation and load mismatches. Since the tuning of FOC and SMES controller parameters is challenging due to nonlinearities, the whale optimization algorithm (WOA) is used to optimize the parameters. The optimized FOC-based SMES is tested under fluctuating wind and solar powers. The extensive simulations are carried out using MATLAB Simulink environment considering different scenarios, such as light and high load profile variations, multiple load profile variations, and reduced system inertia. It is observed that the proposed FOC-based SMES improves several performance indices, such as settling time, overshoot, undershoot compared to the conventional technique.
Alam; Majed Alotaibi; Alam; Alamgir Hossain; Shafiullah; Fahad Al-Ismail; Mamun Ur Rashid; Mohammad Abido. High-Level Renewable Energy Integrated System Frequency Control with SMES-Based Optimized Fractional Order Controller. Electronics 2021, 10, 511 .
AMA StyleAlam, Majed Alotaibi, Alam, Alamgir Hossain, Shafiullah, Fahad Al-Ismail, Mamun Ur Rashid, Mohammad Abido. High-Level Renewable Energy Integrated System Frequency Control with SMES-Based Optimized Fractional Order Controller. Electronics. 2021; 10 (4):511.
Chicago/Turabian StyleAlam; Majed Alotaibi; Alam; Alamgir Hossain; Shafiullah; Fahad Al-Ismail; Mamun Ur Rashid; Mohammad Abido. 2021. "High-Level Renewable Energy Integrated System Frequency Control with SMES-Based Optimized Fractional Order Controller." Electronics 10, no. 4: 511.
From a residential point of view, home energy management (HEM) is an essential requirement in order to diminish peak demand and utility tariffs. The integration of renewable energy sources (RESs) together with battery energy storage systems (BESSs) and central battery storage system (CBSS) may promote energy and cost minimization. However, proper home appliance scheduling along with energy storage options is essential to significantly decrease the energy consumption profile and overall expenditure in real-time operation. This paper proposes a cost-effective HEM scheme in the microgrid framework to promote curtailing of energy usage and relevant utility tariff considering both energy storage and renewable sources integration. Usually, the household appliances have different runtime preferences and duration of operation based on user demand. This work considers a simulator designed in the C++ platform to address the domestic customer’s HEM issue based on usages priorities. The positive aspects of merging RESs, BESSs, and CBSSs with the proposed optimal power sharing algorithm (OPSA) are evaluated by considering three distinct case scenarios. Comprehensive analysis of each scenario considering the real-time scheduling of home appliances is conducted to substantiate the efficacy of the outlined energy and cost mitigation schemes. The results obtained demonstrate the effectiveness of the proposed algorithm to enable energy and cost savings up to 37.5% and 45% in comparison to the prevailing methodology.
Mamun Ur Rashid; Majed Alotaibi; Abdul Chowdhury; Muaz Rahman; Shafiul Alam; Alamgir Hossain; Mohammad Abido. Home Energy Management for Community Microgrids Using Optimal Power Sharing Algorithm. Energies 2021, 14, 1060 .
AMA StyleMamun Ur Rashid, Majed Alotaibi, Abdul Chowdhury, Muaz Rahman, Shafiul Alam, Alamgir Hossain, Mohammad Abido. Home Energy Management for Community Microgrids Using Optimal Power Sharing Algorithm. Energies. 2021; 14 (4):1060.
Chicago/Turabian StyleMamun Ur Rashid; Majed Alotaibi; Abdul Chowdhury; Muaz Rahman; Shafiul Alam; Alamgir Hossain; Mohammad Abido. 2021. "Home Energy Management for Community Microgrids Using Optimal Power Sharing Algorithm." Energies 14, no. 4: 1060.
Although several energy management schemes are developed in the literature, they are not thoroughly examined in order to reduce the impact of uncertain power generation, demand and electricity prices to minimise the operating cost of a small-scale power system. This paper investigates energy management schemes under uncertain environments and proposes a scheduling scheme to minimise the operating cost of a grid-connected microgrid. Optimisation problems are formulated as real-time scheduling approaches, and are solved by developing modified particle swarm optimisation (MPSO) algorithms. The modification in the MPSO algorithm is performed through structural change in incorporating a mechanism that regulates the selection procedure for decision variables. The effectiveness of the proposed algorithm is justified by comparing the results with other recent algorithms. All the algorithms are separately tuned using the Taguchi technique to demonstrate a fair comparison in solving the scheduling problem. The scheduling program demonstrates superior performance in all cases, including when there is uncertainty in prediction, as compared to other energy management approaches, although solutions have significant deviations due to prediction errors. It is also shown that the proposed MPSO algorithm for the scheduling program can save 16.80 per cent operational cost as compared to the PSO algorithm.
Alamgir Hossain; Ripon K. Chakrabortty; Michael J. Ryan; Hemanshu Roy Pota. Energy management of community energy storage in grid-connected microgrid under uncertain real-time prices. Sustainable Cities and Society 2020, 66, 102658 .
AMA StyleAlamgir Hossain, Ripon K. Chakrabortty, Michael J. Ryan, Hemanshu Roy Pota. Energy management of community energy storage in grid-connected microgrid under uncertain real-time prices. Sustainable Cities and Society. 2020; 66 ():102658.
Chicago/Turabian StyleAlamgir Hossain; Ripon K. Chakrabortty; Michael J. Ryan; Hemanshu Roy Pota. 2020. "Energy management of community energy storage in grid-connected microgrid under uncertain real-time prices." Sustainable Cities and Society 66, no. : 102658.
The main goal of an interconnected power system is to transfer power from one area to another while the network frequency and tie‐line flow remain within the prescribed limits. However, both of these quantities may violate their desired values during this transfer due to disturbances in the network. This paper proposes a stratagem for choosing the right feedback path for an interconnected power system to maintain the system frequency and tie‐line flows within the prescribed limits while external disturbances exist. Area control error (ACE), a combination of frequency error and tie‐flow deviations, is used as the performance indicator. In the proposed approach, feedback control is designed using active disturbance rejection controller (ADRC) based load frequency control to tackle ACE. It is observed that the individual load change monitoring is sufficient for selecting the right feedback paths rather than the consideration of simultaneous load changes of all load centres. The effectiveness of the proposed controller for selecting the feedback paths has been tested by conducting several case studies. The results demonstrate that the proposed controller can reduce transient magnitude around 57% for ACE, 55% for frequency error and 72% for tie‐line error as compared to the PID controller.
A. Hasib Chowdhury; Mijanur Rahman; Alamgir Hossain. Optimal feedback path selection for interconnected power systems using load frequency control strategy. IET Generation, Transmission & Distribution 2020, 15, 619 -630.
AMA StyleA. Hasib Chowdhury, Mijanur Rahman, Alamgir Hossain. Optimal feedback path selection for interconnected power systems using load frequency control strategy. IET Generation, Transmission & Distribution. 2020; 15 (4):619-630.
Chicago/Turabian StyleA. Hasib Chowdhury; Mijanur Rahman; Alamgir Hossain. 2020. "Optimal feedback path selection for interconnected power systems using load frequency control strategy." IET Generation, Transmission & Distribution 15, no. 4: 619-630.
To maximize renewable energy usage to combat climate change, the penetration of electric vehicles (EVs) has increased significantly in developed countries. This can cause serious power quality issues, such as increased voltage imbalance and neutral currents, which severely impact the operation of power systems. Although the power quality issue is not a new problem, it requires an improved strategy for the growing penetration of photovoltaic (PV) solar energy and electric vehicles in low-voltage distribution grids and their uncoordinated operation. This paper presents a new control strategy to reduce the number of coordinated EVs to mitigate voltage unbalance and compensate for the neutral current. The proposed control strategy consists of two controllers arranged in a hierarchical structure with the central controller at the top layer and the local controller at the bottom layer. It is evident that the proposed control strategy reduces the number of EVs that need to be coordinated, and further, EV coordination is not required if the grid imbalance is less. This new hierarchical control strategy can improve power quality and reduce data processing overhead and computational complexity.
Rabiul Islam; Haiyan Lu; M. Jahangir Hossain; Li Li. Optimal Coordination of Electric Vehicles and Distributed Generators for Voltage Unbalance and Neutral Current Compensation. IEEE Transactions on Industry Applications 2020, 57, 1069 -1080.
AMA StyleRabiul Islam, Haiyan Lu, M. Jahangir Hossain, Li Li. Optimal Coordination of Electric Vehicles and Distributed Generators for Voltage Unbalance and Neutral Current Compensation. IEEE Transactions on Industry Applications. 2020; 57 (1):1069-1080.
Chicago/Turabian StyleRabiul Islam; Haiyan Lu; M. Jahangir Hossain; Li Li. 2020. "Optimal Coordination of Electric Vehicles and Distributed Generators for Voltage Unbalance and Neutral Current Compensation." IEEE Transactions on Industry Applications 57, no. 1: 1069-1080.
Multi-carrier energy systems have received wide attentions due their flexibility and sustainable characteristics. Although these systems show significant efficiency in providing and consuming energy, the performance of the whole system can be degraded owing to uncertainties arising from different sources. This paper presents a stochastic decentralized model for considering the uncertainties of a system including different types of thermal and electrical private loads using a multi-agent framework. In other words, agents have private ownership and seek for social welfare as well as optimized personal profits. In the proposed model, the gradient projection method is used to implement a fully-decentralize energy trading model. Also, various stochastic scenarios of solar irradiance, prices, and loads are considered using the fast-forward selection algorithm to take into account the uncertainties. Then, to assess the proposed stochastic multi-agent model, “AnyLogic” is used for conducting the simulation studies. The numerical results show that the clearing price is directly affected by the renewable agent without any supervisory control. Moreover, the total operating cost of the considered multi-carrier energy system decreases by ∼7 % considering these uncertainties compared with a deterministic one. However, social welfare declines due to the intrinsically beneficial behavior in private cooperation.
Mohammad Javad Salehpour; Amir Mohammad Alishavandi; M. Jahangir Hossain; Seyyed Mohammad Hosseini Rostami; Jin Wang; Xiaofeng Yu. A stochastic decentralized model for the privately interactive operation of a multi-carrier energy system. Sustainable Cities and Society 2020, 64, 102551 .
AMA StyleMohammad Javad Salehpour, Amir Mohammad Alishavandi, M. Jahangir Hossain, Seyyed Mohammad Hosseini Rostami, Jin Wang, Xiaofeng Yu. A stochastic decentralized model for the privately interactive operation of a multi-carrier energy system. Sustainable Cities and Society. 2020; 64 ():102551.
Chicago/Turabian StyleMohammad Javad Salehpour; Amir Mohammad Alishavandi; M. Jahangir Hossain; Seyyed Mohammad Hosseini Rostami; Jin Wang; Xiaofeng Yu. 2020. "A stochastic decentralized model for the privately interactive operation of a multi-carrier energy system." Sustainable Cities and Society 64, no. : 102551.
Several efforts have been taken to promote clean energy towards a sustainable and green economy. Existing sources of electricity present some complications concerning consumers, utility owners, and the environment. Utility operators encourage household applicants to employ residential energy management (REM) systems. Renewable energy sources (RESs), energy storage systems (ESS), and optimal energy allocation strategies are used to resolve these difficulties. In this paper, the development of a cluster-based energy management scheme for residential consumers of a smart grid community is proposed to reduce energy use and monetary cost. Normally, residential consumers deal with household appliances with various operating time slots depending on consumer preferences. A simulator is designed and developed using C++ software to resolve the residential consumer’s REM problem. The benefits of the RESs, ESS, and optimal energy allocation techniques are analyzed by taking in account three different scenarios. Extensive case studies are carried out to validate the effectiveness of the proposed cluster-based energy management scheme. It is demonstrated that the proposed method can save energy and costs up to 45% and 56% compared to the existing methods.
Mamun Ur Rashid; Fabrizio Granelli; Alamgir Hossain; Shafiul Alam; Fahad Al-Ismail; Rakibuzzaman Shah. Development of Cluster-Based Energy Management Scheme for Residential Usages in the Smart Grid Community. Electronics 2020, 9, 1462 .
AMA StyleMamun Ur Rashid, Fabrizio Granelli, Alamgir Hossain, Shafiul Alam, Fahad Al-Ismail, Rakibuzzaman Shah. Development of Cluster-Based Energy Management Scheme for Residential Usages in the Smart Grid Community. Electronics. 2020; 9 (9):1462.
Chicago/Turabian StyleMamun Ur Rashid; Fabrizio Granelli; Alamgir Hossain; Shafiul Alam; Fahad Al-Ismail; Rakibuzzaman Shah. 2020. "Development of Cluster-Based Energy Management Scheme for Residential Usages in the Smart Grid Community." Electronics 9, no. 9: 1462.
In recent years, photovoltaic (PV) systems have emerged as economical solutions for irrigation systems in rural areas. However, they are characterized by low voltage output and less reliable configurations. To address this issue in this paper, a promising inverter configuration called Impedance (Z)-source inverter (ZSI) is designed and implemented to obtain high voltage output with single-stage power conversion, particularly suitable for irrigation application. An improved and efficient modulation scheme and design specifications of the network parameters are derived. Additionally, a suitable fault-tolerant strategy is developed and implemented to improve reliability and efficiency. It incorporates an additional redundant leg with an improved control strategy to facilitate the fault-tolerant operation. The proposed fault-tolerant circuit is designed to handle switch failures of the inverter modules due to the open-circuit and short-circuit faults. The relevant simulation and experimental results under normal, faulty and post-fault operation are presented. The post-fault operation characteristics are identical to the normal operation. The motor performance characteristics such as load current, torque, harmonic spectrum, and efficiency are thoroughly analysed to prove the suitability of the proposed system for irrigation applications. This study provides an efficient and economical solution for rural irrigation utilized in developing countries, for example, India.
Vivek Sharma; M. Hossain; S. Ali; Muhammad Kashif. A Photovoltaic-Fed Z-Source Inverter Motor Drive with Fault-Tolerant Capability for Rural Irrigation. Energies 2020, 13, 4630 .
AMA StyleVivek Sharma, M. Hossain, S. Ali, Muhammad Kashif. A Photovoltaic-Fed Z-Source Inverter Motor Drive with Fault-Tolerant Capability for Rural Irrigation. Energies. 2020; 13 (18):4630.
Chicago/Turabian StyleVivek Sharma; M. Hossain; S. Ali; Muhammad Kashif. 2020. "A Photovoltaic-Fed Z-Source Inverter Motor Drive with Fault-Tolerant Capability for Rural Irrigation." Energies 13, no. 18: 4630.
Rabiul Islam; Haiyan Lu; Jahangir Hossain; Li Li. Multiobjective Optimization Technique for Mitigating Unbalance and Improving Voltage Considering Higher Penetration of Electric Vehicles and Distributed Generation. IEEE Systems Journal 2020, 14, 3676 -3686.
AMA StyleRabiul Islam, Haiyan Lu, Jahangir Hossain, Li Li. Multiobjective Optimization Technique for Mitigating Unbalance and Improving Voltage Considering Higher Penetration of Electric Vehicles and Distributed Generation. IEEE Systems Journal. 2020; 14 (3):3676-3686.
Chicago/Turabian StyleRabiul Islam; Haiyan Lu; Jahangir Hossain; Li Li. 2020. "Multiobjective Optimization Technique for Mitigating Unbalance and Improving Voltage Considering Higher Penetration of Electric Vehicles and Distributed Generation." IEEE Systems Journal 14, no. 3: 3676-3686.