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The process of constructing a reliable mathematical model of solid oxide fuel cell (SOFC) is a challenge due to its complex nature. This paper proposes a new methodology incorporated a recent meta-heuristic algorithm named parasitism-predation algorithm (PPA) to estimate the optimal parameters of SOFC equivalent circuit. Two experiments are conducted in this work; the first one comprises four measured datasets for a commercial enhanced cylindrical SOFC manufactured by Siemen Energy. While the second series consists of five measured datasets for a theoretical dynamic SOFC stack with 96 connected cells. The collected datasets are measured at different operating conditions. An excessive comparative study is presented with other optimizers of comprehensive learning particle swarm optimization (CLPSO), improved PSO with difference mean with perturbation (DMP_PSO), heterogeneous CLPSO (HCLPSO), locally informed PSO (LIPS), modified CSO with tri-competitive mechanism (MCSO), opposition-based learning competitive PSO (OBLCPSO), ranking-based biased learning swarm optimizer (RBLSO), competitive swarm optimizer (CSO), hybrid Jaya with DE (JayaDE), and social learning PSO (SLPSO). Furthermore, statistical analyses of the ranking tests, multiple sign tests, Friedman tests, and ANOVA are performed. The obtained results confirmed the proposed PPA's competence in constructing a reliable model of SOFC as it provides the least mean square error (MSE) between the measured and estimated characteristics of 2.164e−6 in the first series of experiments at 1073 K, in contrast, the most peer (CLPSO) provides 5.57e−6. Similarly, in the second series of experiments, PPA achieves lease MSE of 7.17e−2 at 973 K; meanwhile, the most peer (CLPSO) attains 5.44e−1.
Dalia Yousri; Ahmed Fathy; Thanikanti Sudhakar Babu; Mohamed R. Berber. Estimating the optimal parameters of solid oxide fuel cell‐based circuit using parasitism‐predation algorithm. International Journal of Energy Research 2021, 1 .
AMA StyleDalia Yousri, Ahmed Fathy, Thanikanti Sudhakar Babu, Mohamed R. Berber. Estimating the optimal parameters of solid oxide fuel cell‐based circuit using parasitism‐predation algorithm. International Journal of Energy Research. 2021; ():1.
Chicago/Turabian StyleDalia Yousri; Ahmed Fathy; Thanikanti Sudhakar Babu; Mohamed R. Berber. 2021. "Estimating the optimal parameters of solid oxide fuel cell‐based circuit using parasitism‐predation algorithm." International Journal of Energy Research , no. : 1.
Integrating distributed generators (DGs) in radial distribution networks plays a vital role in improving the system performance via enhancing the bus voltage and minimizing the system losses. Nonetheless, uncoordinated DGs integration may cause technical issues if they are not efficiently planned, controlled, and operated. Therefore, this paper proposes a new methodology based on the recent metaheuristic chimp optimizer approach (CO) to identify DGs’ optimal allocations and rated powers. This work's objective function is minimizing the total active power loss of the network; the considered constraints are load flow, buses’ voltages, and transmission lines. The proposed CO is characterized by ease of implementation, high convergence rate, and avoiding stuck in local optima. CO is adapted such that the first design variables are integer numbers representing the locations of DGs while the others are assigned to be the DGs’ powers. The proposed CO is applied on three radial networks, 33-bus, 69-bus, and 119-bus, moreover two modes of DGs, unity power factor (DGs generate only active power) and non-unity power factor (DGs generate active and reactive powers), are studied. The results obtained via the proposed CO are compared to other reported approaches of exhaustive load flow (ELF), genetic algorithm (GA), and different programmed approaches of particle swarm optimizer (PSO) and Archimedes optimization algorithm (AOA). The obtained results confirmed the superiority and reliability of the proposed CO methodology in achieving a minor power loss via installing the DGs in the correct sites.
Ahmed Fathy; Dalia Yousri; Almoataz Y. Abdelaziz; Haitham S. Ramadan. Robust approach based chimp optimization algorithm for minimizing power loss of electrical distribution networks via allocating distributed generators. Sustainable Energy Technologies and Assessments 2021, 47, 101359 .
AMA StyleAhmed Fathy, Dalia Yousri, Almoataz Y. Abdelaziz, Haitham S. Ramadan. Robust approach based chimp optimization algorithm for minimizing power loss of electrical distribution networks via allocating distributed generators. Sustainable Energy Technologies and Assessments. 2021; 47 ():101359.
Chicago/Turabian StyleAhmed Fathy; Dalia Yousri; Almoataz Y. Abdelaziz; Haitham S. Ramadan. 2021. "Robust approach based chimp optimization algorithm for minimizing power loss of electrical distribution networks via allocating distributed generators." Sustainable Energy Technologies and Assessments 47, no. : 101359.
In multi-interconnected power system, keeping the changes in frequencies and tie-line powers at their specified values is vital process especially during operation under load disturbance. This target can be achieved by installing load frequency control (LFC), the main object of LFC is damping the deviations of frequencies and tie-line powers to zero. This paper proposes a new approach incorporated recent optimizer of movable damped wave algorithm (MDVA) to identify the unknown parameters of LFC represented by fractional-order proportional integral derivative (FOPID). FOPID controller is selected as it has better and robust performance, the controller is installed in multi-interconnected system with multi-sources considering renewable energy-based plants. Minimizing the integral time absolute error (ITAE) of the change in frequencies and tie-line powers is the main target. Two power systems are considered in this work, the first one comprises photovoltaic (PV) and thermal generating units while the second system includes four plants of PV, wind turbine (WT), and two thermal based plants. Moreover, the generation rate constraints and governor dead-band of thermal plant are considered. Different load disturbances in both studied systems are investigated and the obtained results via the proposed DMVA are compared to coronavirus herd immunity optimizer (CHIO), antlion optimizer (ALO), sooty tern optimization algorithm (STOA), manta ray foraging optimizer (MRFO), and sin-cos algorithm (SCA). Regarding the two-interconnected system, the proposed DMVA succeeded in achieving the best ITAE of 6.3911 during 10% disturbance applied on PV plant. Regarding to the multi-interconnected system, the best (minimum) fitness function is 0.015029 achieved via the proposed DMVA at 1% load disturbance on the first area. The results confirmed the robustness of the proposed algorithm in solving the LFC parameter estimation.
Ahmed Fathy; Abdullah G. Alharbi. Recent Approach Based Movable Damped Wave Algorithm for Designing Fractional-Order PID Load Frequency Control Installed in Multi-Interconnected Plants With Renewable Energy. IEEE Access 2021, 9, 71072 -71089.
AMA StyleAhmed Fathy, Abdullah G. Alharbi. Recent Approach Based Movable Damped Wave Algorithm for Designing Fractional-Order PID Load Frequency Control Installed in Multi-Interconnected Plants With Renewable Energy. IEEE Access. 2021; 9 (99):71072-71089.
Chicago/Turabian StyleAhmed Fathy; Abdullah G. Alharbi. 2021. "Recent Approach Based Movable Damped Wave Algorithm for Designing Fractional-Order PID Load Frequency Control Installed in Multi-Interconnected Plants With Renewable Energy." IEEE Access 9, no. 99: 71072-71089.
This paper identifies the best energy management strategy of hybrid photovoltaic–diesel battery-based water desalination systems in isolated regions using technical, economic and techno–economic criteria. The employed procedures include Criteria Importance Through Intercriteria Correlation (CRITIC) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) as tools for the solution. Twelve alternatives, containing three–four energy management strategies; four energy management strategies, load following (LF), cycle charging (CC), combined LF–CC, and predictive strategy; and three different sizes of brackish water reverse osmosis (BWRO) water desalination units, BWRO-150, BWRO-250, and BWRO-500, are investigated with capacity of 150, 250, and 500 m3/day, respectively. Eight attributes comprising different technical and economic metrics are considered during the evaluation procedure. HOMER Pro® software is utilized to perform the simulation and optimization. The main findings confirmed that the best energy management strategies are predictive strategies and the reverse osmosis (RO) unit’s optimal size is RO-250. For such an option, the annual operating cost and initial costs are $4590 and $78,435, respectively, whereas the cost of energy is $0.156/kWh. The excess energy and unmet loads are 27,532 kWh and 20.3 kWh, respectively. The breakeven grid extension distance and the amount of CO2 are 6.02 km and 14,289 kg per year, respectively. Compared with CC–RO-150, the amount of CO2 has been sharply decreased by 61.2%.
Hegazy Rezk; Basem Alamri; Mokhtar Aly; Ahmed Fathy; Abdul Olabi; Mohammad Abdelkareem; Hamdy Ziedan. Multicriteria Decision-Making to Determine the Optimal Energy Management Strategy of Hybrid PV–Diesel Battery-Based Desalination System. Sustainability 2021, 13, 4202 .
AMA StyleHegazy Rezk, Basem Alamri, Mokhtar Aly, Ahmed Fathy, Abdul Olabi, Mohammad Abdelkareem, Hamdy Ziedan. Multicriteria Decision-Making to Determine the Optimal Energy Management Strategy of Hybrid PV–Diesel Battery-Based Desalination System. Sustainability. 2021; 13 (8):4202.
Chicago/Turabian StyleHegazy Rezk; Basem Alamri; Mokhtar Aly; Ahmed Fathy; Abdul Olabi; Mohammad Abdelkareem; Hamdy Ziedan. 2021. "Multicriteria Decision-Making to Determine the Optimal Energy Management Strategy of Hybrid PV–Diesel Battery-Based Desalination System." Sustainability 13, no. 8: 4202.
In hybrid renewable energy sources containing different storage devices like fuel cells, batteries, and supercapacitors, minimizing the hydrogen consumption is the main target for economic aspects and operation enhancement. External energy maximization strategy (EEMS) is the most popular energy management strategy used with hybrid renewable energy sources. However, gradient-based method is employed in EEMS which has low convergence, moreover it doesn’t guarantee the optimum solution. Therefore, this paper proposes for first time an energy management strategy based on recent metaheuristic optimizer of parasitism-predation algorithm employed in hybrid source comprises photovoltaic/fuel cell/battery/supercapacitor for supplying aircraft in emergency state during landing. The main target is hydrogen consumption minimization, this helps in enhancing the power durability to the aircraft in case of curtailment of the main power source. The selection of parasitism-predation algorithm (PPA) is due to requirement of less parameters defined by the user and its high convergence ability. The proposed strategy is compared to other conventional and programmed approaches of state machine control, water cycle algorithm, dynamic differential annealed optimization, spotted hyena optimizer, EEMS, marine predator algorithm, and mayfly optimization algorithm. The obtained results confirmed the superiority of the proposed method achieving efficiency of 95.34% and minimum hydrogen consumption of 15.7559 gm.
Ahmed Fathy; Dalia Yousri; Turki Alanazi; Hegazy Rezk. Minimum hydrogen consumption based control strategy of fuel cell/PV/battery/supercapacitor hybrid system using recent approach based parasitism-predation algorithm. Energy 2021, 225, 120316 .
AMA StyleAhmed Fathy, Dalia Yousri, Turki Alanazi, Hegazy Rezk. Minimum hydrogen consumption based control strategy of fuel cell/PV/battery/supercapacitor hybrid system using recent approach based parasitism-predation algorithm. Energy. 2021; 225 ():120316.
Chicago/Turabian StyleAhmed Fathy; Dalia Yousri; Turki Alanazi; Hegazy Rezk. 2021. "Minimum hydrogen consumption based control strategy of fuel cell/PV/battery/supercapacitor hybrid system using recent approach based parasitism-predation algorithm." Energy 225, no. : 120316.
The output power of the fuel cell (FC) is mainly depending on the membrane water content, temperature, the hydrogen and oxygen partial pressures. The polarization curve has one global maximum power to be tracked. Therefore, a robust maximum power point tracking (MPPT) is highly required to follow the optimal operating point under any operating conditions. In this article, a recent approach of forensic-based investigation (FBI) algorithm is proposed to identify the optimal parameters of fractional order PID-based MPPT with proton exchange membrane (PEM) fuel cell. FBI is selected due to its high accuracy and requirement of less computational efforts. The considered objective function to be minimized is the error between the voltage at maximum power (V MP ) and the actual one at FC terminals. To prove the robustness of the proposed methodology, four cases of operating conditions are analyzed which are constant membrane water content and temperature, constant membrane water content with variable temperature, variable membrane water content with constant temperature, and variable membrane water content with variable temperature. The obtained results are compared to other approaches such as incremental resistance (INCR), particle swarm optimizer (PSO), invasive weed optimizer (IWO), sin-cosine algorithm (SCA), and artificial ecosystem optimizer (AEO). In case (1), the proposed FBI-FOPID succeeded in achieving maximum power of 5185.101 W. While the minimum objective functions in the second, third, and fourth cases are 2.5736 V, 1.4436 V, and 1.1568 V respectively obtained via the proposed approach. The comparison confirmed the superiority of the proposed FBI-based MPPT compared with other methods.
Ahmed Fathy; Hegazy Rezk; Turki M. Alanazi. Recent Approach of Forensic-Based Investigation Algorithm for Optimizing Fractional Order PID-Based MPPT With Proton Exchange Membrane Fuel Cell. IEEE Access 2021, 9, 18974 -18992.
AMA StyleAhmed Fathy, Hegazy Rezk, Turki M. Alanazi. Recent Approach of Forensic-Based Investigation Algorithm for Optimizing Fractional Order PID-Based MPPT With Proton Exchange Membrane Fuel Cell. IEEE Access. 2021; 9 ():18974-18992.
Chicago/Turabian StyleAhmed Fathy; Hegazy Rezk; Turki M. Alanazi. 2021. "Recent Approach of Forensic-Based Investigation Algorithm for Optimizing Fractional Order PID-Based MPPT With Proton Exchange Membrane Fuel Cell." IEEE Access 9, no. : 18974-18992.
Among the various fuel cell types, proton exchange membrane fuel cells (PEMFCs) have prominent characteristics that make them unique in applications. However, the preciseness of results of a PEMFC model depends on the availability of the parameters, which are missing in the datasheets provided by the manufacturers and vendors, and this explains why it becomes convenient to estimate such parameters for a complete and precise PEMFC model that closely matches the experimental measures under different operation conditions. In this work, a novel solution methodology based on applying an ensemble sinusoidal parameter adaptation incorporated with L‐SHADE, called LSHADE‐EpSin optimization algorithm, is proposed to solve the PEMFC parameter extraction problem. The proposed methodology is applied to four commercial PEMFCs: 250 W PEMFC; NedStack PS6, 6 kW; BCS 500 W; and SR‐12500 W, and the results obtained are compared with the results obtained by using other recent optimization algorithms. Furthermore, several statistical tests were performed to validate the proposed model's performance and compare between the investigated algorithms. The results show the effectiveness of the approach proposed using the LSHADE‐EpSin algorithm in estimating the optimal PEMFC parameters under different operating conditions compared to the other optimizers for the four studied stacks.
Ahmed Fathy; Shady H. E. Abdel Aleem; Hegazy Rezk. A novel approach for PEM fuel cell parameter estimation using LSHADE ‐ EpSin optimization algorithm. International Journal of Energy Research 2020, 45, 6922 -6942.
AMA StyleAhmed Fathy, Shady H. E. Abdel Aleem, Hegazy Rezk. A novel approach for PEM fuel cell parameter estimation using LSHADE ‐ EpSin optimization algorithm. International Journal of Energy Research. 2020; 45 (5):6922-6942.
Chicago/Turabian StyleAhmed Fathy; Shady H. E. Abdel Aleem; Hegazy Rezk. 2020. "A novel approach for PEM fuel cell parameter estimation using LSHADE ‐ EpSin optimization algorithm." International Journal of Energy Research 45, no. 5: 6922-6942.
The operation of photovoltaic (PV) array under shade faces great challenges due to the power loss that reduces the extracted power. Reconfiguration process is one of the most promising solutions to reduce the effect of shadow on the array. However, the physical array relocation methods like total cross-tied (TCT) and Su Do Ku have some limitations due to their complicated arrangements. Therefore, this paper presents a recent metaheuristic approach of coyote optimization algorithm (COA) to solve the reconfiguration process of the partially shaded PV array. The main target is maximizing the global maximum power (GMP) extracted from the array. The proposed COA is applied on 9×9 PV array operated under four standard shadow patterns which are short wide (SW), long wide (LW), short narrow (SN), and long narrow (LN). The obtained configurations via the proposed COA method are compared to TCT, Su Do Ku, flower pollination algorithm (FPA), marine predators algorithm (MPA), and butterfly optimization algorithm (BOA) based arrangements. The best enhancement of GMP obtained via the proposed COA with respect to TCT configuration occurs in SW shadow pattern of 26.58% while the least on is 7.68% placed in SN pattern. The obtained results confirmed the competence and superiority of the proposed COA in reconfiguring the shaded array optimally.
Hegazy Rezk; Ahmed Fathy; Mokhtar Aly. A robust photovoltaic array reconfiguration strategy based on coyote optimization algorithm for enhancing the extracted power under partial shadow condition. Energy Reports 2020, 7, 109 -124.
AMA StyleHegazy Rezk, Ahmed Fathy, Mokhtar Aly. A robust photovoltaic array reconfiguration strategy based on coyote optimization algorithm for enhancing the extracted power under partial shadow condition. Energy Reports. 2020; 7 ():109-124.
Chicago/Turabian StyleHegazy Rezk; Ahmed Fathy; Mokhtar Aly. 2020. "A robust photovoltaic array reconfiguration strategy based on coyote optimization algorithm for enhancing the extracted power under partial shadow condition." Energy Reports 7, no. : 109-124.
The appropriate planning of electric power systems has a significant effect on the economic situation of countries. For the protection and reliability of the power system, the optimal reactive power dispatch (ORPD) problem is an essential issue. The ORPD is a non-linear, non-convex, and continuous or non-continuous optimization problem. Therefore, introducing a reliable optimizer is a challenging task to solve this optimization problem. This study proposes a robust and flexible optimization algorithm with the minimum adjustable parameters named Improved Marine Predators Algorithm and Particle Swarm Optimization (IMPAPSO) algorithm, for dealing with the non-linearity of ORPD. The IMPAPSO is evaluated using various test cases, including IEEE 30 bus, IEEE 57 bus, and IEEE 118 bus systems. An effectiveness of the proposed optimization algorithm was verified through a rigorous comparative study with other optimization methods. There was a noticeable enhancement in the electric power networks behavior when using the IMPAPSO method. Moreover, the IMPAPSO high convergence speed was an observed feature in a comparison with its peers.
Mohamed Shaheen; Dalia Yousri; Ahmed Fathy; Hany Hasanien; Abdulaziz Alkuhayli; S. Muyeen. A Novel Application of Improved Marine Predators Algorithm and Particle Swarm Optimization for Solving the ORPD Problem. Energies 2020, 13, 5679 .
AMA StyleMohamed Shaheen, Dalia Yousri, Ahmed Fathy, Hany Hasanien, Abdulaziz Alkuhayli, S. Muyeen. A Novel Application of Improved Marine Predators Algorithm and Particle Swarm Optimization for Solving the ORPD Problem. Energies. 2020; 13 (21):5679.
Chicago/Turabian StyleMohamed Shaheen; Dalia Yousri; Ahmed Fathy; Hany Hasanien; Abdulaziz Alkuhayli; S. Muyeen. 2020. "A Novel Application of Improved Marine Predators Algorithm and Particle Swarm Optimization for Solving the ORPD Problem." Energies 13, no. 21: 5679.
A significant growth in PV (photovoltaic) system installations have been observed during the last decade. The PV array has a nonlinear output characteristic because of weather intermittency. Partial shading is an environmental phenomenon that causes multiple peaks in the power curve and has a negative effect on the efficiency of the conventional maximum power point tracking (MPPT) methods. This tends to have a substantial effect on the overall performance of the PV system. Therefore, to enhance the performance of the PV system under shading conditions, the global MPPT technique is mandatory to force the PV system to operate close to the global maximum. In this paper, for the first time, a stochastic fractal search (SFS) optimization algorithm is applied to solve the dilemma of tracking the global power of PV system based triple-junction solar cells under shading conditions. SFS has been nominated because it can converge to the best solution at a fast rate. Moreover, balance between exploration and exploitation phases is one of its main advantages. Therefore, the SFS algorithm has been selected to extract the global maximum power point (MPP) under partial shading conditions. To prove the superiority of the proposed global MPPT–SFS based tracker, several shading scenarios have been considered. The idea of changing the shading scenario is to change the position of the global MPP. The obtained results are compared with common optimizers: Antlion Optimizer (ALO), Cuckoo Search (CS), Flower Pollination Algorithm (FPA), Firefly-Algorithm (FA), Invasive-Weed-Optimization (IWO), JAYA and Gravitational Search Algorithm (GSA). The results of comparison confirmed the effectiveness and robustness of the proposed global MPPT–SFS based tracker over ALO, CS, FPA, FA, IWO, JAYA, and GSA.
Hegazy Rezk; Ahmed Fathy. Stochastic Fractal Search Optimization Algorithm Based Global MPPT for Triple-Junction Photovoltaic Solar System. Energies 2020, 13, 4971 .
AMA StyleHegazy Rezk, Ahmed Fathy. Stochastic Fractal Search Optimization Algorithm Based Global MPPT for Triple-Junction Photovoltaic Solar System. Energies. 2020; 13 (18):4971.
Chicago/Turabian StyleHegazy Rezk; Ahmed Fathy. 2020. "Stochastic Fractal Search Optimization Algorithm Based Global MPPT for Triple-Junction Photovoltaic Solar System." Energies 13, no. 18: 4971.
One of the worst negative phenomena faced by photovoltaic (PV) array is the operation under the shadow phenomenon, which significantly affects the generated power. Multiple local maximum power point (MPP) and unique global MPP are generated from the shaded array. Therefore, regular dispersion of the shadow falling on the PV array surface is a vital issue to extract the GMP via reconfiguration of the shaded modules in the array. This paper proposes a recent approach based on Multi-objective grey wolf optimizer (MOGWO) to reconfigure the shaded PV array optimally. The main objective of the proposed MOGWO is providing the optimal structure for the switching matrix to minimize the row current difference and maximize the output power. The benefits of the proposed strategy is performing a dynamic reconfiguration process which closes to the reality. The proposed method is validated across 9 × 9 PV array with six shade patterns. MOGWO schemes results are compared with TCT and modified SuDoKu based on several statistical metrics. The comparison reveals the superiority of MOGWO in tackling the multi-peak issue in the P-V characteristics with harvesting the highest power levels.
Dalia Yousri; Sudhakar Babu Thanikanti; Karthik Balasubramanian; Ahmed Osama; Ahmed Fathy. Multi-Objective Grey Wolf Optimizer for Optimal Design of Switching Matrix for Shaded PV array Dynamic Reconfiguration. IEEE Access 2020, 8, 159931 -159946.
AMA StyleDalia Yousri, Sudhakar Babu Thanikanti, Karthik Balasubramanian, Ahmed Osama, Ahmed Fathy. Multi-Objective Grey Wolf Optimizer for Optimal Design of Switching Matrix for Shaded PV array Dynamic Reconfiguration. IEEE Access. 2020; 8 (99):159931-159946.
Chicago/Turabian StyleDalia Yousri; Sudhakar Babu Thanikanti; Karthik Balasubramanian; Ahmed Osama; Ahmed Fathy. 2020. "Multi-Objective Grey Wolf Optimizer for Optimal Design of Switching Matrix for Shaded PV array Dynamic Reconfiguration." IEEE Access 8, no. 99: 159931-159946.
Identifying accurate and precise photovoltaic models' parameters is the primary gate in providing a proper PV system design simulate its real behavior. Therefore, this article proposed a new approach based on a recent meta‐heuristic algorithm of artificial ecosystem‐based optimization (AEO) to identify the optimal parameters of PV cell and module models. Various PV models are considered in this work as single diode (SD), double diode (DD), and triple diode (TD)‐based circuits. The analysis is performed on which are R.T.C. France silicon solar cell, FSM‐25 PV module, and Canadian‐Solar‐(CS6P‐240P) multi‐crystalline solar panel with the aid of experimental data under different operating conditions. Moreover, Lambert form is employed to validate the constructed model. Furthermore, comparative analysis with Harris hawks optimizer (HHO), gray wolf optimizer (GWO), and salp swarm algorithm (SSA) is performed. Additionally, statistical analysis using the Wilcoxon signed rank test is implemented across the three series of experiments for all employed optimizers. The obtained results confirmed the competence of the proposed approach in identifying the PV cell and modules equivalent circuits' parameters.
Dalia Yousri; Hegazy Rezk; Ahmed Fathy. Identifying the parameters of different configurations of photovoltaic models based on recent artificial ecosystem‐based optimization approach. International Journal of Energy Research 2020, 44, 11302 -11322.
AMA StyleDalia Yousri, Hegazy Rezk, Ahmed Fathy. Identifying the parameters of different configurations of photovoltaic models based on recent artificial ecosystem‐based optimization approach. International Journal of Energy Research. 2020; 44 (14):11302-11322.
Chicago/Turabian StyleDalia Yousri; Hegazy Rezk; Ahmed Fathy. 2020. "Identifying the parameters of different configurations of photovoltaic models based on recent artificial ecosystem‐based optimization approach." International Journal of Energy Research 44, no. 14: 11302-11322.
The output power of a fuel cell mainly depends on the operating conditions such as cell temperature and membrane water content. The fuel cell (FC) power versus FC current graph has a unique maximum power point (MPP). The location of the MPP is variable, depending on the operating condition. Consequently, a maximum power point tracker (MPPT) is highly required to ensure that the fuel cell operates at an MPP to increase its performance. In this research work, a variable step-size incremental resistance (VSS-INR) tracking method was suggested to track the MPP of the proton exchange membrane (PEMFC). Most of MPPT methods used with PEMFC require at least three sensors: temperature sensor, water content sensor, and voltage sensor. However, the proposed VSS-INR needs only two sensors: voltage and current sensors. The step size of the VSS-INR is directly proportional to the error signal. Therefore, the step size will become small as the error becomes very small nearby the maximum power point. Accordingly, the accuracy of the VSS-INR tracking method is high in a steady state. To test and validate the VSS-INR, nine different scenarios of operating conditions, including normal operation, only temperature variation, only variation of water content in the membrane, and both variations of temperature and water content simultaneously, were used. The obtained results were compared with previously proposed methods, including particle swarm optimization (PSO), perturb and observe (P&O), and sliding mode (SM), under different operating conditions. The results of the comparison confirmed the superiority of VSS-INR compared with other methods in terms of the tracking efficiency and steady-state fluctuations.
Hegazy Rezk; Ahmed Fathy. Performance Improvement of PEM Fuel Cell Using Variable Step-Size Incremental Resistance MPPT Technique. Sustainability 2020, 12, 5601 .
AMA StyleHegazy Rezk, Ahmed Fathy. Performance Improvement of PEM Fuel Cell Using Variable Step-Size Incremental Resistance MPPT Technique. Sustainability. 2020; 12 (14):5601.
Chicago/Turabian StyleHegazy Rezk; Ahmed Fathy. 2020. "Performance Improvement of PEM Fuel Cell Using Variable Step-Size Incremental Resistance MPPT Technique." Sustainability 12, no. 14: 5601.
This paper proposes a recent approach-based moth-flame optimizer (MFO) to enhance the output power of solid oxide fuel cell (SOFC) via identifying the optimal parameters of its model. The cell is modeled via artificial neural network (ANN) trained by experimental dataset. Six inputs are fed to ANN to get the SOFC terminal voltage. Levenberg-Marquardt is used in training process with minimizing the mean squared error (MSE). The SOFC model polarization curves are compared to experimental data under variable operating conditions. The proposed MFO is employed to estimate the optimal values of SOFC model, anode support layer (ASL) thickness; ASL porosity; thickness of electrolyte and cathode functional layer (CFL) thickness to enhance the SOFC extracted power. Furthermore, a quantitative and qualitative comparative study with ANN-based SOFC optimized via Genetic Algorithm (GA), Social Spider Optimizer (SSO), Radial Movement Optimizer (RMO) and the experimental data is presented under different operating conditions. Sensitivity analysis is performed by changing the upper and lower thresholds of the estimated variables. The proposed ANN-MFO approach enhanced the SOFC power by 18.92% and 5.56% in comparison with ANN-GA and ANN-RMO respectively. The obtained results confirmed the significance of the proposed MFO in enhancing of the SOFC output power.
Ahmed Fathy; Hegazy Rezk; Haitham Saad Mohamed Ramadan. Recent moth-flame optimizer for enhanced solid oxide fuel cell output power via optimal parameters extraction process. Energy 2020, 207, 118326 .
AMA StyleAhmed Fathy, Hegazy Rezk, Haitham Saad Mohamed Ramadan. Recent moth-flame optimizer for enhanced solid oxide fuel cell output power via optimal parameters extraction process. Energy. 2020; 207 ():118326.
Chicago/Turabian StyleAhmed Fathy; Hegazy Rezk; Haitham Saad Mohamed Ramadan. 2020. "Recent moth-flame optimizer for enhanced solid oxide fuel cell output power via optimal parameters extraction process." Energy 207, no. : 118326.
The high efficiency triple-junction solar cells (TJSC) have received considerable attention in the concentrated PV systems nonetheless the harvested electrical energy generated by TJSC-based system has been reduced under the partial shading conditions. Tracking the global maximum power point in the TJSC-based system characteristics is a main challenge faced the traditional trackers like perturb and observe (P&O). Therefore, this paper proposes a new global maximum power point tracker (MPPT) based on recent metaheuristic approach of Manta ray foraging optimization (MRFO). The proposed MRFO based MPPT is employed to extract the global maximum power point (GMPP) from the Triple-Junction solar based array operated under shadow conditions. Seven shadow patterns are studied on seven topologies of triple junction solar based arrays. The obtained results are compared with differential evolution (DE) and crow search algorithm (CSA). The obtained results confirmed the superiority of the proposed MPPT based MRFO in extracting the GMPP under different partial shadow patterns followed by CSA and DE optimizers.
Ahmed Fathy; Hegazy Rezk; Dalia Yousri. A robust global MPPT to mitigate partial shading of triple-junction solar cell-based system using manta ray foraging optimization algorithm. Solar Energy 2020, 207, 305 -316.
AMA StyleAhmed Fathy, Hegazy Rezk, Dalia Yousri. A robust global MPPT to mitigate partial shading of triple-junction solar cell-based system using manta ray foraging optimization algorithm. Solar Energy. 2020; 207 ():305-316.
Chicago/Turabian StyleAhmed Fathy; Hegazy Rezk; Dalia Yousri. 2020. "A robust global MPPT to mitigate partial shading of triple-junction solar cell-based system using manta ray foraging optimization algorithm." Solar Energy 207, no. : 305-316.
The operation of the photovoltaic (PV) array under partial shadow conditions (PSCs) has negative effects on the extracted global maximum power (GMP) which is decreased due to the presence of power loss. Rearranging the shaded panels in the array is essential to enhance the GMP and diminish the effect of shadow, this process is known as PV array reconfiguration. Most of reported approaches did not guarantee the GMP and applicable to specific dimension of the PV array. Therefore, this paper proposes a novel methodology incorporated recent metaheuristic approach of butterfly optimization algorithm (BOA) to reconfigure the shaded PV array optimally and extract the GMP. BOA is selected due to its multiple advantages like ease of implementation, simple in construction, requirement of less controlling parameters, and effectiveness in solving real-time problems. Five shadow patterns are studied and the obtained results via BOA are compared to series–parallel total-cross-tied (SP-TCT), novel structure (NS) puzzle pattern, shade dispersion with NS and grey wolf optimizer (GWO) based arrangements. The proposed BOA succeeded in achieving maximum GMP enhancement of 27.43% compared to SP-TCT configuration. Moreover, Wilcoxon test is investigated for the results of the proposed BOA and GWO. Furthermore, the statistical parameters of both approaches are calculated. The obtained results confirmed the availability of the proposed BOA in reconfiguring the PV array operated under PSCs optimally.
Ahmed Fathy. Butterfly optimization algorithm based methodology for enhancing the shaded photovoltaic array extracted power via reconfiguration process. Energy Conversion and Management 2020, 220, 113115 .
AMA StyleAhmed Fathy. Butterfly optimization algorithm based methodology for enhancing the shaded photovoltaic array extracted power via reconfiguration process. Energy Conversion and Management. 2020; 220 ():113115.
Chicago/Turabian StyleAhmed Fathy. 2020. "Butterfly optimization algorithm based methodology for enhancing the shaded photovoltaic array extracted power via reconfiguration process." Energy Conversion and Management 220, no. : 113115.
This paper presents a recent metaheuristic optimization approach of multi-verse optimizer (MVO) to design load frequency control (LFC) based model predictive control (MPC) incorporated in large multi-interconnected system. The constructed system comprises six plants with renewable energy sources (RESs). MVO is employed to determine the optimal parameters of MPC-LFC to achieve the desired output of the interconnected system in case of load disturbances. The presented system comprises reheat thermal, hydro, photovoltaic (PV) model with maximum power point tracker (MPPT), wind turbine (WT), diesel generation (DG), and superconducting magnetic energy storage (SMES). The integral time absolute error (ITAE) of the frequencies and tie-line powers deviations is proposed as objective function. The effects of governor dead zone and generation rate constraint (GRC) of thermal plants are considered. The performance of the proposed MPC optimized via MVO is compared with the other designed via intelligent water drops (IWD) and genetic algorithm (GA). Additionally, the robustness of the proposed MPC-LFC based MVO with variation of the system parameters is presented. The obtained results confirmed the superiority and reliability of the proposed controller compared to the others.
Hossam Hassan Ali; Ahmed M. Kassem; Mujahed Al-Dhaifallah; Ahmed Fathy. Multi-Verse Optimizer for Model Predictive Load Frequency Control of Hybrid Multi-Interconnected Plants Comprising Renewable Energy. IEEE Access 2020, 8, 114623 -114642.
AMA StyleHossam Hassan Ali, Ahmed M. Kassem, Mujahed Al-Dhaifallah, Ahmed Fathy. Multi-Verse Optimizer for Model Predictive Load Frequency Control of Hybrid Multi-Interconnected Plants Comprising Renewable Energy. IEEE Access. 2020; 8 (99):114623-114642.
Chicago/Turabian StyleHossam Hassan Ali; Ahmed M. Kassem; Mujahed Al-Dhaifallah; Ahmed Fathy. 2020. "Multi-Verse Optimizer for Model Predictive Load Frequency Control of Hybrid Multi-Interconnected Plants Comprising Renewable Energy." IEEE Access 8, no. 99: 114623-114642.
Supercapacitor (SC) is completely valuable as an energy storage device for different applications such as electric vehicles and hybrid renewable systems. A simple SC model that composed of series RC circuit is insufficient to precisely characterize its dynamic performance. Consequently, an accurate mathematical model must be created for reliable and safe operation of SC. The equivalent circuit model that contains three RC branches, immediate, delayed and long is considered, the first branch includes voltage-dependent capacitance. The second one determines the terminal behavior in minute-time range while the last branch determines the behavior for time longer than 10 min. Such model has eight unknown parameters to be determined. A new formula to estimate the SC voltage is derived. For first time Interior Search Algorithm (ISA) is employed to identify these parameters. The obtained results are compared with those obtained by other different optimization algorithms like Genetic Algorithm (GA), Moth Flam Optimization (MFO), Antlion Optimizer (ALO), Gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Artificial ecosystem-based optimization (AEO). Two different capacitors with values of 470-F and 1500-F are considered during the validation process. Extensive statistical analysis is carried out to prove the reliability of the proposed methodology. The results confirmed high level of agreement between experimental data and optimized model circuit.
Ahmed Fathy; Hegazy Rezk. Robust electrical parameter extraction methodology based on Interior Search Optimization Algorithm applied to supercapacitor. ISA Transactions 2020, 105, 86 -97.
AMA StyleAhmed Fathy, Hegazy Rezk. Robust electrical parameter extraction methodology based on Interior Search Optimization Algorithm applied to supercapacitor. ISA Transactions. 2020; 105 ():86-97.
Chicago/Turabian StyleAhmed Fathy; Hegazy Rezk. 2020. "Robust electrical parameter extraction methodology based on Interior Search Optimization Algorithm applied to supercapacitor." ISA Transactions 105, no. : 86-97.
This paper proposes a reliable approach-based Harris Hawks Optimizer (HHO) to evaluate the optimal parameters of the Proportional–Integral (PI) controller simulating load frequency control (LFC) incorporated in a multi-interconnected system with renewable energy sources (RESs). During the optimization process, the integral time absolute error (ITAE) of the frequency and tie-line power is handled as the objective function. The HHO is selected due to its ease and requirement of less controlling parameters. Two different interconnected power systems are investigated as test systems to demonstrate the robustness of the proposed controller based HHO by comparing to other optimizers and traditional controller. The considered two systems include, one comprises two interconnected area of thermal and photovoltaic (PV) plants while the other system has four plants of PV, wind turbine (WT), and two thermal plants considering governor dead-band (GDB) and generation rate constraint (GRC). Different cases of load disturbance are studied, and the obtained results via the HHO are compared to Sine Cosine Algorithm (SCA), Multi-verse optimizer (MVO), Antlion Optimizer (ALO), and Grey wolf optimizer (GWO) as well as traditional controller. Moreover, sensitivity analysis is performed by changing the system parameters in a range of ± 10% and the performance of the proposed HHO-LFC is evaluated. The obtained results confirmed the reliability and superiority of the proposed approach based HHO in designing LFC for the considered systems.
Dalia Yousri; Thaniaknti Sudhakar Babu; Ahmed Fathy. Recent methodology based Harris Hawks optimizer for designing load frequency control incorporated in multi-interconnected renewable energy plants. Sustainable Energy, Grids and Networks 2020, 22, 100352 .
AMA StyleDalia Yousri, Thaniaknti Sudhakar Babu, Ahmed Fathy. Recent methodology based Harris Hawks optimizer for designing load frequency control incorporated in multi-interconnected renewable energy plants. Sustainable Energy, Grids and Networks. 2020; 22 ():100352.
Chicago/Turabian StyleDalia Yousri; Thaniaknti Sudhakar Babu; Ahmed Fathy. 2020. "Recent methodology based Harris Hawks optimizer for designing load frequency control incorporated in multi-interconnected renewable energy plants." Sustainable Energy, Grids and Networks 22, no. : 100352.
Recently, the penetration of the electric vehicles (EVs) in distribution networks gained a great attention. This paper proposes a new solution methodology incorporated recent metaheuristic approach of competition over resource (COR) for evaluating the optimal allocations and sizes of parking lots installed in radial distribution network. The main target is enhancing the network reliability with achieving minimum cost. COR is easily implemented with achieving feasible convergence for confident solution. A new formula of objective function comprising reliability enhancement cost, power loss improvement cost, and investment cost is proposed. The COR approach is applied on 9-bus, 33-bus, and 69-bus radial distribution networks. Additionally, the algorithms of grey wolf optimizer (GWO), whale optimization algorithm (WOA), and water cycle algorithm (WCA) are programmed and their results are compared with those obtained via the proposed COR. For 9-bus network, the proposed COR achieved saving of 6628.835 $ with percentage of 1.47% than GWO and time benefits of 84.11%. Moreover, it achieved economic benefits of 5315.4578 $ with percentage of 0.57% with time benefits of 7.6% compared to WCA for 33-bus network. Furthermore, COR provided 0.037% saving in total benefits compared to GWO for 69-bus system. The obtained results confirmed the superiority of COR in evaluating the optimal allocations and sizes of the parking lots in radial distribution networks.
Ahmed Fathy; Almoataz Y. Abdelaziz. Competition over resource optimization algorithm for optimal allocating and sizing parking lots in radial distribution network. Journal of Cleaner Production 2020, 264, 121397 .
AMA StyleAhmed Fathy, Almoataz Y. Abdelaziz. Competition over resource optimization algorithm for optimal allocating and sizing parking lots in radial distribution network. Journal of Cleaner Production. 2020; 264 ():121397.
Chicago/Turabian StyleAhmed Fathy; Almoataz Y. Abdelaziz. 2020. "Competition over resource optimization algorithm for optimal allocating and sizing parking lots in radial distribution network." Journal of Cleaner Production 264, no. : 121397.