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Abdullah Shaheen
Department of Electrical Engineering, Faculty of Engineering, Suez University, Suez 43533, Egypt

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Journal article
Published: 26 August 2021 in Mathematics
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This paper proposes a hybrid algorithm that combines two prominent nature-inspired meta-heuristic strategies to solve the combined heat and power (CHP) economic dispatch. In this line, an innovative hybrid heap-based and jellyfish search algorithm (HBJSA) is developed to enhance the performance of two recent algorithms: heap-based algorithm (HBA) and jellyfish search algorithm (JSA). The proposed hybrid HBJSA seeks to make use of the explorative features of HBA and the exploitative features of the JSA to overcome some of the problems found in their standard forms. The proposed hybrid HBJSA, HBA, and JSA are validated and statistically compared by attempting to solve a real-world optimization issue of the CHP economic dispatch. It aims to satisfy the power and heat demands and minimize the whole fuel cost (WFC) of the power and heat generation units. Additionally, a series of operational and electrical constraints such as non-convex feasible operating regions of CHP and valve-point effects of power-only plants, respectively, are considered in solving such a problem. The proposed hybrid HBJSA, HBA, and JSA are employed on two medium systems, which are 24-unit and 48-unit systems, and two large systems, which are 84- and 96-unit systems. The experimental results demonstrate that the proposed hybrid HBJSA outperforms the standard HBA and JSA and other reported techniques when handling the CHP economic dispatch. Otherwise, comparative analyses are carried out to demonstrate the suggested HBJSA’s strong stability and robustness in determining the lowest minimum, average, and maximum WFC values compared to the HBA and JSA.

ACS Style

Ahmed Ginidi; Abdallah Elsayed; Abdullah Shaheen; Ehab Elattar; Ragab El-Sehiemy. An Innovative Hybrid Heap-Based and Jellyfish Search Algorithm for Combined Heat and Power Economic Dispatch in Electrical Grids. Mathematics 2021, 9, 2053 .

AMA Style

Ahmed Ginidi, Abdallah Elsayed, Abdullah Shaheen, Ehab Elattar, Ragab El-Sehiemy. An Innovative Hybrid Heap-Based and Jellyfish Search Algorithm for Combined Heat and Power Economic Dispatch in Electrical Grids. Mathematics. 2021; 9 (17):2053.

Chicago/Turabian Style

Ahmed Ginidi; Abdallah Elsayed; Abdullah Shaheen; Ehab Elattar; Ragab El-Sehiemy. 2021. "An Innovative Hybrid Heap-Based and Jellyfish Search Algorithm for Combined Heat and Power Economic Dispatch in Electrical Grids." Mathematics 9, no. 17: 2053.

Journal article
Published: 23 August 2021 in Sustainability
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The extraction of parameters of solar photovoltaic generating systems is a difficult problem because of the complex nonlinear variables of current-voltage and power-voltage. In this article, a new implementation of the Gorilla Troops Optimization (GTO) technique for parameter extraction of several PV models is created. GTO is inspired by gorilla group activities in which numerous strategies are imitated, including migration to an unknown area, moving to other gorillas, migration in the direction of a defined site, following the silverback, and competition for adult females. With numerical analyses of the Kyocera KC200GT PV and STM6-40/36 PV modules for the Single Diode (SD) and Double-Diode (DD), the validity of GTO is illustrated. Furthermore, the developed GTO is compared with the outcomes of recent algorithms in 2020, which are Forensic-Based Investigation Optimizer, Equilibrium Optimizer, Jellyfish Search Optimizer, HEAP Optimizer, Marine Predator Algorithm, and an upgraded MPA. GTO’s efficacy and superiority are expressed by calculating the standard deviations of the fitness values, which indicates that the SD and DD models are smaller than 1E−16, and 1E−6, respectively. In addition, validation of GTO for the KC200GT module is demonstrated with diverse irradiations and temperatures where great closeness between the emulated and experimental P-V and I-V curves is achieved under various operating conditions (temperatures and irradiations).

ACS Style

Ahmed Ginidi; Sherif M. Ghoneim; Abdallah Elsayed; Ragab El-Sehiemy; Abdullah Shaheen; Attia El-Fergany. Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems. Sustainability 2021, 13, 9459 .

AMA Style

Ahmed Ginidi, Sherif M. Ghoneim, Abdallah Elsayed, Ragab El-Sehiemy, Abdullah Shaheen, Attia El-Fergany. Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems. Sustainability. 2021; 13 (16):9459.

Chicago/Turabian Style

Ahmed Ginidi; Sherif M. Ghoneim; Abdallah Elsayed; Ragab El-Sehiemy; Abdullah Shaheen; Attia El-Fergany. 2021. "Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems." Sustainability 13, no. 16: 9459.

Journal article
Published: 23 July 2021 in Alexandria Engineering Journal
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This paper proposes an improved marine predators’ optimization algorithm (IMPOA) for solving the combined heat and power (CHP) economic dispatch problem. This problem provides optimal scheduling of heat and power generation supplies and pursues to minimize the overall fuel cost (OFC) supply of cogeneration units considering their operational constraints. Four test systems are considered to check the performance of both the MPOA and the proposed IMPOA. The first test system is small sized which involve 5-unit, whereas the second system is medium sized which contains 48-unit system. The third and fourth test systems are large sized systems. The third test system includes 84-unit, which are divided into 40 power-only units, 20 heat only units, and 24 CHP units. The fourth test system includes 96-unit, which are divided into 52 power-only units, 20 heat-only units, and 24 CHP units. The obtained results clearly show the capability, efficiency, and feasibility of the IMPOA with respect to other relevant optimization techniques for optimal solutions of small, medium and large-scale systems. Additionally, the convergence characteristics of the proposed IMPOA are stable and the arrival of the optimal solution is faster than the conventional MPOA.

ACS Style

Abdullah M. Shaheen; Abdallah M. Elsayed; Ahmed R. Ginidi; Ragab A. El-Sehiemy; Mosleh M. Alharthi; Sherif S.M. Ghoneim. A novel improved marine predators algorithm for combined heat and power economic dispatch problem. Alexandria Engineering Journal 2021, 1 .

AMA Style

Abdullah M. Shaheen, Abdallah M. Elsayed, Ahmed R. Ginidi, Ragab A. El-Sehiemy, Mosleh M. Alharthi, Sherif S.M. Ghoneim. A novel improved marine predators algorithm for combined heat and power economic dispatch problem. Alexandria Engineering Journal. 2021; ():1.

Chicago/Turabian Style

Abdullah M. Shaheen; Abdallah M. Elsayed; Ahmed R. Ginidi; Ragab A. El-Sehiemy; Mosleh M. Alharthi; Sherif S.M. Ghoneim. 2021. "A novel improved marine predators algorithm for combined heat and power economic dispatch problem." Alexandria Engineering Journal , no. : 1.

Journal article
Published: 13 July 2021 in Energy
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An enhanced multi-objective Quasi-Reflected Jellyfish Search Optimizer (MOQRJFS) is presented in this article for solving multi-dimensional Optimal Power Flow (MDOPF) issue with diverse objectives which display the minimization of economic fuel cost, total emissions, and the active power loss with satisfying operational constraints. Despite the simple structure of JFS with control of exploitation and exploration, searching capability of the JFS requires more support. Hence, two modifications are performed on the standard JFS algorithm. the first modification is that a cluster with a random size has been proposed which illustrates the social community that can share the data in the cluster and are dissimilar from one to another. The second modification is that a quasi-opposition-based learning is emerged in JFS to support the exploration phase. As selection criteria for the best solutions, a fuzzy decision-making strategy is joint into MOQRJFS optimizer. Additionally, the Pareto optimality concept is added to extract the non-dominated solutions. The superiority of the MOQRJFS is proved throughout application on IEEE 30-bus system, IEEE 57-bus system, the West Delta Region System of 52 bus (WDRS-52) in Egypt, and a large scale 118-bus system. Thirteen cases with economic, environmental, and technical objectives of MDOPF are included in this study. The outcomes of the proposed MOQRJFS have been compared with the conventional MOJFS and the reported techniques in the literature. It is clearly observed that the MOQRJFS give the minimum values compared with these techniques which reveals its robustness, effectiveness, and superiority when handling MDOPF among other techniques.

ACS Style

Abdullah M. Shaheen; Ragab A. El-Sehiemy; Mosleh M. Alharthi; Sherif S.M. Ghoneim; Ahmed R. Ginidi. Multi-objective jellyfish search optimizer for efficient power system operation based on multi-dimensional OPF framework. Energy 2021, 237, 121478 .

AMA Style

Abdullah M. Shaheen, Ragab A. El-Sehiemy, Mosleh M. Alharthi, Sherif S.M. Ghoneim, Ahmed R. Ginidi. Multi-objective jellyfish search optimizer for efficient power system operation based on multi-dimensional OPF framework. Energy. 2021; 237 ():121478.

Chicago/Turabian Style

Abdullah M. Shaheen; Ragab A. El-Sehiemy; Mosleh M. Alharthi; Sherif S.M. Ghoneim; Ahmed R. Ginidi. 2021. "Multi-objective jellyfish search optimizer for efficient power system operation based on multi-dimensional OPF framework." Energy 237, no. : 121478.

Journal article
Published: 25 June 2021 in Studies in Informatics and Control
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ACS Style

Mosleh Alharthi; Sherif Ghoneim; Abdallah Elsayed; Ragab El-Sehiemy; Abdullah Shaheen; Ahmed Ginidi. A Multi-Objective Marine Predator Optimizer for Optimal Techno-Economic Operation of AC/DC Grids. Studies in Informatics and Control 2021, 30, 89 -99.

AMA Style

Mosleh Alharthi, Sherif Ghoneim, Abdallah Elsayed, Ragab El-Sehiemy, Abdullah Shaheen, Ahmed Ginidi. A Multi-Objective Marine Predator Optimizer for Optimal Techno-Economic Operation of AC/DC Grids. Studies in Informatics and Control. 2021; 30 (2):89-99.

Chicago/Turabian Style

Mosleh Alharthi; Sherif Ghoneim; Abdallah Elsayed; Ragab El-Sehiemy; Abdullah Shaheen; Ahmed Ginidi. 2021. "A Multi-Objective Marine Predator Optimizer for Optimal Techno-Economic Operation of AC/DC Grids." Studies in Informatics and Control 30, no. 2: 89-99.

Journal article
Published: 25 June 2021 in IEEE Access
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Power system operators and planners have progressively shown an interest in maximizing distribution automation technologies. The automated distribution systems (ADS) provide the capability of efficient and reliable control which require an optimal operation strategy to control the status of the line switches and also dispatch the controllable devices. Therefore, this paper introduces an efficient and robust technique based on Jellyfish Search Algorithm (JFSA) for optimal Volt/VAr coordination in ADSs based on joint distribution system reconfiguration (DSR), distributed generation units (DGs) integration and Distribution static VAr compensators (SVCs) operation. The suggested technique is used for the dynamic operation of ADS in order to minimize losses and reduce emissions when considering regular daily loading conditions. The 33-bus and 69-bus delivery DSs have been subjected to a variety of scenarios. These situations are mostly concerned with achieving optimum distribution system operation and control, as well as validating the proposed methodology. Despite the problem’s complexity, the proposed technique based on JFSA is shown to be the best solution in all of the cases considered. Furthermore, a comparison of the proposed JFSA with other similar approaches demonstrates its usefulness as a method to be used in modern ADS control centers.

ACS Style

Abdullah M. Shaheen; Abdullah M. ElSAYED; Ahmed R. Ginidi; Ehab E. Elattar; Ragab A. El-Sehiemy. Effective Automation of Distribution Systems with Joint Integration of DGs/ SVCs considering reconfiguration capability by Jellyfish Search Algorithm. IEEE Access 2021, 9, 1 -1.

AMA Style

Abdullah M. Shaheen, Abdullah M. ElSAYED, Ahmed R. Ginidi, Ehab E. Elattar, Ragab A. El-Sehiemy. Effective Automation of Distribution Systems with Joint Integration of DGs/ SVCs considering reconfiguration capability by Jellyfish Search Algorithm. IEEE Access. 2021; 9 ():1-1.

Chicago/Turabian Style

Abdullah M. Shaheen; Abdullah M. ElSAYED; Ahmed R. Ginidi; Ehab E. Elattar; Ragab A. El-Sehiemy. 2021. "Effective Automation of Distribution Systems with Joint Integration of DGs/ SVCs considering reconfiguration capability by Jellyfish Search Algorithm." IEEE Access 9, no. : 1-1.

Journal article
Published: 08 June 2021 in IEEE Systems Journal
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This article develops a nonlinear, multimodal, and multiobjective formulation of the combined economic environmental operation (CEEO) problem in hybrid AC-multiterminal (AC-MT) high-voltage direct current grids. In these grids, the technologies of voltage source converters support more active and reactive power control in AC grids. The aim of the CEEO issue is to minimize the overall cost of fuel and the pollutant emissions of generators. Also, transmission loss minimization is another target. An improved crow search algorithm (ICSA) is proposed for obtaining the solution of the formulated problem. The proposed ICSA combines the merits of CSA by randomly switching into local search around the best crows’ position. Pareto dominance is activated to improve the crow's memory and the external repository for multiobjective models. The ICSA is tested on modified IEEE 30-bus, the Egyptian West Delta Power Network, and the large-scale 118-bus system to solve the CEEO problem in AC-MTDC grids. The simulation results illustrate the proposed ICSA capability for finding diversified Pareto solutions with several possible operating points. Furthermore, the effectiveness of the proposed ICSA is demonstrated in terms of its solution robustness compared with previous techniques.

ACS Style

Abdullah M. Shaheen; Abdallah M. Elsayed; Ragab A. El-Sehiemy. Optimal Economic–Environmental Operation for AC-MTDC Grids by Improved Crow Search Algorithm. IEEE Systems Journal 2021, PP, 1 -8.

AMA Style

Abdullah M. Shaheen, Abdallah M. Elsayed, Ragab A. El-Sehiemy. Optimal Economic–Environmental Operation for AC-MTDC Grids by Improved Crow Search Algorithm. IEEE Systems Journal. 2021; PP (99):1-8.

Chicago/Turabian Style

Abdullah M. Shaheen; Abdallah M. Elsayed; Ragab A. El-Sehiemy. 2021. "Optimal Economic–Environmental Operation for AC-MTDC Grids by Improved Crow Search Algorithm." IEEE Systems Journal PP, no. 99: 1-8.

Journal article
Published: 08 June 2021 in IEEE Access
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Cogeneration systems economic dispatch (CSED) provides an optimal scheduling of heat/ power generating units. The CSED aims to minimize the whole fuel cost (WFC) of the cogeneration units taking into consideration their technical and operational limits. Then, the current paper examines the first implementation of dominant bio-inspired metaheuristic called heap-based optimization algorithm (HBOA). The HBOA is powered by an adaptive penalty functions for getting the optimal operating points. The HBOA is inspired from the organization hierarchy, where the mechanism consists of the interaction among the subordinates and their immediate boss, the interaction among the colleagues, and the employee’s self-contribution. Based on the infeasible solutions’ remoteness from the nearest feasible point, HBOA penalizes them with various degrees. Four case studies of the CSED are implemented and analyzed, which comprise of 4, 24, 84 and 96 generating units. The HBOA is proposed to solve CSED problem with consideration of transmission losses and the valve point impacts. An investigation with the recent optimization algorithms, which are supply demand optimization (SDO), jellyfish search optimization algorithm (JFSOA), and marine predators’ optimization algorithm (MPOA), the improved MPOA (IMPOA) and manta ray foraging (MRF), is developed and elaborated. From the obtained results, it is clearly observed that the optimal solutions gained, in terms of WFC, reveal the feasibility, capability, and efficiency of HBOA compared with other optimizers especially for large-scale systems. case

ACS Style

Ahmed R. Ginidi; Abdallah M. Elsayed; Abdullah M. Shaheen; Ehab E. Elattar; Ragab A. El-Sehiemy. A Novel Heap based Optimizer for Scheduling of Large-scale Combined Heat and Power Economic Dispatch. IEEE Access 2021, 9, 1 -1.

AMA Style

Ahmed R. Ginidi, Abdallah M. Elsayed, Abdullah M. Shaheen, Ehab E. Elattar, Ragab A. El-Sehiemy. A Novel Heap based Optimizer for Scheduling of Large-scale Combined Heat and Power Economic Dispatch. IEEE Access. 2021; 9 ():1-1.

Chicago/Turabian Style

Ahmed R. Ginidi; Abdallah M. Elsayed; Abdullah M. Shaheen; Ehab E. Elattar; Ragab A. El-Sehiemy. 2021. "A Novel Heap based Optimizer for Scheduling of Large-scale Combined Heat and Power Economic Dispatch." IEEE Access 9, no. : 1-1.

Journal article
Published: 29 April 2021 in IEEE Access
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This paper presents an Enhanced Marine Predators Algorithm (EMPA) for simultaneous optimal distribution system reconfigurations (DSRs) and distributed generations (DGs) addition. The proposed EMPA recognizes the changes opportunity in environmental and climatic conditions. The EMPA handles a multi-objective model to minimize the power losses and enhance the voltage stability index (VSI) at different loading levels. The proposed EMPA is performed on IEEE 33-bus and large-scale 137-bus distribution systems (DSs) where three distinct loading conditions are beheld through light, nominal and heavy levels. For the 33-bus DS, the proposed EMPA successfully reduces the cumulative losses by 72.4% compared to 70.36% for MPA for the three-loading levels simultaneously. As a result, significant voltage improvement is achieved for heavy, nominal and light loadings to be 95.05, 97, 98.3%, respectively. For the 137-bus DS, it successfully minimizes the losses of 81.16% under small standard deviation 4.76%. Also, the 83-bus test system is considered for fair comparative between the proposed and previous techniques. The simulation outputs revealed significant improvements in the standard MPA and demonstrated the superiority and effectiveness of the proposed EMPA compared to other reported results by recent algorithms for DSRs associated with DGs integration.

ACS Style

Abdullah M. Shaheen; Ragab A. El-Sehiemy; Salah Kamel; Ehab E. Elattar; Abdallah M. Elsayed. Improving Distribution Networks’ Consistency by Optimal Distribution System Reconfiguration and Distributed Generations. IEEE Access 2021, 9, 67186 -67200.

AMA Style

Abdullah M. Shaheen, Ragab A. El-Sehiemy, Salah Kamel, Ehab E. Elattar, Abdallah M. Elsayed. Improving Distribution Networks’ Consistency by Optimal Distribution System Reconfiguration and Distributed Generations. IEEE Access. 2021; 9 ():67186-67200.

Chicago/Turabian Style

Abdullah M. Shaheen; Ragab A. El-Sehiemy; Salah Kamel; Ehab E. Elattar; Abdallah M. Elsayed. 2021. "Improving Distribution Networks’ Consistency by Optimal Distribution System Reconfiguration and Distributed Generations." IEEE Access 9, no. : 67186-67200.

Journal article
Published: 02 April 2021 in Processes
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Recently, the use of diverse renewable energy resources has been intensively expanding due to their technical and environmental benefits. One of the important issues in the modeling and simulation of renewable energy resources is the extraction of the unknown parameters in photovoltaic models. In this regard, the parameters of three models of photovoltaic (PV) cells are extracted in this paper with a new optimization method called turbulent flow of water-based optimization (TFWO). The applications of the proposed TFWO algorithm for extracting the optimal values of the parameters for various PV models are implemented on the real data of a 55 mm diameter commercial R.T.C. France solar cell and experimental data of a KC200GT module. Further, an assessment study is employed to show the capability of the proposed TFWO algorithm compared with several recent optimization techniques such as the marine predators algorithm (MPA), equilibrium optimization (EO), and manta ray foraging optimization (MRFO). For a fair performance evaluation, the comparative study is carried out with the same dataset and the same computation burden for the different optimization algorithms. Statistical analysis is also used to analyze the performance of the proposed TFWO against the other optimization algorithms. The findings show a high closeness between the estimated power–voltage (P–V) and current–voltage (I–V) curves achieved by the proposed TFWO compared with the experimental data as well as the competitive optimization algorithms, thanks to the effectiveness of the developed TFWO solution mechanism.

ACS Style

Mokhtar Said; Abdullah Shaheen; Ahmed Ginidi; Ragab El-Sehiemy; Karar Mahmoud; Matti Lehtonen; Mohamed Darwish. Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer. Processes 2021, 9, 627 .

AMA Style

Mokhtar Said, Abdullah Shaheen, Ahmed Ginidi, Ragab El-Sehiemy, Karar Mahmoud, Matti Lehtonen, Mohamed Darwish. Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer. Processes. 2021; 9 (4):627.

Chicago/Turabian Style

Mokhtar Said; Abdullah Shaheen; Ahmed Ginidi; Ragab El-Sehiemy; Karar Mahmoud; Matti Lehtonen; Mohamed Darwish. 2021. "Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer." Processes 9, no. 4: 627.

Research article
Published: 01 April 2021 in Engineering Optimization
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A modified marine predators optimizer (MMPO) is proposed for simultaneous distribution network reconfiguration (DNR) associated with the allocation of distributed generators (DGs). In the MMPO, the predator’s strategies are merged to consider the possibilities for variation in the environmental and climatic circumstances. The suggested MMPO is contrasted with the standard marine predators optimizer (MPO) and genetic, harmony search, fireworks, firefly and improved sine–cosine optimizers. The proposed MMPO is validated on single and multiple objectives using 33- and 69-bus distribution systems at light, nominal and heavy loading levels. The results obtained by the proposed MMPO are compared with those obtained by the original MPO and other optimizers. The achieved simulation outputs reveal a great improvement over the standard MPO and demonstrate the superiority of the proposed MMPO for simultaneous DNR and DG allocation.

ACS Style

Abdullah M. Shaheen; Abdallah M. Elsayed; Ragab A. El-Sehiemy; Salah Kamel; Sherif S. M. Ghoneim. A modified marine predators optimization algorithm for simultaneous network reconfiguration and distributed generator allocation in distribution systems under different loading conditions. Engineering Optimization 2021, 1 -22.

AMA Style

Abdullah M. Shaheen, Abdallah M. Elsayed, Ragab A. El-Sehiemy, Salah Kamel, Sherif S. M. Ghoneim. A modified marine predators optimization algorithm for simultaneous network reconfiguration and distributed generator allocation in distribution systems under different loading conditions. Engineering Optimization. 2021; ():1-22.

Chicago/Turabian Style

Abdullah M. Shaheen; Abdallah M. Elsayed; Ragab A. El-Sehiemy; Salah Kamel; Sherif S. M. Ghoneim. 2021. "A modified marine predators optimization algorithm for simultaneous network reconfiguration and distributed generator allocation in distribution systems under different loading conditions." Engineering Optimization , no. : 1-22.

Journal article
Published: 04 March 2021 in Energy
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Economic Dispatch in Cogeneration Systems (EDCS) provides the optimal scheduling of heat and power of generation units. This can be achieved by minimizing the total cost of fuel (TCF) of the cogeneration units taking into consideration their operational limits. A manta ray foraging MRF optimizer, in this paper, is developed to solve the EDCS problem including the valve point impacts, and wind power. MRF optimizer is designed with adaptive penalty functions for acquiring the most feasible and best operational points for the EDCS problem. Infeasible solutions are handled with various degrees and penalized depending on their remoteness from the closest possible point. The overall power and heat loading are completely achieved by the equality constraints. Also, the cogeneration units’ dynamic operating limits are not adversely affected since its concerning limitations of heat-only and power-only units are fulfilled. Two test systems of small 5 and large 96-units, are analyzed. In addition to this, an assessment of the recent optimization techniques, which are applied on to EDCS, has been developed and discussed. The applications are carried out for two scenarios at peak and daily variation in the power and heat loading condition. The wind power inclusion is assessed for each scenario in terms of the overall reduction in the total fuel costs. It was proven also; the inclusion of wind power achieves more economical solution at different scenarios with reduction up to 8%. It is crystal clear that the outputs obtained illustrate MRF optimizer efficiency, feasibility, and capability to obtain better solutions in minimizing the fuel cost compared to other optimization techniques at acceptable convergence rates. Moreover, the solutions demonstrate the ability of MRF optimizer application on the large-scale 96-unit systems.

ACS Style

Abdullah M. Shaheen; Ahmed R. Ginidi; Ragab A. El-Sehiemy; Ehab E. Elattar. Optimal economic power and heat dispatch in Cogeneration Systems including wind power. Energy 2021, 225, 120263 .

AMA Style

Abdullah M. Shaheen, Ahmed R. Ginidi, Ragab A. El-Sehiemy, Ehab E. Elattar. Optimal economic power and heat dispatch in Cogeneration Systems including wind power. Energy. 2021; 225 ():120263.

Chicago/Turabian Style

Abdullah M. Shaheen; Ahmed R. Ginidi; Ragab A. El-Sehiemy; Ehab E. Elattar. 2021. "Optimal economic power and heat dispatch in Cogeneration Systems including wind power." Energy 225, no. : 120263.

Journal article
Published: 22 February 2021 in IEEE Access
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This paper proposes a modified crow search optimizer (MCSO) for solving the combined economic emission power flow (EEPF) problem. In the proposed approach, the local search ability is enhanced into the crow search optimizer (CSO) and aggregated with a novel bat algorithm (NBA). Close accord between CSO, NBA, and MCSO is employed for solving the single and multi-objective frameworks. Moreover, the proposed MCSO incorporates external archive and dominance comparison to handle multi-objective frameworks while the best compromise solution is extracted by using a fuzzy based mechanism. The proposed MCSO, CSO, and NBA are developed and tested to on IEEE 30 bus and West Delta power grid (WDPG) systems. Added to the that, the proposed methodology is tested on a large-scale power system, IEEE 118-bus test system, for measure the scalability of the proposed method. Their output results are compared with the reported algorithms in the literature to demonstrate the MCSO outperformance in terms of solution quality and robustness. Significant economical solutions of the EEPF problem are achieved with respecting the environment concerns at acceptable emission levels. Added to that, the multi objective framework is assessed with hypervolume indictor that show the high capability of the proposed MCSO compared with CSO.

ACS Style

Abdullah M. Shaheen; Ragab A. El-Sehiemy; Ehab E. Elattar; Ahmed S. Abd-Elrazek. A Modified Crow Search Optimizer for Solving Non-Linear OPF Problem With Emissions. IEEE Access 2021, 9, 43107 -43120.

AMA Style

Abdullah M. Shaheen, Ragab A. El-Sehiemy, Ehab E. Elattar, Ahmed S. Abd-Elrazek. A Modified Crow Search Optimizer for Solving Non-Linear OPF Problem With Emissions. IEEE Access. 2021; 9 ():43107-43120.

Chicago/Turabian Style

Abdullah M. Shaheen; Ragab A. El-Sehiemy; Ehab E. Elattar; Ahmed S. Abd-Elrazek. 2021. "A Modified Crow Search Optimizer for Solving Non-Linear OPF Problem With Emissions." IEEE Access 9, no. : 43107-43120.

Journal article
Published: 21 January 2021 in IEEE Access
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Nowadays, distribution utilities expend large investments on Distributed System Automation (DSA) based on smart secondary substations at load, capacitor, and distributed generator points with installed automatic sectionalizing switches on their branches. This article addresses the optimal control and operation of distribution systems that minimize the wasted energy and introducing quantitative and qualitative power services to meet consumers’ satisfaction. Simultaneous allocations of Distributed Generators (DGs) and Capacitor Banks (CBs) are handled at peak loading condition. Then, the DSA is optimally activated for optimal Distribution Network Reconfiguration (DNR), optimal DGs commitment, and optimal CBs switching for losses minimization in coordination with different loading conditions. Practical daily load variation is applied to simulate the dynamic operation of automated distribution systems. For achieving these targets, the Manta Ray Foraging Optimization Algorithm (MRFOA) is adopted. MRFOA is an effective and simple structure optimizer that emulates three various individual manta rays foraging organizations. The capability of the MRFOA is applied to the IEEE 33-bus, 69-bus and practical distribution network of 84-bus due to the Taiwan Power Company (TPC). A comparison with recent techniques has been conducted to prove the effectiveness of MRFOA. The accomplished results demonstrate that the proposed MRFOA has great effectiveness and robustness among other optimization techniques.

ACS Style

Ehab E. Elattar; Abdullah M. Shaheen; Abdullah M. El-Sayed; Ragab A. El-Sehiemy; Ahmed R. Ginidi. Optimal Operation of Automated Distribution Networks Based-MRFO Algorithm. IEEE Access 2021, 9, 19586 -19601.

AMA Style

Ehab E. Elattar, Abdullah M. Shaheen, Abdullah M. El-Sayed, Ragab A. El-Sehiemy, Ahmed R. Ginidi. Optimal Operation of Automated Distribution Networks Based-MRFO Algorithm. IEEE Access. 2021; 9 ():19586-19601.

Chicago/Turabian Style

Ehab E. Elattar; Abdullah M. Shaheen; Abdullah M. El-Sayed; Ragab A. El-Sehiemy; Ahmed R. Ginidi. 2021. "Optimal Operation of Automated Distribution Networks Based-MRFO Algorithm." IEEE Access 9, no. : 19586-19601.

Original article
Published: 09 January 2021 in Neural Computing and Applications
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Recently, the renewable energy has been occupied a lot of attention around the world since it presents cheap and sustainable energy. Consequently, its presence in power systems becomes a fact that had to deal with. Hence, load frequency control (LFC) in multi-area power systems that constitute photovoltaic (PV) and thermal plant sources is proposed. Two forms of competing cascaded controllers, namely proportional integral–proportional integral (PI–PI) and proportional–derivative with filter-PI (PDn-PI), are investigated, and their performances are compared with traditional PI and PIDn controller. An enhanced coyote optimization algorithm (ECOA) is proposed for finding the optimal tuned parameters of the proposed controllers. Furthermore, the uncertainty is considered under the variation of system parameters by ± 40%. The performance of the proposed competing controllers is tested under dynamic load change that is applied individually in each area. These controllers are applied on two dissimilar test cases with various sets of disturbances. The obtained results are compared with various reported techniques. The simulated comparisons declare the great efficiency with high superiority robustness of the proposed cascaded PDn-PI based on ECOA for handling the LFC in multi-area power systems.

ACS Style

Adel A. Abou El-Ela; Ragab A. El-Sehiemy; Abdullah M. Shaheen; Abd El-Gelil Diab. Enhanced coyote optimizer-based cascaded load frequency controllers in multi-area power systems with renewable. Neural Computing and Applications 2021, 1 -19.

AMA Style

Adel A. Abou El-Ela, Ragab A. El-Sehiemy, Abdullah M. Shaheen, Abd El-Gelil Diab. Enhanced coyote optimizer-based cascaded load frequency controllers in multi-area power systems with renewable. Neural Computing and Applications. 2021; ():1-19.

Chicago/Turabian Style

Adel A. Abou El-Ela; Ragab A. El-Sehiemy; Abdullah M. Shaheen; Abd El-Gelil Diab. 2021. "Enhanced coyote optimizer-based cascaded load frequency controllers in multi-area power systems with renewable." Neural Computing and Applications , no. : 1-19.

Original research paper
Published: 31 December 2020 in IET Generation, Transmission & Distribution
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The current paper presents a multi‐objective manta ray foraging algorithm (MO‐MRFA) for efficient operation of hybrid AC and multi‐terminal direct current (MTDC) power grids. The multi‐objective framework aims at achieving economical, technical and environmental benefits by minimising the total production fuel costs, minimising the transmission power losses and minimising the environmental emissions in the AC/MTDC transmission systems. The MRFA imitates three separate independent foraging organisations of the manta rays. It is updated incorporating an additional Pareto archive to preserve the non‐dominated solutions. A dynamic adaptation of the fitness feature is employed by iteratively varying the form of the employed fitness function. Furthermore, a fuzzy decision‐making technique is activated to finally pick the appropriate operating point of the AC/MTDC power grids. The proposed technique is compared with other reported algorithms in the literatures. The applications are conducted on three test systems. These systems are IEEE 30‐bus, IEEE 57‐bus test power systems in addition to real part of the Egyptian grid at West Delta region. Numerical results demonstrate that the proposed MO‐MRFA has great effectiveness and robustness indices over the others. Nevertheless, the proposed MO‐MRFA is successfully extracting several Pareto solutions that meet the techno‐economic requirements with accepted environmental concerns.

ACS Style

Abdullah M. Shaheen; Ragab A. El‐Sehiemy; Abdallah M. Elsayed; Ehab E. Elattar. Multi‐objective manta ray foraging algorithm for efficient operation of hybrid AC/DC power grids with emission minimisation. IET Generation, Transmission & Distribution 2020, 15, 1314 -1336.

AMA Style

Abdullah M. Shaheen, Ragab A. El‐Sehiemy, Abdallah M. Elsayed, Ehab E. Elattar. Multi‐objective manta ray foraging algorithm for efficient operation of hybrid AC/DC power grids with emission minimisation. IET Generation, Transmission & Distribution. 2020; 15 (8):1314-1336.

Chicago/Turabian Style

Abdullah M. Shaheen; Ragab A. El‐Sehiemy; Abdallah M. Elsayed; Ehab E. Elattar. 2020. "Multi‐objective manta ray foraging algorithm for efficient operation of hybrid AC/DC power grids with emission minimisation." IET Generation, Transmission & Distribution 15, no. 8: 1314-1336.

Journal article
Published: 28 December 2020 in IEEE Access
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All over the world, the operators of the power distribution networks (DNs) are still looking for improving the efficiency of their networks. The performance of DNs and lifetime of its component have been significantly affected by its capability of varying their topologies with accurate load gathering via smart grid functions. This paper investigates making use of the smart DNs features and proposes a model of handling the capability of re-allocating the capacitors integrating with configuring the DNs topology. Using the developed formulation, the efficiency of DNs can be improved not only by minimizing the operational costs related to the network losses but also by optimizing the investment costs associated with capacitor re-allocations. Also, various load patterns are employed in the developed formulation to imitate the daily load variations over a year. The improved sunflower optimization algorithm (ISFOA) is proposed in this paper to get the optimal solution of the presented problem. The standard IEEE 33-node feeder and practical 84-node system of Taiwan Power Company (TPC) are the considered test systems. Besides, the uncertainties due to a distributed generation of wind power are investigated via Monte Carlo simulation involved with the proposed ISFOA. Furthermore, to verify the ability of ISFOA to obtain better solutions compared with different recent optimizers, a statistical comparison is carried out based on a large scale 118-node distribution systems. The simulation results reveal that significant technical and economic benefits are obtained by applying the proposed algorithm with higher superiority and effectiveness.

ACS Style

Abdullah M. Shaheen; Ehab E. Elattar; Ragab A. El-Sehiemy; Abdallah M. Elsayed. An Improved Sunflower Optimization Algorithm-Based Monte Carlo Simulation for Efficiency Improvement of Radial Distribution Systems Considering Wind Power Uncertainty. IEEE Access 2020, 9, 2332 -2344.

AMA Style

Abdullah M. Shaheen, Ehab E. Elattar, Ragab A. El-Sehiemy, Abdallah M. Elsayed. An Improved Sunflower Optimization Algorithm-Based Monte Carlo Simulation for Efficiency Improvement of Radial Distribution Systems Considering Wind Power Uncertainty. IEEE Access. 2020; 9 ():2332-2344.

Chicago/Turabian Style

Abdullah M. Shaheen; Ehab E. Elattar; Ragab A. El-Sehiemy; Abdallah M. Elsayed. 2020. "An Improved Sunflower Optimization Algorithm-Based Monte Carlo Simulation for Efficiency Improvement of Radial Distribution Systems Considering Wind Power Uncertainty." IEEE Access 9, no. : 2332-2344.

Journal article
Published: 22 December 2020 in IEEE Access
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The accurate parameter extraction of photovoltaic (PV) module is pivotal for determining and optimizing the energy output of PV systems into electric power networks. Consequently, a Photovoltaic Single-Diode Model (PVSDM), Double Diode Model (PVDDM), and Triple- Diode Model (PVTDM) is demonstrated to consider the PV losses. This article introduces a new application of the Forensic-Based Investigation Algorithm (FBIA), which is a new meta-heuristic optimization technique, to accurately extract the electrical parameters of different PV models. The FBIA is inspired by the suspect investigation, location, and pursuit processes that are used by police officers. The FBIA has two phases, which are the investigation phase applying by the investigators team, and the pursuit phase employing by the police agents team. The validity of the FBIA for PVSDM, PVDDM, and PVTDM is commonly considered by the numerical analysis executing under diverse values of solar irradiations and temperatures. The optimal five, seven, and nine parameters of PVSDM, PVDDM, and PVTDM, respectively, are accomplished using the FBIA and compared with those manifested by various optimization techniques. The numerical results are compared for the marketable Photowatt-PWP 201 polycrystalline and Kyocera KC200GT modules. The efficacy of the FBIA for the three models is properly carried out checking its standard deviation error with that obtained from various recently proposed optimization techniques in 2020 which are Jellyfish search (JFS) optimizer, Manta Ray Foraging optimizer (MRFO), Marine Predators Algorithm(MPA), Equilibrium Optimizer (EO), Heap Based Optimizer (HBO). The standard deviations of the fitness values over 30 runs are developed to be less than $1 \times 10^{-6}$ for the three models, which make the FBIA results are extremely consistent. Therefore, FBIA is foreseen to be a competitive technique for PV module parameter extraction.

ACS Style

Abdullah M. Shaheen; Ahmed Rabie Ginidi; Ragab A. El-Sehiemy; Sherif S. M. Ghoneim. A Forensic-Based Investigation Algorithm for Parameter Extraction of Solar Cell Models. IEEE Access 2020, 9, 1 -20.

AMA Style

Abdullah M. Shaheen, Ahmed Rabie Ginidi, Ragab A. El-Sehiemy, Sherif S. M. Ghoneim. A Forensic-Based Investigation Algorithm for Parameter Extraction of Solar Cell Models. IEEE Access. 2020; 9 ():1-20.

Chicago/Turabian Style

Abdullah M. Shaheen; Ahmed Rabie Ginidi; Ragab A. El-Sehiemy; Sherif S. M. Ghoneim. 2020. "A Forensic-Based Investigation Algorithm for Parameter Extraction of Solar Cell Models." IEEE Access 9, no. : 1-20.

Research article
Published: 07 December 2020 in International Transactions on Electrical Energy Systems
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The majority of power utilities have targeted pretentious clean and efficient energy plans. These plans are accompanied in driving down the cost of biomass distributed generation units (BDGs). This paper proposes an optimal allocation procedure of BDGs to enhance the performance of the distribution systems and to reduce the related environmental emissions. The proposed procedure aims to maximize the power utilities' benefits in terms of power loss reduction, energy sales excess, and pollutant emission reduction. It also takes into consideration the minimization of the annual operational and maintenance costs of the BDGs. The load growth with several loading levels over the BDGs planning period is presented. For achieving this target, an adaptive equilibrium optimizer (EO) technique is developed which is characterized by simple structure and dynamic control parameters. The proposed procedure is applied to IEEE 33‐bus and practical large‐scale 141‐bus system of AES‐Venezuela in the metropolitan area of Caracas. The simulation results declare the effectiveness and capability of the employed EO technique in achieving great benefits in terms of energy sales and environmental emissions with a high improvement of the voltage profile and minimizing power losses. In addition, a comparative and statistical analysis is executed with several recent techniques to show the superior capability of the proposed procedure using the EO technique.

ACS Style

Adel A. Abo El‐Ela; Sohir M. Allam; Abdullah M. Shaheen; Nadia A. Nagem. Optimal allocation of biomass distributed generation in distribution systems using equilibrium algorithm. International Transactions on Electrical Energy Systems 2020, 31, 1 .

AMA Style

Adel A. Abo El‐Ela, Sohir M. Allam, Abdullah M. Shaheen, Nadia A. Nagem. Optimal allocation of biomass distributed generation in distribution systems using equilibrium algorithm. International Transactions on Electrical Energy Systems. 2020; 31 (2):1.

Chicago/Turabian Style

Adel A. Abo El‐Ela; Sohir M. Allam; Abdullah M. Shaheen; Nadia A. Nagem. 2020. "Optimal allocation of biomass distributed generation in distribution systems using equilibrium algorithm." International Transactions on Electrical Energy Systems 31, no. 2: 1.

Journal article
Published: 17 November 2020 in IEEE Access
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Economic Power and Heat Dispatch (EPHD) in Cogeneration Energy Systems (CES) is considered as one of non linear hard optimization problems. It is optimally scheduling the of heat and power generation units. It aims at minimizing the total fuel cost (TFC) of cogeneration units considering their operational limits. In this paper, a Manta Ray Foraging Optimization Algorithm (MRFOA), which is a recent meta-heuristic optimization technique, is developed to solve the EPHD problem in CES with additional non-convex valve point effects. The simplicity and effectiveness motivate the attempt of employing the MRFOA to minimize the TFC for power units only, cogeneration units and heat units only. The equality constraints by supplying the total loading of power and heat is are maintained. In addition, the inequality operational bounds of power only and heat only units are satisfied while the dynamic operational bounds of cogeneration units are not jeopardized. Three test systems are analyzed to estimate the MRFOA performance for solving the EPHD problem in CES, which involve 5 units, 7 units, and 48 units. It is worth noticing that the optimal solutions demonstrate MRFOA capability, feasibility and efficiency of better solutions obtained in terms of TFC compared with other optimization methods and the ability of implementation of MRFOA on EPHD issue in CES.

ACS Style

Abdullah M. Shaheen; Ahmed Rabie Ginidi; Ragab A. El-Sehiemy; Sherif S. M. Ghoneim. Economic Power and Heat Dispatch in Cogeneration Energy Systems Using Manta Ray Foraging Optimizer. IEEE Access 2020, 8, 208281 -208295.

AMA Style

Abdullah M. Shaheen, Ahmed Rabie Ginidi, Ragab A. El-Sehiemy, Sherif S. M. Ghoneim. Economic Power and Heat Dispatch in Cogeneration Energy Systems Using Manta Ray Foraging Optimizer. IEEE Access. 2020; 8 (99):208281-208295.

Chicago/Turabian Style

Abdullah M. Shaheen; Ahmed Rabie Ginidi; Ragab A. El-Sehiemy; Sherif S. M. Ghoneim. 2020. "Economic Power and Heat Dispatch in Cogeneration Energy Systems Using Manta Ray Foraging Optimizer." IEEE Access 8, no. 99: 208281-208295.