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Ahmed Rabie Ginidi received a B.Sc. degree in electrical engineering from Fayoum University, Fayoum, Egypt, in 2007, and M.Sc. and Ph.D. degrees in electrical engineering from Cairo University, Cairo, Egypt, in 2010 and 2015, respectively. He is currently an Assistant Professor of Electrical Engineering with Suez University, Suez, Egypt. His main interests include system engineering, automatic control, intelligent systems, smart grids, power systems operation, fuzzy systems, energy policy, optimization and integration of renewable energy sources, and operations research.
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.
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 StyleAhmed 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 StyleAhmed 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.
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).
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 StyleAhmed 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 StyleAhmed 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.
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.
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 StyleAbdullah 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 StyleAbdullah 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.
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.
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 StyleAbdullah 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 StyleAbdullah 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.
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 StyleMosleh 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 StyleMosleh 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.
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.
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 StyleAbdullah 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 StyleAbdullah 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.
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
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 StyleAhmed 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 StyleAhmed 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.
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.
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 StyleMokhtar 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 StyleMokhtar 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.
This study presents a modified Model Predictive Control (MPC) strategy with primary and secondary levels of control, applied to Distributed Generator (DG) units, to account for limiting overcurrent in case of a faulted autonomous AC microgrid operation. Primary layer involves a Finite Control Set-MPC (FCS-MPC) for reference voltage tracing and droop control with Proportional-Integral (PI) control for power sharing between DGs. Unscented Kalman Filter estimator with MPC-based Voltage control and a communication-less event time-dependent protocol for frequency control are proposed for voltage restoration along with frequency supervision as secondary control stage in islanded DGs operation. The performance under faults of proposed controller is compared with that under conventional hierarchical control and MPC unmodified control. Under the mentioned new strategy, the AC islanded microgrid operation stability is enhanced.
Ahmed M. Taher; Hany M. Hasanien; Ahmed R. Ginidi; Adel T.M. Taha. Hierarchical Model Predictive Control for Performance Enhancement of Autonomous Microgrids. Ain Shams Engineering Journal 2021, 12, 1867 -1881.
AMA StyleAhmed M. Taher, Hany M. Hasanien, Ahmed R. Ginidi, Adel T.M. Taha. Hierarchical Model Predictive Control for Performance Enhancement of Autonomous Microgrids. Ain Shams Engineering Journal. 2021; 12 (2):1867-1881.
Chicago/Turabian StyleAhmed M. Taher; Hany M. Hasanien; Ahmed R. Ginidi; Adel T.M. Taha. 2021. "Hierarchical Model Predictive Control for Performance Enhancement of Autonomous Microgrids." Ain Shams Engineering Journal 12, no. 2: 1867-1881.
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.
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 StyleEhab 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 StyleEhab 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.
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.
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 StyleAbdullah 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 StyleAbdullah 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.
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.
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 StyleAbdullah 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 StyleAbdullah 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.