Multi-objective jellyfish search optimizer for efficient power system operation based on multi-dimensional OPF framework

Shaheen, Abdullah M.; El‐Sehiemy, Ragab A.; Alharthi, Mosleh M.; Ghoneim, Sherif S. M.; Ginidi, Ahmed

doi:10.1016/j.energy.2021.121478

Cited by 51 publications

(16 citation statements)

References 49 publications

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“…This is elaborated in Table 2, which tabulates the comparative results of GTO. It achieved the minimum RMSE value and standard deviation values of 6.367E−4 and 4.405E−8, respectively, with respect to other recent optimization techniques, which were EO [47,55], FBI [46], HEAP [49,56], jellyfish search (JFS) optimizer [48,57], and EMPA [58,59], and other reported optimization techniques, which were PSO [12], ALO [18], flexible PSO (FPSO) [29], PGJAYA [34], classified perturbation mutation PSO (CPMPSO) [60], Hybrid Firefly and Pattern Search (HFAPS) [61], Lightning Attachment Procedure Optimization (LAPO) [62], Barnacles Mating Optimizer (BMA) [63], neighborhood scheme-based Laplacian MBA (NLBMA) [64], hybrid PSO-GWO algorithm (PSOGWO) [65], Enriched Harris Hawks optimization (EHHO) [66], and multi-verse optimizer (MVO) [67]. As shown in Table 2, the GTO technique for the SDM of KC200GT had the minimum error value compared with the various reported algorithms in the literature.…”

Section: Simulation Resultsmentioning

confidence: 96%

Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems

et al. 2021

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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).

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Section: Simulation Resultsmentioning

confidence: 96%

Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems

et al. 2021

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“…Fuel cost ($/h) Emissions (ton/h) MOSGA 42497.0130 1.2712 GBICA [21] 42138.3695 1.3941 MGBICA [21] 42369.0664 1.2940 ESDE [22] 42863.3243 1.2662 ESDE-EC [22] 42863.2116 1.2387 ESDE-MC [22] 42857.4869 1.2191 ISPEA [23] 42444.5535 1.2904 SPEA2 [23] 42320.2545 1.4054 NSGA-II [23] 43567.7653 1.2979 rNSGA-II [23] 42635.7170 1.3784 MPIO-PFM [30] 43205.8477 1.2386 MPIO-COSR [30] 43131.2743 1.2314 MOPSO [31] 43279.6398 1.2546 NMBAS [31] 43117.8602 1.2245 DE-PFA [33] 43331.7568 1.2180 NSGA-II [33] 43353.5661 1.2272 HFBA-COFS [33] 43259.3013 1.2129 MOJFS [35] 43888.2320 1.2383 MOQRJFS [35] 43713.0146 1.3074 IHOA [36] 43864.8798 1.2192 MHBAS [37] 43174.05 1.2211 MDE [37] 43505.90 1.2236 IMOMRFO [38] 41742.9442 1.7912 [22] 42020.7439 12.2155 ESDE-EC [22] 42013.3395 11.9668 ESDE-MC [22] 41998.3588 11.8415 NSGA-II [33] 42125.6042 11.1296 HFBA-COFS [33] 42122.0140 10.6995 MOJFS [35] 42591.8712 15.1461 MOQRJFS [35] 41846.2247 15.8873 IHOA [36] 42419.5253 10.8192 MHBAS [37] 42084.81 10.5043 MDE [37] 42125.83 10.9193…”

Section: Methodsmentioning

confidence: 99%

“…This study considered active power loss, fuel costs, and emissions as three objective functions. Shaheen et al [35] developed a multi-objective quasi-reflected jellyfish search optimization (MOQRJFS) to address the MOOPF problem. The authors validated the performance of MOQRJFS on 30bus, 57-bus, and practical power networks in Egypt.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimal Power Flow Using Multiobjective Search Group Algorithm

Huy

Kim

2022

IEEE Access

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This paper proposes a new multi-objective method that efficiently solves the multi-objective optimal power flow (MOOPF) problem in power systems. The objective of solving the MOOPF problem is to concurrently optimize the fuel cost, emissions, and active power loss. The proposed multi-objective search group algorithm (MOSGA) is an effective method that combines the merits of the original search group algorithm with fast nondominated sorting, crowding distance, and archive selection strategies to acquire a nondominated set in a single run. The MOSGA is employed on IEEE 30-bus and 57-bus systems to validate its robustness and efficiency. It was found that implementing MOSGA to solve the MOOPF significantly enhanced the performance of power systems in terms of economic, environmental, and technical benefits. As for Case 6, the fuel cost, emissions, and active power loss were reduced by 16.5707%, 52.0605%, and 60.9443%, respectively. The simulation results were analyzed and compared with those of previously reported studies based on the best individual solutions, compromise solutions, and performance indicators. The comparative results confirmed the potential and advantage of MOSGA when solving the MOOPF problem efficiently and MOSGA had high-quality optimal solutions. INDEX TERMS Multi-objective search group algorithm, multi-objective optimal power flow, fuel cost, emissions

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“…Firstly, related to the AC network, the control variables are: where, N g , N t , and N q refer to, accordingly the generators number, the number of on-load tap transformers, and the number of VAr devices [55]. Secondly, related to the VSC type, the control variables are [56]:…”

Section: Control and Dependent Variables In Ac-dc Networkmentioning

confidence: 99%

Enhanced Teaching Learning-Based Algorithm for Fuel Costs and Losses Minimization in AC-DC Systems

et al. 2022

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The Teaching Learning-Based Algorithm (TLBA) is a powerful and effective optimization approach. TLBA mimics the teaching-learning process in a classroom, where TLBA’s iterative computing process is separated into two phases, unlike standard evolutionary algorithms and swarm intelligence algorithms, and each phase conducts an iterative learning operation. Advanced technologies of Voltage Source Converters (VSCs) enable greater active and reactive power regulation in these networks. Various objectives are addressed for optimal energy management, with the goal of attaining economic and technical advantages by decreasing overall production fuel costs and transmission power losses in AC-DC transmission networks. In this paper, the TLBA is applied for various sorts of nonlinear and multimodal functioning of hybrid alternating current (AC) and multi-terminal direct current (DC) power grids. The proposed TLBA is evaluated on modified IEEE 30-bus and IEEE 57-bus AC-DC networks and compared to other published methods in the literature. Numerical results demonstrate that the proposed TLBA has great effectiveness and robustness indices over the others. Economically, the reduction percentages of 13.84 and 21.94% are achieved for the IEEE 30-bus and IEEE 57-bus test systems when the fuel costs are minimized. Technically, significant improvement in the transmission power losses with reduction 28.01% and 69.83%, are found for the IEEE 30-bus and IEEE 57-bus test system compared to the initial case. Nevertheless, TLBA has faster convergence, higher quality for the final optimal solution, and more power for escaping from convergence to local optima compared to other published methods in the literature.

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Multi-objective jellyfish search optimizer for efficient power system operation based on multi-dimensional OPF framework

Cited by 51 publications

References 49 publications

Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems

Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems

Multiobjective Optimal Power Flow Using Multiobjective Search Group Algorithm

Enhanced Teaching Learning-Based Algorithm for Fuel Costs and Losses Minimization in AC-DC Systems

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