Optimal Reactive Power Dispatch Using a Chaotic Turbulent Flow of Water-Based Optimization Algorithm

Wahab, Ahmed M. Abd-El; Kamel, Salah; Hassan, Mohamed H.; Mosaad, Mohamed I.; AbdulFattah, Tarek A.

doi:10.3390/math10030346

Cited by 35 publications

(6 citation statements)

References 71 publications

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“…This work considers a medium-scale IEEE 57-test system portrayed in Figure 15, to assess the scalability of the suggested methods. This system has a total generation capacity of 1975.9 MW and is made up of 80 transmission lines, containing 25 control variables including 7 generators, 3 shunt VAR compensators, and 15 transformers [6,16,34]. Furthermore, the system is restricted as follows: the voltage magnitude range for all buses is 0.9 pu to 1.1 pu.…”

Section: Methods Balance Of Active Power Balance Of Reactive Powermentioning

confidence: 99%

“…Figure 2 depicts the IEEE 30-bus test system with 19 control variables including 6 generators, 4 tap changing transformer settings, 9 shunt VAR compensators, 41 branches (37 lines and 4 tap changing transformers), and the total real and reactive power demands are 238.4 MW and 126.2 MVAR, respectively. The exact details of lines and buses for the IEEE-30 bus system are described in [16,34]. Furthermore, in this research, the system is restricted as follows: the magnitude range of all buses' voltages is 0.95 pu to 1.1 pu.…”

Section: Ieee 30-bus Test Systemmentioning

confidence: 99%

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A Novel Stochastic Optimizer Solving Optimal Reactive Power Dispatch Problem Considering Renewable Energy Resources

et al. 2023

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Optimal Reactive Power Dispatch (ORPD is thought of as a noncontinuous, nonlinear global optimization problem. Within the system’s constraints, the ORPD manages to accomplish the reactive power flow. Due to its more intricate linkage of variables, the reactive power issue is more challenging to resolve than the optimum power flow issue. With the existence of renewable energy resources (RERs), solving the ORPD problem to attain the most stable and secure system condition has become a more challenging task. The goal of this article is to solve the objective function of ORPD combined with RERs using a metaheuristic novel optimizer named the African Vultures Optimization Algorithm abbreviated by (AVOA), where the formulation of the ORPD issue including minimization of three single objective functions as follows, voltage deviation, system operating cost, and real power loss, is introduced and also transmission power loss minimization is embraced with the simultaneous incorporation of the optimal renewable energy resources (RERs). Where the ORPD problem complexity grows exponentially with a mixture of continuous and discrete control variables, two distinct continuous and discrete types of optimization variables are considered, and the proposed single objective functions that meet different operating constraints are then transformed into a coefficient multi-objective ORPD problem and elucidated using the weighted sum approach. To validate the suggested algorithm’s effectiveness in addressing the ORPD issue, it is evaluated on three standard IEEE networks: the IEEE-30 bus small-scale network, the IEEE-57 bus medium-scale network, and the IEEE-118 bus large-scale network using different scenarios and the outcomes are compared to these other popular optimization techniques. The findings show that the suggested AVOA algorithm provides an efficient and sturdy high-quality solution for tackling ORPD situations and vastly enhances the overall system performance of power at all scales.

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Section: Methods Balance Of Active Power Balance Of Reactive Powermentioning

confidence: 99%

Section: Ieee 30-bus Test Systemmentioning

confidence: 99%

A Novel Stochastic Optimizer Solving Optimal Reactive Power Dispatch Problem Considering Renewable Energy Resources

et al. 2023

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“…Many modern population based meta-heuristic and nature inspired techniques have been used to resolve this process. Several of the famous optimizations algorithms that attempted this problem include: Genetic Algorithm(GA), 4 Simulated Annealing(SA), 5,6 Differential Evolution(DE), 7,8 Moth Swarm Optimization Algorithm(MSA), 9 Spider Monkey Optimization(SMO), 10 Grey Wolf Optimizer(GWO), 11 Fire Fly Algo-rithm(FFA), 12,13 Harmony Search Algorithm(HSA), 14,15 Spiral Optimization Algorithm(SOA), 16 Artificial Bee Colony(ABC), 17,18 Bacterial Foraging Algorithm (BFA), 19 Particle swarm Optimization(PSO), 20,21 β-hill climbing optimization, 22 Mine Blast Algorithm(MBA), 23 Flower Pollination Algorithm(FPA), 24 Chaotic Turbulent Flow of Water Based Optimization Algorithm(CTFWO), 25 and Coyote Optimization Algorithm(COA). 26 Table 1 addresses the contributions of.…”

Section: Tionmentioning

confidence: 99%

Harris Hawks Approach for Distinct Economic Dispatch Problems

Ali¹

2023

Yanbu Journal of Engineering and Science

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In this paper, a nature-inspired optimization paradigm is suggested, which is called Harris Hawks Approach (HHA). The main inspiration of HHA is the cooperative behavior and chasing style of Harris’ hawks in nature called surprise pounce. In this intelligent strategy, diverse hawks cooperatively pounce victim from distinct directions in an attempt to surprise it. Harris hawks can detect a variety of chasing patterns based on the dynamic nature of scenarios and escaping styles of the victim. HHA is developed for solving the Economic Dispatch (ED) process. Since the ED is a non-linear, complex and constrained optimization process, its main objective is to reduce the total generation cost while matching the equality and inequality constraints of the system. To prove the capabilities of the developed HHA, several test systems including 6, 13, 40 and 10 units have been implemented and tested for solving various ED problems. The simulation results illustrate the strength of the HHA compared with other algorithms recently reported in literature.

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“…The (ORPD) problem, as a sub-problem of OPF, has significant effects on providing reliability and economical performance [37]. A new optimization algorithm which has been named Turbulent Flow Water Based Optimization Algorithm (CTFWO) is used to solve the OPRD which is a complex mixed integer nonlinear optimization problem that includes discrete and continuous control variables (40). The Benchmark table was utilized by Kumar et al (41), which could be an enormous table with awfully expansive numbers that need much time and calculations to run for OPRD while the improved TM, employs OAs that have lower numbers and need lower time and calculations to run for the ORPD problem.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Reactive Power Flow Optimization of Distribution System in Presence of Distributed Units Uncertainty Using Combination of Improved Taguchi Method and Dandelion Algorithm

Najjarpour,

Tousi

2024

IJE

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Nowadays, due to the growing population, rising global warming, environmental pollution, and the reduction of fuel sources, the use of Distributed Generation Sources (DGs) has grown, and because of their random nature, the conventional performance of power systems is being changed. Reactive power has a considerable role in power systems management and control indexes such as loss, stability, reliability, and security, among which the loss index usually can be easily minimized and controlled. Thus the modeling and optimizing of reactive power must be done accurately and correctly. This paper uses a novel metaheuristic algorithm which is called Dandelion, to solve the constrained non-linear optimal reactive power dispatch problem, and the Improved Taguchi method based on orthogonal arrays has been applied in order to the uncertainty of DG units modeling. The applied optimal reactive power dispatch algorithm is tested and validated using standard IEEE 30-bus test power systems. These results show that the Computational time of the applied algorithm in comparison with other used algorithms is the least value and reduces the reactive power from 22.244 to 2.366 Mvar; also, the losses of the power system significantly will be decreased with the tested and introduced algorithm. Genetic Algorithm(GA), Particle swarm optimization algorithm (PSO), and Prairie dog optimization algorithm (PDO) have been utilized to solve the problem.

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Optimal Reactive Power Dispatch Using a Chaotic Turbulent Flow of Water-Based Optimization Algorithm

Cited by 35 publications

References 71 publications

A Novel Stochastic Optimizer Solving Optimal Reactive Power Dispatch Problem Considering Renewable Energy Resources

A Novel Stochastic Optimizer Solving Optimal Reactive Power Dispatch Problem Considering Renewable Energy Resources

Harris Hawks Approach for Distinct Economic Dispatch Problems

Probabilistic Reactive Power Flow Optimization of Distribution System in Presence of Distributed Units Uncertainty Using Combination of Improved Taguchi Method and Dandelion Algorithm

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