Reactive Power Management Based Hybrid GAEO

Hemeida, Mahmoud G.; Senjyu, Tomonobu; Alkhalaf, Salem; Fawzy, Asmaa; Ahmed, Mahrous; Osheba, Dina S. M.

doi:10.3390/su14116933

Cited by 8 publications

(5 citation statements)

References 37 publications

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“…When the DSSC is deployed on the node I side of line ij, and the power flow equations at both ends of the line where the DSSC is deployed are modified, the power flow equations of other nodes remain unchanged [20]. According to the voltage vector diagram constructed in Figure 3, which takes into account dssc, the model parameters can be adjusted according to the actual situation.…”

Section: Power Flow Calculation Methods With Dsscmentioning

confidence: 99%

Study on Reactive Power Optimization Including DSSC for New Energy Access to the Power Grid

Hu,

Gao,

et al. 2024

EAI Endorsed Trans Energy Web

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The vigorous development of new energy has effectively reduced carbon emissions, but it has also brought fluctuating impacts on the carrying capacity of the power grid. In order to improve the voltage stability after integrating new energy sources and promote the scientific consumption of more new energy, this paper proposes the use of Distributed Static Synchronous Compensator (DSSC) devices for flexible and controllable voltage regulation in new energy integration. An improved particle swarm optimization algorithm is then developed to optimize the reactive power considering the regulation of DSSC. The paper conducts power flow calculations based on the DSSC power injection model and establishes a reactive power optimization mathematical model with objectives of minimizing active power loss, minimizing node voltage deviation, and maximizing voltage stability margin in the grid with new energy integration. The improved particle swarm optimization algorithm is utilized to achieve the reactive power optimization. Experimental simulations are conducted using the IEEE 33-node system to analyze the voltage improvement before and after adopting the improved particle swarm optimization algorithm considering the DSSC device in the grid with new energy integration. It is found that the proposed method effectively reduces active power loss and stabilizes voltage fluctuations, demonstrating its practical value.

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Section: Power Flow Calculation Methods With Dsscmentioning

confidence: 99%

Study on Reactive Power Optimization Including DSSC for New Energy Access to the Power Grid

Hu,

Gao,

et al. 2024

EAI Endorsed Trans Energy Web

View full text Add to dashboard Cite

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“…Additionally, it is clear from the experimental results that the proposed GA-EO outperforms existing state-of-the-art metaheuristics for clustering and routing in WSN. In the year 2022 [224], Hemeida et al presented a hybridized EO known as Genetic Algorithm-Equilibrium Optimization (GAEO), which combines the exploitative capabilities of EO with the explorative capabilities of GA to create a more effective technique. Also, the experimental results clearly show that for the reactive power management problem, the suggested GAEO performs better than SCA, CBA, and ISSA.…”

Section: Hybridization With Genetic Algorithm (Ga)mentioning

confidence: 99%

Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

Rai

Dhal

2023

Arch Computat Methods Eng

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There have been many algorithms created and introduced in the literature inspired by various events observable in nature, such as evolutionary phenomena, the actions of social creatures or agents, broad principles based on physical processes, the nature of chemical reactions, human behavior, superiority, and intelligence, intelligent behavior of plants, numerical techniques and mathematics programming procedure and its orientation. Nature-inspired metaheuristic algorithms have dominated the scientific literature and have become a widely used computing paradigm over the past two decades. Equilibrium Optimizer, popularly known as EO, is a population-based, nature-inspired meta-heuristics that belongs to the class of Physics based optimization algorithms, enthused by dynamic source and sink models with a physics foundation that are used to make educated guesses about equilibrium states. EO has achieved massive recognition, and there are quite a few changes made to existing EOs. This article gives a thorough review of EO and its variations. We started with 175 research articles published by several major publishers. Additionally, we discuss the strengths and weaknesses of the algorithms to help researchers find the variant that best suits their needs. The core optimization problems from numerous application areas using EO are also covered in the study, including image classification, scheduling problems, and many others. Lastly, this work recommends a few potential areas for EO research in the future.

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“…In the following, the comparison results of the proposed CO with TLBO 74 and GAEO 75 and cost values have been analyzed [Table 4].…”

Section: Data Matching‐based Rpomentioning

confidence: 99%

Reactive power control of hybrid systems using improved coyote optimizer

Chen

Zhou

et al. 2023

Concurrency and Computation

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SummaryA new method of reactive power control by photovoltaic and hydrogen is presented in this study. Photovoltaic has been employed for harvesting the hydrogen which is based on considering the weather conditions. The proposed system includes a combination of photovoltaic, hydrogen, and fuel cell along with a DG to connect to the grid and to improve the supply power quality. The main contribution of this paper is to direct an improved metaheuristic algorithm, called improved coyote optimization (CO) algorithm for achieving a proper DG placement. The improved version of the CO algorithm has benefitted from a spiral policy that is derived from Whale optimization algorithm. This process makes better control for the social behavior of the coyotes. Reactive power optimization (RPO) has been established after the size selection objective function. Big data technology is also used for improving the historical solution matching‐based RPO appliance. Cosine distance is used for measurement purposes of the historical solution matching‐based similarity technique during the computation time of conventional RPO and PVH‐FC features. As a result of using the suggested CO, the costs for electricity and losses are reduced by around 86.6% and 26.9%, respectively. Additionally, realized profits showed that applying the suggested strategy reduced the overall cost from 9.315e6 to 4.435e6 units. After optimization, the network loss and power are finally reduced to 1325 Kvar and 1371 kW, respectively. The results show that the suggested RPO technique has higher speed of calculation in comparison with some latest algorithms. Achievements also show that the suggested method provides a proper and optimal solution for RPO.

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Reactive Power Management Based Hybrid GAEO

Cited by 8 publications

References 37 publications

Study on Reactive Power Optimization Including DSSC for New Energy Access to the Power Grid

Study on Reactive Power Optimization Including DSSC for New Energy Access to the Power Grid

Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

Reactive power control of hybrid systems using improved coyote optimizer

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