Integration of artificial neural networks for multi-source energy management in a smart grid

Jarmouni, Eziitouni; Mouhsen, Ahmed; Lamhammedi, Mohammed; Ouldzira, Hicham

doi:10.11591/ijpeds.v12.i3.pp1919-1927

Cited by 3 publications

(1 citation statement)

References 16 publications

(16 reference statements)

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“…Machine learning techniques, SVM [42,43], ANN [44,45], and reinforcement learning (RL) [46,47], have also been used to tackle ORPD problems. SVM, a supervised learning algorithm, has been utilized for predicting and optimizing reactive power in power systems.…”

Section: Introductionmentioning

confidence: 99%

A Grey Wolf Optimization Algorithm-Based Optimal Reactive Power Dispatch with Wind-Integrated Power Systems

Varan,

Erduman,

Menevşeoğlu

2023

Energies

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Keeping the bus voltage within acceptable limits depends on dispatching reactive power. Power quality improves as a result of creating an effective power flow system, which also helps to reduce power loss. Therefore, optimal reactive power dispatch (ORPD) studies aim at designing appropriate system configurations to enable a reliable operation of power systems. Establishment of such a configuration is handled through control variables in power systems. Various control variables, such as adjusting generator bus voltages, transformer tap locations, and switchable shunt capacitor sizes, are utilized to achieve this objective. Additionally, the integration of wind power can greatly impact power quality and mitigate power loss. In this study, the Grey Wolf Optimization (GWO) approach was applied to the ORPD issue for the first time to discover the best placement of newly installed wind power in the power system while taking into account tap changer settings, shunt capacitor sizes, and generated power levels. The main objective was to determine optimal wind placement to minimize power loss and voltage deviation, while maintaining control variables within specified limits. On the basis of IEEE 30-bus and IEEE 118-bus systems, the performance of the proposed method was investigated. The results demonstrated the superiority of GWO in multiple scenarios. In IEEE-30, GWO outperformed the PSO, GA, ABC, OGSA, HBMO, and HFA methods, reducing total loss by 10.36%, 18.03%, 9.19%, 7.13%, 5.23%, and 7.73%, respectively, and voltage deviation by 68.00%, 1.59%, 36.34%, 41.97%, 46.29%, and 71.08%, respectively. In wind integration scenarios, GWO achieved the simultaneous reduction of power loss and voltage deviation. In IEEE-118, GWO outperformed the ABC, PSO, GSA, and CFA methods, reducing power loss by approximately 19.91%, 16.83%, 14.09%, and 4.36%, respectively, and voltage deviation by 8.50%, 14.15%, 16.19%, and 7.17%, respectively. These promising results highlighted the potential of the GWO algorithm to facilitate the integration of renewable energy sources, and its role in promoting sustainable energy solutions. In addition, this study conducted an analysis to investigate site-specific wind placement by using the Weibull distribution function and commercial wind turbines.

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Section: Introductionmentioning

confidence: 99%

A Grey Wolf Optimization Algorithm-Based Optimal Reactive Power Dispatch with Wind-Integrated Power Systems

Varan,

Erduman,

Menevşeoğlu

2023

Energies

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Smart and Sustainable Grids Using Data-Driven Methods; Considering Artificial Neural Networks and Decision Trees

Rituraj

Ecker

Annamaria

2022

2022 IEEE 20th Jubilee International Symposium on Intelligent Systems and Informatics (SISY)

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A Grey Wolf Optimization Algorithm Based Optimum Reactive Power Dispatch with Distributed Generation Units

Varan,

Erduman,

Menevseoglu

2023

Preprint

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Keeping the bus voltage within acceptable limits depends on dispatching reactive power. Power quality improves as a result of creating an effective power flow system, which also helps to reduce power loss. Therefore, Optimal Reactive Power Dispatch (ORPD) studies aim at designing appropriate system configurations to enable a reliable operation of power systems. Establishment of such a configuration is handled through control variables in power systems. On the other hand, installing Distributed Generation Unit (DGU) into a power system can have an important effect on power quality and power loss minimization. In this paper, Grey Wolf Optimization (GWO method is applied to ORPD problem for the first time, to find optimal placement of newly installed DGUs at the power system. Active power loss and voltage minimization have been minimized by optimizing the control variables while keeping them within their upper and lower bounds, and by finding the optimal placement of a newly installed DGU in power system. On the basis of IEEE 30 bus and IEEE 118 bus systems, performance of the suggested approach is investigated. A comparison is also performed among the proposed method and some other methods. Test results demonstrate that the resulting configuration contributes to growing the rate of renewable integration to a power system. The results also indicate that the suggested optimization approach applied to ORPD problem has improved power system performance in terms of both on power loss and voltage deviation. The promising results highlight the potential of the GWO algorithm to facilitate the integration of renewable power sources, and its role in promoting sustainable energy solutions.

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Integration of artificial neural networks for multi-source energy management in a smart grid

Cited by 3 publications

References 16 publications

A Grey Wolf Optimization Algorithm-Based Optimal Reactive Power Dispatch with Wind-Integrated Power Systems

A Grey Wolf Optimization Algorithm-Based Optimal Reactive Power Dispatch with Wind-Integrated Power Systems

Smart and Sustainable Grids Using Data-Driven Methods; Considering Artificial Neural Networks and Decision Trees

A Grey Wolf Optimization Algorithm Based Optimum Reactive Power Dispatch with Distributed Generation Units

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