Loss Reduction of Distribution Network by Optimal Reconfiguration and Capacitor Placement Using Cuckoo and Cultural Algorithms

Najjarpour, Majid; Tousi, Behrouz

doi:10.1109/ictem56862.2023.10083643

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…Generally, must be done 128 processes, but by using the OA just eight experiments are needed. So, the calculation steps will be decreased to 6.25% of the total steps; this shows the ability and Advantage of OA (45,46).…”

Section: Orthogonal Arraysmentioning

confidence: 95%

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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Section: Orthogonal Arraysmentioning

confidence: 95%

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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Collaborative deployment of multiple reinforcement methods for network‐loss reduction in distribution system with seasonal loads

Xie,

Xing,

Luo

et al. 2024

Energy Conversion and Econom

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The integration of seasonal loads, such as cereal baking and aquatic‐product processing loads, often leads to significant voltage deviations and severe peak loads of the distribution system during specific periods, resulting in increased network losses. Traditional approaches for reducing network losses are becoming less effective and cost‐efficient due to the spatiotemporally uneven distribution characteristics of seasonal loads. To address this issue, this study proposes an optimisation model that collaboratively integrates mobile energy storage, switching capacitors, and tie lines to minimise annual network losses in special planning scenarios affected by seasonal loads. The deployment strategies of multiple reinforcement methods are thoroughly analysed, greatly enhancing the explainability and feasibility of the collaborative deployment model. Then, the proposed model is reformulated to a mixed‐integer linear programming model using the inscribed regular dodecagon approximation approach, thereby making it trackable for state‐of‐the‐art solvers. To illustrate the effectiveness of the model, case studies are conducted on a unique 55‐bus distribution system located in East China, which contains feeders with substantial seasonal variation aquaculture loads and with general loads. The effectiveness of multiple reinforcement methods is thoroughly analysed through detailed numerical results. Furthermore, a sensitivity analysis of the investment budget is conducted.

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Enhancing efficiency in electrical distribution systems: A novel approach via modified genetic optimization algorithm for loss reduction in optimal network distribution

Rajalakshmi,

Priyan,

Inbakumar

et al. 2024

IFS

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The distribution system plays a pivotal role in connecting power generation sources to vital facilities like nuclear reactors. In this intricate network, losses occur while supplying electricity, demanding a reduction for enhanced performance. The quality of power reaching the nuclear plant is imperative due to the susceptibility of sensitive equipment to poor power conditions. This study presents a reconfiguration strategy to bolster dependability and curtail power losses in distribution networks. Leveraging the Modified Genetic Optimization Algorithm (MGOA), the reconfiguration conundrum is tactfully addressed to determine optimal switch operation schemes. The MGOA-based reconfiguration not only minimizes energy wastage but also refines voltage profiles, elevating operational efficiency. The effectiveness of this approach is substantiated through its successful application to radial distribution systems comprising 33, 69, and 136 buses. Embracing diverse scenarios encompassing normal and abnormal operating states, as well as varying loads, the method’s robustness is showcased. The validity of the proposed methodology is reinforced by comprehensive simulation results, underscoring its reliability and potential for real-world implementation.

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Loss Reduction of Distribution Network by Optimal Reconfiguration and Capacitor Placement Using Cuckoo and Cultural Algorithms

Cited by 4 publications

References 11 publications

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

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

Collaborative deployment of multiple reinforcement methods for network‐loss reduction in distribution system with seasonal loads

Enhancing efficiency in electrical distribution systems: A novel approach via modified genetic optimization algorithm for loss reduction in optimal network distribution

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