Bayesian Learning-Based Multi-Objective Distribution Power Network Reconfiguration

Zhong, Tianwei; Zhang, Haitao; Li, Yuanzheng; Liu, Lan; Lu, Renzhi

doi:10.1109/tsg.2020.3027290

Cited by 20 publications

(16 citation statements)

References 39 publications

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“…• Bus voltages limits (25) • Branch currents limits (26) • DER active power limits (27) • DER reactive power limits (28) • Load balance on each feeder limits (29) (30) In this study, the power balance of the electrical load on each feeder has been considered. This constraint on power balance is one of the essential contributions in this study to improve distribution network performance.…”

Section: B Problem Formulation Of Der Location and Capacity On Distribution Networkmentioning

confidence: 99%

“…The effects of such phenomena may vary according to the location of DER units and weather conditions [23], [24]. Additionally, the system performs better at a certain penetration level of DER units, but, above this level, the system degrades due to substation and feeder loads, voltage variation, and increasing power losses [25]. Additionally, as the number of DER units increases, the operation of the automatic voltage regulator within the OLTC of the transformer becomes more sophisticated and capable due to the occurrence of reverse power flow and associated high voltage and current, which can be controlled using various methods summarized [26].…”

Section: Introductionmentioning

confidence: 99%

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Multiobjective Ant Lion Optimization for Performance Improvement of Modern Distribution Network

Soesanti

Syahputra

2022

IEEE Access

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Multi-objective ant lion optimization (MALO) is a technique developed by imitating the behavior of the ant king foraging. This method has many advantages: straightforward, scalable, flexible, good balance, and fast response. The MALO technique consists of five stages: ants perform optimization by random walking by updating their position, building traps, inserting ants into traps, capturing prey, and rebuilding traps. MALO has attracted the attention of many researchers and has been used successfully to find optimal solutions for power system problems. Computer-assisted operations characterize modern distribution networks to solve complex problems. The complexity of the distribution network problem is due to the integration of distributed energy resources (DER). DER is a renewable energy power plant with up to 10 MW, gaining popularity in recent times. In its application, the integration of DER into the distribution network can cause new problems, namely, load imbalances or excessive voltage increases on the buses where the DER is injected. Therefore, sound planning is needed to place DER. This research proposes a multi-objective optimization technique based on MALO to determine the optimal DER location and capacity. The MALO is a relatively new optimization method that has the potential to improve distribution network performance. Test cases have been carried out for the IEEE 33-bus distribution network. Four scenarios have been carried out, namely integrating DER type-I, type-II, type-III, and type-IV. In each design, the placement of 1 DER, 2 DERs, and 3 DERs is modelled to optimize the location and capacity. The results of multi-objective optimization show that the MALO technique can improve the distribution network performance, characterized by a significant reduction in power losses, an increase in the bus voltage profile, and a balanced load on each feeder.

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Section: B Problem Formulation Of Der Location and Capacity On Distribution Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiobjective Ant Lion Optimization for Performance Improvement of Modern Distribution Network

Soesanti

Syahputra

2022

IEEE Access

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“…In the literature, two approaches have been used in optimizing this problem, which are using (i) a single-objective approach [4][5][6][7][8] and (ii) a multi-objective approach [9][10][11][12]. For the single-objective approach, the author in [4] has focused on minimizing the power/energy losses and network loading index using a hybrid heuristic-genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…There are trade-offs among the objectives to achieve the outcome as the objectives might conflict with each other [9]. In DNR, research has been conducted using multi-objective approaches such as Multi-objective Evolutionary Algorithm [10] and Non-sorting genetic algorithm [11] for service restoration, and Bayesian learning-based evolutionary algorithm for absorption rate of wind power and voltage stability improvement [12]. However, less work has been reported on minimizing power losses and switching operations simultaneously using single-objective or multi-objective approaches.…”

Section: Introductionmentioning

confidence: 99%

Optimal Distribution Network Reconfiguration Using Multi-Objective Cuckoo Search Algorithm

Saedi

Hanifah

Ladin

et al. 2022

IIUMEJ

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In power system electricity delivery, the distribution system has the most electricity loss as the system has the highest R/X ratio and has a radial network at one time. Optimal reconfiguration of the distribution network is needed in order to reduce power losses. However, as it is also involved with multiple objectives and constraint problems such as switching frequency, voltage, and current limits, it is difficult to find the optimal solution. Hence, this paper proposes the Multi-objective Cuckoo Search (MOCS) algorithm to find the optimal reconfiguration of distribution networks by considering minimizing power losses and switch operations. Based on the simulation results on the IEEE-33 bus system, the performance of the MOCS-based scheme has been found to be significantly better than the single-objective algorithm thereby reducing approximately 33% of the power losses. ABSTRAK Melalui sistem penghantaran jana kuasa elektrik, sistem pengagihan mempunyai pembaziran tenaga elektrik terbesar kerana sistem ini mempunyai nisbah R/X paling tinggi dan mempunyai satu rangkaian radial pada tiap-tiap satu masa. Konfigurasi semula rangkaian pengedaran yang optimum diperlukan bagi mengurangkan pembaziran tenaga. Walaubagaimanapun, oleh kerana ia melibatkan objektif dan kekangan masalah yang pelbagai seperti kadar peralihan, had voltan serta arus, adalah sukar bagi mendapatkan bacaan yang optimum. Oleh itu, kajian ini mencadangkan Carian Cuckoo Pelbagai Objektif (MOCS) bagi mencari konfigurasi semula yang optimum bagi sistem pengagihan tenaga dengan mengambil kira pengurangan pembaziran tenaga dan kadar peralihan. Berdasarkan keputusan simulasi pada sistem bas IEEE-33, pretasi MOCS telah menunjukkan peningkatan yang ketara berbanding algoritma objektif tunggal dan pengurangan sebanyak 33% tenaga.

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“…Despite the success of BayesOpt in various fields, it has been seldom used in power system studies. Only a handful of papers have incorporated BayesOpt, and all of them used BayesOpt to train machine learning models such as deep neural networks [17] or Bayesian Networks [18], [19].…”

Section: A Introduction To Bayesian Optimizationmentioning

confidence: 99%

Probabilistic Hosting Capacity Analysis via Bayesian Optimization

Geng¹,

Tong²,

Bhattacharya³

et al. 2020

Preprint

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This paper studies the probabilistic hosting capacity analysis (PHCA) problem in distribution networks considering uncertainties from distributed energy resources (DERs) and residential loads.PHCA aims to compute the hosting capacity, which is defined as the maximal level of DERs that can be securely integrated into a distribution network while satisfying operational constraints with high probability. We formulate PHCA as a chance-constrained optimization problem, and model the uncertainties from DERs and loads using historical data. Due to non-convexities and a substantial number of historical scenarios being used, PHCA is often formulated as large-scale nonlinear optimization problem, thus computationally intractable to solve. To address the core computational challenges, we propose a fast and extensible framework to solve PHCA based on Bayesian Optimization (BayesOpt).Comparing with state-of-the-art algorithms such as interior point and active set, numerical results show that the proposed BayesOpt approach is able to find better solutions (25% higher hosting capacity) with 70% savings in computation time on average.

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Bayesian Learning-Based Multi-Objective Distribution Power Network Reconfiguration

Cited by 20 publications

References 39 publications

Multiobjective Ant Lion Optimization for Performance Improvement of Modern Distribution Network

Multiobjective Ant Lion Optimization for Performance Improvement of Modern Distribution Network

Optimal Distribution Network Reconfiguration Using Multi-Objective Cuckoo Search Algorithm

Probabilistic Hosting Capacity Analysis via Bayesian Optimization

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