Distribution Network Reconfiguration Based on NoisyNet Deep Q-Learning Network

Wang, Beibei; Zhu, Hong; Xu, Huiling; Bao, Yu‐Qing; Di, Huifang

doi:10.1109/access.2021.3089625

Cited by 45 publications

(20 citation statements)

References 10 publications

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“…Among them, the first two are one-off costs as shown in Eq. 2, while the operation and maintenance cost is a non-one-off costs that is adopted (Wang et al, 2021). Therefore, interest and inflation rates are included to calculate the present value of SSs operation and maintenance costs because the life cycle of SSs can last several years.…”

Section: Objective Function At Upper Levelmentioning

confidence: 99%

“…The optimal objective function of the distribution network is closely related to the objective function of distribution network reconstruction. Hsu et al (1993), Skoonpong and Sirisumrannukul (2008), and Wang et al (2021) established distribution, static reconfiguration models, with minimum network losses, balanced load, and maximum reliability as the optimization goals. For the purpose of minimizing the sum of active power, load balance index, and maximum node voltage deviation, Chen et al (2020) employed the gray target decisionmaking strategy to select the best problem in the process of solving multi-objective problems.…”

Section: Introductionmentioning

confidence: 99%

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Bi-Level Coordinated Planning of Sectionalizing Switches and Tie Lines Considering Operation Mode Adjustment

Luo

Gao

et al. 2022

Front. Energy Res.

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Distribution utilities can flexibly control distribution networks by allocating the automatic and remotely controlled sectionalizing switches (SSs), which work with tie lines (TLs) to speed up fault management and are alternative devices for distribution operation mode adjustment. Hence, the SSs and the TLs play an important role in distribution networks. In order to improve the reliability of power supply and achieve economic distribution network operation, this paper proposes a bi-level SSs and TLs planning model, which considers distribution operation mode adjustment to optimize the allocation of SSs and TLs. The upper level of the proposed model aims at minimizing the sum of the total investment cost, the customer interruption loss cost, and the line loss cost. The upper level identifies the number and location of the SSs and the TLs. With the planning scheme obtained from the upper level, the lower level of the model adjusts the distribution operation mode to minimize the distribution line loss. In addition, binary particle swarm optimization (BPSO) is used because it has stable convergence and effectively explores the search space. A second-order cone programming (SOCP) is employed to reduce the complexity of the model and improve the solving process by linearizing the reconstruction calculation of the distribution network. Finally, simulation studies were conducted on bus 2 and bus 4 of the RBTS standard test system to assess the feasibility of the proposed model. The stability and effectiveness of the model are verified through various comparisons.

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Section: Objective Function At Upper Levelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bi-Level Coordinated Planning of Sectionalizing Switches and Tie Lines Considering Operation Mode Adjustment

Luo

Gao

et al. 2022

Front. Energy Res.

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“…In [23], the PNR problem of distribution networks with high wind power penetrations was treated and solved based on Bayesian learning-based evolutionary algorithm. In [24], a NoisyNet deep Q-learning network (DQN) has been carried out to the PNR issue for minimizing the system losses and improving the voltage profile. In [25], AMPL solver as a mathematical programming has been utilized for minimizing the active power losses of distribution systems via PNR issue.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, the considered objective function was conducted with uncertainty probability beside uncertain amounts in load demand. Despite the effective implementations of [21][22][23][24][25], the operational reliability and voltage stability in distribution networks were completely ignored.…”

Section: Introductionmentioning

confidence: 99%

Optimal Operational Reliability and Reconfiguration of Electrical Distribution Network Based on Jellyfish Search Algorithm

et al. 2022

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In this paper, the electricity network automation based on Power Network Reconfiguration (PNR) is implemented to improve the operational reliability of distribution systems using jellyfish search algorithm. For this purpose, system average interruption frequency index (SAIFI), system average interruption unavailability index (SAIUI) and total energy not supplied (TENS) are critical measures. In this paper, a new optimization technique of jellyfish search (JFS) algorithm is employed for distribution network reconfiguration for reliability improvement. It is concerned with the moving patterns of jellyfish. They are divided into three categories. The jellyfish could flow towards the ocean current or between its own swarm. Meanwhile, whenever the food supply is adequate, the jellyfishes are attracted to its location. It is formulated considering the three reliability indices of SAIFI, SAIUI and TENS, simultaneously in a multi-objective model based on the weight factors. The proposed methodology based on the JFS optimizer is implemented on an IEEE 33-node distribution network. According to the numerical results, the SAIFI, SAIUI and TENS is improved by 36.44%, 34.11% and 33.35%, compared to the initial condition. For comparison purposes, tuna swarm optimizer and tunicate swarm algorithm, besides the JFS algorithm, are implemented as well. The simulation results declare the significant outperformance of the JFS algorithm compared to TUNA and TSA in terms of the obtained improvements and the regarding convergence properties.

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“…A supervised learning approach may apply deep neural networks to learn the relationship between the system state and the optimal topology [18], [19], which requires a large data set. The reinforcement learning approach learns the optimal control policy by directly interacting with the real or a simulated physical environment [20], [21]. Its performance is largely dependent on the hyper-parameters selected from experience.…”

Section: Introductionmentioning

confidence: 99%

Improved Successive Branch Reduction for Stochastic Distribution Network Reconfiguration

Huang¹,

Zhao²

2022

Preprint

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Distribution Network Reconfiguration Based on NoisyNet Deep Q-Learning Network

Cited by 45 publications

References 10 publications

Bi-Level Coordinated Planning of Sectionalizing Switches and Tie Lines Considering Operation Mode Adjustment

Bi-Level Coordinated Planning of Sectionalizing Switches and Tie Lines Considering Operation Mode Adjustment

Optimal Operational Reliability and Reconfiguration of Electrical Distribution Network Based on Jellyfish Search Algorithm

Improved Successive Branch Reduction for Stochastic Distribution Network Reconfiguration

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