Multi-stage supply restoration of active distribution networks with SOP integration

Jian, Jie; Li, Peng; Yu, Hao; Ji, Haoran; Ji, Jie; Song, Guanyu; Yan, Jinyue; Wu, Jianzhong; Wang, Chengshan

doi:10.1016/j.segan.2021.100562

Cited by 13 publications

(8 citation statements)

References 37 publications

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“…With the support of DGs and flexible topology of distribution networks, a service restoration strategy is employed in [11] to restore out-of-service loads as much as possible. Reference [12] proposes a supply restoration strategy for active distribution network with soft open points (SOPs), in which the sequential operations of SOP control mode and switching motion are coordinated. Considering the time-series of DGs, energy storage systems (ESSs), and loads, [13] constructs a SOP-based island partition model for load recovery.…”

Section: A Symbolsmentioning

confidence: 99%

Decentralized Bilateral Risk-based Self-healing Strategy for Power Distribution Network with Potentials from Central Energy Stations

Liang

Chai

2023

Journal of Modern Power Systems and Clean Energy

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Owing to potential regulation capacities from flexible resources in energy coupling, storage, and consumption links, central energy stations (CESs) can provide additional support to power distribution network (PDN) in case of power disruption. However, existing research has not explicitly revealed the emergency response of PDN with leveraging multiple CESs. This paper proposes a decentralized self-healing strategy of PDN to minimize the entire load loss, in which multi-area CESs' potentials including thermal storage and building thermal inertia, as well as the flexible topology of PDN, are reasonably exploited for service recovery. For sake of privacy preservation, the co-optimization of PDN and CESs is realized in a decentralized manner using adaptive alternating direction method of multipliers (ADMM). Furtherly, bilateral risk management with conditional value-at-risk (CVaR) for PDN and risk constraints for CESs is integrated to deal with uncertainties from outage duration. Case studies are conducted on a modified IEEE 33-bus PDN with multiple CESs. Numerical results illustrate that the proposed strategy can fully utilize the potentials of multi-area CESs for coordinated load restoration. The effectiveness of the performance and behaviors' adaptation against random risks is also validated.

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Section: A Symbolsmentioning

confidence: 99%

Decentralized Bilateral Risk-based Self-healing Strategy for Power Distribution Network with Potentials from Central Energy Stations

Liang

Chai

2023

Journal of Modern Power Systems and Clean Energy

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“…LBCM (5,1), LBCM (5,2), LBCM (5,3), and LBCM (5,4), are equal to [5,6,7,8,9], [5,6,7,8,9], [], and [53,54,55,56,57,58,59], respectively. Branch 54 is common with LBCM (5,4).…”

Section: Lbcm (2 1)mentioning

confidence: 99%

“…Branch 54 is common with LBCM (5,4). Therefore, branches of LBCM (5,1), LBCM (5,2), LBCM (5,3), and LBCM (5,4) are removed from LBCM (5,5). Since branch 59 does not exist in the new LBCM (5,5), a random branch must be selected from the new LBCM (5,5).…”

Section: Lbcm (2 1)mentioning

confidence: 99%

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Reconfiguration of harmonic polluted distribution network using modified discrete particle swarm optimization equipped with smart radial method

Behbahani

Jalilian

2023

IET Generation Trans & Dist

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Distribution network reconfiguration (DNR) is capable to improve indices of power systems by changing the distribution network (DN) topology under normal conditions. Here, DNR is implemented to improve the power quality (PQ) and power losses of DN. Furthermore, to achieve better optimum in DNR, discrete particle swarm optimization (DPSO) algorithm is equipped with a smart radial method. The proposed algorithm is faster than other metaheuristic methods due to the prevention of regeneration non-radial configurations. In addition, the algorithm increases the probability of finding the optimal configurations using the mutation function. The results clearly demonstrated the positive effect of DNR in improving harmonic losses, total harmonic distortion (THD), and power losses. Furthermore, the proposed method is compared with other metaheuristic algorithms in IEEE 33 bus DN, IEEE 69 bus DN, and real 95-bus DN in presence of photovoltaic (PV). The results show that the developed algorithm leads to the better or same accuracy and speed in all comparisons. In addition, results indicated that considering the summation of fundamental and harmonic losses compared to only fundamental losses in DNR can lead to further energy saving in DNs.

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“…The fault location in the distribution network is a prerequisite for fault isolation and power supply restoration, which is based on the fault information collected by each feeder terminal unit (FTU) to comprehensively determine the zone where the fault occurs [ 30 , 31 ]. When a fault occurs in the distribution system, the FTU installed at each switch node is able to detect a current value greater than the pretuned fault current value.…”

Section: Application Of Decwoamentioning

confidence: 99%

Multistrategy Improved Whale Optimization Algorithm and Its Application

Liu

Zhang

2022

Computational Intelligence and Neuroscience

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To address the shortcomings of the whale optimization algorithm (WOA) in terms of insufficient global search ability and slow convergence speed, a differential evolution chaotic whale optimization algorithm (DECWOA) is proposed in this paper. Firstly, the initial population is generated by introducing the Sine chaos theory at the beginning of the algorithm to increase the population diversity. Secondly, new adaptive inertia weights are introduced into the individual whale position update formula to lay the foundation for the global search and improve the optimization performance of the algorithm. Finally, the differential variance algorithm is fused to improve the global search speed and accuracy of the whale optimization algorithm. The impact of various improvement strategies on the performance of the algorithm is analyzed using different kinds of test functions that are randomly selected. The particle swarm optimization algorithm (PSO), butterfly optimization algorithm (BOA), WOA, chaotic feedback adaptive whale optimization algorithm (CFAWOA), and DECWOA algorithm are compared for the optimal search performance. Experimental simulations are performed using MATLAB software, and the results show that the improved whale optimization algorithm has a better global optimization-seeking capability. The improved whale optimization algorithm is applied to the distribution network fault location of IEEE-33 nodes, and the effectiveness and accuracy of the distribution network fault zone location based on the multistrategy improved whale optimization algorithm is verified.

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Multi-stage supply restoration of active distribution networks with SOP integration

Cited by 13 publications

References 37 publications

Decentralized Bilateral Risk-based Self-healing Strategy for Power Distribution Network with Potentials from Central Energy Stations

Decentralized Bilateral Risk-based Self-healing Strategy for Power Distribution Network with Potentials from Central Energy Stations

Reconfiguration of harmonic polluted distribution network using modified discrete particle swarm optimization equipped with smart radial method

Multistrategy Improved Whale Optimization Algorithm and Its Application

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