Comparative study of modern heuristic algorithms to service restoration in distribution systems

Toune, Sakae; Fudo, Hiroyuki; Genji, Takamu; Fukuyama, Yoshikazu; Nakanishi, Yoshinobu

doi:10.1109/61.974205

Cited by 200 publications

(69 citation statements)

References 11 publications

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“…Some researches adopted intelligent and heuristic algorithms to make restoration strategies. Toune et al [4] compared the efficiency of different kinds of traditional intelligent algorithms such as genetic algorithm, simulated annealing and Tabu search; human experiences and special properties of distribution network restorations are utilized in [5]- [9]. In most heuristic methods, the optimality of solution could not be guaranteed.…”

Section: Nomenclature J ∈ Imentioning

confidence: 99%

Robust Restoration Decision-Making Model for Distribution Networks Based on Information Gap Decision Theory

Chen

Zhang

et al. 2015

IEEE Trans. Smart Grid

128

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Section: Nomenclature J ∈ Imentioning

confidence: 99%

Robust Restoration Decision-Making Model for Distribution Networks Based on Information Gap Decision Theory

Chen

Zhang

et al. 2015

IEEE Trans. Smart Grid

128

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“…We can see more clearly in the above figure than in Table 1, that restoration using the original model cost longer or even insufferable period of time, while using the constraint (9), time consumed is relatively shorter. When constraint (9) and (10), which are the strict and heuristic methods, are used together, the effect is the greatest. It could be predicted that we can benefit more from the proposed efficiency improving method when the computation scale becomes even larger.…”

Section: B Tests For Computational Efficiencymentioning

confidence: 99%

“…The works in [7]- [10] belong to the first three categories, which were time consuming or could not achieve the optimal value. The fourth one could get an optimal value but it usually encounters high computation burden.…”

Section: Introductionmentioning

confidence: 99%

Security evaluation for distribution power system using improved MIQCP based restoration strategy

Chen

Zhang

et al. 2014

Isgt 2014

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Response to reviewers:Reviewer-1:The authors have not explained the main concepts of the paper clearly and it has several grammar issues as well as some unusual words. For example, 'outage load' is not correct. It should be 'interrupted load'. The methodology is not clear, specifically the two heuristics shown in (9) and (10) are not clear. Although heuristics are based on judgment, they should have some physical meaning. Figures are hard to read with very small size and fonts. Figure 4 is not necessary. The results are not explained properly. The authors have tried to focus more on the computational efficiency, but have not explained implementation of the algorithm properly. It would be useful for the readers if they can explain restoration results with an example using the 69-bus system. I think in Figure 5 and 6, large scale and small scale mean 269 and 69 bus system. The statements on page 5 bottom, second column are not true. Figure 9 doesn't support these claim. Overall, the paper needs better explanation of the methodology and the results. Response:(1) In most literatures about distribution power system restoration, outage load is more frequently used term than interrupted loadTo make the methods for improving computation efficiency more comprehensive, new figures and mathematical terms are used. Meanwhile, the illustration to the two methodologies is refined. But the two methods are not both heuristic. The first one is strict in mathematics and the second one is heuristic. (3) The figure of the expanded 269-node system has been removed now. Meanwhile, all the other figures have been improved for better reading by enlarging their sizes and fonts. (4) In the numerical tests part for computation efficiency, Figure 6 of the original manuscript has been removed. Within the last several months, the computation efficiency improvement algorithms have been further developed, so the computing time fluctuation issue is solved and the computing time is further decreased. (5) Figure 8 and Figure 9 of the original manuscript (Figure 7 and 8 in the revised manuscript) should be compared and the conclusion could be made that: the real system used in this paper is more secure than the single-feeder 69-node system, because it has link-branches between feeders to pick up outage loads in severe contingencies. analysis and control, especially the EMS advanced applications in EPCC. He has published more than 300 scientific papers and implemented more than 60 EMS/DTS systems in China. He is a steering member of CIGRE China State Committee and Int. Workshop of EPCC. He won the second rank award of Chinese national science and technology progress in 1992 and the second rank prize of Chinese national technology innovation in 2008.

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“…Other mathematical formulations of DSR problems, which do not depend from a data structure (from an ADS), can be found in references [5] and [6].…”

Section: Modeling Of the Dsr Problemsmentioning

confidence: 99%

Node-Depth Encoding with recombination for multi-objective Evolutionary Algorithm to solve loss reduction problem in large-scale distribution systems

Sanches

Lima

Santos

et al. 2012

2012 IEEE Power and Energy Society General Meeting

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Power loss reduction problem can be achieved by an adequate Distribution System Reconfiguration (DSR). Usually DSR for power loss reduction is formulated as a nonlinear, multiobjective and multi-constrained optimization problem, containing thousands of constraints equations for large-scale networks. As a consequence, to find an adequate solution for such problem is computationally complex. Recently a practical and efficient approach to solve this problem which overcomes such a hurdle was developed. This approach, called MEAN, combines a multi-objective Evolutionary Algorithm (EA), based on subpopulations tables, with a new tree encoding, named Node-Depth Encoding (NDE). This paper presents a new genetic recombination operator, named Evolutionary History Recombination NDE operator, which improves the MEAN performance to solve the loss reduction problem in large-scale networks. Tests with large networks show that with the new operator the MEAN can find network configurations with loss reduction of 29.57% for networks with 5,166 switches. This result is surprising since the practitioners used to expect at most a 10% reduction by means of DSR.

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Comparative study of modern heuristic algorithms to service restoration in distribution systems

Cited by 200 publications

References 11 publications

Robust Restoration Decision-Making Model for Distribution Networks Based on Information Gap Decision Theory

Robust Restoration Decision-Making Model for Distribution Networks Based on Information Gap Decision Theory

Security evaluation for distribution power system using improved MIQCP based restoration strategy

Node-Depth Encoding with recombination for multi-objective Evolutionary Algorithm to solve loss reduction problem in large-scale distribution systems

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